Podcasts about Smol

Agencies and green claims – Walking the talk or just talking? In our final episode of our four part mini-series on navigating green claims and greenwashing we speak to Adrian Ma, founder of Fanclub PR, about the role of agencies in green claims and the rise of purpose-led marketing. Adrian shares his insights from working with impact-driven brands such as Who Gives a Crap, Music Magpie, SMOL and many more, and dives into the challenges agencies face in balancing creativity with compliance. Adrian talks us through the journey Fanclub PR is on and notes the ‘purpose-led' space is gaining a lot of traction, but with a big question mark over the authenticity and ‘trends' many are jumping on. Adrian explains how at a recent roundtable event he attended, many introduced themselves as ‘purpose-led agencies', in addition to that, Meta descriptions of 450 agencies revealed the number one trend was all related to … you guessed it… purpose. However, as Adrian states, “there's a huge, huge disconnect here when it comes to awareness of green claims, I think the awareness is low and the trend is that most agency people are led by the clients (which is fine), but agencies have a duty of care to their own staff to make sure that they know stuff.” At Fanclub PR Adrian and his team work closely with organisations such as Clean Creatives (two-time guests on the podcast - EP 24 and EP 67), and Creatives for Climate, with whom they have helped create an agency guide to greenwashing to support knowledge, education and awareness. (Linked below). On greenwashing vs. greenhushing, Adrian believes, "good creative agencies should help brands be transparent, giving them the confidence to talk about what they're doing”. We discuss the importance of good communication, something we are very passionate about at Can Marketing Save the Planet, given effective communication is critical to driving engagement and change, both internally and externally. Adrian agrees that communication cannot be underestimated as part of the green claims process, telling us “that process of interrogation is only going to make the work stronger”. As well as good communication we also touch on the future of marketing where Adrian is a firm believer in, "if brands stay consistent with their commitments year after year, they build communities, confidence, and leadership—that's where real change happens."  Tune in as we talk to Adrian about: The need for agencies to educate their teams on greenwashing to ensure compliance and ethical, authentic storytelling. How transparency beats perfection and how brands should be sharing both successes and challenges to build trust. The disconnect between sustainability teams (focused on data) and agencies (focused on messaging) and the need for collaboration to bridge that gap. How consistency builds impact and creates deeper engagement than those jumping between campaigns. Why good communication is critical both internally and externally and the need to challenge will only make the work stronger. For more information about Fanclub PR and the work they do… visit https://www.fanclubpr.com/ And here are some related links discussed in the podcast: Creatives for Climate Anti Greenwashing Guide for Agencies Big Brand Came and Stole My Purpose Video case study – Mount Recyclemore Video case study – Winnie The Pooh: Deforested Edition   Across this mini-series you've heard some fantastic insights and we've provided plenty of signposts to training and resources. Don't forget… if you're looking to upskill your team with the green claims landscape - you can also check out our 3 hour Greenwashing 101 short training course via our Sustainable Marketing Training Hub. ________________________________________________________________________ About us… We help Marketers save the planet. 

Začíná doba dovolených a cestování a tak si s hostem Dopoledního expresu Tomášem Smolíkem z pojišťovny právní ochrany D.A.S. budeme povídat o cestovním pojištění.Všechny díly podcastu Host Dopoledního expresu můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Začíná doba dovolených a cestování a tak si s hostem Dopoledního expresu Tomášem Smolíkem z pojišťovny právní ochrany D.A.S. budeme povídat o cestovním pojištění.

s. Města a obce investují nemalé prostředky do modernizace veřejného osvětlení. Moderuje Michal Jánský

s. Města a obce investují nemalé prostředky do modernizace veřejného osvětlení. Moderuje Michal JánskýVšechny díly podcastu Náš host můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

s. Města a obce investují nemalé prostředky do modernizace veřejného osvětlení. Moderuje Michal Jánský

Today's episode is with Paul Klein, founder of Browserbase. We talked about building browser infrastructure for AI agents, the future of agent authentication, and their open source framework Stagehand.* [00:00:00] Introductions* [00:04:46] AI-specific challenges in browser infrastructure* [00:07:05] Multimodality in AI-Powered Browsing* [00:12:26] Running headless browsers at scale* [00:18:46] Geolocation when proxying* [00:21:25] CAPTCHAs and Agent Auth* [00:28:21] Building “User take over” functionality* [00:33:43] Stagehand: AI web browsing framework* [00:38:58] OpenAI's Operator and computer use agents* [00:44:44] Surprising use cases of Browserbase* [00:47:18] Future of browser automation and market competition* [00:53:11] Being a solo founderTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.swyx [00:00:12]: Hey, and today we are very blessed to have our friends, Paul Klein, for the fourth, the fourth, CEO of Browserbase. Welcome.Paul [00:00:21]: Thanks guys. Yeah, I'm happy to be here. I've been lucky to know both of you for like a couple of years now, I think. So it's just like we're hanging out, you know, with three ginormous microphones in front of our face. It's totally normal hangout.swyx [00:00:34]: Yeah. We've actually mentioned you on the podcast, I think, more often than any other Solaris tenant. Just because like you're one of the, you know, best performing, I think, LLM tool companies that have started up in the last couple of years.Paul [00:00:50]: Yeah, I mean, it's been a whirlwind of a year, like Browserbase is actually pretty close to our first birthday. So we are one years old. And going from, you know, starting a company as a solo founder to... To, you know, having a team of 20 people, you know, a series A, but also being able to support hundreds of AI companies that are building AI applications that go out and automate the web. It's just been like, really cool. It's been happening a little too fast. I think like collectively as an AI industry, let's just take a week off together. I took my first vacation actually two weeks ago, and Operator came out on the first day, and then a week later, DeepSeat came out. And I'm like on vacation trying to chill. I'm like, we got to build with this stuff, right? So it's been a breakneck year. But I'm super happy to be here and like talk more about all the stuff we're seeing. And I'd love to hear kind of what you guys are excited about too, and share with it, you know?swyx [00:01:39]: Where to start? So people, you've done a bunch of podcasts. I think I strongly recommend Jack Bridger's Scaling DevTools, as well as Turner Novak's The Peel. And, you know, I'm sure there's others. So you covered your Twilio story in the past, talked about StreamClub, you got acquired to Mux, and then you left to start Browserbase. So maybe we just start with what is Browserbase? Yeah.Paul [00:02:02]: Browserbase is the web browser for your AI. We're building headless browser infrastructure, which are browsers that run in a server environment that's accessible to developers via APIs and SDKs. It's really hard to run a web browser in the cloud. You guys are probably running Chrome on your computers, and that's using a lot of resources, right? So if you want to run a web browser or thousands of web browsers, you can't just spin up a bunch of lambdas. You actually need to use a secure containerized environment. You have to scale it up and down. It's a stateful system. And that infrastructure is, like, super painful. And I know that firsthand, because at my last company, StreamClub, I was CTO, and I was building our own internal headless browser infrastructure. That's actually why we sold the company, is because Mux really wanted to buy our headless browser infrastructure that we'd built. And it's just a super hard problem. And I actually told my co-founders, I would never start another company unless it was a browser infrastructure company. And it turns out that's really necessary in the age of AI, when AI can actually go out and interact with websites, click on buttons, fill in forms. You need AI to do all of that work in an actual browser running somewhere on a server. And BrowserBase powers that.swyx [00:03:08]: While you're talking about it, it occurred to me, not that you're going to be acquired or anything, but it occurred to me that it would be really funny if you became the Nikita Beer of headless browser companies. You just have one trick, and you make browser companies that get acquired.Paul [00:03:23]: I truly do only have one trick. I'm screwed if it's not for headless browsers. I'm not a Go programmer. You know, I'm in AI grant. You know, browsers is an AI grant. But we were the only company in that AI grant batch that used zero dollars on AI spend. You know, we're purely an infrastructure company. So as much as people want to ask me about reinforcement learning, I might not be the best guy to talk about that. But if you want to ask about headless browser infrastructure at scale, I can talk your ear off. So that's really my area of expertise. And it's a pretty niche thing. Like, nobody has done what we're doing at scale before. So we're happy to be the experts.swyx [00:03:59]: You do have an AI thing, stagehand. We can talk about the sort of core of browser-based first, and then maybe stagehand. Yeah, stagehand is kind of the web browsing framework. Yeah.What is Browserbase? Headless Browser Infrastructure ExplainedAlessio [00:04:10]: Yeah. Yeah. And maybe how you got to browser-based and what problems you saw. So one of the first things I worked on as a software engineer was integration testing. Sauce Labs was kind of like the main thing at the time. And then we had Selenium, we had Playbrite, we had all these different browser things. But it's always been super hard to do. So obviously you've worked on this before. When you started browser-based, what were the challenges? What were the AI-specific challenges that you saw versus, there's kind of like all the usual running browser at scale in the cloud, which has been a problem for years. What are like the AI unique things that you saw that like traditional purchase just didn't cover? Yeah.AI-specific challenges in browser infrastructurePaul [00:04:46]: First and foremost, I think back to like the first thing I did as a developer, like as a kid when I was writing code, I wanted to write code that did stuff for me. You know, I wanted to write code to automate my life. And I do that probably by using curl or beautiful soup to fetch data from a web browser. And I think I still do that now that I'm in the cloud. And the other thing that I think is a huge challenge for me is that you can't just create a web site and parse that data. And we all know that now like, you know, taking HTML and plugging that into an LLM, you can extract insights, you can summarize. So it was very clear that now like dynamic web scraping became very possible with the rise of large language models or a lot easier. And that was like a clear reason why there's been more usage of headless browsers, which are necessary because a lot of modern websites don't expose all of their page content via a simple HTTP request. You know, they actually do require you to run this type of code for a specific time. JavaScript on the page to hydrate this. Airbnb is a great example. You go to airbnb.com. A lot of that content on the page isn't there until after they run the initial hydration. So you can't just scrape it with a curl. You need to have some JavaScript run. And a browser is that JavaScript engine that's going to actually run all those requests on the page. So web data retrieval was definitely one driver of starting BrowserBase and the rise of being able to summarize that within LLM. Also, I was familiar with if I wanted to automate a website, I could write one script and that would work for one website. It was very static and deterministic. But the web is non-deterministic. The web is always changing. And until we had LLMs, there was no way to write scripts that you could write once that would run on any website. That would change with the structure of the website. Click the login button. It could mean something different on many different websites. And LLMs allow us to generate code on the fly to actually control that. So I think that rise of writing the generic automation scripts that can work on many different websites, to me, made it clear that browsers are going to be a lot more useful because now you can automate a lot more things without writing. If you wanted to write a script to book a demo call on 100 websites, previously, you had to write 100 scripts. Now you write one script that uses LLMs to generate that script. That's why we built our web browsing framework, StageHand, which does a lot of that work for you. But those two things, web data collection and then enhanced automation of many different websites, it just felt like big drivers for more browser infrastructure that would be required to power these kinds of features.Alessio [00:07:05]: And was multimodality also a big thing?Paul [00:07:08]: Now you can use the LLMs to look, even though the text in the dome might not be as friendly. Maybe my hot take is I was always kind of like, I didn't think vision would be as big of a driver. For UI automation, I felt like, you know, HTML is structured text and large language models are good with structured text. But it's clear that these computer use models are often vision driven, and they've been really pushing things forward. So definitely being multimodal, like rendering the page is required to take a screenshot to give that to a computer use model to take actions on a website. And it's just another win for browser. But I'll be honest, that wasn't what I was thinking early on. I didn't even think that we'd get here so fast with multimodality. I think we're going to have to get back to multimodal and vision models.swyx [00:07:50]: This is one of those things where I forgot to mention in my intro that I'm an investor in Browserbase. And I remember that when you pitched to me, like a lot of the stuff that we have today, we like wasn't on the original conversation. But I did have my original thesis was something that we've talked about on the podcast before, which is take the GPT store, the custom GPT store, all the every single checkbox and plugin is effectively a startup. And this was the browser one. I think the main hesitation, I think I actually took a while to get back to you. The main hesitation was that there were others. Like you're not the first hit list browser startup. It's not even your first hit list browser startup. There's always a question of like, will you be the category winner in a place where there's a bunch of incumbents, to be honest, that are bigger than you? They're just not targeted at the AI space. They don't have the backing of Nat Friedman. And there's a bunch of like, you're here in Silicon Valley. They're not. I don't know.Paul [00:08:47]: I don't know if that's, that was it, but like, there was a, yeah, I mean, like, I think I tried all the other ones and I was like, really disappointed. Like my background is from working at great developer tools, companies, and nothing had like the Vercel like experience. Um, like our biggest competitor actually is partly owned by private equity and they just jacked up their prices quite a bit. And the dashboard hasn't changed in five years. And I actually used them at my last company and tried them and I was like, oh man, like there really just needs to be something that's like the experience of these great infrastructure companies, like Stripe, like clerk, like Vercel that I use in love, but oriented towards this kind of like more specific category, which is browser infrastructure, which is really technically complex. Like a lot of stuff can go wrong on the internet when you're running a browser. The internet is very vast. There's a lot of different configurations. Like there's still websites that only work with internet explorer out there. How do you handle that when you're running your own browser infrastructure? These are the problems that we have to think about and solve at BrowserBase. And it's, it's certainly a labor of love, but I built this for me, first and foremost, I know it's super cheesy and everyone says that for like their startups, but it really, truly was for me. If you look at like the talks I've done even before BrowserBase, and I'm just like really excited to try and build a category defining infrastructure company. And it's, it's rare to have a new category of infrastructure exists. We're here in the Chroma offices and like, you know, vector databases is a new category of infrastructure. Is it, is it, I mean, we can, we're in their office, so, you know, we can, we can debate that one later. That is one.Multimodality in AI-Powered Browsingswyx [00:10:16]: That's one of the industry debates.Paul [00:10:17]: I guess we go back to the LLMOS talk that Karpathy gave way long ago. And like the browser box was very clearly there and it seemed like the people who were building in this space also agreed that browsers are a core primitive of infrastructure for the LLMOS that's going to exist in the future. And nobody was building something there that I wanted to use. So I had to go build it myself.swyx [00:10:38]: Yeah. I mean, exactly that talk that, that honestly, that diagram, every box is a startup and there's the code box and then there's the. The browser box. I think at some point they will start clashing there. There's always the question of the, are you a point solution or are you the sort of all in one? And I think the point solutions tend to win quickly, but then the only ones have a very tight cohesive experience. Yeah. Let's talk about just the hard problems of browser base you have on your website, which is beautiful. Thank you. Was there an agency that you used for that? Yeah. Herb.paris.Paul [00:11:11]: They're amazing. Herb.paris. Yeah. It's H-E-R-V-E. I highly recommend for developers. Developer tools, founders to work with consumer agencies because they end up building beautiful things and the Parisians know how to build beautiful interfaces. So I got to give prep.swyx [00:11:24]: And chat apps, apparently are, they are very fast. Oh yeah. The Mistral chat. Yeah. Mistral. Yeah.Paul [00:11:31]: Late chat.swyx [00:11:31]: Late chat. And then your videos as well, it was professionally shot, right? The series A video. Yeah.Alessio [00:11:36]: Nico did the videos. He's amazing. Not the initial video that you shot at the new one. First one was Austin.Paul [00:11:41]: Another, another video pretty surprised. But yeah, I mean, like, I think when you think about how you talk about your company. You have to think about the way you present yourself. It's, you know, as a developer, you think you evaluate a company based on like the API reliability and the P 95, but a lot of developers say, is the website good? Is the message clear? Do I like trust this founder? I'm building my whole feature on. So I've tried to nail that as well as like the reliability of the infrastructure. You're right. It's very hard. And there's a lot of kind of foot guns that you run into when running headless browsers at scale. Right.Competing with Existing Headless Browser Solutionsswyx [00:12:10]: So let's pick one. You have eight features here. Seamless integration. Scalability. Fast or speed. Secure. Observable. Stealth. That's interesting. Extensible and developer first. What comes to your mind as like the top two, three hardest ones? Yeah.Running headless browsers at scalePaul [00:12:26]: I think just running headless browsers at scale is like the hardest one. And maybe can I nerd out for a second? Is that okay? I heard this is a technical audience, so I'll talk to the other nerds. Whoa. They were listening. Yeah. They're upset. They're ready. The AGI is angry. Okay. So. So how do you run a browser in the cloud? Let's start with that, right? So let's say you're using a popular browser automation framework like Puppeteer, Playwright, and Selenium. Maybe you've written a code, some code locally on your computer that opens up Google. It finds the search bar and then types in, you know, search for Latent Space and hits the search button. That script works great locally. You can see the little browser open up. You want to take that to production. You want to run the script in a cloud environment. So when your laptop is closed, your browser is doing something. The browser is doing something. Well, I, we use Amazon. You can see the little browser open up. You know, the first thing I'd reach for is probably like some sort of serverless infrastructure. I would probably try and deploy on a Lambda. But Chrome itself is too big to run on a Lambda. It's over 250 megabytes. So you can't easily start it on a Lambda. So you maybe have to use something like Lambda layers to squeeze it in there. Maybe use a different Chromium build that's lighter. And you get it on the Lambda. Great. It works. But it runs super slowly. It's because Lambdas are very like resource limited. They only run like with one vCPU. You can run one process at a time. Remember, Chromium is super beefy. It's barely running on my MacBook Air. I'm still downloading it from a pre-run. Yeah, from the test earlier, right? I'm joking. But it's big, you know? So like Lambda, it just won't work really well. Maybe it'll work, but you need something faster. Your users want something faster. Okay. Well, let's put it on a beefier instance. Let's get an EC2 server running. Let's throw Chromium on there. Great. Okay. I can, that works well with one user. But what if I want to run like 10 Chromium instances, one for each of my users? Okay. Well, I might need two EC2 instances. Maybe 10. All of a sudden, you have multiple EC2 instances. This sounds like a problem for Kubernetes and Docker, right? Now, all of a sudden, you're using ECS or EKS, the Kubernetes or container solutions by Amazon. You're spending up and down containers, and you're spending a whole engineer's time on kind of maintaining this stateful distributed system. Those are some of the worst systems to run because when it's a stateful distributed system, it means that you are bound by the connections to that thing. You have to keep the browser open while someone is working with it, right? That's just a painful architecture to run. And there's all this other little gotchas with Chromium, like Chromium, which is the open source version of Chrome, by the way. You have to install all these fonts. You want emojis working in your browsers because your vision model is looking for the emoji. You need to make sure you have the emoji fonts. You need to make sure you have all the right extensions configured, like, oh, do you want ad blocking? How do you configure that? How do you actually record all these browser sessions? Like it's a headless browser. You can't look at it. So you need to have some sort of observability. Maybe you're recording videos and storing those somewhere. It all kind of adds up to be this just giant monster piece of your project when all you wanted to do was run a lot of browsers in production for this little script to go to google.com and search. And when I see a complex distributed system, I see an opportunity to build a great infrastructure company. And we really abstract that away with Browserbase where our customers can use these existing frameworks, Playwright, Publisher, Selenium, or our own stagehand and connect to our browsers in a serverless-like way. And control them, and then just disconnect when they're done. And they don't have to think about the complex distributed system behind all of that. They just get a browser running anywhere, anytime. Really easy to connect to.swyx [00:15:55]: I'm sure you have questions. My standard question with anything, so essentially you're a serverless browser company, and there's been other serverless things that I'm familiar with in the past, serverless GPUs, serverless website hosting. That's where I come from with Netlify. One question is just like, you promised to spin up thousands of servers. You promised to spin up thousands of browsers in milliseconds. I feel like there's no real solution that does that yet. And I'm just kind of curious how. The only solution I know, which is to kind of keep a kind of warm pool of servers around, which is expensive, but maybe not so expensive because it's just CPUs. So I'm just like, you know. Yeah.Browsers as a Core Primitive in AI InfrastructurePaul [00:16:36]: You nailed it, right? I mean, how do you offer a serverless-like experience with something that is clearly not serverless, right? And the answer is, you need to be able to run... We run many browsers on single nodes. We use Kubernetes at browser base. So we have many pods that are being scheduled. We have to predictably schedule them up or down. Yes, thousands of browsers in milliseconds is the best case scenario. If you hit us with 10,000 requests, you may hit a slower cold start, right? So we've done a lot of work on predictive scaling and being able to kind of route stuff to different regions where we have multiple regions of browser base where we have different pools available. You can also pick the region you want to go to based on like lower latency, round trip, time latency. It's very important with these types of things. There's a lot of requests going over the wire. So for us, like having a VM like Firecracker powering everything under the hood allows us to be super nimble and spin things up or down really quickly with strong multi-tenancy. But in the end, this is like the complex infrastructural challenges that we have to kind of deal with at browser base. And we have a lot more stuff on our roadmap to allow customers to have more levers to pull to exchange, do you want really fast browser startup times or do you want really low costs? And if you're willing to be more flexible on that, we may be able to kind of like work better for your use cases.swyx [00:17:44]: Since you used Firecracker, shouldn't Fargate do that for you or did you have to go lower level than that? We had to go lower level than that.Paul [00:17:51]: I find this a lot with Fargate customers, which is alarming for Fargate. We used to be a giant Fargate customer. Actually, the first version of browser base was ECS and Fargate. And unfortunately, it's a great product. I think we were actually the largest Fargate customer in our region for a little while. No, what? Yeah, seriously. And unfortunately, it's a great product, but I think if you're an infrastructure company, you actually have to have a deeper level of control over these primitives. I think it's the same thing is true with databases. We've used other database providers and I think-swyx [00:18:21]: Yeah, serverless Postgres.Paul [00:18:23]: Shocker. When you're an infrastructure company, you're on the hook if any provider has an outage. And I can't tell my customers like, hey, we went down because so-and-so went down. That's not acceptable. So for us, we've really moved to bringing things internally. It's kind of opposite of what we preach. We tell our customers, don't build this in-house, but then we're like, we build a lot of stuff in-house. But I think it just really depends on what is in the critical path. We try and have deep ownership of that.Alessio [00:18:46]: On the distributed location side, how does that work for the web where you might get sort of different content in different locations, but the customer is expecting, you know, if you're in the US, I'm expecting the US version. But if you're spinning up my browser in France, I might get the French version. Yeah.Paul [00:19:02]: Yeah. That's a good question. Well, generally, like on the localization, there is a thing called locale in the browser. You can set like what your locale is. If you're like in the ENUS browser or not, but some things do IP, IP based routing. And in that case, you may want to have a proxy. Like let's say you're running something in the, in Europe, but you want to make sure you're showing up from the US. You may want to use one of our proxy features so you can turn on proxies to say like, make sure these connections always come from the United States, which is necessary too, because when you're browsing the web, you're coming from like a, you know, data center IP, and that can make things a lot harder to browse web. So we do have kind of like this proxy super network. Yeah. We have a proxy for you based on where you're going, so you can reliably automate the web. But if you get scheduled in Europe, that doesn't happen as much. We try and schedule you as close to, you know, your origin that you're trying to go to. But generally you have control over the regions you can put your browsers in. So you can specify West one or East one or Europe. We only have one region of Europe right now, actually. Yeah.Alessio [00:19:55]: What's harder, the browser or the proxy? I feel like to me, it feels like actually proxying reliably at scale. It's much harder than spending up browsers at scale. I'm curious. It's all hard.Paul [00:20:06]: It's layers of hard, right? Yeah. I think it's different levels of hard. I think the thing with the proxy infrastructure is that we work with many different web proxy providers and some are better than others. Some have good days, some have bad days. And our customers who've built browser infrastructure on their own, they have to go and deal with sketchy actors. Like first they figure out their own browser infrastructure and then they got to go buy a proxy. And it's like you can pay in Bitcoin and it just kind of feels a little sus, right? It's like you're buying drugs when you're trying to get a proxy online. We have like deep relationships with these counterparties. We're able to audit them and say, is this proxy being sourced ethically? Like it's not running on someone's TV somewhere. Is it free range? Yeah. Free range organic proxies, right? Right. We do a level of diligence. We're SOC 2. So we have to understand what is going on here. But then we're able to make sure that like we route around proxy providers not working. There's proxy providers who will just, the proxy will stop working all of a sudden. And then if you don't have redundant proxying on your own browsers, that's hard down for you or you may get some serious impacts there. With us, like we intelligently know, hey, this proxy is not working. Let's go to this one. And you can kind of build a network of multiple providers to really guarantee the best uptime for our customers. Yeah. So you don't own any proxies? We don't own any proxies. You're right. The team has been saying who wants to like take home a little proxy server, but not yet. We're not there yet. You know?swyx [00:21:25]: It's a very mature market. I don't think you should build that yourself. Like you should just be a super customer of them. Yeah. Scraping, I think, is the main use case for that. I guess. Well, that leads us into CAPTCHAs and also off, but let's talk about CAPTCHAs. You had a little spiel that you wanted to talk about CAPTCHA stuff.Challenges of Scaling Browser InfrastructurePaul [00:21:43]: Oh, yeah. I was just, I think a lot of people ask, if you're thinking about proxies, you're thinking about CAPTCHAs too. I think it's the same thing. You can go buy CAPTCHA solvers online, but it's the same buying experience. It's some sketchy website, you have to integrate it. It's not fun to buy these things and you can't really trust that the docs are bad. What Browserbase does is we integrate a bunch of different CAPTCHAs. We do some stuff in-house, but generally we just integrate with a bunch of known vendors and continually monitor and maintain these things and say, is this working or not? Can we route around it or not? These are CAPTCHA solvers. CAPTCHA solvers, yeah. Not CAPTCHA providers, CAPTCHA solvers. Yeah, sorry. CAPTCHA solvers. We really try and make sure all of that works for you. I think as a dev, if I'm buying infrastructure, I want it all to work all the time and it's important for us to provide that experience by making sure everything does work and monitoring it on our own. Yeah. Right now, the world of CAPTCHAs is tricky. I think AI agents in particular are very much ahead of the internet infrastructure. CAPTCHAs are designed to block all types of bots, but there are now good bots and bad bots. I think in the future, CAPTCHAs will be able to identify who a good bot is, hopefully via some sort of KYC. For us, we've been very lucky. We have very little to no known abuse of Browserbase because we really look into who we work with. And for certain types of CAPTCHA solving, we only allow them on certain types of plans because we want to make sure that we can know what people are doing, what their use cases are. And that's really allowed us to try and be an arbiter of good bots, which is our long term goal. I want to build great relationships with people like Cloudflare so we can agree, hey, here are these acceptable bots. We'll identify them for you and make sure we flag when they come to your website. This is a good bot, you know?Alessio [00:23:23]: I see. And Cloudflare said they want to do more of this. So they're going to set by default, if they think you're an AI bot, they're going to reject. I'm curious if you think this is something that is going to be at the browser level or I mean, the DNS level with Cloudflare seems more where it should belong. But I'm curious how you think about it.Paul [00:23:40]: I think the web's going to change. You know, I think that the Internet as we have it right now is going to change. And we all need to just accept that the cat is out of the bag. And instead of kind of like wishing the Internet was like it was in the 2000s, we can have free content line that wouldn't be scraped. It's just it's not going to happen. And instead, we should think about like, one, how can we change? How can we change the models of, you know, information being published online so people can adequately commercialize it? But two, how do we rebuild applications that expect that AI agents are going to log in on their behalf? Those are the things that are going to allow us to kind of like identify good and bad bots. And I think the team at Clerk has been doing a really good job with this on the authentication side. I actually think that auth is the biggest thing that will prevent agents from accessing stuff, not captchas. And I think there will be agent auth in the future. I don't know if it's going to happen from an individual company, but actually authentication providers that have a, you know, hidden login as agent feature, which will then you put in your email, you'll get a push notification, say like, hey, your browser-based agent wants to log into your Airbnb. You can approve that and then the agent can proceed. That really circumvents the need for captchas or logging in as you and sharing your password. I think agent auth is going to be one way we identify good bots going forward. And I think a lot of this captcha solving stuff is really short-term problems as the internet kind of reorients itself around how it's going to work with agents browsing the web, just like people do. Yeah.Managing Distributed Browser Locations and Proxiesswyx [00:24:59]: Stitch recently was on Hacker News for talking about agent experience, AX, which is a thing that Netlify is also trying to clone and coin and talk about. And we've talked about this on our previous episodes before in a sense that I actually think that's like maybe the only part of the tech stack that needs to be kind of reinvented for agents. Everything else can stay the same, CLIs, APIs, whatever. But auth, yeah, we need agent auth. And it's mostly like short-lived, like it should not, it should be a distinct, identity from the human, but paired. I almost think like in the same way that every social network should have your main profile and then your alt accounts or your Finsta, it's almost like, you know, every, every human token should be paired with the agent token and the agent token can go and do stuff on behalf of the human token, but not be presumed to be the human. Yeah.Paul [00:25:48]: It's like, it's, it's actually very similar to OAuth is what I'm thinking. And, you know, Thread from Stitch is an investor, Colin from Clerk, Octaventures, all investors in browser-based because like, I hope they solve this because they'll make browser-based submission more possible. So we don't have to overcome all these hurdles, but I think it will be an OAuth-like flow where an agent will ask to log in as you, you'll approve the scopes. Like it can book an apartment on Airbnb, but it can't like message anybody. And then, you know, the agent will have some sort of like role-based access control within an application. Yeah. I'm excited for that.swyx [00:26:16]: The tricky part is just, there's one, one layer of delegation here, which is like, you're authoring my user's user or something like that. I don't know if that's tricky or not. Does that make sense? Yeah.Paul [00:26:25]: You know, actually at Twilio, I worked on the login identity and access. Management teams, right? So like I built Twilio's login page.swyx [00:26:31]: You were an intern on that team and then you became the lead in two years? Yeah.Paul [00:26:34]: Yeah. I started as an intern in 2016 and then I was the tech lead of that team. How? That's not normal. I didn't have a life. He's not normal. Look at this guy. I didn't have a girlfriend. I just loved my job. I don't know. I applied to 500 internships for my first job and I got rejected from every single one of them except for Twilio and then eventually Amazon. And they took a shot on me and like, I was getting paid money to write code, which was my dream. Yeah. Yeah. I'm very lucky that like this coding thing worked out because I was going to be doing it regardless. And yeah, I was able to kind of spend a lot of time on a team that was growing at a company that was growing. So it informed a lot of this stuff here. I think these are problems that have been solved with like the SAML protocol with SSO. I think it's a really interesting stuff with like WebAuthn, like these different types of authentication, like schemes that you can use to authenticate people. The tooling is all there. It just needs to be tweaked a little bit to work for agents. And I think the fact that there are companies that are already. Providing authentication as a service really sets it up. Well, the thing that's hard is like reinventing the internet for agents. We don't want to rebuild the internet. That's an impossible task. And I think people often say like, well, we'll have this second layer of APIs built for agents. I'm like, we will for the top use cases, but instead of we can just tweak the internet as is, which is on the authentication side, I think we're going to be the dumb ones going forward. Unfortunately, I think AI is going to be able to do a lot of the tasks that we do online, which means that it will be able to go to websites, click buttons on our behalf and log in on our behalf too. So with this kind of like web agent future happening, I think with some small structural changes, like you said, it feels like it could all slot in really nicely with the existing internet.Handling CAPTCHAs and Agent Authenticationswyx [00:28:08]: There's one more thing, which is the, your live view iframe, which lets you take, take control. Yeah. Obviously very key for operator now, but like, was, is there anything interesting technically there or that the people like, well, people always want this.Paul [00:28:21]: It was really hard to build, you know, like, so, okay. Headless browsers, you don't see them, right. They're running. They're running in a cloud somewhere. You can't like look at them. And I just want to really make, it's a weird name. I wish we came up with a better name for this thing, but you can't see them. Right. But customers don't trust AI agents, right. At least the first pass. So what we do with our live view is that, you know, when you use browser base, you can actually embed a live view of the browser running in the cloud for your customer to see it working. And that's what the first reason is the build trust, like, okay, so I have this script. That's going to go automate a website. I can embed it into my web application via an iframe and my customer can watch. I think. And then we added two way communication. So now not only can you watch the browser kind of being operated by AI, if you want to pause and actually click around type within this iframe that's controlling a browser, that's also possible. And this is all thanks to some of the lower level protocol, which is called the Chrome DevTools protocol. It has a API called start screencast, and you can also send mouse clicks and button clicks to a remote browser. And this is all embeddable within iframes. You have a browser within a browser, yo. And then you simulate the screen, the click on the other side. Exactly. And this is really nice often for, like, let's say, a capture that can't be solved. You saw this with Operator, you know, Operator actually uses a different approach. They use VNC. So, you know, you're able to see, like, you're seeing the whole window here. What we're doing is something a little lower level with the Chrome DevTools protocol. It's just PNGs being streamed over the wire. But the same thing is true, right? Like, hey, I'm running a window. Pause. Can you do something in this window? Human. Okay, great. Resume. Like sometimes 2FA tokens. Like if you get that text message, you might need a person to type that in. Web agents need human-in-the-loop type workflows still. You still need a person to interact with the browser. And building a UI to proxy that is kind of hard. You may as well just show them the whole browser and say, hey, can you finish this up for me? And then let the AI proceed on afterwards. Is there a future where I stream my current desktop to browser base? I don't think so. I think we're very much cloud infrastructure. Yeah. You know, but I think a lot of the stuff we're doing, we do want to, like, build tools. Like, you know, we'll talk about the stage and, you know, web agent framework in a second. But, like, there's a case where a lot of people are going desktop first for, you know, consumer use. And I think cloud is doing a lot of this, where I expect to see, you know, MCPs really oriented around the cloud desktop app for a reason, right? Like, I think a lot of these tools are going to run on your computer because it makes... I think it's breaking out. People are putting it on a server. Oh, really? Okay. Well, sweet. We'll see. We'll see that. I was surprised, though, wasn't I? I think that the browser company, too, with Dia Browser, it runs on your machine. You know, it's going to be...swyx [00:30:50]: What is it?Paul [00:30:51]: So, Dia Browser, as far as I understand... I used to use Arc. Yeah. I haven't used Arc. But I'm a big fan of the browser company. I think they're doing a lot of cool stuff in consumer. As far as I understand, it's a browser where you have a sidebar where you can, like, chat with it and it can control the local browser on your machine. So, if you imagine, like, what a consumer web agent is, which it lives alongside your browser, I think Google Chrome has Project Marina, I think. I almost call it Project Marinara for some reason. I don't know why. It's...swyx [00:31:17]: No, I think it's someone really likes the Waterworld. Oh, I see. The classic Kevin Costner. Yeah.Paul [00:31:22]: Okay. Project Marinara is a similar thing to the Dia Browser, in my mind, as far as I understand it. You have a browser that has an AI interface that will take over your mouse and keyboard and control the browser for you. Great for consumer use cases. But if you're building applications that rely on a browser and it's more part of a greater, like, AI app experience, you probably need something that's more like infrastructure, not a consumer app.swyx [00:31:44]: Just because I have explored a little bit in this area, do people want branching? So, I have the state. Of whatever my browser's in. And then I want, like, 100 clones of this state. Do people do that? Or...Paul [00:31:56]: People don't do it currently. Yeah. But it's definitely something we're thinking about. I think the idea of forking a browser is really cool. Technically, kind of hard. We're starting to see this in code execution, where people are, like, forking some, like, code execution, like, processes or forking some tool calls or branching tool calls. Haven't seen it at the browser level yet. But it makes sense. Like, if an AI agent is, like, using a website and it's not sure what path it wants to take to crawl this website. To find the information it's looking for. It would make sense for it to explore both paths in parallel. And that'd be a very, like... A road not taken. Yeah. And hopefully find the right answer. And then say, okay, this was actually the right one. And memorize that. And go there in the future. On the roadmap. For sure. Don't make my roadmap, please. You know?Alessio [00:32:37]: How do you actually do that? Yeah. How do you fork? I feel like the browser is so stateful for so many things.swyx [00:32:42]: Serialize the state. Restore the state. I don't know.Paul [00:32:44]: So, it's one of the reasons why we haven't done it yet. It's hard. You know? Like, to truly fork, it's actually quite difficult. The naive way is to open the same page in a new tab and then, like, hope that it's at the same thing. But if you have a form halfway filled, you may have to, like, take the whole, you know, container. Pause it. All the memory. Duplicate it. Restart it from there. It could be very slow. So, we haven't found a thing. Like, the easy thing to fork is just, like, copy the page object. You know? But I think there needs to be something a little bit more robust there. Yeah.swyx [00:33:12]: So, MorphLabs has this infinite branch thing. Like, wrote a custom fork of Linux or something that let them save the system state and clone it. MorphLabs, hit me up. I'll be a customer. Yeah. That's the only. I think that's the only way to do it. Yeah. Like, unless Chrome has some special API for you. Yeah.Paul [00:33:29]: There's probably something we'll reverse engineer one day. I don't know. Yeah.Alessio [00:33:32]: Let's talk about StageHand, the AI web browsing framework. You have three core components, Observe, Extract, and Act. Pretty clean landing page. What was the idea behind making a framework? Yeah.Stagehand: AI web browsing frameworkPaul [00:33:43]: So, there's three frameworks that are very popular or already exist, right? Puppeteer, Playwright, Selenium. Those are for building hard-coded scripts to control websites. And as soon as I started to play with LLMs plus browsing, I caught myself, you know, code-genning Playwright code to control a website. I would, like, take the DOM. I'd pass it to an LLM. I'd say, can you generate the Playwright code to click the appropriate button here? And it would do that. And I was like, this really should be part of the frameworks themselves. And I became really obsessed with SDKs that take natural language as part of, like, the API input. And that's what StageHand is. StageHand exposes three APIs, and it's a super set of Playwright. So, if you go to a page, you may want to take an action, click on the button, fill in the form, etc. That's what the act command is for. You may want to extract some data. This one takes a natural language, like, extract the winner of the Super Bowl from this page. You can give it a Zod schema, so it returns a structured output. And then maybe you're building an API. You can do an agent loop, and you want to kind of see what actions are possible on this page before taking one. You can do observe. So, you can observe the actions on the page, and it will generate a list of actions. You can guide it, like, give me actions on this page related to buying an item. And you can, like, buy it now, add to cart, view shipping options, and pass that to an LLM, an agent loop, to say, what's the appropriate action given this high-level goal? So, StageHand isn't a web agent. It's a framework for building web agents. And we think that agent loops are actually pretty close to the application layer because every application probably has different goals or different ways it wants to take steps. I don't think I've seen a generic. Maybe you guys are the experts here. I haven't seen, like, a really good AI agent framework here. Everyone kind of has their own special sauce, right? I see a lot of developers building their own agent loops, and they're using tools. And I view StageHand as the browser tool. So, we expose act, extract, observe. Your agent can call these tools. And from that, you don't have to worry about it. You don't have to worry about generating playwright code performantly. You don't have to worry about running it. You can kind of just integrate these three tool calls into your agent loop and reliably automate the web.swyx [00:35:48]: A special shout-out to Anirudh, who I met at your dinner, who I think listens to the pod. Yeah. Hey, Anirudh.Paul [00:35:54]: Anirudh's a man. He's a StageHand guy.swyx [00:35:56]: I mean, the interesting thing about each of these APIs is they're kind of each startup. Like, specifically extract, you know, Firecrawler is extract. There's, like, Expand AI. There's a whole bunch of, like, extract companies. They just focus on extract. I'm curious. Like, I feel like you guys are going to collide at some point. Like, right now, it's friendly. Everyone's in a blue ocean. At some point, it's going to be valuable enough that there's some turf battle here. I don't think you have a dog in a fight. I think you can mock extract to use an external service if they're better at it than you. But it's just an observation that, like, in the same way that I see each option, each checkbox in the side of custom GBTs becoming a startup or each box in the Karpathy chart being a startup. Like, this is also becoming a thing. Yeah.Paul [00:36:41]: I mean, like, so the way StageHand works is that it's MIT-licensed, completely open source. You bring your own API key to your LLM of choice. You could choose your LLM. We don't make any money off of the extract or really. We only really make money if you choose to run it with our browser. You don't have to. You can actually use your own browser, a local browser. You know, StageHand is completely open source for that reason. And, yeah, like, I think if you're building really complex web scraping workflows, I don't know if StageHand is the tool for you. I think it's really more if you're building an AI agent that needs a few general tools or if it's doing a lot of, like, web automation-intensive work. But if you're building a scraping company, StageHand is not your thing. You probably want something that's going to, like, get HTML content, you know, convert that to Markdown, query it. That's not what StageHand does. StageHand is more about reliability. I think we focus a lot on reliability and less so on cost optimization and speed at this point.swyx [00:37:33]: I actually feel like StageHand, so the way that StageHand works, it's like, you know, page.act, click on the quick start. Yeah. It's kind of the integration test for the code that you would have to write anyway, like the Puppeteer code that you have to write anyway. And when the page structure changes, because it always does, then this is still the test. This is still the test that I would have to write. Yeah. So it's kind of like a testing framework that doesn't need implementation detail.Paul [00:37:56]: Well, yeah. I mean, Puppeteer, Playwright, and Slenderman were all designed as testing frameworks, right? Yeah. And now people are, like, hacking them together to automate the web. I would say, and, like, maybe this is, like, me being too specific. But, like, when I write tests, if the page structure changes. Without me knowing, I want that test to fail. So I don't know if, like, AI, like, regenerating that. Like, people are using StageHand for testing. But it's more for, like, usability testing, not, like, testing of, like, does the front end, like, has it changed or not. Okay. But generally where we've seen people, like, really, like, take off is, like, if they're using, you know, something. If they want to build a feature in their application that's kind of like Operator or Deep Research, they're using StageHand to kind of power that tool calling in their own agent loop. Okay. Cool.swyx [00:38:37]: So let's go into Operator, the first big agent launch of the year from OpenAI. Seems like they have a whole bunch scheduled. You were on break and your phone blew up. What's your just general view of computer use agents is what they're calling it. The overall category before we go into Open Operator, just the overall promise of Operator. I will observe that I tried it once. It was okay. And I never tried it again.OpenAI's Operator and computer use agentsPaul [00:38:58]: That tracks with my experience, too. Like, I'm a huge fan of the OpenAI team. Like, I think that I do not view Operator as the company. I'm not a company killer for browser base at all. I think it actually shows people what's possible. I think, like, computer use models make a lot of sense. And I'm actually most excited about computer use models is, like, their ability to, like, really take screenshots and reasoning and output steps. I think that using mouse click or mouse coordinates, I've seen that proved to be less reliable than I would like. And I just wonder if that's the right form factor. What we've done with our framework is anchor it to the DOM itself, anchor it to the actual item. So, like, if it's clicking on something, it's clicking on that thing, you know? Like, it's more accurate. No matter where it is. Yeah, exactly. Because it really ties in nicely. And it can handle, like, the whole viewport in one go, whereas, like, Operator can only handle what it sees. Can you hover? Is hovering a thing that you can do? I don't know if we expose it as a tool directly, but I'm sure there's, like, an API for hovering. Like, move mouse to this position. Yeah, yeah, yeah. I think you can trigger hover, like, via, like, the JavaScript on the DOM itself. But, no, I think, like, when we saw computer use, everyone's eyes lit up because they realized, like, wow, like, AI is going to actually automate work for people. And I think seeing that kind of happen from both of the labs, and I'm sure we're going to see more labs launch computer use models, I'm excited to see all the stuff that people build with it. I think that I'd love to see computer use power, like, controlling a browser on browser base. And I think, like, Open Operator, which was, like, our open source version of OpenAI's Operator, was our first take on, like, how can we integrate these models into browser base? And we handle the infrastructure and let the labs do the models. I don't have a sense that Operator will be released as an API. I don't know. Maybe it will. I'm curious to see how well that works because I think it's going to be really hard for a company like OpenAI to do things like support CAPTCHA solving or, like, have proxies. Like, I think it's hard for them structurally. Imagine this New York Times headline, OpenAI CAPTCHA solving. Like, that would be a pretty bad headline, this New York Times headline. Browser base solves CAPTCHAs. No one cares. No one cares. And, like, our investors are bored. Like, we're all okay with this, you know? We're building this company knowing that the CAPTCHA solving is short-lived until we figure out how to authenticate good bots. I think it's really hard for a company like OpenAI, who has this brand that's so, so good, to balance with, like, the icky parts of web automation, which it can be kind of complex to solve. I'm sure OpenAI knows who to call whenever they need you. Yeah, right. I'm sure they'll have a great partnership.Alessio [00:41:23]: And is Open Operator just, like, a marketing thing for you? Like, how do you think about resource allocation? So, you can spin this up very quickly. And now there's all this, like, open deep research, just open all these things that people are building. We started it, you know. You're the original Open. We're the original Open operator, you know? Is it just, hey, look, this is a demo, but, like, we'll help you build out an actual product for yourself? Like, are you interested in going more of a product route? That's kind of the OpenAI way, right? They started as a model provider and then…Paul [00:41:53]: Yeah, we're not interested in going the product route yet. I view Open Operator as a model provider. It's a reference project, you know? Let's show people how to build these things using the infrastructure and models that are out there. And that's what it is. It's, like, Open Operator is very simple. It's an agent loop. It says, like, take a high-level goal, break it down into steps, use tool calling to accomplish those steps. It takes screenshots and feeds those screenshots into an LLM with the step to generate the right action. It uses stagehand under the hood to actually execute this action. It doesn't use a computer use model. And it, like, has a nice interface using the live view that we talked about, the iframe, to embed that into an application. So I felt like people on launch day wanted to figure out how to build their own version of this. And we turned that around really quickly to show them. And I hope we do that with other things like deep research. We don't have a deep research launch yet. I think David from AOMNI actually has an amazing open deep research that he launched. It has, like, 10K GitHub stars now. So he's crushing that. But I think if people want to build these features natively into their application, they need good reference projects. And I think Open Operator is a good example of that.swyx [00:42:52]: I don't know. Actually, I'm actually pretty bullish on API-driven operator. Because that's the only way that you can sort of, like, once it's reliable enough, obviously. And now we're nowhere near. But, like, give it five years. It'll happen, you know. And then you can sort of spin this up and browsers are working in the background and you don't necessarily have to know. And it just is booking restaurants for you, whatever. I can definitely see that future happening. I had this on the landing page here. This might be a slightly out of order. But, you know, you have, like, sort of three use cases for browser base. Open Operator. Or this is the operator sort of use case. It's kind of like the workflow automation use case. And it completes with UiPath in the sort of RPA category. Would you agree with that? Yeah, I would agree with that. And then there's Agents we talked about already. And web scraping, which I imagine would be the bulk of your workload right now, right?Paul [00:43:40]: No, not at all. I'd say actually, like, the majority is browser automation. We're kind of expensive for web scraping. Like, I think that if you're building a web scraping product, if you need to do occasional web scraping or you have to do web scraping that works every single time, you want to use browser automation. Yeah. You want to use browser-based. But if you're building web scraping workflows, what you should do is have a waterfall. You should have the first request is a curl to the website. See if you can get it without even using a browser. And then the second request may be, like, a scraping-specific API. There's, like, a thousand scraping APIs out there that you can use to try and get data. Scraping B. Scraping B is a great example, right? Yeah. And then, like, if those two don't work, bring out the heavy hitter. Like, browser-based will 100% work, right? It will load the page in a real browser, hydrate it. I see.swyx [00:44:21]: Because a lot of people don't render to JS.swyx [00:44:25]: Yeah, exactly.Paul [00:44:26]: So, I mean, the three big use cases, right? Like, you know, automation, web data collection, and then, you know, if you're building anything agentic that needs, like, a browser tool, you want to use browser-based.Alessio [00:44:35]: Is there any use case that, like, you were super surprised by that people might not even think about? Oh, yeah. Or is it, yeah, anything that you can share? The long tail is crazy. Yeah.Surprising use cases of BrowserbasePaul [00:44:44]: One of the case studies on our website that I think is the most interesting is this company called Benny. So, the way that it works is if you're on food stamps in the United States, you can actually get rebates if you buy certain things. Yeah. You buy some vegetables. You submit your receipt to the government. They'll give you a little rebate back. Say, hey, thanks for buying vegetables. It's good for you. That process of submitting that receipt is very painful. And the way Benny works is you use their app to take a photo of your receipt, and then Benny will go submit that receipt for you and then deposit the money into your account. That's actually using no AI at all. It's all, like, hard-coded scripts. They maintain the scripts. They've been doing a great job. And they build this amazing consumer app. But it's an example of, like, all these, like, tedious workflows that people have to do to kind of go about their business. And they're doing it for the sake of their day-to-day lives. And I had never known about, like, food stamp rebates or the complex forms you have to do to fill them. But the world is powered by millions and millions of tedious forms, visas. You know, Emirate Lighthouse is a customer, right? You know, they do the O1 visa. Millions and millions of forms are taking away humans' time. And I hope that Browserbase can help power software that automates away the web forms that we don't need anymore. Yeah.swyx [00:45:49]: I mean, I'm very supportive of that. I mean, forms. I do think, like, government itself is a big part of it. I think the government itself should embrace AI more to do more sort of human-friendly form filling. Mm-hmm. But I'm not optimistic. I'm not holding my breath. Yeah. We'll see. Okay. I think I'm about to zoom out. I have a little brief thing on computer use, and then we can talk about founder stuff, which is, I tend to think of developer tooling markets in impossible triangles, where everyone starts in a niche, and then they start to branch out. So I already hinted at a little bit of this, right? We mentioned more. We mentioned E2B. We mentioned Firecrawl. And then there's Browserbase. So there's, like, all this stuff of, like, have serverless virtual computer that you give to an agent and let them do stuff with it. And there's various ways of connecting it to the internet. You can just connect to a search API, like SERP API, whatever other, like, EXA is another one. That's what you're searching. You can also have a JSON markdown extractor, which is Firecrawl. Or you can have a virtual browser like Browserbase, or you can have a virtual machine like Morph. And then there's also maybe, like, a virtual sort of code environment, like Code Interpreter. So, like, there's just, like, a bunch of different ways to tackle the problem of give a computer to an agent. And I'm just kind of wondering if you see, like, everyone's just, like, happily coexisting in their respective niches. And as a developer, I just go and pick, like, a shopping basket of one of each. Or do you think that you eventually, people will collide?Future of browser automation and market competitionPaul [00:47:18]: I think that currently it's not a zero-sum market. Like, I think we're talking about... I think we're talking about all of knowledge work that people do that can be automated online. All of these, like, trillions of hours that happen online where people are working. And I think that there's so much software to be built that, like, I tend not to think about how these companies will collide. I just try to solve the problem as best as I can and make this specific piece of infrastructure, which I think is an important primitive, the best I possibly can. And yeah. I think there's players that are actually going to like it. I think there's players that are going to launch, like, over-the-top, you know, platforms, like agent platforms that have all these tools built in, right? Like, who's building the rippling for agent tools that has the search tool, the browser tool, the operating system tool, right? There are some. There are some. There are some, right? And I think in the end, what I have seen as my time as a developer, and I look at all the favorite tools that I have, is that, like, for tools and primitives with sufficient levels of complexity, you need to have a solution that's really bespoke to that primitive, you know? And I am sufficiently convinced that the browser is complex enough to deserve a primitive. Obviously, I have to. I'm the founder of BrowserBase, right? I'm talking my book. But, like, I think maybe I can give you one spicy take against, like, maybe just whole OS running. I think that when I look at computer use when it first came out, I saw that the majority of use cases for computer use were controlling a browser. And do we really need to run an entire operating system just to control a browser? I don't think so. I don't think that's necessary. You know, BrowserBase can run browsers for way cheaper than you can if you're running a full-fledged OS with a GUI, you know, operating system. And I think that's just an advantage of the browser. It is, like, browsers are little OSs, and you can run them very efficiently if you orchestrate it well. And I think that allows us to offer 90% of the, you know, functionality in the platform needed at 10% of the cost of running a full OS. Yeah.Open Operator: Browserbase's Open-Source Alternativeswyx [00:49:16]: I definitely see the logic in that. There's a Mark Andreessen quote. I don't know if you know this one. Where he basically observed that the browser is turning the operating system into a poorly debugged set of device drivers, because most of the apps are moved from the OS to the browser. So you can just run browsers.Paul [00:49:31]: There's a place for OSs, too. Like, I think that there are some applications that only run on Windows operating systems. And Eric from pig.dev in this upcoming YC batch, or last YC batch, like, he's building all run tons of Windows operating systems for you to control with your agent. And like, there's some legacy EHR systems that only run on Internet-controlled systems. Yeah.Paul [00:49:54]: I think that's it. I think, like, there are use cases for specific operating systems for specific legacy software. And like, I'm excited to see what he does with that. I just wanted to give a shout out to the pig.dev website.swyx [00:50:06]: The pigs jump when you click on them. Yeah. That's great.Paul [00:50:08]: Eric, he's the former co-founder of banana.dev, too.swyx [00:50:11]: Oh, that Eric. Yeah. That Eric. Okay. Well, he abandoned bananas for pigs. I hope he doesn't start going around with pigs now.Alessio [00:50:18]: Like he was going around with bananas. A little toy pig. Yeah. Yeah. I love that. What else are we missing? I think we covered a lot of, like, the browser-based product history, but. What do you wish people asked you? Yeah.Paul [00:50:29]: I wish people asked me more about, like, what will the future of software look like? Because I think that's really where I've spent a lot of time about why do browser-based. Like, for me, starting a company is like a means of last resort. Like, you shouldn't start a company unless you absolutely have to. And I remain convinced that the future of software is software that you're going to click a button and it's going to do stuff on your behalf. Right now, software. You click a button and it maybe, like, calls it back an API and, like, computes some numbers. It, like, modifies some text, whatever. But the future of software is software using software. So, I may log into my accounting website for my business, click a button, and it's going to go load up my Gmail, search my emails, find the thing, upload the receipt, and then comment it for me. Right? And it may use it using APIs, maybe a browser. I don't know. I think it's a little bit of both. But that's completely different from how we've built software so far. And that's. I think that future of software has different infrastructure requirements. It's going to require different UIs. It's going to require different pieces of infrastructure. I think the browser infrastructure is one piece that fits into that, along with all the other categories you mentioned. So, I think that it's going to require developers to think differently about how they've built software for, you know

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Früher habe ich alles was mit Medien zu tun hatte geliebt. Ich habe es geliebt informiert zu sein und mich ständig mit neuen Informationen zu versorgen. Dann wurden die Nachrichten immer schlimmer und zum ersten Mal stellte ich dann bei der Flüchtlingskrise fest, wie sehr mich diese Nachrichtenflut negativ beeinflusst. Als ich spiritueller wurde, beschloss ich, dass ich meinen Medienkonsum bewusst verändern möchte. Dadurch habe ich festgestellt wie es mir so viel besser geht und wie ich alleine deshalb positiver geworden bin und vor allem mein positives Denken aufrecht erhalten kann. Ich habe euch hierzu auch auf Threats gefragt und teile eure Meinungen hierzu. Endlich ist die neue #Trendbox von @trendbox.official da! Ab sofort trägt die Box einen neuen Namen und Look, aber die tolle Auswahl an nachhaltigen und innovativen Produkten bleibt! Mit meinem Code anziehung15 könnt ihr jetzt 15% sparen. Klicke einfach hier Diesen Monat steht alles im Zeichen von „New Beginnings“ – perfekt, für einen Neustart voller innovativer und nachhaltiger Produkte! Ich habe die Box gerade ausgepackt und sie ist voll mit tollen Produkten. Von Food bis Wellness und Lifestyle ist einfach alles dabei, um mit neuer Energie durchzustarten. Das Beste ist: Durch TrendRaider bekomme ich jeden Monat eine Anzahl cooler innovativer Produkte, sodass ich selber gar nicht erst suchen muss und spare zusätzlich bis zu 60%)! Ich freue mich über die 8 spannenden Produkte im Wert von über 95€. Klicke einfach hier und sichere dir mit meinem Code anziehung15 die neue Januarbox im brandneuen Design vom 01.01. bis zum 31.01. Mit diesen Marken starten wir in das neue Jahr: * NEWKEE - @newkee_care * I WANT YOU NAKED - @i.want.you.naked * BEROUTINE- @b.e.routine * SENTIER - @sentierfragrance * MITOCARE - @mitocare * GLÜCK - @meinglueck * BYODO - @byodo_naturkost * SMOL- @smol_de * GROH - @die_geschenkverlage * PLEASED TO MEET - @pleased_to_meet * WHOLEY - @wholey #trendbox #newbeginnings #lifestyle #nachhaltigkeit #sustainable #unboxing #mysterybox (*Werbung) Beim Gesetz der Anziehung Moment geht es heute um das Wiederbeleben einer Freundschaft. Übrigens gibt es hierzu eine weitere Fortsetzung: Wir sind wieder ein Herz und eine Seele und es ist wieder wie früher

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver. Today, we're proud to share Loubna's highly anticipated talk (slides here)!Synthetic DataWe called out the Synthetic Data debate at last year's NeurIPS, and no surprise that 2024 was dominated by the rise of synthetic data everywhere:* Apple's Rephrasing the Web, Microsoft's Phi 2-4 and Orca/AgentInstruct, Tencent's Billion Persona dataset, DCLM, and HuggingFace's FineWeb-Edu, and Loubna's own Cosmopedia extended the ideas of synthetic textbook and agent generation to improve raw web scrape dataset quality* This year we also talked to the IDEFICS/OBELICS team at HuggingFace who released WebSight this year, the first work on code-vs-images synthetic data.* We called Llama 3.1 the Synthetic Data Model for its extensive use (and documentation!) of synthetic data in its pipeline, as well as its permissive license. * Nemotron CC and Nemotron-4-340B also made a big splash this year for how they used 20k items of human data to synthesize over 98% of the data used for SFT/PFT.* Cohere introduced Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress observing gains of up to 56.5% improvement in win rates comparing multiple teachers vs the single best teacher model* In post training, AI2's Tülu3 (discussed by Luca in our Open Models talk) and Loubna's Smol Talk were also notable open releases this year.This comes in the face of a lot of scrutiny and criticism, with Scale AI as one of the leading voices publishing AI models collapse when trained on recursively generated data in Nature magazine bringing mainstream concerns to the potential downsides of poor quality syndata:Part of the concerns we highlighted last year on low-background tokens are coming to bear: ChatGPT contaminated data is spiking in every possible metric:But perhaps, if Sakana's AI Scientist pans out this year, we will have mostly-AI AI researchers publishing AI research anyway so do we really care as long as the ideas can be verified to be correct?Smol ModelsMeta surprised many folks this year by not just aggressively updating Llama 3 and adding multimodality, but also adding a new series of “small” 1B and 3B “on device” models this year, even working on quantized numerics collaborations with Qualcomm, Mediatek, and Arm. It is near unbelievable that a 1B model today can qualitatively match a 13B model of last year:and the minimum size to hit a given MMLU bar has come down roughly 10x in the last year. We have been tracking this proxied by Lmsys Elo and inference price:The key reads this year are:* MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases* Apple Intelligence Foundation Language Models* Hymba: A Hybrid-head Architecture for Small Language Models* Loubna's SmolLM and SmolLM2: a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters on the pareto efficiency frontier.* and Moondream, which we already covered in the 2024 in Vision talkFull Talk on YouTubeplease like and subscribe!Timestamps* [00:00:05] Loubna Intro* [00:00:33] The Rise of Synthetic Data Everywhere* [00:02:57] Model Collapse* [00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks* [00:12:36] DCLM, Nemotron-CC* [00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage* [00:16:17] Smol Models* [00:18:24] On Device Models* [00:22:45] Smol Vision Models* [00:25:14] What's NextTranscript2024 in Synthetic Data and Smol Models[00:00:00] ​[00:00:05] Loubna Intro[00:00:05] Speaker: ​I'm very happy to be here. Thank you for the invitation. So I'm going to be talking about synthetic data in 2024. And then I'm going to be talking about small on device models. So I think the most interesting thing about synthetic data this year is that like now we have it everywhere in the large language models pipeline.[00:00:33] The Rise of Synthetic Data Everywhere[00:00:33] Speaker: I think initially, synthetic data was mainly used just for post training, because naturally that's the part where we needed human annotators. And then after that, we realized that we don't really have good benchmarks to [00:01:00] measure if models follow instructions well, if they are creative enough, or if they are chatty enough, so we also started using LLMs as judges.[00:01:08] Speaker: Thank you. And I think this year and towards the end of last year, we also went to the pre training parts and we started generating synthetic data for pre training to kind of replace some parts of the web. And the motivation behind that is that you have a lot of control over synthetic data. You can control your prompt and basically also the kind of data that you generate.[00:01:28] Speaker: So instead of just trying to filter the web, you could try to get the LLM to generate what you think the best web pages could look like and then train your models on that. So this is how we went from not having synthetic data at all in the LLM pipeline to having it everywhere. And so the cool thing is like today you can train an LLM with like an entirely synthetic pipeline.[00:01:49] Speaker: For example, you can use our Cosmopedia datasets and you can train a 1B model on like 150 billion tokens that are 100 percent synthetic. And those are also of good quality. And then you can [00:02:00] instruction tune the model on a synthetic SFT dataset. You can also do DPO on a synthetic dataset. And then to evaluate if the model is good, you can use.[00:02:07] Speaker: A benchmark that uses LLMs as a judge, for example, MTBench or AlpacaEvil. So I think this is like a really mind blowing because like just a few years ago, we wouldn't think this is possible. And I think there's a lot of concerns about model collapse, and I'm going to talk about that later. But we'll see that like, if we use synthetic data properly and we curate it carefully, that shouldn't happen.[00:02:29] Speaker: And the reason synthetic data is very popular right now is that we have really strong models, both open and closed. It is really cheap and fast to use compared to human annotations, which cost a lot and take a lot of time. And also for open models right now, we have some really good inference frameworks.[00:02:47] Speaker: So if you have enough GPUs, it's really easy to spawn these GPUs and generate like a lot of synthetic data. Some examples are VLM, TGI, and TensorRT.[00:02:57] Model Collapse[00:02:57] Speaker: Now let's talk about the elephant in the room, model [00:03:00] collapse. Is this the end? If you look at the media and all of like, for example, some papers in nature, it's really scary because there's a lot of synthetic data out there in the web.[00:03:09] Speaker: And naturally we train on the web. So we're going to be training a lot of synthetic data. And if model collapse is going to happen, we should really try to take that seriously. And the other issue is that, as I said, we think, a lot of people think the web is polluted because there's a lot of synthetic data.[00:03:24] Speaker: And for example, when we're building fine web datasets here at Guillerm and Hinek, we're interested in like, how much synthetic data is there in the web? So there isn't really a method to properly measure the amount of synthetic data or to save a webpage synthetic or not. But one thing we can do is to try to look for like proxy words, for example, expressions like as a large language model or words like delve that we know are actually generated by chat GPT.[00:03:49] Speaker: We could try to measure the amount of these words in our data system and compare them to the previous years. For example, here, we measured like a, these words ratio in different dumps of common crawl. [00:04:00] And we can see that like the ratio really increased after chat GPT's release. So if we were to say that synthetic data amount didn't change, you would expect this ratio to stay constant, which is not the case.[00:04:11] Speaker: So there's a lot of synthetic data probably on the web, but does this really make models worse? So what we did is we trained different models on these different dumps. And we then computed their performance on popular, like, NLP benchmarks, and then we computed the aggregated score. And surprisingly, you can see that the latest DOMs are actually even better than the DOMs that are before.[00:04:31] Speaker: So if there's some synthetic data there, at least it did not make the model's worse. Yeah, which is really encouraging. So personally, I wouldn't say the web is positive with Synthetic Data. Maybe it's even making it more rich. And the issue with like model collapse is that, for example, those studies, they were done at like a small scale, and you would ask the model to complete, for example, a Wikipedia paragraph, and then you would train it on these new generations, and you would do that every day.[00:04:56] Speaker: iteratively. I think if you do that approach, it's normal to [00:05:00] observe this kind of behavior because the quality is going to be worse because the model is already small. And then if you train it just on its generations, you shouldn't expect it to become better. But what we're really doing here is that we take a model that is very large and we try to distill its knowledge into a model that is smaller.[00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks[00:05:14] Speaker: And in this way, you can expect to get like a better performance for your small model. And using synthetic data for pre-training has become really popular. After the textbooks are all you need papers where Microsoft basically trained a series of small models on textbooks that were using a large LLM.[00:05:32] Speaker: And then they found that these models were actually better than models that are much larger. So this was really interesting. It was like first of its time, but it was also met with a lot of skepticism, which is a good thing in research. It pushes you to question things because the dataset that they trained on was not public, so people were not really sure if these models are really good or maybe there's just some data contamination.[00:05:55] Speaker: So it was really hard to check if you just have the weights of the models. [00:06:00] And as Hugging Face, because we like open source, we tried to reproduce what they did. So this is our Cosmopedia dataset. We basically tried to follow a similar approach to what they documented in the paper. And we created a synthetic dataset of textbooks and blog posts and stories that had almost 30 billion tokens.[00:06:16] Speaker: And we tried to train some models on that. And we found that like the key ingredient to getting a good data set that is synthetic is trying as much as possible to keep it diverse. Because if you just throw the same prompts as your model, like generate like a textbook about linear algebra, and even if you change the temperature, the textbooks are going to look alike.[00:06:35] Speaker: So there's no way you could scale to like millions of samples. And the way you do that is by creating prompts that have some seeds that make them diverse. In our case, the prompt, we would ask the model to generate a textbook, but make it related to an extract from a webpage. And also we try to frame it within, to stay within topic.[00:06:55] Speaker: For example, here, we put like an extract about cardiovascular bioimaging, [00:07:00] and then we ask the model to generate a textbook related to medicine that is also related to this webpage. And this is a really nice approach because there's so many webpages out there. So you can. Be sure that your generation is not going to be diverse when you change the seed example.[00:07:16] Speaker: One thing that's challenging with this is that you want the seed samples to be related to your topics. So we use like a search tool to try to go all of fine web datasets. And then we also do a lot of experiments with the type of generations we want the model to generate. For example, we ask it for textbooks for middle school students or textbook for college.[00:07:40] Speaker: And we found that like some generation styles help on some specific benchmarks, while others help on other benchmarks. For example, college textbooks are really good for MMLU, while middle school textbooks are good for benchmarks like OpenBookQA and Pico. This is like a sample from like our search tool.[00:07:56] Speaker: For example, you have a top category, which is a topic, and then you have some [00:08:00] subtopics, and then you have the topic hits, which are basically the web pages in fine web does belong to these topics. And here you can see the comparison between Cosmopedia. We had two versions V1 and V2 in blue and red, and you can see the comparison to fine web, and as you can see throughout the training training on Cosmopedia was consistently better.[00:08:20] Speaker: So we managed to get a data set that was actually good to train these models on. It's of course so much smaller than FineWeb, it's only 30 billion tokens, but that's the scale that Microsoft data sets was, so we kind of managed to reproduce a bit what they did. And the data set is public, so everyone can go there, check if everything is all right.[00:08:38] Speaker: And now this is a recent paper from NVIDIA, Neumatron CC. They took things a bit further, and they generated not a few billion tokens, but 1. 9 trillion tokens, which is huge. And we can see later how they did that. It's more of, like, rephrasing the web. So we can see today that there's, like, some really huge synthetic datasets out there, and they're public, so, [00:09:00] like, you can try to filter them even further if you want to get, like, more high quality corpses.[00:09:04] Speaker: So for this, rephrasing the web this approach was suggested in this paper by Pratyush, where basically in this paper, they take some samples from C4 datasets, and then they use an LLM to rewrite these samples into a better format. For example, they ask an LLM to rewrite the sample into a Wikipedia passage or into a Q& A page.[00:09:25] Speaker: And the interesting thing in this approach is that you can use a model that is Small because it doesn't, rewriting doesn't require knowledge. It's just rewriting a page into a different style. So the model doesn't need to have like knowledge that is like extensive of what is rewriting compared to just asking a model to generate a new textbook and not giving it like ground truth.[00:09:45] Speaker: So here they rewrite some samples from C4 into Q& A, into Wikipedia, and they find that doing this works better than training just on C4. And so what they did in Nemo Trans CC is a similar approach. [00:10:00] They rewrite some pages from Common Crawl for two reasons. One is to, like improve Pages that are low quality, so they rewrite them into, for example, Wikipedia page, so they look better.[00:10:11] Speaker: And another reason is to create more diverse datasets. So they have a dataset that they already heavily filtered, and then they take these pages that are already high quality, and they ask the model to rewrite them in Question and Answer format. into like open ended questions or like multi choice questions.[00:10:27] Speaker: So this way they can reuse the same page multiple times without fearing like having multiple duplicates, because it's the same information, but it's going to be written differently. So I think that's also a really interesting approach for like generating synthetic data just by rephrasing the pages that you already have.[00:10:44] Speaker: There's also this approach called Prox where they try to start from a web page and then they generate a program which finds how to write that page to make it better and less noisy. For example, here you can see that there's some leftover metadata in the web page and you don't necessarily want to keep that for training [00:11:00] your model.[00:11:00] Speaker: So So they train a model that can generate programs that can like normalize and remove lines that are extra. So I think this approach is also interesting, but it's maybe less scalable than the approaches that I presented before. So that was it for like rephrasing and generating new textbooks.[00:11:17] Speaker: Another approach that I think is really good and becoming really popular for using synthetic data for pre training is basically building a better classifiers. For filtering the web for example, here we release the data sets called fine web edu. And the way we built it is by taking Llama3 and asking it to rate the educational content of web pages from zero to five.[00:11:39] Speaker: So for example, if a page is like a really good textbook that could be useful in a school setting, it would get a really high score. And if a page is just like an advertisement or promotional material, it would get a lower score. And then after that, we take these synthetic annotations and we train a classifier on them.[00:11:57] Speaker: It's a classifier like a BERT model. [00:12:00] And then we run this classifier on all of FineWeb, which is a 15 trillion tokens dataset. And then we only keep the pages that have like a score that's higher than 3. So for example, in our case, we went from 15 trillion tokens to 3. to just 1. 5 trillion tokens. Those are really highly educational.[00:12:16] Speaker: And as you can see here, a fine web EDU outperforms all the other public web datasets by a larger margin on a couple of benchmarks here, I show the aggregated score and you can see that this approach is really effective for filtering web datasets to get like better corpuses for training your LLMs.[00:12:36] DCLM, Nemotron-CC[00:12:36] Speaker: Others also try to do this approach. There's, for example, the DCLM datasets where they also train the classifier, but not to detect educational content. Instead, they trained it on OpenHermes dataset, which is a dataset for instruction tuning. And also they explain like IAM5 subreddits, and then they also get really high quality dataset which is like very information dense and can help [00:13:00] you train some really good LLMs.[00:13:01] Speaker: And then Nemotron Common Crawl, they also did this approach, but instead of using one classifier, they used an ensemble of classifiers. So they used, for example, the DCLM classifier, and also classifiers like the ones we used in FineWebEducational, and then they combined these two. Scores into a, with an ensemble method to only retain the best high quality pages, and they get a data set that works even better than the ones we develop.[00:13:25] Speaker: So that was it for like synthetic data for pre-training.[00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage[00:13:28] Speaker: Now we can go back to post training. I think there's a lot of interesting post training data sets out there. One that was released recently, the agent instructs by Microsoft where they basically try to target some specific skills. And improve the performance of models on them.[00:13:43] Speaker: For example, here, you can see code, brain teasers, open domain QA, and they managed to get a dataset that outperforms that's when fine tuning Mistral 7b on it, it outperforms the original instruct model that was released by Mistral. And as I said, to get good synthetic data, you really [00:14:00] have to have a framework to make sure that your data is diverse.[00:14:03] Speaker: So for example, for them, they always. And then they see the generations on either source code or raw text documents, and then they rewrite them to make sure they're easier to generate instructions from, and then they use that for their like instruction data generation. There's also the Tool3SFT mixture, which was released recently by Allen AI.[00:14:23] Speaker: It's also really good quality and it covers a wide range of tasks. And the way they make sure that this dataset is diverse is by using personas from the persona hub datasets. Which is basically a data set of like I think over a million personas. And for example, in the tool mixture to generate like a new code snippet, they would give like the model persona, for example, a machine learning researcher interested in neural networks, and then ask it to generate like a coding problem.[00:14:49] Speaker: This way you make sure that your data set is really diverse, and then you can further filter the data sets, for example, using the reward models. We also released a dataset called Smalltalk, [00:15:00] and we also tried to cover the wide range of tasks, and as you can see here, for example, when fine tuning Mistral 7b on the dataset, we also outperformed the original Mistral instructs on a number of benchmarks, notably on mathematics and instruction following with ifevil.[00:15:18] Speaker: Another paper that's really interesting I wanted to mention is this one called Multilingual Data Arbitrage by Cohere. And basically they want to generate a data set for post training that is multilingual. And they have a really interesting problem. It's the fact that there isn't like one model that's really good at all the languages they wanted.[00:15:36] Speaker: So what they do is that like they use not just one teacher model, but multiple teachers. And then they have a router which basically sends the prompts they have to all these models. And then they get the completions and they have a reward model that traces all these generations and only keeps the best one.[00:15:52] Speaker: And this is like arbitrage and finance. So well, I think what's interesting in this, it shows that like synthetic data, it doesn't have to come from a single model. [00:16:00] And because we have so many good models now, you could like pull these models together and get like a dataset that's really high quality and that's diverse and that's covers all your needs.[00:16:12] Speaker: I was supposed to put a meme there, but. Yeah, so that was it for like a synthetic data.[00:16:17] Smol Models[00:16:17] Speaker: Now we can go to see what's happening in the small models field in 2024. I don't know if you know, but like now we have some really good small models. For example, Lama 3. 2 1B is. It matches Lama 2. 13b from, that was released last year on the LMSYS arena, which is basically the default go to leaderboard for evaluating models using human evaluation.[00:16:39] Speaker: And as you can see here, the scores of the models are really close. So I think we've made like hugely forward in terms of small models. Of course, that's one, just one data point, but there's more. For example, if you look at this chart from the Quint 2. 5 blog post, it shows that today we have some really good models that are only like 3 billion parameters [00:17:00] and 4 billion that score really high on MMLU.[00:17:03] Speaker: Which is a really popular benchmark for evaluating models. And you can see here that the red, the blue dots have more than 65 on MMLU. And the grey ones have less. And for example, Llama33b had less. So now we have a 3b model that outperforms a 33b model that was released earlier. So I think now people are starting to realize that like, we shouldn't just scale and scale models, but we should try to make them more efficient.[00:17:33] Speaker: I don't know if you knew, but you can also chat with a 3B plus model on your iPhone. For example, here, this is an app called PocketPal, where you can go and select a model from Hugging Face. It has a large choice. For example, here we loaded the 5. 3. 5, which is 3. 8 billion parameters on this iPhone. And we can chat with this and you can see that even the latency is also acceptable.[00:17:57] Speaker: For example, here, I asked it to give me a joke about [00:18:00] NeurIPS. So let's see what it has to say.[00:18:06] Speaker: Okay, why did the neural network attend NeurIPS? Because it heard there would be a lot of layers and fun and it wanted to train its sense of humor. So not very funny, but at least it can run on device. Yeah, so I think now we have good small models, but we also have like good frameworks and tools to use these small models.[00:18:24] On Device Models[00:18:24] Speaker: So I think we're really close to having like really on edge and on device models that are really good. And I think for a while we've had this narrative. But just training larger models is better. Of course, this is supported by science scaling laws. As you can see here, for example, when we scale the model size, the loss is lower and obviously you get a better model.[00:18:46] Speaker: But and we can see this, for example, in the GPT family of models, how we went from just a hundred million parameters to more than a trillion. parameters. And of course, we all observed the performance improvement when using the latest model. But [00:19:00] one thing that we shouldn't forget is that when we scale the model, we also scale the inference costs and time.[00:19:05] Speaker: And so the largest models were are going to cost so much more. So I think now instead of just building larger models, we should be focusing on building more efficient models. It's no longer a race for the largest models since these models are really expensive to run and they require like a really good infrastructure to do that and they cannot run on, for example, consumer hardware.[00:19:27] Speaker: And when you try to build more efficient models that match larger models, that's when you can really unlock some really interesting on device use cases. And I think a trend that we're noticing now is the trend of training smaller models longer. For example, if you compare how much, how long LLAMA was trained compared to LLAMA3, there is a huge increase in the pre training length.[00:19:50] Speaker: LLAMA was trained on 1 trillion tokens, but LLAMA3 8b was trained on 15 trillion tokens. So Meta managed to get a model that's the same size, but But it performs so much [00:20:00] better by choosing to like spend the sacrifice during training, because as we know, training is a one time cost, but inference is something that's ongoing.[00:20:08] Speaker: If we want to see what are like the small models reads in 2024, I think this mobile LLM paper by Meta is interesting. They try to study different models that are like have the less than 1 billion parameters and find which architecture makes most sense for these models. For example, they find that depth is more important than width.[00:20:29] Speaker: So it's more important to have models that have like more layers than just one. making them more wide. They also find that GQA helps, that tying the embedding helps. So I think it's a nice study overall for models that are just a few hundred million parameters. There's also the Apple intelligence tech report, which is interesting.[00:20:48] Speaker: So for Apple intelligence, they had two models, one that was like on server and another model that was on device. It had 3 billion parameters. And I think the interesting part is that they trained this model using [00:21:00] pruning. And then distillation. And for example, they have this table where they show that, like, using pruning and distillation works much better than training from scratch.[00:21:08] Speaker: And they also have some interesting insights about, like, how they specialize their models on specific tasks, like, for example, summarization and rewriting. There's also this paper by NVIDIA that was released recently. I think you've already had a talk about, like, hybrid models that was all interesting.[00:21:23] Speaker: And this model, they used, like, a hybrid architecture between state space models and transformers. And they managed to train a 1B model that's really performant without needing to train it on a lot of tokens. And regarding our work, we just recently released SmallM2, so it's a series of three models, which are the best in class in each model size.[00:21:46] Speaker: For example, our 1. 7b model outperforms Lama 1b and also Qt 2. 5. And how we managed to train this model is the following. That's where you spent a lot of time trying to curate the pre training datasets. We did a lot of [00:22:00] ablations, trying to find which datasets are good and also how to mix them. We also created some new math and code datasets that we're releasing soon.[00:22:08] Speaker: But you basically really spent a lot of time trying to find what's the best mixture that you can train these models on. And then we spent some time trying to like we also trained these models for very long. For example, small M1 was trained only on 1 trillion tokens, but this model is trained on 11 trillion tokens.[00:22:24] Speaker: And we saw that the performance kept improving. The models didn't really plateau mid training, which I think is really interesting. It shows that you can train such small models for very long and keep getting performance gains. What's interesting about SmallLM2 is that it's fully open. We also released, like the pre training code base, the fine tuning code, the datasets, and also evaluation in this repository.[00:22:45] Smol Vision Models[00:22:45] Speaker: Also there's, like, really interesting small models for text, but also for vision. For example, here you can see SmallVLM, which is a 2B model that's really efficient. It doesn't consume a lot of RAM, and it also has a good performance. There's also Moondream 0. [00:23:00] 5b, which was released recently. It's like the smallest visual language model.[00:23:04] Speaker: And as you can see, there isn't like a big trade off compared to Moondream 2b. So now I showed you that we have some really good small models. We also have the tools to use them, but why should you consider using small models and when? I think, like, small models are really interesting because of the on device feature.[00:23:23] Speaker: Because these models are small and they can run fast, you can basically run them on your laptop, but also on your mobile phone. And this means that your dataset stays locally. You don't have to send your queries to third parties. And this really enhances privacy. That was, for example, one of the big selling points for Apple Intelligence.[00:23:42] Speaker: Also, right now, we really have a lot of work to do. So many frameworks to do on device inference. For example, there's MLX, MLC, Llama, CPP, Transformers, JS. So we have a lot of options and each of them have like great features. So you have so many options for doing that. Small models are also really powerful if you choose to specialize them.[00:24:00][00:24:00] Speaker: For example, here there's a startup called Numind, which took small LM and then they fine tuned it on text extraction datasets. And they managed to get a model that's not very far from models that are much larger. So I think text extraction is like one use case where small models can be really performant and it makes sense to use them instead of just using larger models.[00:24:19] Speaker: You can also chat with these models in browser. For example, here, you can go there, you can load the model, you can even turn off your internet and just start chatting with the model locally. Speaking of text extraction, if you don't want to fine tune the models, there's a really good method of structure generation.[00:24:36] Speaker: We can basically force the models to follow a JSON schema that you defined. For example, here, we try to force the model to follow a schema for extracting key information from GitHub issues. So you can input free text, which is a complaint about a GitHub repository, something not working. And then you can run it there and the model can extract anything that is relevant for your GitHub issue creation.[00:24:58] Speaker: For example, the [00:25:00] priority, for example, here, priority is high, the type of the issue bug, and then a title and the estimation of how long this will take to fix. And you can just like do this in the browser, you can transform your text into a GitHub issue that's properly formatted.[00:25:14] What's Next[00:25:14] Speaker: So what's next for synthetic data and small models?[00:25:18] Speaker: I think that domain specific synthetic data is going to be, it's already important, it's going to be even more important. For example, generating synthetic data for math. I think this really would help improve the reasoning of a lot of models. And a lot of people are doing it, for example, Quint 2. 12 math, everyone's trying to reproduce a one.[00:25:37] Speaker: And so I think for synthetic data, trying to specialize it on some domains is going to be really important. And then for small models, I think specializing them through fine tuning, it's also going to be really important because I think a lot of companies are just trying to use these large models because they are better.[00:25:53] Speaker: But on some tasks, I think you can already get decent performance with small models. So you don't need to Pay like a [00:26:00] cost that's much larger just to make your model better at your task by a few percent. And this is not just for text. And I think it also applies for other modalities like vision and audio.[00:26:11] Speaker: And I think you should also watch out for on device frameworks and applications. For example, like the app I showed, or lama, all these frameworks are becoming really popular and I'm pretty sure that we're gonna get like more of them in 2025. And users really like that. Maybe for other, I should also say hot take.[00:26:28] Speaker: I think that like in AI, we just started like with fine tuning, for example, trying to make BERT work on some specific use cases, and really struggling to do that. And then we had some models that are much larger. So we just switched to like prompt engineering to get the models And I think we're going back to fine tuning where we realize these models are really costly.[00:26:47] Speaker: It's better to use just a small model or try to specialize it. So I think it's a little bit of a cycle and we're going to start to see like more fine tuning and less of just like a prompt engineering the models. So that was my talk. Thank you for following. And if you have [00:27:00] any questions, we can take them now. 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The full schedule for Latent Space LIVE! at NeurIPS has been announced, featuring Best of 2024 overview talks for the AI Startup Landscape, Computer Vision, Open Models, Transformers Killers, Synthetic Data, Agents, and Scaling, and speakers from Sarah Guo of Conviction, Roboflow, AI2/Meta, Recursal/Together, HuggingFace, OpenHands and SemiAnalysis. Join us for the IRL event/Livestream! Alessio will also be holding a meetup at AWS Re:Invent in Las Vegas this Wednesday. See our new Events page for dates of AI Engineer Summit, Singapore, and World's Fair in 2025. LAST CALL for questions for our big 2024 recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!When we first observed that GPT Wrappers are Good, Actually, we did not even have Bolt on our radar. Since we recorded our Anthropic episode discussing building Agents with the new Claude 3.5 Sonnet, Bolt.new (by Stackblitz) has easily cleared the $8m ARR bar, repeating and accelerating its initial $4m feat.There are very many AI code generators and VS Code forks out there, but Bolt probably broke through initially because of its incredible zero shot low effort app generation:But as we explain in the pod, Bolt also emphasized deploy (Netlify)/ backend (Supabase)/ fullstack capabilities on top of Stackblitz's existing WebContainer full-WASM-powered-developer-environment-in-the-browser tech. Since then, the team has been shipping like mad (with weekly office hours), with bugfixing, full screen, multi-device, long context, diff based edits (using speculative decoding like we covered in Inference, Fast and Slow).All of this has captured the imagination of low/no code builders like Greg Isenberg and many others on YouTube/TikTok/Reddit/X/Linkedin etc:Just as with Fireworks, our relationship with Bolt/Stackblitz goes a bit deeper than normal - swyx advised the launch and got a front row seat to this epic journey, as well as demoed it with Realtime Voice at the recent OpenAI Dev Day. So we are very proud to be the first/closest to tell the full open story of Bolt/Stackblitz!Flow Engineering + Qodo/AlphaCodium UpdateIn year 2 of the pod we have been on a roll getting former guests to return as guest cohosts (Harrison Chase, Aman Sanger, Jon Frankle), and it was a pleasure to catch Itamar Friedman back on the pod, giving us an update on all things Qodo and Testing Agents from our last catchup a year and a half ago:Qodo (they renamed in September) went viral in early January this year with AlphaCodium (paper here, code here) beating DeepMind's AlphaCode with high efficiency:With a simple problem solving code agent:* The first step is to have the model reason about the problem. They describe it using bullet points and focus on the goal, inputs, outputs, rules, constraints, and any other relevant details.* Then, they make the model reason about the public tests and come up with an explanation of why the input leads to that particular output. * The model generates two to three potential solutions in text and ranks them in terms of correctness, simplicity, and robustness. * Then, it generates more diverse tests for the problem, covering cases not part of the original public tests. * Iteratively, pick a solution, generate the code, and run it on a few test cases. * If the tests fail, improve the code and repeat the process until the code passes every test.swyx has previously written similar thoughts on types vs tests for putting bounds on program behavior, but AlphaCodium extends this to AI generated tests and code.More recently, Itamar has also shown that AlphaCodium's techniques also extend well to the o1 models:Making Flow Engineering a useful technique to improve code model performance on every model. This is something we see AI Engineers uniquely well positioned to do compared to ML Engineers/Researchers.Full Video PodcastLike and subscribe!Show Notes* Itamar* Qodo* First episode* Eric* Bolt* StackBlitz* Thinkster* AlphaCodium* WebContainersChapters* 00:00:00 Introductions & Updates* 00:06:01 Generic vs. Specific AI Agents* 00:07:40 Maintaining vs Creating with AI* 00:17:46 Human vs Agent Computer Interfaces* 00:20:15 Why Docker doesn't work for Bolt* 00:24:23 Creating Testing and Code Review Loops* 00:28:07 Bolt's Task Breakdown Flow* 00:31:04 AI in Complex Enterprise Environments* 00:41:43 AlphaCodium* 00:44:39 Strategies for Breaking Down Complex Tasks* 00:45:22 Building in Open Source* 00:50:35 Choosing a product as a founder* 00:59:03 Reflections on Bolt Success* 01:06:07 Building a B2C GTM* 01:18:11 AI Capabilities and Pricing Tiers* 01:20:28 What makes Bolt unique* 01:23:07 Future Growth and Product Development* 01:29:06 Competitive Landscape in AI Engineering* 01:30:01 Advice to Founders and Embracing AI* 01:32:20 Having a baby and completing an Iron ManTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:12]: Hey, and today we're still in our sort of makeshift in-between studio, but we're very delighted to have a former returning guest host, Itamar. Welcome back.Itamar [00:00:21]: Great to be here after a year or more. Yeah, a year and a half.Swyx [00:00:24]: You're one of our earliest guests on Agents. Now you're CEO co-founder of Kodo. Right. Which has just been renamed. You also raised a $40 million Series A, and we can get caught up on everything, but we're also delighted to have our new guest, Eric. Welcome.Eric [00:00:42]: Thank you. Excited to be here. Should I say Bolt or StackBlitz?Swyx [00:00:45]: Like, is it like its own company now or?Eric [00:00:47]: Yeah. Bolt's definitely bolt.new. That's the thing that we're probably the most known for, I imagine, at this point.Swyx [00:00:54]: Which is ridiculous to say because you were working at StackBlitz for so long.Eric [00:00:57]: Yeah. I mean, within a week, we were doing like double the amount of traffic. And StackBlitz had been online for seven years, and we were like, what? But anyways, yeah. So we're StackBlitz, the company behind bolt.new. If you've heard of bolt.new, that's our stuff. Yeah.Swyx [00:01:12]: Yeah.Itamar [00:01:13]: Excellent. I see, by the way, that the founder mode, you need to know to capture opportunities. So kudos on doing that, right? You're working on some technology, and then suddenly you can exploit that to a new world. Yeah.Eric [00:01:24]: Totally. And I think, well, not to jump, but 100%, I mean, a couple of months ago, we had the idea for Bolt earlier this year, but we haven't really shared this too much publicly. But we actually had tried to build it with some of those state-of-the-art models back in January, February, you can kind of imagine which, and they just weren't good enough to actually do the code generation where the code was accurate and it was fast and whatever have you without a ton of like rag, but then there was like issues with that. So we put it on the shelf and then we got kind of a sneak peek of some of the new models that have come out in the past couple of months now. And so once we saw that, once we actually saw the code gen from it, we were like, oh my God, like, okay, we can build a product around this. And so that was really the impetus of us building the thing. But with that, it was StackBlitz, the core StackBlitz product the past seven years has been an IDE for developers. So the entire user experience flow we've built up just didn't make sense. And so when we kind of went out to build Bolt, we just thought, you know, if we were inventing our product today, what would the interface look like given what is now possible with the AI code gen? And so there's definitely a lot of conversations we had internally, but you know, just kind of when we logically laid it out, we were like, yeah, I think it makes sense to just greenfield a new thing and let's see what happens. If it works great, then we'll figure it out. If it doesn't work great, then it'll get deleted at some point. So that's kind of how it actually came to be.Swyx [00:02:49]: I'll mention your background a little bit. You were also founder of Thinkster before you started StackBlitz. So both of you are second time founders. Both of you have sort of re-founded your company recently. Yours was more of a rename. I think a slightly different direction as well. And then we can talk about both. Maybe just chronologically, should we get caught up on where Kodo is first and then you know, just like what people should know since the last pod? Sure.Itamar [00:03:12]: The last pod was two months after we launched and we basically had the vision that we talked about. The idea that software development is about specification, test and code, etc. We are more on the testing part as in essence, we think that if you solve testing, you solve software development. The beautiful chart that we'll put up on screen. And testing is a really big field, like there are many dimensions, unit testing, the level of the component, how big it is, how large it is. And then there is like different type of testing, is it regression or smoke or whatever. So back then we only had like one ID extension with unit tests as in focus. One and a half year later, first ID extension supports more type of testing as context aware. We index local, local repos, but also 10,000s of repos for Fortune 500 companies. We have another agent, another tool that is called, the pure agent is the open source and the commercial one is CodoMerge. And then we have another open source called CoverAgent, which is not yet a commercial product coming very soon. It's very impressive. It could be that already people are approving automated pull requests that they don't even aware in really big open sources. So once we have enough of these, we will also launch another agent. So for the first one and a half year, what we did is grew in our offering and mostly on the side of, does this code actually works, testing, code review, et cetera. And we believe that's the critical milestone that needs to be achieved to actually have the AI engineer for enterprise software. And then like for the first year was everything bottom up, getting to 1 million installation. 2024, that was 2023, 2024 was starting to monetize, to feel like how it is to make the first buck. So we did the teams offering, it went well with a thousand of teams, et cetera. And then we started like just a few months ago to do enterprise with everything you need, which is a lot of things that discussed in the last post that was just released by Codelm. So that's how we call it at Codelm. Just opening the brackets, our company name was Codelm AI, and we renamed to Codo and we call our models Codelm. So back to my point, so we started Enterprise Motion and already have multiple Fortune 100 companies. And then with that, we raised a series of $40 million. And what's exciting about it is that enables us to develop more agents. That's our focus. I think it's very different. We're not coming very soon with an ID or something like that.Swyx [00:06:01]: You don't want to fork this code?Itamar [00:06:03]: Maybe we'll fork JetBrains or something just to be different.Swyx [00:06:08]: I noticed that, you know, I think the promise of general purpose agents has kind of died. Like everyone is doing kind of what you're doing. There's Codogen, Codomerge, and then there's a third one. What's the name of it?Itamar [00:06:17]: Yeah. Codocover. Cover. Which is like a commercial version of a cover agent. It's coming soon.Swyx [00:06:23]: Yeah. It's very similar with factory AI, also doing like droids. They all have special purpose doing things, but people don't really want general purpose agents. Right. The last time you were here, we talked about AutoGBT, the biggest thing of 2023. This year, not really relevant anymore. And I think it's mostly just because when you give me a general purpose agent, I don't know what to do with it.Eric [00:06:42]: Yeah.Itamar [00:06:43]: I totally agree with that. We're seeing it for a while and I think it will stay like that despite the computer use, et cetera, that supposedly can just replace us. You can just like prompt it to be, hey, now be a QA or be a QA person or a developer. I still think that there's a few reasons why you see like a dedicated agent. Again, I'm a bit more focused, like my head is more on complex software for big teams and enterprise, et cetera. And even think about permissions and what are the data sources and just the same way you manage permissions for users. Developers, you probably want to have dedicated guardrails and dedicated approvals for agents. I intentionally like touched a point on not many people think about. And of course, then what you can think of, like maybe there's different tools, tool use, et cetera. But just the first point by itself is a good reason why you want to have different agents.Alessio [00:07:40]: Just to compare that with Bot.new, you're almost focused on like the application is very complex and now you need better tools to kind of manage it and build on top of it. On Bot.new, it's almost like I was using it the other day. There's basically like, hey, look, I'm just trying to get started. You know, I'm not very opinionated on like how you're going to implement this. Like this is what I want to do. And you build a beautiful app with it. What people ask as the next step, you know, going back to like the general versus like specific, have you had people say, hey, you know, this is great to start, but then I want a specific Bot.new dot whatever else to do a more vertical integration and kind of like development or what's the, what do people say?Eric [00:08:18]: Yeah. I think, I think you kind of hit the, hit it head on, which is, you know, kind of the way that we've, we've kind of talked about internally is it's like people are using Bolt to go from like 0.0 to 1.0, like that's like kind of the biggest unlock that Bolt has versus most other things out there. I mean, I think that's kind of what's, what's very unique about Bolt. I think the, you know, the working on like existing enterprise applications is, I mean, it's crazy important because, you know, there's a, you look, when you look at the fortune 500, I mean, these code bases, some of these have been around for 20, 30 plus years. And so it's important to be going from, you know, 101.3 to 101.4, et cetera. I think for us, so what's been actually pretty interesting is we see there's kind of two different users for us that are coming in and it's very distinct. It's like people that are developers already. And then there's people that have never really written software and more if they have, it's been very, very minimal. And so in the first camp, what these developers are doing, like to go from zero to one, they're coming to Bolt and then they're ejecting the thing to get up or just downloading it and, you know, opening cursor, like whatever to, to, you know, keep iterating on the thing. And sometimes they'll bring it back to Bolt to like add in a huge piece of functionality or something. Right. But for the people that don't know how to code, they're actually just, they, they live in this thing. And that was one of the weird things when we launched is, you know, within a day of us being online, one of the most popular YouTube videos, and there's been a ton since, which was, you know, there's like, oh, Bolt is the cursor killer. And I originally saw the headlines and I was like, thanks for the views. I mean, I don't know. This doesn't make sense to me. That's not, that's not what we kind of thought.Swyx [00:09:44]: It's how YouTubers talk to each other. Well, everything kills everything else.Eric [00:09:47]: Totally. But what blew my mind was that there was any comparison because it's like cursor is a, is a local IDE product. But when, when we actually kind of dug into it and we, and we have people that are using our product saying this, I'm not using cursor. And I was like, what? And it turns out there are hundreds of thousands of people that we have seen that we're using cursor and we're trying to build apps with that where they're not traditional software does, but we're heavily leaning on the AI. And as you can imagine, it is very complicated, right? To do that with cursor. So when Bolt came out, they're like, wow, this thing's amazing because it kind of inverts the complexity where it's like, you know, it's not an IDE, it's, it's a, it's a chat-based sort of interface that we have. So that's kind of the split, which is rather interesting. We've had like the first startups now launch off of Bolt entirely where this, you know, tomorrow I'm doing a live stream with this guy named Paul, who he's built an entire CRM using this thing and you know, with backend, et cetera. And people have made their first money on the internet period, you know, launching this with Stripe or whatever have you. So that's, that's kind of the two main, the two main categories of folks that we see using Bolt though.Itamar [00:10:51]: I agree that I don't understand the comparison. It doesn't make sense to me. I think like we have like two type of families of tools. One is like we re-imagine the software development. I think Bolt is there and I think like a cursor is more like a evolution of what we already have. It's like taking the IDE and it's, it's amazing and it's okay, let's, let's adapt the IDE to an era where LLMs can do a lot for us. And Bolt is more like, okay, let's rethink everything totally. And I think we see a few tools there, like maybe Vercel, Veo and maybe Repl.it in that area. And then in the area of let's expedite, let's change, let's, let's progress with what we already have. You can see Cursor and Kodo, but we're different between ourselves, Cursor and Kodo, but definitely I think that comparison doesn't make sense.Alessio [00:11:42]: And just to set the context, this is not a Twitter demo. You've made 4 million of revenue in four weeks. So this is, this is actually working, you know, it's not a, what, what do you think that is? Like, there's been so many people demoing coding agents on Twitter and then it doesn't really work. And then you guys were just like, here you go, it's live, go use it, pay us for it. You know, is there anything in the development that was like interesting and maybe how that compares to building your own agents?Eric [00:12:08]: We had no idea, honestly, like we, we, we've been pretty blown away and, and things have just kind of continued to grow faster since then. We're like, oh, today is week six. So I, I kind of came back to the point you just made, right, where it's, you, you kind of outlined, it's like, there's kind of this new market of like kind of rethinking the software development and then there's heavily augmenting existing developers. I think that, you know, both of which are, you know, AI code gen being extremely good, it's allowed existing developers, it's allowing existing developers to camera out software far faster than they could have ever before, right? It's like the ultimate power tool for an existing developer. But this code gen stuff is now so good. And then, and we saw this over the past, you know, from the beginning of the year when we tried to first build, it's actually lowered the barrier to people that, that aren't traditionally software engineers. But the kind of the key thing is if you kind of think about it from, imagine you've never written software before, right? My co-founder and I, he and I grew up down the street from each other in Chicago. We learned how to code when we were 13 together and we've been building stuff ever since. And this is back in like the mid 2000s or whatever, you know, there was nothing for free to learn from online on the internet and how to code. For our 13th birthdays, we asked our parents for, you know, O'Reilly books cause you couldn't get this at the library, right? And so instead of like an Xbox, we got, you know, programming books. But the hardest part for everyone learning to code is getting an environment set up locally, you know? And so when we built StackBlitz, like kind of the key thesis, like seven years ago, the insight we had was that, Hey, it seems like the browser has a lot of new APIs like WebAssembly and service workers, et cetera, where you could actually write an operating system that ran inside the browser that could boot in milliseconds. And you, you know, basically there's this missing capability of the web. Like the web should be able to build apps for the web, right? You should be able to build the web on the web. Every other platform has that, Visual Studio for Windows, Xcode for Mac. The web has no built in primitive for this. And so just like our built in kind of like nerd instinct on this was like, that seems like a huge hole and it's, you know, it will be very valuable or like, you know, very valuable problem to solve. So if you want to set up that environments, you know, this is what we spent the past seven years doing. And the reality is existing developers have running locally. They already know how to set up that environment. So the problem isn't as acute for them. When we put Bolt online, we took that technology called WebContainer and married it with these, you know, state of the art frontier models. And the people that have the most pain with getting stuff set up locally is people that don't code. I think that's been, you know, really the big explosive reason is no one else has been trying to make dev environments work inside of a browser tab, you know, for the past if since ever, other than basically our company, largely because there wasn't an immediate demand or need. So I think we kind of find ourselves at the right place at the right time. And again, for this market of people that don't know how to write software, you would kind of expect that you should be able to do this without downloading something to your computer in the same way that, hey, I don't have to download Photoshop now to make designs because there's Figma. I don't have to download Word because there's, you know, Google Docs. They're kind of looking at this as that sort of thing, right? Which was kind of the, you know, our impetus and kind of vision from the get-go. But you know, the code gen, the AI code gen stuff that's come out has just been, you know, an order of magnitude multiplier on how magic that is, right? So that's kind of my best distillation of like, what is going on here, you know?Alessio [00:15:21]: And you can deploy too, right?Eric [00:15:22]: Yeah.Alessio [00:15:23]: Yeah.Eric [00:15:24]: And so that's, what's really cool is it's, you know, we have deployment built in with Netlify and this is actually, I think, Sean, you actually built this at Netlify when you were there. Yeah. It's one of the most brilliant integrations actually, because, you know, effectively the API that Sean built, maybe you can speak to it, but like as a provider, we can just effectively give files to Netlify without the user even logging in and they have a live website. And if they want to keep, hold onto it, they can click a link and claim it to their Netlify account. But it basically is just this really magic experience because when you come to Bolt, you say, I want a website. Like my mom, 70, 71 years old, made her first website, you know, on the internet two weeks ago, right? It was about her nursing days.Swyx [00:16:03]: Oh, that's fantastic though. It wouldn't have been made.Eric [00:16:06]: A hundred percent. Cause even in, you know, when we've had a lot of people building personal, like deeply personal stuff, like in the first week we launched this, the sales guy from the East Coast, you know, replied to a tweet of mine and he said, thank you so much for building this to your team. His daughter has a medical condition and so for her to travel, she has to like line up donors or something, you know, so ahead of time. And so he actually used Bolt to make a website to do that, to actually go and send it to folks in the region she was going to travel to ahead of time. I was really touched by it, but I also thought like, why, you know, why didn't he use like Wix or Squarespace? Right? I mean, this is, this is a solved problem, quote unquote, right? And then when I thought, I actually use Squarespace for my, for my, uh, the wedding website for my wife and I, like back in 2021, so I'm familiar, you know, it was, it was faster. I know how to code. I was like, this is faster. Right. And I thought back and I was like, there's a whole interface you have to learn how to use. And it's actually not that simple. There's like a million things you can configure in that thing. When you come to Bolt, there's a, there's a text box. You just say, I need a, I need a wedding website. Here's the date. Here's where it is. And here's a photo of me and my wife, put it somewhere relevant. It's actually the simplest way. And that's what my, when my mom came, she said, uh, I'm Pat Simons. I was a nurse in the seventies, you know, and like, here's the things I did and a website came out. So coming back to why is this such a, I think, why are we seeing this sort of growth? It's, this is the simplest interface I think maybe ever created to actually build it, a deploy a website. And then that website, my mom made, she's like, okay, this looks great. And there's, there's one button, you just click it, deploy, and it's live and you can buy a domain name, attach it to it. And you know, it's as simple as it gets, it's getting even simpler with some of the stuff we're working on. But anyways, so that's, it's, it's, uh, it's been really interesting to see some of the usage like that.Swyx [00:17:46]: I can offer my perspective. So I, you know, I probably should have disclosed a little bit that, uh, I'm a, uh, stack list investor.Alessio [00:17:53]: Canceled the episode. I know, I know. Don't play it now. Pause.Eric actually reached out to ShowMeBolt before the launch. And we, you know, we talked a lot about, like, the framing of, of what we're going to talk about how we marketed the thing, but also, like, what we're So that's what Bolt was going to need, like a whole sort of infrastructure.swyx: Netlify, I was a maintainer but I won't take claim for the anonymous upload. That's actually the origin story of Netlify. We can have Matt Billman talk about it, but that was [00:18:00] how Netlify started. You could drag and drop your zip file or folder from your desktop onto a website, it would have a live URL with no sign in.swyx: And so that was the origin story of Netlify. And it just persists to today. And it's just like it's really nice, interesting that both Bolt and CognitionDevIn and a bunch of other sort of agent type startups, they all use Netlify to deploy because of this one feature. They don't really care about the other features.swyx: But, but just because it's easy for computers to use and talk to it, like if you build an interface for computers specifically, that it's easy for them to Navigate, then they will be used in agents. And I think that's a learning that a lot of developer tools companies are having. That's my bolt launch story and now if I say all that stuff.swyx: And I just wanted to come back to, like, the Webcontainers things, right? Like, I think you put a lot of weight on the technical modes. I think you also are just like, very good at product. So you've, you've like, built a better agent than a lot of people, the rest of us, including myself, who have tried to build these things, and we didn't get as far as you did.swyx: Don't shortchange yourself on products. But I think specifically [00:19:00] on, on infra, on like the sandboxing, like this is a thing that people really want. Alessio has Bax E2B, which we'll have on at some point, talking about like the sort of the server full side. But yours is, you know, inside of the browser, serverless.swyx: It doesn't cost you anything to serve one person versus a million people. It doesn't, doesn't cost you anything. I think that's interesting. I think in theory, we should be able to like run tests because you can run the full backend. Like, you can run Git, you can run Node, you can run maybe Python someday.swyx: We talked about this. But ideally, you should be able to have a fully gentic loop, running code, seeing the errors, correcting code, and just kind of self healing, right? Like, I mean, isn't that the dream?Eric: Totally.swyx: Yeah,Eric: totally. At least in bold, we've got, we've got a good amount of that today. I mean, there's a lot more for us to do, but one of the nice things, because like in web container, you know, there's a lot of kind of stuff you go Google like, you know, turn docker container into wasm.Eric: You'll find a lot of stuff out there that will do that. The problem is it's very big, it's slow, and that ruins the experience. And so what we ended up doing is just writing an operating system from [00:20:00] scratch that was just purpose built to, you know, run in a browser tab. And the reason being is, you know, Docker 2 awesome things will give you an image that's like out 60 to 100 megabits, you know, maybe more, you know, and our, our OS, you know, kind of clocks in, I think, I think we're in like a, maybe, maybe a megabyte or less or something like that.Eric: I mean, it's, it's, you know, really, really, you know, stripped down.swyx: This is basically the task involved is I understand that it's. Mapping every single, single Linux call to some kind of web, web assembly implementation,Eric: but more or less, and, and then there's a lot of things actually, like when you're looking at a dev environment, there's a lot of things that you don't need that a traditional OS is gonna have, right?Eric: Like, you know audio drivers or you like, there's just like, there's just tons of things. Oh, yeah. Right. Yeah. That goes . Yeah. You can just kind, you can, you can kind of tos them. Or alternatively, what you can do is you can actually be the nice thing. And this is, this kind of comes back to the origins of browsers, which is, you know, they're, they're at the beginning of the web and, you know, the late nineties, there was two very different kind of visions for the web where Alan Kay vehemently [00:21:00] disagree with the idea that should be document based, which is, you know, Tim Berners Lee, you know, that, and that's kind of what ended up winning, winning was this document based kind of browsing documents on the web thing.Eric: Alan Kay, he's got this like very famous quote where he said, you know, you want web browsers to be mini operating systems. They should download little mini binaries and execute with like a little mini virtualized operating system in there. And what's kind of interesting about the history, not to geek out on this aspect, what's kind of interesting about the history is both of those folks ended up being right.Eric: Documents were actually the pragmatic way that the web worked. Was, you know, became the most ubiquitous platform in the world to the degree now that this is why WebAssembly has been invented is that we're doing, we need to do more low level things in a browser, same thing with WebGPU, et cetera. And so all these APIs, you know, to build an operating system came to the browser.Eric: And that was actually the realization we had in 2017 was, holy heck, like you can actually, you know, service workers, which were designed for allowing your app to work offline. That was the kind of the key one where it was like, wait a second, you can actually now run. Web servers within a [00:22:00] browser, like you can run a server that you open up.Eric: That's wild. Like full Node. js. Full Node. js. Like that capability. Like, I can have a URL that's programmatically controlled. By a web application itself, boom. Like the web can build the web. The primitive is there. Everyone at the time, like we talked to people that like worked on, you know Chrome and V8 and they were like, uhhhh.Eric: You know, like I don't know. But it's one of those things you just kind of have to go do it to find out. So we spent a couple of years, you know, working on it and yeah. And, and, and got to work in back in 2021 is when we kind of put the first like data of web container online. Butswyx: in partnership with Google, right?swyx: Like Google actually had to help you get over the finish line with stuff.Eric: A hundred percent, because well, you know, over the years of when we were doing the R and D on the thing. Kind of the biggest challenge, the two ways that you can kind of test how powerful and capable a platform are, the two types of applications are one, video games, right, because they're just very compute intensive, a lot of calculations that have to happen, right?Eric: The second one are IDEs, because you're talking about actually virtualizing the actual [00:23:00] runtime environment you are in to actually build apps on top of it, which requires sophisticated capabilities, a lot of access to data. You know, a good amount of compute power, right, to effectively, you know, building app in app sort of thing.Eric: So those, those are the stress tests. So if your platform is missing stuff, those are the things where you find out. Those are, those are the people building games and IDEs. They're the ones filing bugs on operating system level stuff. And for us, browser level stuff.Eric [00:23:47]: yeah, what ended up happening is we were just hammering, you know, the Chromium bug tracker, and they're like, who are these guys? Yeah. And, and they were amazing because I mean, just making Chrome DevTools be able to debug, I mean, it's, it's not, it wasn't originally built right for debugging an operating system, right? They've been phenomenal working with us and just kind of really pushing the limits, but that it's a rising tide that's kind of lifted all boats because now there's a lot of different types of applications that you can debug with Chrome Dev Tools that are running a browser that runs more reliably because just the stress testing that, that we and, you know, games that are coming to the web are kind of pushing as well, but.Itamar [00:24:23]: That's awesome. About the testing, I think like most, let's say coding assistant from different kinds will need this loop of testing. And even I would add code review to some, to some extent that you mentioned. How is testing different from code review? Code review could be, for example, PR review, like a code review that is done at the point of when you want to merge branches. But I would say that code review, for example, checks best practices, maintainability, and so on. It's not just like CI, but more than CI. And testing is like a more like checking functionality, et cetera. So it's different. We call, by the way, all of these together code integrity, but that's a different story. Just to go back to the, to the testing and specifically. Yeah. It's, it's, it's since the first slide. Yeah. We're consistent. So if we go back to the testing, I think like, it's not surprising that for us testing is important and for Bolt it's testing important, but I want to shed some light on a different perspective of it. Like let's think about autonomous driving. Those startups that are doing autonomous driving for highway and autonomous driving for the city. And I think like we saw the autonomous of the highway much faster and reaching to a level, I don't know, four or so much faster than those in the city. Now, in both cases, you need testing and quote unquote testing, you know, verifying validation that you're doing the right thing on the road and you're reading and et cetera. But it's probably like so different in the city that it could be like actually different technology. And I claim that we're seeing something similar here. So when you're building the next Wix, and if I was them, I was like looking at you and being a bit scared. That's what you're disrupting, what you just said. Then basically, I would say that, for example, the UX UI is freaking important. And because you're you're more aiming for the end user. In this case, maybe it's an end user that doesn't know how to develop for developers. It's also important. But let alone those that do not know to develop, they need a slick UI UX. And I think like that's one reason, for example, I think Cursor have like really good technology. I don't know the underlying what's under the hood, but at least what they're saying. But I think also their UX UI is great. It's a lot because they did their own ID. While if you're aiming for the city AI, suddenly like there's a lot of testing and code review technology that it's not necessarily like that important. For example, let's talk about integration tests. Probably like a lot of what you're building involved at the moment is isolated applications. Maybe the vision or the end game is maybe like having one solution for everything. It could be that eventually the highway companies will go into the city and the other way around. But at the beginning, there is a difference. And integration tests are a good example. I guess they're a bit less important. And when you think about enterprise software, they're really important. So to recap, like I think like the idea of looping and verifying your test and verifying your code in different ways, testing or code review, et cetera, seems to be important in the highway AI and the city AI, but in different ways and different like critical for the city, even more and more variety. Actually, I was looking to ask you like what kind of loops you guys are doing. For example, when I'm using Bolt and I'm enjoying it a lot, then I do see like sometimes you're trying to catch the errors and fix them. And also, I noticed that you're breaking down tasks into smaller ones and then et cetera, which is already a common notion for a year ago. But it seems like you're doing it really well. So if you're willing to share anything about it.Eric [00:28:07]: Yeah, yeah. I realized I never actually hit the punchline of what I was saying before. I mentioned the point about us kind of writing an operating system from scratch because what ended up being important about that is that to your point, it's actually a very, like compared to like a, you know, if you're like running cursor on anyone's machine, you kind of don't know what you're dealing with, with the OS you're running on. There could be an error happens. It could be like a million different things, right? There could be some config. There could be, it could be God knows what, right? The thing with WebConnect is because we wrote the entire thing from scratch. It's actually a unified image basically. And we can instrument it at any level that we think is going to be useful, which is exactly what we did when we started building Bolt is we instrumented stuff at like the process level, at the runtime level, you know, et cetera, et cetera, et cetera. Stuff that would just be not impossible to do on local, but to do that in a way that works across any operating system, whatever is, I mean, would just be insanely, you know, insanely difficult to do right and reliably. And that's what you saw when you've used Bolt is that when an error actually will occur, whether it's in the build process or the actual web application itself is failing or anything kind of in between, you can actually capture those errors. And today it's a very primitive way of how we've implemented it largely because the product just didn't exist 90 days ago. So we're like, we got some work ahead of us and we got to hire some more a little bit, but basically we present and we say, Hey, this is, here's kind of the things that went wrong. There's a fix it button and then a ignore button, and then you can just hit fix it. And then we take all that telemetry through our agent, you run it through our agent and say, kind of, here's the state of the application. Here's kind of the errors that we got from Node.js or the browser or whatever, and like dah, dah, dah, dah. And it can take a crack at actually solving it. And it's actually pretty darn good at being able to do that. That's kind of been a, you know, closing the loop and having it be a reliable kind of base has seemed to be a pretty big upgrade over doing stuff locally, just because I think that's a pretty key ingredient of it. And yeah, I think breaking things down into smaller tasks, like that's, that's kind of a key part of our agent. I think like Claude did a really good job with artifacts. I think, you know, us and kind of everyone else has, has kind of taken their approach of like actually breaking out certain tasks in a certain order into, you know, kind of a concrete way. And, and so actually the core of Bolt, I know we actually made open source. So you can actually go and check out like the system prompts and et cetera, and you can run it locally and whatever have you. So anyone that's interested in this stuff, I'd highly recommend taking a look at. There's not a lot of like stuff that's like open source in this realm. It's, that was one of the fun things that we've we thought would be cool to do. And people, people seem to like it. I mean, there's a lot of forks and people adding different models and stuff. So it's been cool to see.Swyx [00:30:41]: Yeah. I'm happy to add, I added real-time voice for my opening day demo and it was really fun to hack with. So thank you for doing that. Yeah. Thank you. I'm going to steal your code.Eric [00:30:52]: Because I want that.Swyx [00:30:52]: It's funny because I built on top of the fork of Bolt.new that already has the multi LLM thing. And so you just told me you're going to merge that in. So then you're going to merge two layers of forks down into this thing. So it'll be fun.Eric [00:31:03]: Heck yeah.Alessio [00:31:04]: Just to touch on like the environment, Itamar, you maybe go into the most complicated environments that even the people that work there don't know how to run. How much of an impact does that have on your performance? Like, you know, it's most of the work you're doing actually figuring out environment and like the libraries, because I'm sure they're using outdated version of languages, they're using outdated libraries, they're using forks that have not been on the public internet before. How much of the work that you're doing is like there versus like at the LLM level?Itamar [00:31:32]: One of the reasons I was asking about, you know, what are the steps to break things down, because it really matters. Like, what's the tech stack? How complicated the software is? It's hard to figure it out when you're dealing with the real world, any environment of enterprise as a city, when I'm like, while maybe sometimes like, I think you do enable like in Bolt, like to install stuff, but it's quite a like controlled environment. And that's a good thing to do, because then you narrow down and it's easier to make things work. So definitely, there are two dimensions, I think, actually spaces. One is the fact just like installing our software without yet like doing anything, making it work, just installing it because we work with enterprise and Fortune 500, etc. Many of them want on prem solution.Swyx [00:32:22]: So you have how many deployment options?Itamar [00:32:24]: Basically, we had, we did a metric metrics, say 96 options, because, you know, they're different dimensions. Like, for example, one dimension, we connect to your code management system to your Git. So are you having like GitHub, GitLab? Subversion? Is it like on cloud or deployed on prem? Just an example. Which model agree to use its APIs or ours? Like we have our Is it TestGPT? Yeah, when we started with TestGPT, it was a huge mistake name. It was cool back then, but I don't think it's a good idea to name a model after someone else's model. Anyway, that's my opinion. So we gotSwyx [00:33:02]: I'm interested in these learnings, like things that you change your mind on.Itamar [00:33:06]: Eventually, when you're building a company, you're building a brand and you want to create your own brand. By the way, when I thought about Bolt.new, I also thought about if it's not a problem, because when I think about Bolt, I do think about like a couple of companies that are already called this way.Swyx [00:33:19]: Curse companies. You could call it Codium just to...Itamar [00:33:24]: Okay, thank you. Touche. Touche.Eric [00:33:27]: Yeah, you got to imagine the board meeting before we launched Bolt, one of our investors, you can imagine they're like, are you sure? Because from the investment side, it's kind of a famous, very notorious Bolt. And they're like, are you sure you want to go with that name? Oh, yeah. Yeah, absolutely.Itamar [00:33:43]: At this point, we have actually four models. There is a model for autocomplete. There's a model for the chat. There is a model dedicated for more for code review. And there is a model that is for code embedding. Actually, you might notice that there isn't a good code embedding model out there. Can you name one? Like dedicated for code?Swyx [00:34:04]: There's code indexing, and then you can do sort of like the hide for code. And then you can embed the descriptions of the code.Itamar [00:34:12]: Yeah, but you do see a lot of type of models that are dedicated for embedding and for different spaces, different fields, etc. And I'm not aware. And I know that if you go to the bedrock, try to find like there's a few code embedding models, but none of them are specialized for code.Swyx [00:34:31]: Is there a benchmark that you would tell us to pay attention to?Itamar [00:34:34]: Yeah, so it's coming. Wait for that. Anyway, we have our models. And just to go back to the 96 option of deployment. So I'm closing the brackets for us. So one is like dimensional, like what Git deployment you have, like what models do you agree to use? Dotter could be like if it's air-gapped completely, or you want VPC, and then you have Azure, GCP, and AWS, which is different. Do you use Kubernetes or do not? Because we want to exploit that. There are companies that do not do that, etc. I guess you know what I mean. So that's one thing. And considering that we are dealing with one of all four enterprises, we needed to deal with that. So you asked me about how complicated it is to solve that complex code. I said, it's just a deployment part. And then now to the software, we see a lot of different challenges. For example, some companies, they did actually a good job to build a lot of microservices. Let's not get to if it's good or not, but let's first assume that it is a good thing. A lot of microservices, each one of them has their own repo. And now you have tens of thousands of repos. And you as a developer want to develop something. And I remember me coming to a corporate for the first time. I don't know where to look at, like where to find things. So just doing a good indexing for that is like a challenge. And moreover, the regular indexing, the one that you can find, we wrote a few blogs on that. By the way, we also have some open source, different than yours, but actually three and growing. Then it doesn't work. You need to let the tech leads and the companies influence your indexing. For example, Mark with different repos with different colors. This is a high quality repo. This is a lower quality repo. This is a repo that we want to deprecate. This is a repo we want to grow, etc. And let that be part of your indexing. And only then things actually work for enterprise and they don't get to a fatigue of, oh, this is awesome. Oh, but I'm starting, it's annoying me. I think Copilot is an amazing tool, but I'm quoting others, meaning GitHub Copilot, that they see not so good retention of GitHub Copilot and enterprise. Ooh, spicy. Yeah. I saw snapshots of people and we have customers that are Copilot users as well. And also I saw research, some of them is public by the way, between 38 to 50% retention for users using Copilot and enterprise. So it's not so good. By the way, I don't think it's that bad, but it's not so good. So I think that's a reason because, yeah, it helps you auto-complete, but then, and especially if you're working on your repo alone, but if it's need that context of remote repos that you're code-based, that's hard. So to make things work, there's a lot of work on that, like giving the controllability for the tech leads, for the developer platform or developer experience department in the organization to influence how things are working. A short example, because if you have like really old legacy code, probably some of it is not so good anymore. If you just fine tune on these code base, then there is a bias to repeat those mistakes or old practices, etc. So you need, for example, as I mentioned, to influence that. For example, in Coda, you can have a markdown of best practices by the tech leads and Coda will include that and relate to that and will not offer suggestions that are not according to the best practices, just as an example. So that's just a short list of things that you need to do in order to deal with, like you mentioned, the 100.1 to 100.2 version of software. I just want to say what you're doing is extremelyEric [00:38:32]: impressive because it's very difficult. I mean, the business of Stackplus, kind of before bulk came online, we sold a version of our IDE that went on-prem. So I understand what you're saying about the difficulty of getting stuff just working on-prem. Holy heck. I mean, that is extremely hard. I guess the question I have for you is, I mean, we were just doing that with kind of Kubernetes-based stuff, but the spread of Fortune 500 companies that you're working with, how are they doing the inference for this? Are you kind of plugging into Azure's OpenAI stuff and AWS's Bedrock, you know, Cloud stuff? Or are they just like running stuff on GPUs? Like, what is that? How are these folks approaching that? Because, man, what we saw on the enterprise side, I mean, I got to imagine that that's a huge challenge. Everything you said and more, like,Itamar [00:39:15]: for example, like someone could be, and I don't think any of these is bad. Like, they made their decision. Like, for example, some people, they're, I want only AWS and VPC on AWS, no matter what. And then they, some of them, like there is a subset, I will say, I'm willing to take models only for from Bedrock and not ours. And we have a problem because there is no good code embedding model on Bedrock. And that's part of what we're doing now with AWS to solve that. We solve it in a different way. But if you are willing to run on AWS VPC, but run your run models on GPUs or inferentia, like the new version of the more coming out, then our models can run on that. But everything you said is right. Like, we see like on-prem deployment where they have their own GPUs. We see Azure where you're using OpenAI Azure. We see cases where you're running on GCP and they want OpenAI. Like this cross, like a case, although there is Gemini or even Sonnet, I think is available on GCP, just an example. So all the options, that's part of the challenge. I admit that we thought about it, but it was even more complicated. And it took us a few months to actually, that metrics that I mentioned, to start clicking each one of the blocks there. A few months is impressive. I mean,Eric [00:40:35]: honestly, just that's okay. Every one of these enterprises is, their networking is different. Just everything's different. Every single one is different. I see you understand. Yeah. So that just cannot be understated. That it is, that's extremely impressive. Hats off.Itamar [00:40:50]: It could be, by the way, like, for example, oh, we're only AWS, but our GitHub enterprise is on-prem. Oh, we forgot. So we need like a private link or whatever, like every time like that. It's not, and you do need to think about it if you want to work with an enterprise. And it's important. Like I understand like their, I respect their point of view.Swyx [00:41:10]: And this primarily impacts your architecture, your tech choices. Like you have to, you can't choose some vendors because...Itamar [00:41:15]: Yeah, definitely. To be frank, it makes us hard for a startup because it means that we want, we want everyone to enjoy all the variety of models. By the way, it was hard for us with our technology. I want to open a bracket, like a window. I guess you're familiar with our Alpha Codium, which is an open source.Eric [00:41:33]: We got to go over that. Yeah. So I'll do that quickly.Itamar [00:41:36]: Yeah. A pin in that. Yeah. Actually, we didn't have it in the last episode. So, so, okay.Swyx [00:41:41]: Okay. We'll come back to that later, but let's talk about...Itamar [00:41:43]: Yeah. So, so just like shortly, and then we can double click on Alpha Codium. But Alpha Codium is a open source tool. You can go and try it and lets you compete on CodeForce. This is a website and a competition and actually reach a master level level, like 95% with a click of a button. You don't need to do anything. And part of what we did there is taking a problem and breaking it to different, like smaller blocks. And then the models are doing a much better job. Like we all know it by now that taking small tasks and solving them, by the way, even O1, which is supposed to be able to do system two thinking like Greg from OpenAI like hinted, is doing better on these kinds of problems. But still, it's very useful to break it down for O1, despite O1 being able to think by itself. And that's what we presented like just a month ago, OpenAI released that now they are doing 93 percentile with O1 IOI left and International Olympiad of Formation. Sorry, I forgot. Exactly. I told you I forgot. And we took their O1 preview with Alpha Codium and did better. Like it just shows like, and there is a big difference between the preview and the IOI. It shows like that these models are not still system two thinkers, and there is a big difference. So maybe they're not complete system two. Yeah, they need some guidance. I call them system 1.5. We can, we can have it. I thought about it. Like, you know, I care about this philosophy stuff. And I think like we didn't see it even close to a system two thinking. I can elaborate later. But closing the brackets, like we take Alpha Codium and as our principle of thinking, we take tasks and break them down to smaller tasks. And then we want to exploit the best model to solve them. So I want to enable anyone to enjoy O1 and SONET and Gemini 1.5, etc. But at the same time, I need to develop my own models as well, because some of the Fortune 500 want to have all air gapped or whatever. So that's a challenge. Now you need to support so many models. And to some extent, I would say that the flow engineering, the breaking down to two different blocks is a necessity for us. Why? Because when you take a big block, a big problem, you need a very different prompt for each one of the models to actually work. But when you take a big problem and break it into small tasks, we can talk how we do that, then the prompt matters less. What I want to say, like all this, like as a startup trying to do different deployment, getting all the juice that you can get from models, etc. is a big problem. And one need to think about it. And one of our mitigation is that process of taking tasks and breaking them down. That's why I'm really interested to know how you guys are doing it. And part of what we do is also open source. So you can see.Swyx [00:44:39]: There's a lot in there. But yeah, flow over prompt. I do believe that that does make sense. I feel like there's a lot that both of you can sort of exchange notes on breaking down problems. And I just want you guys to just go for it. This is fun to watch.Eric [00:44:55]: Yeah. I mean, what's super interesting is the context you're working in is, because for us too with Bolt, we've started thinking because our kind of existing business line was going behind the firewall, right? We were like, how do we do this? Adding the inference aspect on, we're like, okay, how does... Because I mean, there's not a lot of prior art, right? I mean, this is all new. This is all new. So I definitely am going to have a lot of questions for you.Itamar [00:45:17]: I'm here. We're very open, by the way. We have a paper on a blog or like whatever.Swyx [00:45:22]: The Alphacodeum, GitHub, and we'll put all this in the show notes.Itamar [00:45:25]: Yeah. And even the new results of O1, we published it.Eric [00:45:29]: I love that. And I also just, I think spiritually, I like your approach of being transparent. Because I think there's a lot of hype-ium around AI stuff. And a lot of it is, it's just like, you have these companies that are just kind of keep their stuff closed source and then just max hype it, but then it's kind of nothing. And I think it kind of gives a bad rep to the incredible stuff that's actually happening here. And so I think it's stuff like what you're doing where, I mean, true merit and you're cracking open actual code for others to learn from and use. That strikes me as the right approach. And it's great to hear that you're making such incredible progress.Itamar [00:46:02]: I have something to share about the open source. Most of our tools are, we have an open source version and then a premium pro version. But it's not an easy decision to do that. I actually wanted to ask you about your strategy, but I think in your case, there is, in my opinion, relatively a good strategy where a lot of parts of open source, but then you have the deployment and the environment, which is not right if I get it correctly. And then there's a clear, almost hugging face model. Yeah, you can do that, but why should you try to deploy it yourself, deploy it with us? But in our case, and I'm not sure you're not going to hit also some competitors, and I guess you are. I wanted to ask you, for example, on some of them. In our case, one day we looked on one of our competitors that is doing code review. We're a platform. We have the code review, the testing, et cetera, spread over the ID to get. And in each agent, we have a few startups or a big incumbents that are doing only that. So we noticed one of our competitors having not only a very similar UI of our open source, but actually even our typo. And you sit there and you're kind of like, yeah, we're not that good. We don't use enough Grammarly or whatever. And we had a couple of these and we saw it there. And then it's a challenge. And I want to ask you, Bald is doing so well, and then you open source it. So I think I know what my answer was. I gave it before, but still interestingEric [00:47:29]: to hear what you think. GeoHot said back, I don't know who he was up to at this exact moment, but I think on comma AI, all that stuff's open source. And someone had asked him, why is this open source? And he's like, if you're not actually confident that you can go and crush it and build the best thing, then yeah, you should probably keep your stuff closed source. He said something akin to that. I'm probably kind of butchering it, but I thought it was kind of a really good point. And that's not to say that you should just open source everything, because for obvious reasons, there's kind of strategic things you have to kind of take in mind. But I actually think a pretty liberal approach, as liberal as you kind of can be, it can really make a lot of sense. Because that is so validating that one of your competitors is taking your stuff and they're like, yeah, let's just kind of tweak the styles. I mean, clearly, right? I think it's kind of healthy because it keeps, I'm sure back at HQ that day when you saw that, you're like, oh, all right, well, we have to grind even harder to make sure we stay ahead. And so I think it's actually a very useful, motivating thing for the teams. Because you might feel this period of comfort. I think a lot of companies will have this period of comfort where they're not feeling the competition and one day they get disrupted. So kind of putting stuff out there and letting people push it forces you to face reality soon, right? And actually feel that incrementally so you can kind of adjust course. And that's for us, the open source version of Bolt has had a lot of features people have been begging us for, like persisting chat messages and checkpoints and stuff. Within the first week, that stuff was landed in the open source versions. And they're like, why can't you ship this? It's in the open, so people have forked it. And we're like, we're trying to keep our servers and GPUs online. But it's been great because the folks in the community did a great job, kept us on our toes. And we've got to know most of these folks too at this point that have been building these things. And so it actually was very instructive. Like, okay, well, if we're going to go kind of land this, there's some UX patterns we can kind of look at and the code is open source to this stuff. What's great about these, what's not. So anyways, NetNet, I think it's awesome. I think from a competitive point of view for us, I think in particular, what's interesting is the core technology of WebContainer going. And I think that right now, there's really nothing that's kind of on par with that. And we also, we have a business of, because WebContainer runs in your browser, but to make it work, you have to install stuff from NPM. You have to make cores bypass requests, like connected databases, which all require server-side proxying or acceleration. And so we actually sell WebContainer as a service. One of the core reasons we open-sourced kind of the core components of Bolt when we launched was that we think that there's going to be a lot more of these AI, in-your-browser AI co-gen experiences, kind of like what Anthropic did with Artifacts and Clod. By the way, Artifacts uses WebContainers. Not yet. No, yeah. Should I strike that? I think that they've got their own thing at the moment, but there's been a lot of interest in WebContainers from folks doing things in that sort of realm and in the AI labs and startups and everything in between. So I think there'll be, I imagine, over the coming months, there'll be lots of things being announced to folks kind of adopting it. But yeah, I think effectively...Swyx [00:50:35]: Okay, I'll say this. If you're a large model lab and you want to build sandbox environments inside of your chat app, you should call Eric.Itamar [00:50:43]: But wait, wait, wait, wait, wait, wait. I have a question about that. I think OpenAI, they felt that people are not using their model as they would want to. So they built ChatGPT. But I would say that ChatGPT now defines OpenAI. I know they're doing a lot of business from their APIs, but still, is this how you think? Isn't Bolt.new your business now? Why don't you focus on that instead of the...Swyx [00:51:16]: What's your advice as a founder?Eric [00:51:18]: You're right. And so going into it, we, candidly, we were like, Bolt.new, this thing is super cool. We think people are stoked. We think people will be stoked. But we were like, maybe that's allowed. Best case scenario, after month one, we'd be mind blown if we added a couple hundred K of error or something. And we were like, but we think there's probably going to be an immediate huge business. Because there was some early poll on folks wanting to put WebContainer into their product offerings, kind of similar to what Bolt is doing or whatever. We were actually prepared for the inverse outcome here. But I mean, well, I guess we've seen poll on both. But I mean, what's happened with Bolt, and you're right, it's actually the same strategy as like OpenAI or Anthropic, where we have our ChatGPT to OpenAI's APIs is Bolt to WebContainer. And so we've kind of taken that same approach. And we're seeing, I guess, some of the similar results, except right now, the revenue side is extremely lopsided to Bolt.Itamar [00:52:16]: I think if you ask me what's my advice, I think you have three options. One is to focus on Bolt. The other is to focus on the WebContainer. The third is to raise one billion dollars and do them both. I'm serious. I think otherwise, you need to choose. And if you raise enough money, and I think it's big bucks, because you're going to be chased by competitors. And I think it will be challenging to do both. And maybe you can. I don't know. We do see these numbers right now, raising above $100 million, even without havingEric [00:52:49]: a product. You can see these. It's excellent advice. And I think what's been amazing, but also kind of challenging is we're trying to forecast, okay, well, where are these things going? I mean, in the initial weeks, I think us and all the investors in the company that we're sharing this with, it was like, this is cool. Okay, we added 500k. Wow, that's crazy. Wow, we're at a million now. Most things, you have this kind of the tech crunch launch of initiation and then the thing of sorrow. And if there's going to be a downtrend, it's just not coming yet. Now that we're kind of looking ahead, we're six weeks in. So now we're getting enough confidence in our convictions to go, okay, this se

What might be the cause of international suffering? In this week's episode, we are joined by freelance film writer Christopher Smol to discuss the examinations of complicity, gentility and retrospective guilt in James Ivory's 1993 drama, The Remains of the Day. We also discuss: The Wind Rises (2013) d. Hayao Miyazaki Oppenheimer (2023) d. Christopher Nolan Contact Us E: ⁠⁠⁠⁠⁠⁠⁠⁠⁠contact@jimmybernasconi.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ IG: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/filmsfortoday/⁠⁠⁠

Alessio will be at AWS re:Invent next week and hosting a casual coffee meetup on Wednesday, RSVP here! And subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!If you've been following the AI agents space, you have heard of Lindy AI; while founder Flo Crivello is hesitant to call it "blowing up," when folks like Andrew Wilkinson start obsessing over your product, you're definitely onto something.In our latest episode, Flo walked us through Lindy's evolution from late 2022 to now, revealing some design choices about agent platform design that go against conventional wisdom in the space.The Great Reset: From Text Fields to RailsRemember late 2022? Everyone was "LLM-pilled," believing that if you just gave a language model enough context and tools, it could do anything. Lindy 1.0 followed this pattern:* Big prompt field ✅* Bunch of tools ✅* Prayer to the LLM gods ✅Fast forward to today, and Lindy 2.0 looks radically different. As Flo put it (~17:00 in the episode): "The more you can put your agent on rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user."Instead of a giant, intimidating text field, users now build workflows visually:* Trigger (e.g., "Zendesk ticket received")* Required actions (e.g., "Check knowledge base")* Response generationThis isn't just a UI change - it's a fundamental rethinking of how to make AI agents reliable. As Swyx noted during our discussion: "Put Shoggoth in a box and make it a very small, minimal viable box. Everything else should be traditional if-this-then-that software."The Surprising Truth About Model LimitationsHere's something that might shock folks building in the space: with Claude 3.5 Sonnet, the model is no longer the bottleneck. Flo's exact words (~31:00): "It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small."Some context: Lindy started when context windows were 4K tokens. Today, their system prompt alone is larger than that. But what's really interesting is what this means for platform builders:* Raw capabilities aren't the constraint anymore* Integration quality matters more than model performance* User experience and workflow design are the new bottlenecksThe Search Engine Parallel: Why Horizontal Platforms Might WinOne of the spiciest takes from our conversation was Flo's thesis on horizontal vs. vertical agent platforms. He draws a fascinating parallel to search engines (~56:00):"I find it surprising the extent to which a horizontal search engine has won... You go through Google to search Reddit. You go through Google to search Wikipedia... search in each vertical has more in common with search than it does with each vertical."His argument: agent platforms might follow the same pattern because:* Agents across verticals share more commonalities than differences* There's value in having agents that can work together under one roof* The R&D cost of getting agents right is better amortized across use casesThis might explain why we're seeing early vertical AI companies starting to expand horizontally. The core agent capabilities - reliability, context management, tool integration - are universal needs.What This Means for BuildersIf you're building in the AI agents space, here are the key takeaways:* Constrain First: Rather than maximizing capabilities, focus on reliable execution within narrow bounds* Integration Quality Matters: With model capabilities plateauing, your competitive advantage lies in how well you integrate with existing tools* Memory Management is Key: Flo revealed they actively prune agent memories - even with larger context windows, not all memories are useful* Design for Discovery: Lindy's visual workflow builder shows how important interface design is for adoptionThe Meta LayerThere's a broader lesson here about AI product development. Just as Lindy evolved from "give the LLM everything" to "constrain intelligently," we might see similar evolution across the AI tooling space. The winners might not be those with the most powerful models, but those who best understand how to package AI capabilities in ways that solve real problems reliably.Full Video PodcastFlo's talk at AI Engineer SummitChapters* 00:00:00 Introductions * 00:04:05 AI engineering and deterministic software * 00:08:36 Lindys demo* 00:13:21 Memory management in AI agents * 00:18:48 Hierarchy and collaboration between Lindys * 00:21:19 Vertical vs. horizontal AI tools * 00:24:03 Community and user engagement strategies * 00:26:16 Rickrolling incident with Lindy * 00:28:12 Evals and quality control in AI systems * 00:31:52 Model capabilities and their impact on Lindy * 00:39:27 Competition and market positioning * 00:42:40 Relationship between Factorio and business strategy * 00:44:05 Remote work vs. in-person collaboration * 00:49:03 Europe vs US Tech* 00:58:59 Testing the Overton window and free speech * 01:04:20 Balancing AI safety concerns with business innovation Show Notes* Lindy.ai* Rick Rolling* Flo on X* TeamFlow* Andrew Wilkinson* Dust* Poolside.ai* SB1047* Gathertown* Sid Sijbrandij* Matt Mullenweg* Factorio* Seeing Like a StateTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:12]: Hey, and today we're joined in the studio by Florent Crivello. Welcome.Flo [00:00:15]: Hey, yeah, thanks for having me.Swyx [00:00:17]: Also known as Altimore. I always wanted to ask, what is Altimore?Flo [00:00:21]: It was the name of my character when I was playing Dungeons & Dragons. Always. I was like 11 years old.Swyx [00:00:26]: What was your classes?Flo [00:00:27]: I was an elf. I was a magician elf.Swyx [00:00:30]: Well, you're still spinning magic. Right now, you're a solo founder and CEO of Lindy.ai. What is Lindy?Flo [00:00:36]: Yeah, we are a no-code platform letting you build your own AI agents easily. So you can think of we are to LangChain as Airtable is to MySQL. Like you can just pin up AI agents super easily by clicking around and no code required. You don't have to be an engineer and you can automate business workflows that you simply could not automate before in a few minutes.Swyx [00:00:55]: You've been in our orbit a few times. I think you spoke at our Latent Space anniversary. You spoke at my summit, the first summit, which was a really good keynote. And most recently, like we actually already scheduled this podcast before this happened. But Andrew Wilkinson was like, I'm obsessed by Lindy. He's just created a whole bunch of agents. So basically, why are you blowing up?Flo [00:01:16]: Well, thank you. I think we are having a little bit of a moment. I think it's a bit premature to say we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0. I would say we started working on that six months ago. We've actually not really announced it yet. It's just, I guess, I guess that's what we're doing now. And so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think I'll list you, actually, the last time you tried the product, it was still Lindy 1.0. Oh, yeah. If you log in now, the platform looks very different. There's like a ton more features. And I think one realization that we made, and I think a lot of folks in the agent space made the same realization, is that there is such a thing as too much of a good thing. I think many people, when they started working on agents, they were very LLM peeled and chat GPT peeled, right? They got ahead of themselves in a way, and us included, and they thought that agents were actually, and LLMs were actually more advanced than they actually were. And so the first version of Lindy was like just a giant prompt and a bunch of tools. And then the realization we had was like, hey, actually, the more you can put your agent on Rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user, because you can really, as a user, you get, instead of just getting this big, giant, intimidating text field, and you type words in there, and you have no idea if you're typing the right word or not, here you can really click and select step by step, and tell your agent what to do, and really give as narrow or as wide a guardrail as you want for your agent. We started working on that. We called it Lindy on Rails about six months ago, and we started putting it into the hands of users over the last, I would say, two months or so, and I think things really started going pretty well at that point. The agent is way more reliable, way easier to set up, and we're already seeing a ton of new use cases pop up.Swyx [00:03:00]: Yeah, just a quick follow-up on that. You launched the first Lindy in November last year, and you were already talking about having a DSL, right? I remember having this discussion with you, and you were like, it's just much more reliable. Is this still the DSL under the hood? Is this a UI-level change, or is it a bigger rewrite?Flo [00:03:17]: No, it is a much bigger rewrite. I'll give you a concrete example. Suppose you want to have an agent that observes your Zendesk tickets, and it's like, hey, every time you receive a Zendesk ticket, I want you to check my knowledge base, so it's like a RAG module and whatnot, and then answer the ticket. The way it used to work with Lindy before was, you would type the prompt asking it to do that. You check my knowledge base, and so on and so forth. The problem with doing that is that it can always go wrong. You're praying the LLM gods that they will actually invoke your knowledge base, but I don't want to ask it. I want it to always, 100% of the time, consult the knowledge base after it receives a Zendesk ticket. And so with Lindy, you can actually have the trigger, which is Zendesk ticket received, have the knowledge base consult, which is always there, and then have the agent. So you can really set up your agent any way you want like that.Swyx [00:04:05]: This is something I think about for AI engineering as well, which is the big labs want you to hand over everything in the prompts, and only code of English, and then the smaller brains, the GPU pours, always want to write more code to make things more deterministic and reliable and controllable. One way I put it is put Shoggoth in a box and make it a very small, the minimal viable box. Everything else should be traditional, if this, then that software.Flo [00:04:29]: I love that characterization, put the Shoggoth in the box. Yeah, we talk about using as much AI as necessary and as little as possible.Alessio [00:04:37]: And what was the choosing between kind of like this drag and drop, low code, whatever, super code-driven, maybe like the Lang chains, auto-GPT of the world, and maybe the flip side of it, which you don't really do, it's like just text to agent, it's like build the workflow for me. Like what have you learned actually putting this in front of users and figuring out how much do they actually want to add it versus like how much, you know, kind of like Ruby on Rails instead of Lindy on Rails, it's kind of like, you know, defaults over configuration.Flo [00:05:06]: I actually used to dislike when people said, oh, text is not a great interface. I was like, ah, this is such a mid-take, I think text is awesome. And I've actually come around, I actually sort of agree now that text is really not great. I think for people like you and me, because we sort of have a mental model, okay, when I type a prompt into this text box, this is what it's going to do, it's going to map it to this kind of data structure under the hood and so forth. I guess it's a little bit blackmailing towards humans. You jump on these calls with humans and you're like, here's a text box, this is going to set up an agent for you, do it. And then they type words like, I want you to help me put order in my inbox. Oh, actually, this is a good one. This is actually a good one. What's a bad one? I would say 60 or 70% of the prompts that people type don't mean anything. Me as a human, as AGI, I don't understand what they mean. I don't know what they mean. It is actually, I think whenever you can have a GUI, it is better than to have just a pure text interface.Alessio [00:05:58]: And then how do you decide how much to expose? So even with the tools, you have Slack, you have Google Calendar, you have Gmail. Should people by default just turn over access to everything and then you help them figure out what to use? I think that's the question. When I tried to set up Slack, it was like, hey, give me access to all channels and everything, which for the average person probably makes sense because you don't want to re-prompt them every time you add new channels. But at the same time, for maybe the more sophisticated enterprise use cases, people are like, hey, I want to really limit what you have access to. How do you kind of thread that balance?Flo [00:06:35]: The general philosophy is we ask for the least amount of permissions needed at any given moment. I don't think Slack, I could be mistaken, but I don't think Slack lets you request permissions for just one channel. But for example, for Google, obviously there are hundreds of scopes that you could require for Google. There's a lot of scopes. And sometimes it's actually painful to set up your Lindy because you're going to have to ask Google and add scopes five or six times. We've had sessions like this. But that's what we do because, for example, the Lindy email drafter, she's going to ask you for your authorization once for, I need to be able to read your email so I can draft a reply, and then another time for I need to be able to write a draft for them. We just try to do it very incrementally like that.Alessio [00:07:15]: Do you think OAuth is just overall going to change? I think maybe before it was like, hey, we need to set up OAuth that humans only want to kind of do once. So we try to jam-pack things all at once versus what if you could on-demand get different permissions every time from different parts? Do you ever think about designing things knowing that maybe AI will use it instead of humans will use it? Yeah, for sure.Flo [00:07:37]: One pattern we've started to see is people provisioning accounts for their AI agents. And so, in particular, Google Workspace accounts. So, for example, Lindy can be used as a scheduling assistant. So you can just CC her to your emails when you're trying to find time with someone. And just like a human assistant, she's going to go back and forth and offer other abilities and so forth. Very often, people don't want the other party to know that it's an AI. So it's actually funny. They introduce delays. They ask the agent to wait before replying, so it's not too obvious that it's an AI. And they provision an account on Google Suite, which costs them like $10 a month or something like that. So we're seeing that pattern more and more. I think that does the job for now. I'm not optimistic on us actually patching OAuth. Because I agree with you, ultimately, we would want to patch OAuth because the new account thing is kind of a clutch. It's really a hack. You would want to patch OAuth to have more granular access control and really be able to put your sugar in the box. I'm not optimistic on us doing that before AGI, I think. That's a very close timeline.Swyx [00:08:36]: I'm mindful of talking about a thing without showing it. And we already have the setup to show it. Why don't we jump into a screen share? For listeners, you can jump on the YouTube and like and subscribe. But also, let's have a look at how you show off Lindy. Yeah, absolutely.Flo [00:08:51]: I'll give an example of a very simple Lindy and then I'll graduate to a much more complicated one. A super simple Lindy that I have is, I unfortunately bought some investment properties in the south of France. It was a really, really bad idea. And I put them on a Holydew, which is like the French Airbnb, if you will. And so I received these emails from time to time telling me like, oh, hey, you made 200 bucks. Someone booked your place. When I receive these emails, I want to log this reservation in a spreadsheet. Doing this without an AI agent or without AI in general is a pain in the butt because you must write an HTML parser for this email. And so it's just hard. You may not be able to do it and it's going to break the moment the email changes. By contrast, the way it works with Lindy, it's really simple. It's two steps. It's like, okay, I receive an email. If it is a reservation confirmation, I have this filter here. Then I append a row to this spreadsheet. And so this is where you can see the AI part where the way this action is configured here, you see these purple fields on the right. Each of these fields is a prompt. And so I can say, okay, you extract from the email the day the reservation begins on. You extract the amount of the reservation. You extract the number of travelers of the reservation. And now you can see when I look at the task history of this Lindy, it's really simple. It's like, okay, you do this and boom, appending this row to this spreadsheet. And this is the information extracted. So effectively, this node here, this append row node is a mini agent. It can see everything that just happened. It has context over the task and it's appending the row. And then it's going to send a reply to the thread. That's a very simple example of an agent.Swyx [00:10:34]: A quick follow-up question on this one while we're still on this page. Is that one call? Is that a structured output call? Yeah. Okay, nice. Yeah.Flo [00:10:41]: And you can see here for every node, you can configure which model you want to power the node. Here I use cloud. For this, I use GPT-4 Turbo. Much more complex example, my meeting recorder. It looks very complex because I've added to it over time, but at a high level, it's really simple. It's like when a meeting begins, you record the meeting. And after the meeting, you send me a summary and you send me coaching notes. So I receive, like my Lindy is constantly coaching me. And so you can see here in the prompt of the coaching notes, I've told it, hey, you know, was I unnecessarily confrontational at any point? I'm French, so I have to watch out for that. Or not confrontational enough. Should I have double-clicked on any issue, right? So I can really give it exactly the kind of coaching that I'm expecting. And then the interesting thing here is, like, you can see the agent here, after it sent me these coaching notes, moves on. And it does a bunch of other stuff. So it goes on Slack. It disseminates the notes on Slack. It does a bunch of other stuff. But it's actually able to backtrack and resume the automation at the coaching notes email if I responded to that email. So I'll give a super concrete example. This is an actual coaching feedback that I received from Lindy. She was like, hey, this was a sales call I had with a customer. And she was like, I found your explanation of Lindy too technical. And I was able to follow up and just ask a follow-up question in the thread here. And I was like, why did you find too technical about my explanation? And Lindy restored the context. And so she basically picked up the automation back up here in the tree. And she has all of the context of everything that happened, including the meeting in which I was. So she was like, oh, you used the words deterministic and context window and agent state. And that concept exists at every level for every channel and every action that Lindy takes. So another example here is, I mentioned she also disseminates the notes on Slack. So this was a meeting where I was not, right? So this was a teammate. He's an indie meeting recorder, posts the meeting notes in this customer discovery channel on Slack. So you can see, okay, this is the onboarding call we had. This was the use case. Look at the questions. How do I make Lindy slower? How do I add delays to make Lindy slower? And I was able, in the Slack thread, to ask follow-up questions like, oh, what did we answer to these questions? And it's really handy because I know I can have this sort of interactive Q&A with these meetings. It means that very often now, I don't go to meetings anymore. I just send my Lindy. And instead of going to like a 60-minute meeting, I have like a five-minute chat with my Lindy afterwards. And she just replied. She was like, well, this is what we replied to this customer. And I can just be like, okay, good job, Jack. Like, no notes about your answers. So that's the kind of use cases people have with Lindy. It's a lot of like, there's a lot of sales automations, customer support automations, and a lot of this, which is basically personal assistance automations, like meeting scheduling and so forth.Alessio [00:13:21]: Yeah, and I think the question that people might have is memory. So as you get coaching, how does it track whether or not you're improving? You know, if these are like mistakes you made in the past, like, how do you think about that?Flo [00:13:31]: Yeah, we have a memory module. So I'll show you my meeting scheduler, Lindy, which has a lot of memories because by now I've used her for so long. And so every time I talk to her, she saves a memory. If I tell her, you screwed up, please don't do this. So you can see here, oh, it's got a double memory here. This is the meeting link I have, or this is the address of the office. If I tell someone to meet me at home, this is the address of my place. This is the code. I guess we'll have to edit that out. This is not the code of my place. No dogs. Yeah, so Lindy can just manage her own memory and decide when she's remembering things between executions. Okay.Swyx [00:14:11]: I mean, I'm just going to take the opportunity to ask you, since you are the creator of this thing, how come there's so few memories, right? Like, if you've been using this for two years, there should be thousands of thousands of things. That is a good question.Flo [00:14:22]: Agents still get confused if they have too many memories, to my point earlier about that. So I just am out of a call with a member of the Lama team at Meta, and we were chatting about Lindy, and we were going into the system prompt that we sent to Lindy, and all of that stuff. And he was amazed, and he was like, it's a miracle that it's working, guys. He was like, this kind of system prompt, this does not exist, either pre-training or post-training. These models were never trained to do this kind of stuff. It's a miracle that they can be agents at all. And so what I do, I actually prune the memories. You know, it's actually something I've gotten into the habit of doing from back when we had GPT 3.5, being Lindy agents. I suspect it's probably not as necessary in the Cloud 3.5 Sunette days, but I prune the memories. Yeah, okay.Swyx [00:15:05]: The reason is because I have another assistant that also is recording and trying to come up with facts about me. It comes up with a lot of trivial, useless facts that I... So I spend most of my time pruning. Actually, it's not super useful. I'd much rather have high-quality facts that it accepts. Or maybe I was even thinking, were you ever tempted to add a wake word to only memorize this when I say memorize this? And otherwise, don't even bother.Flo [00:15:30]: I have a Lindy that does this. So this is my inbox processor, Lindy. It's kind of beefy because there's a lot of different emails. But somewhere in here,Swyx [00:15:38]: there is a rule where I'm like,Flo [00:15:39]: aha, I can email my inbox processor, Lindy. It's really handy. So she has her own email address. And so when I process my email inbox, I sometimes forward an email to her. And it's a newsletter, or it's like a cold outreach from a recruiter that I don't care about, or anything like that. And I can give her a rule. And I can be like, hey, this email I want you to archive, moving forward. Or I want you to alert me on Slack when I have this kind of email. It's really important. And so you can see here, the prompt is, if I give you a rule about a kind of email, like archive emails from X, save it as a new memory. And I give it to the memory saving skill. And yeah.Swyx [00:16:13]: One thing that just occurred to me, so I'm a big fan of virtual mailboxes. I recommend that everybody have a virtual mailbox. You could set up a physical mail receive thing for Lindy. And so then Lindy can process your physical mail.Flo [00:16:26]: That's actually a good idea. I actually already have something like that. I use like health class mail. Yeah. So yeah, most likely, I can process my physical mail. Yeah.Swyx [00:16:35]: And then the other product's idea I have, looking at this thing, is people want to brag about the complexity of their Lindys. So this would be like a 65 point Lindy, right?Flo [00:16:43]: What's a 65 point?Swyx [00:16:44]: Complexity counting. Like how many nodes, how many things, how many conditions, right? Yeah.Flo [00:16:49]: This is not the most complex one. I have another one. This designer recruiter here is kind of beefy as well. Right, right, right. So I'm just saying,Swyx [00:16:56]: let people brag. Let people be super users. Oh, right.Flo [00:16:59]: Give them a score. Give them a score.Swyx [00:17:01]: Then they'll just be like, okay, how high can you make this score?Flo [00:17:04]: Yeah, that's a good point. And I think that's, again, the beauty of this on-rails phenomenon. It's like, think of the equivalent, the prompt equivalent of this Lindy here, for example, that we're looking at. It'd be monstrous. And the odds that it gets it right are so low. But here, because we're really holding the agent's hand step by step by step, it's actually super reliable. Yeah.Swyx [00:17:22]: And is it all structured output-based? Yeah. As far as possible? Basically. Like, there's no non-structured output?Flo [00:17:27]: There is. So, for example, here, this AI agent step, right, or this send message step, sometimes it gets to... That's just plain text.Swyx [00:17:35]: That's right.Flo [00:17:36]: Yeah. So I'll give you an example. Maybe it's TMI. I'm having blood pressure issues these days. And so this Lindy here, I give it my blood pressure readings, and it updates a log that I have of my blood pressure that it sends to my doctor.Swyx [00:17:49]: Oh, so every Lindy comes with a to-do list?Flo [00:17:52]: Yeah. Every Lindy has its own task history. Huh. Yeah. And so you can see here, this is my main Lindy, my personal assistant, and I've told it, where is this? There is a point where I'm like, if I am giving you a health-related fact, right here, I'm giving you health information, so then you update this log that I have in this Google Doc, and then you send me a message. And you can see, I've actually not configured this send message node. I haven't told it what to send me a message for. Right? And you can see, it's actually lecturing me. It's like, I'm giving it my blood pressure ratings. It's like, hey, it's a bit high. Here are some lifestyle changes you may want to consider.Alessio [00:18:27]: I think maybe this is the most confusing or new thing for people. So even I use Lindy and I didn't even know you could have multiple workflows in one Lindy. I think the mental model is kind of like the Zapier workflows. It starts and it ends. It doesn't choose between. How do you think about what's a Lindy versus what's a sub-function of a Lindy? Like, what's the hierarchy?Flo [00:18:48]: Yeah. Frankly, I think the line is a little arbitrary. It's kind of like when you code, like when do you start to create a new class versus when do you overload your current class. I think of it in terms of like jobs to be done and I think of it in terms of who is the Lindy serving. This Lindy is serving me personally. It's really my day-to-day Lindy. I give it a bunch of stuff, like very easy tasks. And so this is just the Lindy I go to. Sometimes when a task is really more specialized, so for example, I have this like summarizer Lindy or this designer recruiter Lindy. These tasks are really beefy. I wouldn't want to add this to my main Lindy, so I just created a separate Lindy for it. Or when it's a Lindy that serves another constituency, like our customer support Lindy, I don't want to add that to my personal assistant Lindy. These are two very different Lindys.Alessio [00:19:31]: And you can call a Lindy from within another Lindy. That's right. You can kind of chain them together.Flo [00:19:36]: Lindys can work together, absolutely.Swyx [00:19:38]: A couple more things for the video portion. I noticed you have a podcast follower. We have to ask about that. What is that?Flo [00:19:46]: So this one wakes me up every... So wakes herself up every week. And she sends me... So she woke up yesterday, actually. And she searches for Lenny's podcast. And she looks for like the latest episode on YouTube. And once she finds it, she transcribes the video and then she sends me the summary by email. I don't listen to podcasts as much anymore. I just like read these summaries. Yeah.Alessio [00:20:09]: We should make a latent space Lindy. Marketplace.Swyx [00:20:12]: Yeah. And then you have a whole bunch of connectors. I saw the list briefly. Any interesting one? Complicated one that you're proud of? Anything that you want to just share? Connector stories.Flo [00:20:23]: So many of our workflows are about meeting scheduling. So we had to build some very open unity tools around meeting scheduling. So for example, one that is surprisingly hard is this find available times action. You would not believe... This is like a thousand lines of code or something. It's just a very beefy action. And you can pass it a bunch of parameters about how long is the meeting? When does it start? When does it end? What are the meetings? The weekdays in which I meet? How many time slots do you return? What's the buffer between my meetings? It's just a very, very, very complex action. I really like our GitHub action. So we have a Lindy PR reviewer. And it's really handy because anytime any bug happens... So the Lindy reads our guidelines on Google Docs. By now, the guidelines are like 40 pages long or something. And so every time any new kind of bug happens, we just go to the guideline and we add the lines. Like, hey, this has happened before. Please watch out for this category of bugs. And it's saving us so much time every day.Alessio [00:21:19]: There's companies doing PR reviews. Where does a Lindy start? When does a company start? Or maybe how do you think about the complexity of these tasks when it's going to be worth having kind of like a vertical standalone company versus just like, hey, a Lindy is going to do a good job 99% of the time?Flo [00:21:34]: That's a good question. We think about this one all the time. I can't say that we've really come up with a very crisp articulation of when do you want to use a vertical tool versus when do you want to use a horizontal tool. I think of it as very similar to the internet. I find it surprising the extent to which a horizontal search engine has won. But I think that Google, right? But I think the even more surprising fact is that the horizontal search engine has won in almost every vertical, right? You go through Google to search Reddit. You go through Google to search Wikipedia. I think maybe the biggest exception is e-commerce. Like you go to Amazon to search e-commerce, but otherwise you go through Google. And I think that the reason for that is because search in each vertical has more in common with search than it does with each vertical. And search is so expensive to get right. Like Google is a big company that it makes a lot of sense to aggregate all of these different use cases and to spread your R&D budget across all of these different use cases. I have a thesis, which is, it's a really cool thesis for Lindy, is that the same thing is true for agents. I think that by and large, in a lot of verticals, agents in each vertical have more in common with agents than they do with each vertical. I also think there are benefits in having a single agent platform because that way your agents can work together. They're all like under one roof. That way you only learn one platform and so you can create agents for everything that you want. And you don't have to like pay for like a bunch of different platforms and so forth. So I think ultimately, it is actually going to shake out in a way that is similar to search in that search is everywhere on the internet. Every website has a search box, right? So there's going to be a lot of vertical agents for everything. I think AI is going to completely penetrate every category of software. But then I also think there are going to be a few very, very, very big horizontal agents that serve a lot of functions for people.Swyx [00:23:14]: That is actually one of the questions that we had about the agent stuff. So I guess we can transition away from the screen and I'll just ask the follow-up, which is, that is a hot topic. You're basically saying that the current VC obsession of the day, which is vertical AI enabled SaaS, is mostly not going to work out. And then there are going to be some super giant horizontal SaaS.Flo [00:23:34]: Oh, no, I'm not saying it's either or. Like SaaS today, vertical SaaS is huge and there's also a lot of horizontal platforms. If you look at like Airtable or Notion, basically the entire no-code space is very horizontal. I mean, Loom and Zoom and Slack, there's a lot of very horizontal tools out there. Okay.Swyx [00:23:49]: I was just trying to get a reaction out of you for hot takes. Trying to get a hot take.Flo [00:23:54]: No, I also think it is natural for the vertical solutions to emerge first because it's just easier to build. It's just much, much, much harder to build something horizontal. Cool.Swyx [00:24:03]: Some more Lindy-specific questions. So we covered most of the top use cases and you have an academy. That was nice to see. I also see some other people doing it for you for free. So like Ben Spites is doing it and then there's some other guy who's also doing like lessons. Yeah. Which is kind of nice, right? Yeah, absolutely. You don't have to do any of that.Flo [00:24:20]: Oh, we've been seeing it more and more on like LinkedIn and Twitter, like people posting their Lindys and so forth.Swyx [00:24:24]: I think that's the flywheel that you built the platform where creators see value in allying themselves to you. And so then, you know, your incentive is to make them successful so that they can make other people successful and then it just drives more and more engagement. Like it's earned media. Like you don't have to do anything.Flo [00:24:39]: Yeah, yeah. I mean, community is everything.Swyx [00:24:41]: Are you doing anything special there? Any big wins?Flo [00:24:44]: We have a Slack community that's pretty active. I can't say we've invested much more than that so far.Swyx [00:24:49]: I would say from having, so I have some involvement in the no-code community. I would say that Webflow going very hard after no-code as a category got them a lot more allies than just the people using Webflow. So it helps you to grow the community beyond just Lindy. And I don't know what this is called. Maybe it's just no-code again. Maybe you want to call it something different. But there's definitely an appetite for this and you are one of a broad category, right? Like just before you, we had Dust and, you know, they're also kind of going after a similar market. Zapier obviously is not going to try to also compete with you. Yeah. There's no question there. It's just like a reaction about community. Like I think a lot about community. Lanespace is growing the community of AI engineers. And I think you have a slightly different audience of, I don't know what.Flo [00:25:33]: Yeah. I think the no-code tinkerers is the community. Yeah. It is going to be the same sort of community as what Webflow, Zapier, Airtable, Notion to some extent.Swyx [00:25:43]: Yeah. The framing can be different if you were, so I think tinkerers has this connotation of not serious or like small. And if you framed it to like no-code EA, we're exclusively only for CEOs with a certain budget, then you just have, you tap into a different budget.Flo [00:25:58]: That's true. The problem with EA is like, the CEO has no willingness to actually tinker and play with the platform.Swyx [00:26:05]: Maybe Andrew's doing that. Like a lot of your biggest advocates are CEOs, right?Flo [00:26:09]: A solopreneur, you know, small business owners, I think Andrew is an exception. Yeah. Yeah, yeah, he is.Swyx [00:26:14]: He's an exception in many ways. Yep.Alessio [00:26:16]: Just before we wrap on the use cases, is Rick rolling your customers? Like a officially supported use case or maybe tell that story?Flo [00:26:24]: It's one of the main jobs to be done, really. Yeah, we woke up recently, so we have a Lindy obviously doing our customer support and we do check after the Lindy. And so we caught this email exchange where someone was asking Lindy for video tutorials. And at the time, actually, we did not have video tutorials. We do now on the Lindy Academy. And Lindy responded to the email. It's like, oh, absolutely, here's a link. And we were like, what? Like, what kind of link did you send? And so we clicked on the link and it was a recall. We actually reacted fast enough that the customer had not yet opened the email. And so we reacted immediately. Like, oh, hey, actually, sorry, this is the right link. And so the customer never reacted to the first link. And so, yeah, I tweeted about that. It went surprisingly viral. And I checked afterwards in the logs. We did like a database query and we found, I think, like three or four other instances of it having happened before.Swyx [00:27:12]: That's surprisingly low.Flo [00:27:13]: It is low. And we fixed it across the board by just adding a line to the system prompt that's like, hey, don't recall people, please don't recall.Swyx [00:27:21]: Yeah, yeah, yeah. I mean, so, you know, you can explain it retroactively, right? Like, that YouTube slug has been pasted in so many different corpuses that obviously it learned to hallucinate that.Alessio [00:27:31]: And it pretended to be so many things. That's the thing.Swyx [00:27:34]: I wouldn't be surprised if that takes one token. Like, there's this one slug in the tokenizer and it's just one token.Flo [00:27:41]: That's the idea of a YouTube video.Swyx [00:27:43]: Because it's used so much, right? And you have to basically get it exactly correct. It's probably not. That's a long speech.Flo [00:27:52]: It would have been so good.Alessio [00:27:55]: So this is just a jump maybe into evals from here. How could you possibly come up for an eval that says, make sure my AI does not recall my customer? I feel like when people are writing evals, that's not something that they come up with. So how do you think about evals when it's such like an open-ended problem space?Flo [00:28:12]: Yeah, it is tough. We built quite a bit of infrastructure for us to create evals in one click from any conversation history. So we can point to a conversation and we can be like, in one click we can turn it into effectively a unit test. It's like, this is a good conversation. This is how you're supposed to handle things like this. Or if it's a negative example, then we modify a little bit the conversation after generating the eval. So it's very easy for us to spin up this kind of eval.Alessio [00:28:36]: Do you use an off-the-shelf tool which is like Brain Trust on the podcast? Or did you just build your own?Flo [00:28:41]: We unfortunately built our own. We're most likely going to switch to Brain Trust. Well, when we built it, there was nothing. Like there was no eval tool, frankly. I mean, we started this project at the end of 2022. It was like, it was very, very, very early. I wouldn't recommend it to build your own eval tool. There's better solutions out there and our eval tool breaks all the time and it's a nightmare to maintain. And that's not something we want to be spending our time on.Swyx [00:29:04]: I was going to ask that basically because I think my first conversations with you about Lindy was that you had a strong opinion that everyone should build their own tools. And you were very proud of your evals. You're kind of showing off to me like how many evals you were running, right?Flo [00:29:16]: Yeah, I think that was before all of these tools came around. I think the ecosystem has matured a fair bit.Swyx [00:29:21]: What is one thing that Brain Trust has nailed that you always struggled to do?Flo [00:29:25]: We're not using them yet, so I couldn't tell. But from what I've gathered from the conversations I've had, like they're doing what we do with our eval tool, but better.Swyx [00:29:33]: And like they do it, but also like 60 other companies do it, right? So I don't know how to shop apart from brand. Word of mouth.Flo [00:29:41]: Same here.Swyx [00:29:42]: Yeah, like evals or Lindys, there's two kinds of evals, right? Like in some way, you don't have to eval your system as much because you've constrained the language model so much. And you can rely on open AI to guarantee that the structured outputs are going to be good, right? We had Michelle sit where you sit and she explained exactly how they do constraint grammar sampling and all that good stuff. So actually, I think it's more important for your customers to eval their Lindys than you evaling your Lindy platform because you just built the platform. You don't actually need to eval that much.Flo [00:30:14]: Yeah. In an ideal world, our customers don't need to care about this. And I think the bar is not like, look, it needs to be at 100%. I think the bar is it needs to be better than a human. And for most use cases we serve today, it is better than a human, especially if you put it on Rails.Swyx [00:30:30]: Is there a limiting factor of Lindy at the business? Like, is it adding new connectors? Is it adding new node types? Like how do you prioritize what is the most impactful to your company?Flo [00:30:41]: Yeah. The raw capabilities for sure are a big limit. It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small. It's kind of insane that we started building this when the context windows were like 4,000 tokens. Like today, our system prompt is more than 4,000 tokens. So yeah, the model is actually very much not a limit anymore. It almost gives me pause because I'm like, I want the model to be a limit. And so no, the integrations are ones, the core capabilities are ones. So for example, we are investing in a system that's basically, I call it like the, it's a J hack. Give me these names, like the poor man's RLHF. So you can turn on a toggle on any step of your Lindy workflow to be like, ask me for confirmation before you actually execute this step. So it's like, hey, I receive an email, you send a reply, ask me for confirmation before actually sending it. And so today you see the email that's about to get sent and you can either approve, deny, or change it and then approve. And we are making it so that when you make a change, we are then saving this change that you're making or embedding it in the vector database. And then we are retrieving these examples for future tasks and injecting them into the context window. So that's the kind of capability that makes a huge difference for users. That's the bottleneck today. It's really like good old engineering and product work.Swyx [00:31:52]: I assume you're hiring. We'll do a call for hiring at the end.Alessio [00:31:54]: Any other comments on the model side? When did you start feeling like the model was not a bottleneck anymore? Was it 4.0? Was it 3.5? 3.5.Flo [00:32:04]: 3.5 Sonnet, definitely. I think 4.0 is overhyped, frankly. We don't use 4.0. I don't think it's good for agentic behavior. Yeah, 3.5 Sonnet is when I started feeling that. And then with prompt caching with 3.5 Sonnet, like that fills the cost, cut the cost again. Just cut it in half. Yeah.Swyx [00:32:21]: Your prompts are... Some of the problems with agentic uses is that your prompts are kind of dynamic, right? Like from caching to work, you need the front prefix portion to be stable.Flo [00:32:32]: Yes, but we have this append-only ledger paradigm. So every node keeps appending to that ledger and every filled node inherits all the context built up by all the previous nodes. And so we can just decide, like, hey, every X thousand nodes, we trigger prompt caching again.Swyx [00:32:47]: Oh, so you do it like programmatically, not all the time.Flo [00:32:50]: No, sorry. Anthropic manages that for us. But basically, it's like, because we keep appending to the prompt, the prompt caching works pretty well.Alessio [00:32:57]: We have this small podcaster tool that I built for the podcast and I rewrote all of our prompts because I noticed, you know, I was inputting stuff early on. I wonder how much more money OpenAN and Anthropic are making just because people don't rewrite their prompts to be like static at the top and like dynamic at the bottom.Flo [00:33:13]: I think that's the remarkable thing about what we're having right now. It's insane that these companies are routinely cutting their costs by two, four, five. Like, they basically just apply constraints. They want people to take advantage of these innovations. Very good.Swyx [00:33:25]: Do you have any other competitive commentary? Commentary? Dust, WordWare, Gumloop, Zapier? If not, we can move on.Flo [00:33:31]: No comment.Alessio [00:33:32]: I think the market is,Flo [00:33:33]: look, I mean, AGI is coming. All right, that's what I'm talking about.Swyx [00:33:38]: I think you're helping. Like, you're paving the road to AGI.Flo [00:33:41]: I'm playing my small role. I'm adding my small brick to this giant, giant, giant castle. Yeah, look, when it's here, we are going to, this entire category of software is going to create, it's going to sound like an exaggeration, but it is a fact it is going to create trillions of dollars of value in a few years, right? It's going to, for the first time, we're actually having software directly replace human labor. I see it every day in sales calls. It's like, Lindy is today replacing, like, we talk to even small teams. It's like, oh, like, stop, this is a 12-people team here. I guess we'll set up this Lindy for one or two days, and then we'll have to decide what to do with this 12-people team. And so, yeah. To me, there's this immense uncapped market opportunity. It's just such a huge ocean, and there's like three sharks in the ocean. I'm focused on the ocean more than on the sharks.Swyx [00:34:25]: So we're moving on to hot topics, like, kind of broadening out from Lindy, but obviously informed by Lindy. What are the high-order bits of good agent design?Flo [00:34:31]: The model, the model, the model, the model. I think people fail to truly, and me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like, you just scale the model. Like, GPUs go brr, it's all that matters. I think it also holds for the cognitive architecture. I used to be very cognitive architecture-filled, and I was like, ah, and I was like a critic, and I was like a generator, and all this, and then it's just like, GPUs go brr, like, just like let the model do its job. I think we're seeing it a little bit right now with O1. I'm seeing some tweets that say that the new 3.5 SONNET is as good as O1, but with none of all the crazy...Swyx [00:35:09]: It beats O1 on some measures. On some reasoning tasks. On AIME, it's still a lot lower. Like, it's like 14 on AIME versus O1, it's like 83.Flo [00:35:17]: Got it. Right. But even O1 is still the model. Yeah.Swyx [00:35:22]: Like, there's no cognitive architecture on top of it.Flo [00:35:23]: You can just wait for O1 to get better.Alessio [00:35:25]: And so, as a founder, how do you think about that, right? Because now, knowing this, wouldn't you just wait to start Lindy? You know, you start Lindy, it's like 4K context, the models are not that good. It's like, but you're still kind of like going along and building and just like waiting for the models to get better. How do you today decide, again, what to build next, knowing that, hey, the models are going to get better, so maybe we just shouldn't focus on improving our prompt design and all that stuff and just build the connectors instead or whatever? Yeah.Flo [00:35:51]: I mean, that's exactly what we do. Like, all day, we always ask ourselves, oh, when we have a feature idea or a feature request, we ask ourselves, like, is this the kind of thing that just gets better while we sleep because models get better? I'm reminded, again, when we started this in 2022, we spent a lot of time because we had to around context pruning because 4,000 tokens is really nothing. You really can't do anything with 4,000 tokens. All that work was throwaway work. Like, now it's like it was for nothing, right? Now we just assume that infinite context windows are going to be here in a year or something, a year and a half, and infinitely cheap as well, and dynamic compute is going to be here. Like, we just assume all of these things are going to happen, and so we really focus, our job to be done in the industry is to provide the input and output to the model. I really compare it all the time to the PC and the CPU, right? Apple is busy all day. They're not like a CPU wrapper. They have a lot to build, but they don't, well, now actually they do build the CPU as well, but leaving that aside, they're busy building a laptop. It's just a lot of work to build these things. It's interesting because, like,Swyx [00:36:45]: for example, another person that we're close to, Mihaly from Repl.it, he often says that the biggest jump for him was having a multi-agent approach, like the critique thing that you just said that you don't need, and I wonder when, in what situations you do need that and what situations you don't. Obviously, the simple answer is for coding, it helps, and you're not coding, except for, are you still generating code? In Indy? Yeah.Flo [00:37:09]: No, we do. Oh, right. No, no, no, the cognitive architecture changed. We don't, yeah.Swyx [00:37:13]: Yeah, okay. For you, you're one shot, and you chain tools together, and that's it. And if the user really wantsFlo [00:37:18]: to have this kind of critique thing, you can also edit the prompt, you're welcome to. I have some of my Lindys, I've told them, like, hey, be careful, think step by step about what you're about to do, but that gives you a little bump for some use cases, but, yeah.Alessio [00:37:30]: What about unexpected model releases? So, Anthropic released computer use today. Yeah. I don't know if many people were expecting computer use to come out today. Do these things make you rethink how to design, like, your roadmap and things like that, or are you just like, hey, look, whatever, that's just, like, a small thing in their, like, AGI pursuit, that, like, maybe they're not even going to support, and, like, it's still better for us to build our own integrations into systems and things like that. Because maybe people will say, hey, look, why am I building all these API integrationsFlo [00:38:02]: when I can just do computer use and never go to the product? Yeah. No, I mean, we did take into account computer use. We were talking about this a year ago or something, like, we've been talking about it as part of our roadmap. It's been clear to us that it was coming, My philosophy about it is anything that can be done with an API must be done by an API or should be done by an API for a very long time. I think it is dangerous to be overly cavalier about improvements of model capabilities. I'm reminded of iOS versus Android. Android was built on the JVM. There was a garbage collector, and I can only assume that the conversation that went down in the engineering meeting room was, oh, who cares about the garbage collector? Anyway, Moore's law is here, and so that's all going to go to zero eventually. Sure, but in the meantime, you are operating on a 400 MHz CPU. It was like the first CPU on the iPhone 1, and it's really slow, and the garbage collector is introducing a tremendous overhead on top of that, especially a memory overhead. For the longest time, and it's really only been recently that Android caught up to iOS in terms of how smooth the interactions were, but for the longest time, Android phones were significantly slowerSwyx [00:39:07]: and laggierFlo [00:39:08]: and just not feeling as good as iOS devices. Look, when you're talking about modules and magnitude of differences in terms of performance and reliability, which is what we are talking about when we're talking about API use versus computer use, then you can't ignore that, right? And so I think we're going to be in an API use world for a while.Swyx [00:39:27]: O1 doesn't have API use today. It will have it at some point, and it's on the roadmap. There is a future in which OpenAI goes much harder after your business, your market, than it is today. Like, ChatGPT, it's its own business. All they need to do is add tools to the ChatGPT, and now they're suddenly competing with you. And by the way, they have a GPT store where a bunch of people have already configured their tools to fit with them. Is that a concern?Flo [00:39:56]: I think even the GPT store, in a way, like the way they architect it, for example, their plug-in systems are actually grateful because we can also use the plug-ins. It's very open. Now, again, I think it's going to be such a huge market. I think there's going to be a lot of different jobs to be done. I know they have a huge enterprise offering and stuff, but today, ChatGPT is a consumer app. And so, the sort of flow detail I showed you, this sort of workflow, this sort of use cases that we're going after, which is like, we're doing a lot of lead generation and lead outreach and all of that stuff. That's not something like meeting recording, like Lindy Today right now joins your Zoom meetings and takes notes, all of that stuff.Swyx [00:40:34]: I don't see that so farFlo [00:40:35]: on the OpenAI roadmap.Swyx [00:40:36]: Yeah, but they do have an enterprise team that we talk to You're hiring GMs?Flo [00:40:42]: We did.Swyx [00:40:43]: It's a fascinating way to build a business, right? Like, what should you, as CEO, be in charge of? And what should you basically hireFlo [00:40:52]: a mini CEO to do? Yeah, that's a good question. I think that's also something we're figuring out. The GM thing was inspired from my days at Uber, where we hired one GM per city or per major geo area. We had like all GMs, regional GMs and so forth. And yeah, Lindy is so horizontal that we thought it made sense to hire GMs to own each vertical and the go-to market of the vertical and the customization of the Lindy templates for these verticals and so forth. What should I own as a CEO? I mean, the canonical reply here is always going to be, you know, you own the fundraising, you own the culture, you own the... What's the rest of the canonical reply? The culture, the fundraising.Swyx [00:41:29]: I don't know,Flo [00:41:30]: products. Even that, eventually, you do have to hand out. Yes, the vision, the culture, and the foundation. Well, you've done your job as a CEO. In practice, obviously, yeah, I mean, all day, I do a lot of product work still and I want to keep doing product work for as long as possible.Swyx [00:41:48]: Obviously, like you're recording and managing the team. Yeah.Flo [00:41:52]: That one feels like the most automatable part of the job, the recruiting stuff.Swyx [00:41:56]: Well, yeah. You saw myFlo [00:41:59]: design your recruiter here. Relationship between Factorio and building Lindy. We actually very often talk about how the business of the future is like a game of Factorio. Yeah. So, in the instance, it's like Slack and you've got like 5,000 Lindys in the sidebar and your job is to somehow manage your 5,000 Lindys. And it's going to be very similar to company building because you're going to look for like the highest leverage way to understand what's going on in your AI company and understand what levels do you have to make impact in that company. So, I think it's going to be very similar to like a human company except it's going to go infinitely faster. Today, in a human company, you could have a meeting with your team and you're like, oh, I'm going to build a facility and, you know, now it's like, okay,Swyx [00:42:40]: boom, I'm going to spin up 50 designers. Yeah. Like, actually, it's more important that you can clone an existing designer that you know works because the hiring process, you cannot clone someone because every new person you bring in is going to have their own tweaksFlo [00:42:54]: and you don't want that. Yeah.Swyx [00:42:56]: That's true. You want an army of mindless dronesFlo [00:42:59]: that all work the same way.Swyx [00:43:00]: The reason I bring this, bring Factorio up as well is one, Factorio Space just came out. Apparently, a whole bunch of people stopped working. I tried out Factorio. I never really got that much into it. But the other thing was, you had a tweet recently about how the sort of intentional top-down design was not as effective as just build. Yeah. Just ship.Flo [00:43:21]: I think people read a little bit too much into that tweet. It went weirdly viral. I was like, I did not intend it as a giant statement online.Swyx [00:43:28]: I mean, you notice you have a pattern with this, right? Like, you've done this for eight years now.Flo [00:43:33]: You should know. I legit was just hearing an interesting story about the Factorio game I had. And everybody was like, oh my God, so deep. I guess this explains everything about life and companies. There is something to be said, certainly, about focusing on the constraint. And I think it is Patrick Collison who said, people underestimate the extent to which moonshots are just one pragmatic step taken after the other. And I think as long as you have some inductive bias about, like, some loose idea about where you want to go, I think it makes sense to follow a sort of greedy search along that path. I think planning and organizing is important. And having older is important.Swyx [00:44:05]: I'm wrestling with that. There's two ways I encountered it recently. One with Lindy. When I tried out one of your automation templates and one of them was quite big and I just didn't understand it, right? So, like, it was not as useful to me as a small one that I can just plug in and see all of. And then the other one was me using Cursor. I was very excited about O1 and I just up frontFlo [00:44:27]: stuffed everythingSwyx [00:44:28]: I wanted to do into my prompt and expected O1 to do everything. And it got itself into a huge jumbled mess and it was stuck. It was really... There was no amount... I wasted, like, two hours on just, like, trying to get out of that hole. So I threw away the code base, started small, switched to Clouds on it and build up something working and just add it over time and it just worked. And to me, that was the factorial sentiment, right? Maybe I'm one of those fanboys that's just, like, obsessing over the depth of something that you just randomly tweeted out. But I think it's true for company building, for Lindy building, for coding.Flo [00:45:02]: I don't know. I think it's fair and I think, like, you and I talked about there's the Tuft & Metal principle and there's this other... Yes, I love that. There's the... I forgot the name of this other blog post but it's basically about this book Seeing Like a State that talks about the need for legibility and people who optimize the system for its legibility and anytime you make a system... So legible is basically more understandable. Anytime you make a system more understandable from the top down, it performs less well from the bottom up. And it's fine but you should at least make this trade-off with your eyes wide open. You should know, I am sacrificing performance for understandability, for legibility. And in this case, for you, it makes sense. It's like you are actually optimizing for legibility. You do want to understand your code base but in some other cases it may not make sense. Sometimes it's better to leave the system alone and let it be its glorious, chaotic, organic self and just trust that it's going to perform well even though you don't understand it completely.Swyx [00:45:55]: It does remind me of a common managerial issue or dilemma which you experienced in the small scale of Lindy where, you know, do you want to organize your company by functional sections or by products or, you know, whatever the opposite of functional is. And you tried it one way and it was more legible to you as CEO but actually it stopped working at the small level. Yeah.Flo [00:46:17]: I mean, one very small example, again, at a small scale is we used to have everything on Notion. And for me, as founder, it was awesome because everything was there. The roadmap was there. The tasks were there. The postmortems were there. And so, the postmortem was linkedSwyx [00:46:31]: to its task.Flo [00:46:32]: It was optimized for you. Exactly. And so, I had this, like, one pane of glass and everything was on Notion. And then the team, one day,Swyx [00:46:39]: came to me with pitchforksFlo [00:46:40]: and they really wanted to implement Linear. And I had to bite my fist so hard. I was like, fine, do it. Implement Linear. Because I was like, at the end of the day, the team needs to be able to self-organize and pick their own tools.Alessio [00:46:51]: Yeah. But it did make the company slightly less legible for me. Another big change you had was going away from remote work, every other month. The discussion comes up again. What was that discussion like? How did your feelings change? Was there kind of like a threshold of employees and team size where you felt like, okay, maybe that worked. Now it doesn't work anymore. And how are you thinking about the futureFlo [00:47:12]: as you scale the team? Yeah. So, for context, I used to have a business called TeamFlow. The business was about building a virtual office for remote teams. And so, being remote was not merely something we did. It was, I was banging the remote drum super hard and helping companies to go remote. And so, frankly, in a way, it's a bit embarrassing for me to do a 180 like that. But I guess, when the facts changed, I changed my mind. What happened? Well, I think at first, like everyone else, we went remote by necessity. It was like COVID and you've got to go remote. And on paper, the gains of remote are enormous. In particular, from a founder's standpoint, being able to hire from anywhere is huge. Saving on rent is huge. Saving on commute is huge for everyone and so forth. But then, look, we're all here. It's like, it is really making it much harder to work together. And I spent three years of my youth trying to build a solution for this. And my conclusion is, at least we couldn't figure it out and no one else could. Zoom didn't figure it out. We had like a bunch of competitors. Like, Gathertown was one of the bigger ones. We had dozens and dozens of competitors. No one figured it out. I don't know that software can actually solve this problem. The reality of it is, everyone just wants to get off the darn Zoom call. And it's not a good feeling to be in your home office if you're even going to have a home office all day. It's harder to build culture. It's harder to get in sync. I think software is peculiar because it's like an iceberg. It's like the vast majority of it is submerged underwater. And so, the quality of the software that you ship is a function of the alignment of your mental models about what is below that waterline. Can you actually get in sync about what it is exactly fundamentally that we're building? What is the soul of our product? And it is so much harder to get in sync about that when you're remote. And then you waste time in a thousand ways because people are offline and you can't get a hold of them or you can't share your screen. It's just like you feel like you're walking in molasses all day. And eventually, I was like, okay, this is it. We're not going to do this anymore.Swyx [00:49:03]: Yeah. I think that is the current builder San Francisco consensus here. Yeah. But I still have a big... One of my big heroes as a CEO is Sid Subban from GitLab.Flo [00:49:14]: Mm-hmm.Swyx [00:49:15]: Matt MullenwegFlo [00:49:16]: used to be a hero.Swyx [00:49:17]: But these people run thousand-person remote businesses. The main idea is that at some company

In this episode of Today in YGK, CFRC’s Broadcast Journalist Christena Lawrie covers a car-sharing pilot program coming to Kingston next year, sitting down with Soren Christianson, a project manager in the city’s Climate Leadership division. In campus news, reporter Ali Al-Safadi sits down with Queen's University researchers Dr. Kerry Rowe and Dr. John Smol, who were honoured with top NSERC awards for their pioneering environmental work. Dr. Rowe's innovative landfill design protects groundwater from contamination, while Dr. Smol's research on lake sediment reveals human impacts on ecosystems, guiding conservation efforts. Sports broadcaster Jesse Bell recaps the latest in Queen’s women’s volleyball and men’s rugby. Tune in for all these stories and more!

We are recording our next big recap episode and taking questions! Submit questions and messages on Speakpipe here for a chance to appear on the show!Also subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!In our first ever episode with Logan Kilpatrick we called out the two hottest LLM frameworks at the time: LangChain and Dust. We've had Harrison from LangChain on twice (as a guest and as a co-host), and we've now finally come full circle as Stanislas from Dust joined us in the studio.After stints at Oracle and Stripe, Stan had joined OpenAI to work on mathematical reasoning capabilities. He describes his time at OpenAI as "the PhD I always wanted to do" while acknowledging the challenges of research work: "You're digging into a field all day long for weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, 'oh, yeah, that was obvious.' And you go back to digging." This experience, combined with early access to GPT-4's capabilities, shaped his decision to start Dust: "If we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down."The History of DustDust's journey can be broken down into three phases:* Developer Framework (2022): Initially positioned as a competitor to LangChain, Dust started as a developer tooling platform. While both were open source, their approaches differed – LangChain focused on broad community adoption and integration as a pure developer experience, while Dust emphasized UI-driven development and better observability that wasn't just `print` statements.* Browser Extension (Early 2023): The company pivoted to building XP1, a browser extension that could interact with web content. This experiment helped validate user interaction patterns with AI, even while using less capable models than GPT-4.* Enterprise Platform (Current): Today, Dust has evolved into an infrastructure platform for deploying AI agents within companies, with impressive metrics like 88% daily active users in some deployments.The Case for Being HorizontalThe big discussion for early stage companies today is whether or not to be horizontal or vertical. Since models are so good at general tasks, a lot of companies are building vertical products that take care of a workflow end-to-end in order to offer more value and becoming more of “Services as Software”. Dust on the other hand is a platform for the users to build their own experiences, which has had a few advantages:* Maximum Penetration: Dust reports 60-70% weekly active users across entire companies, demonstrating the potential reach of horizontal solutions rather than selling into a single team.* Emergent Use Cases: By allowing non-technical users to create agents, Dust enables use cases to emerge organically from actual business needs rather than prescribed solutions.* Infrastructure Value: The platform approach creates lasting value through maintained integrations and connections, similar to how Stripe's value lies in maintaining payment infrastructure. Rather than relying on third-party integration providers, Dust maintains its own connections to ensure proper handling of different data types and structures.The Vertical ChallengeHowever, this approach comes with trade-offs:* Harder Go-to-Market: As Stan talked about: "We spike at penetration... but it makes our go-to-market much harder. Vertical solutions have a go-to-market that is much easier because they're like, 'oh, I'm going to solve the lawyer stuff.'"* Complex Infrastructure: Building a horizontal platform requires maintaining numerous integrations and handling diverse data types appropriately – from structured Salesforce data to unstructured Notion pages. As you scale integrations, the cost of maintaining them also scales. * Product Surface Complexity: Creating an interface that's both powerful and accessible to non-technical users requires careful design decisions, down to avoiding technical terms like "system prompt" in favor of "instructions." The Future of AI PlatformsStan initially predicted we'd see the first billion-dollar single-person company in 2023 (a prediction later echoed by Sam Altman), but he's now more focused on a different milestone: billion-dollar companies with engineering teams of just 20 people, enabled by AI assistance.This vision aligns with Dust's horizontal platform approach – building the infrastructure that allows small teams to achieve outsized impact through AI augmentation. Rather than replacing entire job functions (the vertical approach), they're betting on augmenting existing workflows across organizations.Full YouTube EpisodeChapters* 00:00:00 Introductions* 00:04:33 Joining OpenAI from Paris* 00:09:54 Research evolution and compute allocation at OpenAI* 00:13:12 Working with Ilya Sutskever and OpenAI's vision* 00:15:51 Leaving OpenAI to start Dust* 00:18:15 Early focus on browser extension and WebGPT-like functionality* 00:20:20 Dust as the infrastructure for agents* 00:24:03 Challenges of building with early AI models* 00:28:17 LLMs and Workflow Automation* 00:35:28 Building dependency graphs of agents* 00:37:34 Simulating API endpoints* 00:40:41 State of AI models* 00:43:19 Running evals* 00:46:36 Challenges in building AI agents infra* 00:49:21 Buy vs. build decisions for infrastructure components* 00:51:02 Future of SaaS and AI's Impact on Software* 00:53:07 The single employee $1B company race* 00:56:32 Horizontal vs. vertical approaches to AI agentsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:11]: Hey, and today we're in a studio with Stanislas, welcome.Stan [00:00:14]: Thank you very much for having me.Swyx [00:00:16]: Visiting from Paris.Stan [00:00:17]: Paris.Swyx [00:00:18]: And you have had a very distinguished career. It's very hard to summarize, but you went to college in both Ecopolytechnique and Stanford, and then you worked in a number of places, Oracle, Totems, Stripe, and then OpenAI pre-ChatGPT. We'll talk, we'll spend a little bit of time about that. About two years ago, you left OpenAI to start Dust. I think you were one of the first OpenAI alum founders.Stan [00:00:40]: Yeah, I think it was about at the same time as the Adept guys, so that first wave.Swyx [00:00:46]: Yeah, and people really loved our David episode. We love a few sort of OpenAI stories, you know, for back in the day, like we're talking about pre-recording. Probably the statute of limitations on some of those stories has expired, so you can talk a little bit more freely without them coming after you. But maybe we'll just talk about, like, what was your journey into AI? You know, you were at Stripe for almost five years, there are a lot of Stripe alums going into OpenAI. I think the Stripe culture has come into OpenAI quite a bit.Stan [00:01:11]: Yeah, so I think the buses of Stripe people really started flowing in, I guess, after ChatGPT. But, yeah, my journey into AI is a... I mean, Greg Brockman. Yeah, yeah. From Greg, of course. And Daniela, actually, back in the days, Daniela Amodei.Swyx [00:01:27]: Yes, she was COO, I mean, she is COO, yeah. She had a pretty high job at OpenAI at the time, yeah, for sure.Stan [00:01:34]: My journey started as anybody else, you're fascinated with computer science and you want to make them think, it's awesome, but it doesn't work. I mean, it was a long time ago, it was like maybe 16, so it was 25 years ago. Then the first big exposure to AI would be at Stanford, and I'm going to, like, disclose a whole lamb, because at the time it was a class taught by Andrew Ng, and there was no deep learning. It was half features for vision and a star algorithm. So it was fun. But it was the early days of deep learning. At the time, I think a few years after, it was the first project at Google. But you know, that cat face or the human face trained from many images. I went to, hesitated doing a PhD, more in systems, eventually decided to go into getting a job. Went at Oracle, started a company, did a gazillion mistakes, got acquired by Stripe, worked with Greg Buckman there. And at the end of Stripe, I started interesting myself in AI again, felt like it was the time, you had the Atari games, you had the self-driving craziness at the time. And I started exploring projects, it felt like the Atari games were incredible, but there were still games. And I was looking into exploring projects that would have an impact on the world. And so I decided to explore three things, self-driving cars, cybersecurity and AI, and math and AI. It's like I sing it by a decreasing order of impact on the world, I guess.Swyx [00:03:01]: Discovering new math would be very foundational.Stan [00:03:03]: It is extremely foundational, but it's not as direct as driving people around.Swyx [00:03:07]: Sorry, you're doing this at Stripe, you're like thinking about your next move.Stan [00:03:09]: No, it was at Stripe, kind of a bit of time where I started exploring. I did a bunch of work with friends on trying to get RC cars to drive autonomously. Almost started a company in France or Europe about self-driving trucks. We decided to not go for it because it was probably very operational. And I think the idea of the company, of the team wasn't there. And also I realized that if I wake up a day and because of a bug I wrote, I killed a family, it would be a bad experience. And so I just decided like, no, that's just too crazy. And then I explored cybersecurity with a friend. We're trying to apply transformers to cut fuzzing. So cut fuzzing, you have kind of an algorithm that goes really fast and tries to mutate the inputs of a library to find bugs. And we tried to apply a transformer to that and do reinforcement learning with the signal of how much you propagate within the binary. Didn't work at all because the transformers are so slow compared to evolutionary algorithms that it kind of didn't work. Then I started interested in math and AI and started working on SAT solving with AI. And at the same time, OpenAI was kind of starting the reasoning team that were tackling that project as well. I was in touch with Greg and eventually got in touch with Ilya and finally found my way to OpenAI. I don't know how much you want to dig into that. The way to find your way to OpenAI when you're in Paris was kind of an interesting adventure as well.Swyx [00:04:33]: Please. And I want to note, this was a two-month journey. You did all this in two months.Stan [00:04:38]: The search.Swyx [00:04:40]: Your search for your next thing, because you left in July 2019 and then you joined OpenAI in September.Stan [00:04:45]: I'm going to be ashamed to say that.Swyx [00:04:47]: You were searching before. I was searching before.Stan [00:04:49]: I mean, it's normal. No, the truth is that I moved back to Paris through Stripe and I just felt the hardship of being remote from your team nine hours away. And so it kind of freed a bit of time for me to start the exploration before. Sorry, Patrick. Sorry, John.Swyx [00:05:05]: Hopefully they're listening. So you joined OpenAI from Paris and from like, obviously you had worked with Greg, but notStan [00:05:13]: anyone else. No. Yeah. So I had worked with Greg, but not Ilya, but I had started chatting with Ilya and Ilya was kind of excited because he knew that I was a good engineer through Greg, I presume, but I was not a trained researcher, didn't do a PhD, never did research. And I started chatting and he was excited all the way to the point where he was like, hey, come pass interviews, it's going to be fun. I think he didn't care where I was, he just wanted to try working together. So I go to SF, go through the interview process, get an offer. And so I get Bob McGrew on the phone for the first time, he's like, hey, Stan, it's awesome. You've got an offer. When are you coming to SF? I'm like, hey, it's awesome. I'm not coming to the SF. I'm based in Paris and we just moved. He was like, hey, it's awesome. Well, you don't have an offer anymore. Oh, my God. No, it wasn't as hard as that. But that's basically the idea. And it took me like maybe a couple more time to keep chatting and they eventually decided to try a contractor set up. And that's how I kind of started working at OpenAI, officially as a contractor, but in practice really felt like being an employee.Swyx [00:06:14]: What did you work on?Stan [00:06:15]: So it was solely focused on math and AI. And in particular in the application, so the study of the larger grid models, mathematical reasoning capabilities, and in particular in the context of formal mathematics. The motivation was simple, transformers are very creative, but yet they do mistakes. Formal math systems are of the ability to verify a proof and the tactics they can use to solve problems are very mechanical, so you miss the creativity. And so the idea was to try to explore both together. You would get the creativity of the LLMs and the kind of verification capabilities of the formal system. A formal system, just to give a little bit of context, is a system in which a proof is a program and the formal system is a type system, a type system that is so evolved that you can verify the program. If the type checks, it means that the program is correct.Swyx [00:07:06]: Is the verification much faster than actually executing the program?Stan [00:07:12]: Verification is instantaneous, basically. So the truth is that what you code in involves tactics that may involve computation to search for solutions. So it's not instantaneous. You do have to do the computation to expand the tactics into the actual proof. The verification of the proof at the very low level is instantaneous.Swyx [00:07:32]: How quickly do you run into like, you know, halting problem PNP type things, like impossibilities where you're just like that?Stan [00:07:39]: I mean, you don't run into it at the time. It was really trying to solve very easy problems. So I think the... Can you give an example of easy? Yeah, so that's the mass benchmark that everybody knows today. The Dan Hendricks one. The Dan Hendricks one, yeah. And I think it was the low end part of the mass benchmark at the time, because that mass benchmark includes AMC problems, AMC 8, AMC 10, 12. So these are the easy ones. Then AIME problems, somewhat harder, and some IMO problems, like Crazy Arm.Swyx [00:08:07]: For our listeners, we covered this in our Benchmarks 101 episode. AMC is literally the grade of like high school, grade 8, grade 10, grade 12. So you can solve this. Just briefly to mention this, because I don't think we'll touch on this again. There's a bit of work with like Lean, and then with, you know, more recently with DeepMind doing like scoring like silver on the IMO. Any commentary on like how math has evolved from your early work to today?Stan [00:08:34]: I mean, that result is mind blowing. I mean, from my perspective, spent three years on that. At the same time, Guillaume Lampe in Paris, we were both in Paris, actually. He was at FAIR, was working on some problems. We were pushing the boundaries, and the goal was the IMO. And we cracked a few problems here and there. But the idea of getting a medal at an IMO was like just remote. So this is an impressive result. And we can, I think the DeepMind team just did a good job of scaling. I think there's nothing too magical in their approach, even if it hasn't been published. There's a Dan Silver talk from seven days ago where it goes a little bit into more details. It feels like there's nothing magical there. It's really applying reinforcement learning and scaling up the amount of data that can generate through autoformalization. So we can dig into what autoformalization means if you want.Alessio [00:09:26]: Let's talk about the tail end, maybe, of the OpenAI. So you joined, and you're like, I'm going to work on math and do all of these things. I saw on one of your blog posts, you mentioned you fine-tuned over 10,000 models at OpenAI using 10 million A100 hours. How did the research evolve from the GPD 2, and then getting closer to DaVinci 003? And then you left just before ChatGPD was released, but tell people a bit more about the research path that took you there.Stan [00:09:54]: I can give you my perspective of it. I think at OpenAI, there's always been a large chunk of the compute that was reserved to train the GPTs, which makes sense. So it was pre-entropic splits. Most of the compute was going to a product called Nest, which was basically GPT-3. And then you had a bunch of, let's say, remote, not core research teams that were trying to explore maybe more specific problems or maybe the algorithm part of it. The interesting part, I don't know if it was where your question was going, is that in those labs, you're managing researchers. So by definition, you shouldn't be managing them. But in that space, there's a managing tool that is great, which is compute allocation. Basically by managing the compute allocation, you can message the team of where you think the priority should go. And so it was really a question of, you were free as a researcher to work on whatever you wanted. But if it was not aligned with OpenAI mission, and that's fair, you wouldn't get the compute allocation. As it happens, solving math was very much aligned with the direction of OpenAI. And so I was lucky to generally get the compute I needed to make good progress.Swyx [00:11:06]: What do you need to show as incremental results to get funded for further results?Stan [00:11:12]: It's an imperfect process because there's a bit of a... If you're working on math and AI, obviously there's kind of a prior that it's going to be aligned with the company. So it's much easier than to go into something much more risky, much riskier, I guess. You have to show incremental progress, I guess. It's like you ask for a certain amount of compute and you deliver a few weeks after and you demonstrate that you have a progress. Progress might be a positive result. Progress might be a strong negative result. And a strong negative result is actually often much harder to get or much more interesting than a positive result. And then it generally goes into, as any organization, you would have people finding your project or any other project cool and fancy. And so you would have that kind of phase of growing up compute allocation for it all the way to a point. And then maybe you reach an apex and then maybe you go back mostly to zero and restart the process because you're going in a different direction or something else. That's how I felt. Explore, exploit. Yeah, exactly. Exactly. Exactly. It's a reinforcement learning approach.Swyx [00:12:14]: Classic PhD student search process.Alessio [00:12:17]: And you were reporting to Ilya, like the results you were kind of bringing back to him or like what's the structure? It's almost like when you're doing such cutting edge research, you need to report to somebody who is actually really smart to understand that the direction is right.Stan [00:12:29]: So we had a reasoning team, which was working on reasoning, obviously, and so math in general. And that team had a manager, but Ilya was extremely involved in the team as an advisor, I guess. Since he brought me in OpenAI, I was lucky to mostly during the first years to have kind of a direct access to him. He would really coach me as a trainee researcher, I guess, with good engineering skills. And Ilya, I think at OpenAI, he was the one showing the North Star, right? He was his job and I think he really enjoyed it and he did it super well, was going through the teams and saying, this is where we should be going and trying to, you know, flock the different teams together towards an objective.Swyx [00:13:12]: I would say like the public perception of him is that he was the strongest believer in scaling. Oh, yeah. Obviously, he has always pursued the compression thesis. You have worked with him personally, what does the public not know about how he works?Stan [00:13:26]: I think he's really focused on building the vision and communicating the vision within the company, which was extremely useful. I was personally surprised that he spent so much time, you know, working on communicating that vision and getting the teams to work together versus...Swyx [00:13:40]: To be specific, vision is AGI? Oh, yeah.Stan [00:13:42]: Vision is like, yeah, it's the belief in compression and scanning computes. I remember when I started working on the Reasoning team, the excitement was really about scaling the compute around Reasoning and that was really the belief we wanted to ingrain in the team. And that's what has been useful to the team and with the DeepMind results shows that it was the right approach with the success of GPT-4 and stuff shows that it was the right approach.Swyx [00:14:06]: Was it according to the neural scaling laws, the Kaplan paper that was published?Stan [00:14:12]: I think it was before that, because those ones came with GPT-3, basically at the time of GPT-3 being released or being ready internally. But before that, there really was a strong belief in scale. I think it was just the belief that the transformer was a generic enough architecture that you could learn anything. And that was just a question of scaling.Alessio [00:14:33]: Any other fun stories you want to tell? Sam Altman, Greg, you know, anything.Stan [00:14:37]: Weirdly, I didn't work that much with Greg when I was at OpenAI. He had always been mostly focused on training the GPTs and rightfully so. One thing about Sam Altman, he really impressed me because when I joined, he had joined not that long ago and it felt like he was kind of a very high level CEO. And I was mind blown by how deep he was able to go into the subjects within a year or something, all the way to a situation where when I was having lunch by year two, I was at OpenAI with him. He would just quite know deeply what I was doing. With no ML background. Yeah, with no ML background, but I didn't have any either, so I guess that explains why. But I think it's a question about, you don't necessarily need to understand the very technicalities of how things are done, but you need to understand what's the goal and what's being done and what are the recent results and all of that in you. And we could have kind of a very productive discussion. And that really impressed me, given the size at the time of OpenAI, which was not negligible.Swyx [00:15:44]: Yeah. I mean, you've been a, you were a founder before, you're a founder now, and you've seen Sam as a founder. How has he affected you as a founder?Stan [00:15:51]: I think having that capability of changing the scale of your attention in the company, because most of the time you operate at a very high level, but being able to go deep down and being in the known of what's happening on the ground is something that I feel is really enlightening. That's not a place in which I ever was as a founder, because first company, we went all the way to 10 people. Current company, there's 25 of us. So the high level, the sky and the ground are pretty much at the same place. No, you're being too humble.Swyx [00:16:21]: I mean, Stripe was also like a huge rocket ship.Stan [00:16:23]: Stripe, I was a founder. So I was, like at OpenAI, I was really happy being on the ground, pushing the machine, making it work. Yeah.Swyx [00:16:31]: Last OpenAI question. The Anthropic split you mentioned, you were around for that. Very dramatic. David also left around that time, you left. This year, we've also had a similar management shakeup, let's just call it. Can you compare what it was like going through that split during that time? And then like, does that have any similarities now? Like, are we going to see a new Anthropic emerge from these folks that just left?Stan [00:16:54]: That I really, really don't know. At the time, the split was pretty surprising because they had been trying GPT-3, it was a success. And to be completely transparent, I wasn't in the weeds of the splits. What I understood of it is that there was a disagreement of the commercialization of that technology. I think the focal point of that disagreement was the fact that we started working on the API and wanted to make those models available through an API. Is that really the core disagreement? I don't know.Swyx [00:17:25]: Was it safety?Stan [00:17:26]: Was it commercialization?Swyx [00:17:27]: Or did they just want to start a company?Stan [00:17:28]: Exactly. Exactly. That I don't know. But I think what I was surprised of is how quickly OpenAI recovered at the time. And I think it's just because we were mostly a research org and the mission was so clear that some divergence in some teams, some people leave, the mission is still there. We have the compute. We have a site. So it just keeps going.Swyx [00:17:50]: Very deep bench. Like just a lot of talent. Yeah.Alessio [00:17:53]: So that was the OpenAI part of the history. Exactly. So then you leave OpenAI in September 2022. And I would say in Silicon Valley, the two hottest companies at the time were you and Lanktrain. What was that start like and why did you decide to start with a more developer focused kind of like an AI engineer tool rather than going back into some more research and something else?Stan [00:18:15]: Yeah. First, I'm not a trained researcher. So going through OpenAI was really kind of the PhD I always wanted to do. But research is hard. You're digging into a field all day long for weeks and weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, oh, yeah, that was obvious. And you go back to digging. I'm not a trained, like formally trained researcher, and it wasn't kind of a necessarily an ambition of me of creating, of having a research career. And I felt the hardness of it. I enjoyed a lot of like that a ton. But at the time, I decided that I wanted to go back to something more productive. And the other fun motivation was like, I mean, if we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down. And so that was kind of the true motivation for like trying to go there. So that's kind of the core motivation at the beginning of personally. And the motivation for starting a company was pretty simple. I had seen GPT-4 internally at the time, it was September 2022. So it was pre-GPT, but GPT-4 was ready since, I mean, I'd been ready for a few months internally. I was like, okay, that's obvious, the capabilities are there to create an insane amount of value to the world. And yet the deployment is not there yet. The revenue of OpenAI at the time were ridiculously small compared to what it is today. So the thesis was, there's probably a lot to be done at the product level to unlock the usage.Alessio [00:19:49]: Yeah. Let's talk a bit more about the form factor, maybe. I think one of the first successes you had was kind of like the WebGPT-like thing, like using the models to traverse the web and like summarize things. And the browser was really the interface. Why did you start with the browser? Like what was it important? And then you built XP1, which was kind of like the browser extension.Stan [00:20:09]: So the starting point at the time was, if you wanted to talk about LLMs, it was still a rather small community, a community of mostly researchers and to some extent, very early adopters, very early engineers. It was almost inconceivable to just build a product and go sell it to the enterprise, though at the time there was a few companies doing that. The one on marketing, I don't remember its name, Jasper. But so the natural first intention, the first, first, first intention was to go to the developers and try to create tooling for them to create product on top of those models. And so that's what Dust was originally. It was quite different than Lanchain, and Lanchain just beat the s**t out of us, which is great. It's a choice.Swyx [00:20:53]: You were cloud, in closed source. They were open source.Stan [00:20:56]: Yeah. So technically we were open source and we still are open source, but I think that doesn't really matter. I had the strong belief from my research time that you cannot create an LLM-based workflow on just one example. Basically, if you just have one example, you overfit. So as you develop your interaction, your orchestration around the LLM, you need a dozen examples. Obviously, if you're running a dozen examples on a multi-step workflow, you start paralyzing stuff. And if you do that in the console, you just have like a messy stream of tokens going out and it's very hard to observe what's going there. And so the idea was to go with an UI so that you could kind of introspect easily the output of each interaction with the model and dig into there through an UI, which is-Swyx [00:21:42]: Was that open source? I actually didn't come across it.Stan [00:21:44]: Oh yeah, it wasn't. I mean, Dust is entirely open source even today. We're not going for an open source-Swyx [00:21:48]: If it matters, I didn't know that.Stan [00:21:49]: No, no, no, no, no. The reason why is because we're not open source because we're not doing an open source strategy. It's not an open source go-to-market at all. We're open source because we can and it's fun.Swyx [00:21:59]: Open source is marketing. You have all the downsides of open source, which is like people can clone you.Stan [00:22:03]: But I think that downside is a big fallacy. Okay. Yes, anybody can clone Dust today, but the value of Dust is not the current state. The value of Dust is the number of eyeballs and hands of developers that are creating to it in the future. And so yes, anybody can clone it today, but that wouldn't change anything. There is some value in being open source. In a discussion with the security team, you can be extremely transparent and just show the code. When you have discussion with users and there's a bug or a feature missing, you can just point to the issue, show the pull request, show the, show the, exactly, oh, PR welcome. That doesn't happen that much, but you can show the progress if the person that you're chatting with is a little bit technical, they really enjoy seeing the pull request advancing and seeing all the way to deploy. And then the downsides are mostly around security. You never want to do security by obfuscation. But the truth is that your vector of attack is facilitated by you being open source. But at the same time, it's a good thing because if you're doing anything like a bug bountying or stuff like that, you just give much more tools to the bug bountiers so that their output is much better. So there's many, many, many trade-offs. I don't believe in the value of the code base per se. I think it's really the people that are on the code base that have the value and go to market and the product and all of those things that are around the code base. Obviously, that's not true for every code base. If you're working on a very secret kernel to accelerate the inference of LLMs, I would buy that you don't want to be open source. But for product stuff, I really think there's very little risk. Yeah.Alessio [00:23:39]: I signed up for XP1, I was looking, January 2023. I think at the time you were on DaVinci 003. Given that you had seen GPD 4, how did you feel having to push a product out that was using this model that was so inferior? And you're like, please, just use it today. I promise it's going to get better. Just overall, as a founder, how do you build something that maybe doesn't quite work with the model today, but you're just expecting the new model to be better?Stan [00:24:03]: Yeah, so actually, XP1 was even on a smaller one that was the post-GDPT release, small version, so it was... Ada, Babbage... No, no, no, not that far away. But it was the small version of GDPT, basically. I don't remember its name. Yes, you have a frustration there. But at the same time, I think XP1 was designed, was an experiment, but was designed as a way to be useful at the current capability of the model. If you just want to extract data from a LinkedIn page, that model was just fine. If you want to summarize an article on a newspaper, that model was just fine. And so it was really a question of trying to find a product that works with the current capability, knowing that you will always have tailwinds as models get better and faster and cheaper. So that was kind of a... There's a bit of a frustration because you know what's out there and you know that you don't have access to it yet. It's also interesting to try to find a product that works with the current capability.Alessio [00:24:55]: And we highlighted XP1 in our anatomy of autonomy post in April of last year, which was, you know, where are all the agents, right? So now we spent 30 minutes getting to what you're building now. So you basically had a developer framework, then you had a browser extension, then you had all these things, and then you kind of got to where Dust is today. So maybe just give people an overview of what Dust is today and the courtesies behind it. Yeah, of course.Stan [00:25:20]: So Dust, we really want to build the infrastructure so that companies can deploy agents within their teams. We are horizontal by nature because we strongly believe in the emergence of use cases from the people having access to creating an agent that don't need to be developers. They have to be thinkers. They have to be curious. But anybody can create an agent that will solve an operational thing that they're doing in their day-to-day job. And to make those agents useful, there's two focus, which is interesting. The first one is an infrastructure focus. You have to build the pipes so that the agent has access to the data. You have to build the pipes such that the agents can take action, can access the web, et cetera. So that's really an infrastructure play. Maintaining connections to Notion, Slack, GitHub, all of them is a lot of work. It is boring work, boring infrastructure work, but that's something that we know is extremely valuable in the same way that Stripe is extremely valuable because it maintains the pipes. And we have that dual focus because we're also building the product for people to use it. And there it's fascinating because everything started from the conversational interface, obviously, which is a great starting point. But we're only scratching the surface, right? I think we are at the pong level of LLM productization. And we haven't invented the C3. We haven't invented Counter-Strike. We haven't invented Cyberpunk 2077. So this is really our mission is to really create the product that lets people equip themselves to just get away all the work that can be automated or assisted by LLMs.Alessio [00:26:57]: And can you just comment on different takes that people had? So maybe the most open is like auto-GPT. It's just kind of like just trying to do anything. It's like it's all magic. There's no way for you to do anything. Then you had the ADAPT, you know, we had David on the podcast. They're very like super hands-on with each individual customer to build super tailored. How do you decide where to draw the line between this is magic? This is exposed to you, especially in a market where most people don't know how to build with AI at all. So if you expect them to do the thing, they're probably not going to do it. Yeah, exactly.Stan [00:27:29]: So the auto-GPT approach obviously is extremely exciting, but we know that the agentic capability of models are not quite there yet. It just gets lost. So we're starting, we're starting where it works. Same with the XP one. And where it works is pretty simple. It's like simple workflows that involve a couple tools where you don't even need to have the model decide which tools it's used in the sense of you just want people to put it in the instructions. It's like take that page, do that search, pick up that document, do the work that I want in the format I want, and give me the results. There's no smartness there, right? In terms of orchestrating the tools, it's mostly using English for people to program a workflow where you don't have the constraint of having compatible API between the two.Swyx [00:28:17]: That kind of personal automation, would you say it's kind of like an LLM Zapier type ofStan [00:28:22]: thing?Swyx [00:28:22]: Like if this, then that, and then, you know, do this, then this. You're programming with English?Stan [00:28:28]: So you're programming with English. So you're just saying, oh, do this and then that. You can even create some form of APIs. You say, when I give you the command X, do this. When I give you the command Y, do this. And you describe the workflow. But you don't have to create boxes and create the workflow explicitly. It just needs to describe what are the tasks supposed to be and make the tool available to the agent. The tool can be a semantic search. The tool can be querying into a structured database. The tool can be searching on the web. And obviously, the interesting tools that we're only starting to scratch are actually creating external actions like reimbursing something on Stripe, sending an email, clicking on a button in the admin or something like that.Swyx [00:29:11]: Do you maintain all these integrations?Stan [00:29:13]: Today, we maintain most of the integrations. We do always have an escape hatch for people to kind of custom integrate. But the reality is that the reality of the market today is that people just want it to work, right? And so it's mostly us maintaining the integration. As an example, a very good source of information that is tricky to productize is Salesforce. Because Salesforce is basically a database and a UI. And they do the f**k they want with it. And so every company has different models and stuff like that. So right now, we don't support it natively. And the type of support or real native support will be slightly more complex than just osing into it, like is the case with Slack as an example. Because it's probably going to be, oh, you want to connect your Salesforce to us? Give us the SQL. That's the Salesforce QL language. Give us the queries you want us to run on it and inject in the context of dust. So that's interesting how not only integrations are cool, and some of them require a bit of work on the user. And for some of them that are really valuable to our users, but we don't support yet, they can just build them internally and push the data to us.Swyx [00:30:18]: I think I understand the Salesforce thing. But let me just clarify, are you using browser automation because there's no API for something?Stan [00:30:24]: No, no, no, no. In that case, so we do have browser automation for all the use cases and apply the public web. But for most of the integration with the internal system of the company, it really runs through API.Swyx [00:30:35]: Haven't you felt the pull to RPA, browser automation, that kind of stuff?Stan [00:30:39]: I mean, what I've been saying for a long time, maybe I'm wrong, is that if the future is that you're going to stand in front of a computer and looking at an agent clicking on stuff, then I'll hit my computer. And my computer is a big Lenovo. It's black. Doesn't sound good at all compared to a Mac. And if the APIs are there, we should use them. There is going to be a long tail of stuff that don't have APIs, but as the world is moving forward, that's disappearing. So the core API value in the past has really been, oh, this old 90s product doesn't have an API. So I need to use the UI to automate. I think for most of the ICP companies, the companies that ICP for us, the scale ups that are between 500 and 5,000 people, tech companies, most of the SaaS they use have APIs. Now there's an interesting question for the open web, because there are stuff that you want to do that involve websites that don't necessarily have APIs. And the current state of web integration from, which is us and OpenAI and Anthropic, I don't even know if they have web navigation, but I don't think so. The current state of affair is really, really broken because you have what? You have basically search and headless browsing. But headless browsing, I think everybody's doing basically body.innertext and fill that into the model, right?Swyx [00:31:56]: MARK MIRCHANDANI There's parsers into Markdown and stuff.Stan [00:31:58]: FRANCESC CAMPOY I'm super excited by the companies that are exploring the capability of rendering a web page into a way that is compatible for a model, being able to maintain the selector. So that's basically the place where to click in the page through that process, expose the actions to the model, have the model select an action in a way that is compatible with model, which is not a big page of a full DOM that is very noisy, and then being able to decompress that back to the original page and take the action. And that's something that is really exciting and that will kind of change the level of things that agents can do on the web. That I feel exciting, but I also feel that the bulk of the useful stuff that you can do within the company can be done through API. The data can be retrieved by API. The actions can be taken through API.Swyx [00:32:44]: For listeners, I'll note that you're basically completely disagreeing with David Wan. FRANCESC CAMPOY Exactly, exactly. I've seen it since it's summer. ADEPT is where it is, and Dust is where it is. So Dust is still standing.Alessio [00:32:55]: Can we just quickly comment on function calling? You mentioned you don't need the models to be that smart to actually pick the tools. Have you seen the models not be good enough? Or is it just like, you just don't want to put the complexity in there? Like, is there any room for improvement left in function calling? Or do you feel you usually consistently get always the right response, the right parametersStan [00:33:13]: and all of that?Alessio [00:33:13]: FRANCESC CAMPOY So that's a tricky product question.Stan [00:33:15]: Because if the instructions are good and precise, then you don't have any issue, because it's scripted for you. And the model will just look at the scripts and just follow and say, oh, he's probably talking about that action, and I'm going to use it. And the parameters are kind of abused from the state of the conversation. I'll just go with it. If you provide a very high level, kind of an auto-GPT-esque level in the instructions and provide 16 different tools to your model, yes, we're seeing the models in that state making mistakes. And there is obviously some progress can be made on the capabilities. But the interesting part is that there is already so much work that can assist, augment, accelerate by just going with pretty simply scripted for actions agents. What I'm excited about by pushing our users to create rather simple agents is that once you have those working really well, you can create meta agents that use the agents as actions. And all of a sudden, you can kind of have a hierarchy of responsibility that will probably get you almost to the point of the auto-GPT value. It requires the construction of intermediary artifacts, but you're probably going to be able to achieve something great. I'll give you some example. We have our incidents are shared in Slack in a specific channel, or shipped are shared in Slack. We have a weekly meeting where we have a table about incidents and shipped stuff. We're not writing that weekly meeting table anymore. We have an assistant that just go find the right data on Slack and create the table for us. And that assistant works perfectly. It's trivially simple, right? Take one week of data from that channel and just create the table. And then we have in that weekly meeting, obviously some graphs and reporting about our financials and our progress and our ARR. And we've created assistants to generate those graphs directly. And those assistants works great. By creating those assistants that cover those small parts of that weekly meeting, slowly we're getting to in a world where we'll have a weekly meeting assistance. We'll just call it. You don't need to prompt it. You don't need to say anything. It's going to run those different assistants and get that notion page just ready. And by doing that, if you get there, and that's an objective for us to us using Dust, get there, you're saving an hour of company time every time you run it. Yeah.Alessio [00:35:28]: That's my pet topic of NPM for agents. How do you build dependency graphs of agents? And how do you share them? Because why do I have to rebuild some of the smaller levels of what you built already?Swyx [00:35:40]: I have a quick follow-up question on agents managing other agents. It's a topic of a lot of research, both from Microsoft and even in startups. What you've discovered best practice for, let's say like a manager agent controlling a bunch of small agents. It's two-way communication. I don't know if there should be a protocol format.Stan [00:35:59]: To be completely honest, the state we are at right now is creating the simple agents. So we haven't even explored yet the meta agents. We know it's there. We know it's going to be valuable. We know it's going to be awesome. But we're starting there because it's the simplest place to start. And it's also what the market understands. If you go to a company, random SaaS B2B company, not necessarily specialized in AI, and you take an operational team and you tell them, build some tooling for yourself, they'll understand the small agents. If you tell them, build AutoGP, they'll be like, Auto what?Swyx [00:36:31]: And I noticed that in your language, you're very much focused on non-technical users. You don't really mention API here. You mention instruction instead of system prompt, right? That's very conscious.Stan [00:36:41]: Yeah, it's very conscious. It's a mark of our designer, Ed, who kind of pushed us to create a friendly product. I was knee-deep into AI when I started, obviously. And my co-founder, Gabriel, was a Stripe as well. We started a company together that got acquired by Stripe 15 years ago. It was at Alain, a healthcare company in Paris. After that, it was a little bit less so knee-deep in AI, but really focused on product. And I didn't realize how important it is to make that technology not scary to end users. It didn't feel scary to me, but it was really seen by Ed, our designer, that it was feeling scary to the users. And so we were very proactive and very deliberate about creating a brand that feels not too scary and creating a wording and a language, as you say, that really tried to communicate the fact that it's going to be fine. It's going to be easy. You're going to make it.Alessio [00:37:34]: And another big point that David had about ADAPT is we need to build an environment for the agents to act. And then if you have the environment, you can simulate what they do. How's that different when you're interacting with APIs and you're kind of touching systems that you cannot really simulate? If you call it the Salesforce API, you're just calling it.Stan [00:37:52]: So I think that goes back to the DNA of the companies that are very different. ADAPT, I think, was a product company with a very strong research DNA, and they were still doing research. One of their goals was building a model. And that's why they raised a large amount of money, et cetera. We are 100% deliberately a product company. We don't do research. We don't train models. We don't even run GPUs. We're using the models that exist, and we try to push the product boundary as far as possible with the existing models. So that creates an issue. Indeed, so to answer your question, when you're interacting in the real world, well, you cannot simulate, so you cannot improve the models. Even improving your instructions is complicated for a builder. The hope is that you can use models to evaluate the conversations so that you can get at least feedback and you could get contradictive information about the performance of the assistance. But if you take actual trace of interaction of humans with those agents, it is even for us humans extremely hard to decide whether it was a productive interaction or a really bad interaction. You don't know why the person left. You don't know if they left happy or not. So being extremely, extremely, extremely pragmatic here, it becomes a product issue. We have to build a product that identifies the end users to provide feedback so that as a first step, the person that is building the agent can iterate on it. As a second step, maybe later when we start training model and post-training, et cetera, we can optimize around that for each of those companies. Yeah.Alessio [00:39:17]: Do you see in the future products offering kind of like a simulation environment, the same way all SaaS now kind of offers APIs to build programmatically? Like in cybersecurity, there are a lot of companies working on building simulative environments so that then you can use agents like Red Team, but I haven't really seen that.Stan [00:39:34]: Yeah, no, me neither. That's a super interesting question. I think it's really going to depend on how much, because you need to simulate to generate data, you need to train data to train models. And the question at the end is, are we going to be training models or are we just going to be using frontier models as they are? On that question, I don't have a strong opinion. It might be the case that we'll be training models because in all of those AI first products, the model is so close to the product surface that as you get big and you want to really own your product, you're going to have to own the model as well. Owning the model doesn't mean doing the pre-training, that would be crazy. But at least having an internal post-training realignment loop, it makes a lot of sense. And so if we see many companies going towards that all the time, then there might be incentives for the SaaS's of the world to provide assistance in getting there. But at the same time, there's a tension because those SaaS, they don't want to be interacted by agents, they want the human to click on the button. Yeah, they got to sell seats. Exactly.Swyx [00:40:41]: Just a quick question on models. I'm sure you've used many, probably not just OpenAI. Would you characterize some models as better than others? Do you use any open source models? What have been the trends in models over the last two years?Stan [00:40:53]: We've seen over the past two years kind of a bit of a race in between models. And at times, it's the OpenAI model that is the best. At times, it's the Anthropic models that is the best. Our take on that is that we are agnostic and we let our users pick their model. Oh, they choose? Yeah, so when you create an assistant or an agent, you can just say, oh, I'm going to run it on GP4, GP4 Turbo, or...Swyx [00:41:16]: Don't you think for the non-technical user, that is actually an abstraction that you should take away from them?Stan [00:41:20]: We have a sane default. So we move the default to the latest model that is cool. And we have a sane default, and it's actually not very visible. In our flow to create an agent, you would have to go in advance and go pick your model. So this is something that the technical person will care about. But that's something that obviously is a bit too complicated for the...Swyx [00:41:40]: And do you care most about function calling or instruction following or something else?Stan [00:41:44]: I think we care most for function calling because you want to... There's nothing worse than a function call, including incorrect parameters or being a bit off because it just drives the whole interaction off.Swyx [00:41:56]: Yeah, so got the Berkeley function calling.Stan [00:42:00]: These days, it's funny how the comparison between GP4O and GP4 Turbo is still up in the air on function calling. I personally don't have proof, but I know many people, and I'm probably part of them, to think that GP4 Turbo is still better than GP4O on function calling. Wow. We'll see what comes out of the O1 class if it ever gets function calling. And Cloud 3.5 Summit is great as well. They kind of innovated in an interesting way, which was never quite publicized. But it's that they have that kind of chain of thought step whenever you use a Cloud model or Summit model with function calling. That chain of thought step doesn't exist when you just interact with it just for answering questions. But when you use function calling, you get that step, and it really helps getting better function calling.Swyx [00:42:43]: Yeah, we actually just recorded a podcast with the Berkeley team that runs that leaderboard this week. So they just released V3.Stan [00:42:49]: Yeah.Swyx [00:42:49]: It was V1 like two months ago, and then they V2, V3. Turbo is on top.Stan [00:42:53]: Turbo is on top. Turbo is over 4.0.Swyx [00:42:54]: And then the third place is XLAM from Salesforce, which is a large action model they've been trying to popularize.Stan [00:43:01]: Yep.Swyx [00:43:01]: O1 Mini is actually on here, I think. O1 Mini is number 11.Stan [00:43:05]: But arguably, O1 Mini has been in a line for that. Yeah.Alessio [00:43:09]: Do you use leaderboards? Do you have your own evals? I mean, this is kind of intuitive, right? Like using the older model is better. I think most people just upgrade. Yeah. What's the eval process like?Stan [00:43:19]: It's funny because I've been doing research for three years, and we have bigger stuff to cook. When you're deploying in a company, one thing where we really spike is that when we manage to activate the company, we have a crazy penetration. The highest penetration we have is 88% daily active users within the entire employee of the company. The kind of average penetration and activation we have in our current enterprise customers is something like more like 60% to 70% weekly active. So we basically have the entire company interacting with us. And when you're there, there is so many stuff that matters most than getting evals, getting the best model. Because there is so many places where you can create products or do stuff that will give you the 80% with the work you do. Whereas deciding if it's GPT-4 or GPT-4 Turbo or et cetera, you know, it'll just give you the 5% improvement. But the reality is that you want to focus on the places where you can really change the direction or change the interaction more drastically. But that's something that we'll have to do eventually because we still want to be serious people.Swyx [00:44:24]: It's funny because in some ways, the model labs are competing for you, right? You don't have to do any effort. You just switch model and then it'll grow. What are you really limited by? Is it additional sources?Stan [00:44:36]: It's not models, right?Swyx [00:44:37]: You're not really limited by quality of model.Stan [00:44:40]: Right now, we are limited by the infrastructure part, which is the ability to connect easily for users to all the data they need to do the job they want to do.Swyx [00:44:51]: Because you maintain all your own stuff.Stan [00:44:53]: You know, there are companies out thereSwyx [00:44:54]: that are starting to provide integrations as a service, right? I used to work in an integrations company. Yeah, I know.Stan [00:44:59]: It's just that there is some intricacies about how you chunk stuff and how you process information from one platform to the other. If you look at the end of the spectrum, you could think of, you could say, oh, I'm going to support AirByte and AirByte has- I used to work at AirByte.Swyx [00:45:12]: Oh, really?Stan [00:45:13]: That makes sense.Swyx [00:45:14]: They're the French founders as well.Stan [00:45:15]: I know Jean very well. I'm seeing him today. And the reality is that if you look at Notion, AirByte does the job of taking Notion and putting it in a structured way. But that's the way it is not really usable to actually make it available to models in a useful way. Because you get all the blocks, details, et cetera, which is useful for many use cases.Swyx [00:45:35]: It's also for data scientists and not for AI.Stan [00:45:38]: The reality of Notion is that sometimes you have a- so when you have a page, there's a lot of structure in it and you want to capture the structure and chunk the information in a way that respects that structure. In Notion, you have databases. Sometimes those databases are real tabular data. Sometimes those databases are full of text. You want to get the distinction and understand that this database should be considered like text information, whereas this other one is actually quantitative information. And to really get a very high quality interaction with that piece of information, I haven't found a solution that will work without us owning the connection end-to-end.Swyx [00:46:15]: That's why I don't invest in, there's Composio, there's All Hands from Graham Newbig. There's all these other companies that are like, we will do the integrations for you. You just, we have the open source community. We'll do off the shelf. But then you are so specific in your needs that you want to own it.Swyx [00:46:28]: Yeah, exactly.Stan [00:46:29]: You can talk to Michel about that.Swyx [00:46:30]: You know, he wants to put the AI in there, but you know. Yeah, I will. I will.Stan [00:46:35]: Cool. What are we missing?Alessio [00:46:36]: You know, what are like the things that are like sneakily hard that you're tackling that maybe people don't even realize they're like really hard?Stan [00:46:43]: The real parts as we kind of touch base throughout the conversation is really building the infra that works for those agents because it's a tenuous walk. It's an evergreen piece of work because you always have an extra integration that will be useful to a non-negligible set of your users. I'm super excited about is that there's so many interactions that shouldn't be conversational interactions and that could be very useful. Basically, know that we have the firehose of information of those companies and there's not going to be that many companies that capture the firehose of information. When you have the firehose of information, you can do a ton of stuff with models that are just not accelerating people, but giving them superhuman capability, even with the current model capability because you can just sift through much more information. An example is documentation repair. If I have the firehose of Slack messages and new Notion pages, if somebody says, I own that page, I want to be updated when there is a piece of information that should update that page, this is not possible. You get an email saying, oh, look at that Slack message. It says the opposite of what you have in that paragraph. Maybe you want to update or just ping that person. I think there is a lot to be explored on the product layer in terms of what it means to interact productively with those models. And that's a problem that's extremely hard and extremely exciting.Swyx [00:48:00]: One thing you keep mentioning about infra work, obviously, Dust is building that infra and serving that in a very consumer-friendly way. You always talk about infra being additional sources, additional connectors. That is very important. But I'm also interested in the vertical infra. There is an orchestrator underlying all these things where you're doing asynchronous work. For example, the simplest one is a cron job. You just schedule things. But also, for if this and that, you have to wait for something to be executed and proceed to the next task. I used to work on an orchestrator as well, Temporal.Stan [00:48:31]: We used Temporal. Oh, you used Temporal? Yeah. Oh, how was the experience?Swyx [00:48:34]: I need the NPS.Stan [00:48:36]: We're doing a self-discovery call now.Swyx [00:48:39]: But you can also complain to me because I don't work there anymore.Stan [00:48:42]: No, we love Temporal. There's some edges that are a bit rough, surprisingly rough. And you would say, why is it so complicated?Swyx [00:48:49]: It's always versioning.Stan [00:48:50]: Yeah, stuff like that. But we really love it. And we use it for exactly what you said, like managing the entire set of stuff that needs to happen so that in semi-real time, we get all the updates from Slack or Notion or GitHub into the system. And whenever we see that piece of information goes through, maybe trigger workflows to run agents because they need to provide alerts to users and stuff like that. And Temporal is great. Love it.Swyx [00:49:17]: You haven't evaluated others. You don't want to build your own. You're happy with...Stan [00:49:21]: Oh, no, we're not in the business of replacing Temporal. And Temporal is so... I mean, it is or any other competitive product. They're very general. If it's there, there's an interesting theory about buy versus build. I think in that case, when you're a high-growth company, your buy-build trade-off is very much on the side of buy. Because if you have the capability, you're just going to be saving time, you can focus on your core competency, etc. And it's funny because we're seeing, we're starting to see the post-high-growth company, post-SKF company, going back on that trade-off, interestingly. So that's the cloud news about removing Zendesk and Salesforce. Do you believe that, by the way?Alessio [00:49:56]: Yeah, I did a podcast with them.Stan [00:49:58]: Oh, yeah?Alessio [00:49:58]: It's true.Swyx [00:49:59]: No, no, I know.Stan [00:50:00]: Of course they say it's true,Swyx [00:50:00]: but also how well is it going to go?Stan [00:50:02]: So I'm not talking about deflecting the customer traffic. I'm talking about building AI on top of Salesforce and Zendesk, basically, if I understand correctly. And all of a sudden, your product surface becomes much smaller because you're interacting with an AI system that will take some actions. And so all of a sudden, you don't need the product layer anymore. And you realize that, oh, those things are just databases that I pay a hundred times the price, right? Because you're a post-SKF company and you have tech capabilities, you are incentivized to reduce your costs and you have the capability to do so. And then it makes sense to just scratch the SaaS away. So it's interesting that we might see kind of a bad time for SaaS in post-hyper-growth tech companies. So it's still a big market, but it's not that big because if you're not a tech company, you don't have the capabilities to reduce that cost. If you're a high-growth company, always going to be buying because you go faster with that. But that's an interesting new space, new category of companies that might remove some SaaS. Yeah, Alessio's firmSwyx [00:51:02]: has an interesting thesis on the future of SaaS in AI.Alessio [00:51:05]: Service as a software, we call it. It's basically like, well, the most extreme is like, why is there any software at all? You know, ideally, it's all a labor interface where you're asking somebody to do something for you, whether that's a person, an AI agent or whatnot.Stan [00:51:17]: Yeah, yeah, that's interesting. I have to ask.Swyx [00:51:19]: Are you paying for Temporal Cloud or are you self-hosting?Stan [00:51:22]: Oh, no, no, we're paying, we're paying. Oh, okay, interesting.Swyx [00:51:24]: We're paying way too much.Stan [00:51:26]: It's crazy expensive, but it makes us-Swyx [00:51:28]: That's why as a shareholder, I like to hear that. It makes us go faster,Stan [00:51:31]: so we're happy to pay.Swyx [00:51:33]: Other things in the infrastack, I just want a list for other founders to think about. Ops, API gateway, evals, you know, anything interesting there that you build or buy?Stan [00:51:41]: I mean, there's always an interesting question. We've been building a lot around the interface between models and because Dust, the original version, was an orchestration platform and we basically provide a unified interface to every model providers.Swyx [00:51:56]: That's what I call gateway.Stan [00:51:57]: That we add because Dust was that and so we continued building upon and we own it. But that's an interesting question was in you, you want to build that or buy it?Swyx [00:52:06]: Yeah, I always say light LLM is the current open source consensus.Stan [00:52:09]: Exactly, yeah. There's an interesting question there.Swyx [00:52:12]: Ops, Datadog, just tracking.Stan [00:52:14]: Oh yeah, so Datadog is an obvious... What are the mistakes that I regret? I started as pure JavaScript, not TypeScript, and I think you want to, if you're wondering, oh, I want to go fast, I'll do a little bit of JavaScript. No, don't, just start with TypeScript. I see, okay.Swyx [00:52:30]: So interesting, you are a research engineer that came out of OpenAI that bet on TypeScript.Stan [00:52:36]: Well, the reality is that if you're building a product, you're going to be doing a lot of JavaScript, right? And Next, we're using Next as an example. It's

Apologies for lower audio quality; we lost recordings and had to use backup tracks. Our guests today are Anastasios Angelopoulos and Wei-Lin Chiang, leads of Chatbot Arena, fka LMSYS, the crowdsourced AI evaluation platform developed by the LMSys student club at Berkeley, which became the de facto standard for comparing language models. Arena ELO is often more cited than MMLU scores to many folks, and they have attracted >1,000,000 people to cast votes since its launch, leading top model trainers to cite them over their own formal academic benchmarks:The Limits of Static BenchmarksWe've done two benchmarks episodes: Benchmarks 101 and Benchmarks 201. One issue we've always brought up with static benchmarks is that 1) many are getting saturated, with models scoring almost perfectly on them 2) they often don't reflect production use cases, making it hard for developers and users to use them as guidance. The fundamental challenge in AI evaluation isn't technical - it's philosophical. How do you measure something that increasingly resembles human intelligence? Rather than trying to define intelligence upfront, Arena let users interact naturally with models and collect comparative feedback. It's messy and subjective, but that's precisely the point - it captures the full spectrum of what people actually care about when using AI.The Pareto Frontier of Cost vs IntelligenceBecause the Elo scores are remarkably stable over time, we can put all the chat models on a map against their respective cost to gain a view of at least 3 orders of magnitude of model sizes/costs and observe the remarkable shift in intelligence per dollar over the past year:This frontier stood remarkably firm through the recent releases of o1-preview and price cuts of Gemini 1.5:The Statistics of SubjectivityIn our Benchmarks 201 episode, Clémentine Fourrier from HuggingFace thought this design choice was one of shortcomings of arenas: they aren't reproducible. You don't know who ranked what and what exactly the outcome was at the time of ranking. That same person might rank the same pair of outputs differently on a different day, or might ask harder questions to better models compared to smaller ones, making it imbalanced. Another argument that people have brought up is confirmation bias. We know humans prefer longer responses and are swayed by formatting - Rob Mulla from Dreadnode had found some interesting data on this in May:The approach LMArena is taking is to use logistic regression to decompose human preferences into constituent factors. As Anastasios explains: "We can say what components of style contribute to human preference and how they contribute." By adding these style components as parameters, they can mathematically "suck out" their influence and isolate the core model capabilities.This extends beyond just style - they can control for any measurable factor: "What if I want to look at the cost adjusted performance? Parameter count? We can ex post facto measure that." This is one of the most interesting things about Arena: You have a data generation engine which you can clean and turn into leaderboards later. If you wanted to create a leaderboard for poetry writing, you could get existing data from Arena, normalize it by identifying these style components. Whether or not it's possible to really understand WHAT bias the voters have, that's a different question.Private EvalsOne of the most delicate challenges LMSYS faces is maintaining trust while collaborating with AI labs. The concern is that labs could game the system by testing multiple variants privately and only releasing the best performer. This was brought up when 4o-mini released and it ranked as the second best model on the leaderboard:But this fear misunderstands how Arena works. Unlike static benchmarks where selection bias is a major issue, Arena's live nature means any initial bias gets washed out by ongoing evaluation. As Anastasios explains: "In the long run, there's way more fresh data than there is data that was used to compare these five models." The other big question is WHAT model is actually being tested; as people often talk about on X / Discord, the same endpoint will randomly feel “nerfed” like it happened for “Claude European summer” and corresponding conspiracy theories:It's hard to keep track of these performance changes in Arena as these changes (if real…?) are not observable.The Future of EvaluationThe team's latest work on RouteLLM points to an interesting future where evaluation becomes more granular and task-specific. But they maintain that even simple routing strategies can be powerful - like directing complex queries to larger models while handling simple tasks with smaller ones.Arena is now going to expand beyond text into multimodal evaluation and specialized domains like code execution and red teaming. But their core insight remains: the best way to evaluate intelligence isn't to simplify it into metrics, but to embrace its complexity and find rigorous ways to analyze it. To go after this vision, they are spinning out Arena from LMSys, which will stay as an academia-driven group at Berkeley.Full Video PodcastChapters* 00:00:00 - Introductions* 00:01:16 - Origin and development of Chatbot Arena* 00:05:41 - Static benchmarks vs. Arenas* 00:09:03 - Community building* 00:13:32 - Biases in human preference evaluation* 00:18:27 - Style Control and Model Categories* 00:26:06 - Impact of o1* 00:29:15 - Collaborating with AI labs* 00:34:51 - RouteLLM and router models* 00:38:09 - Future of LMSys / ArenaShow Notes* Anastasios Angelopoulos* Anastasios' NeurIPS Paper Conformal Risk Control* Wei-Lin Chiang* Chatbot Arena* LMSys* MTBench* ShareGPT dataset* Stanford's Alpaca project* LLMRouter* E2B* DreadnodeTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, Partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:14]: Hey, and today we're very happy and excited to welcome Anastasios and Wei Lin from LMSys. Welcome guys.Wei Lin [00:00:21]: Hey, how's it going? Nice to see you.Anastasios [00:00:23]: Thanks for having us.Swyx [00:00:24]: Anastasios, I actually saw you, I think at last year's NeurIPS. You were presenting a paper, which I don't really super understand, but it was some theory paper about how your method was very dominating over other sort of search methods. I don't remember what it was, but I remember that you were a very confident speaker.Anastasios [00:00:40]: Oh, I totally remember you. Didn't ever connect that, but yes, that's definitely true. Yeah. Nice to see you again.Swyx [00:00:46]: Yeah. I was frantically looking for the name of your paper and I couldn't find it. Basically I had to cut it because I didn't understand it.Anastasios [00:00:51]: Is this conformal PID control or was this the online control?Wei Lin [00:00:55]: Blast from the past, man.Swyx [00:00:57]: Blast from the past. It's always interesting how NeurIPS and all these academic conferences are sort of six months behind what people are actually doing, but conformal risk control, I would recommend people check it out. I have the recording. I just never published it just because I was like, I don't understand this enough to explain it.Anastasios [00:01:14]: People won't be interested.Wei Lin [00:01:15]: It's all good.Swyx [00:01:16]: But ELO scores, ELO scores are very easy to understand. You guys are responsible for the biggest revolution in language model benchmarking in the last few years. Maybe you guys want to introduce yourselves and maybe tell a little bit of the brief history of LMSysWei Lin [00:01:32]: Hey, I'm Wei Lin. I'm a fifth year PhD student at UC Berkeley, working on Chatbot Arena these days, doing crowdsourcing AI benchmarking.Anastasios [00:01:43]: I'm Anastasios. I'm a sixth year PhD student here at Berkeley. I did most of my PhD on like theoretical statistics and sort of foundations of model evaluation and testing. And now I'm working 150% on this Chatbot Arena stuff. It's great.Alessio [00:02:00]: And what was the origin of it? How did you come up with the idea? How did you get people to buy in? And then maybe what were one or two of the pivotal moments early on that kind of made it the standard for these things?Wei Lin [00:02:12]: Yeah, yeah. Chatbot Arena project was started last year in April, May, around that. Before that, we were basically experimenting in a lab how to fine tune a chatbot open source based on the Llama 1 model that I released. At that time, Lama 1 was like a base model and people didn't really know how to fine tune it. So we were doing some explorations. We were inspired by Stanford's Alpaca project. So we basically, yeah, grow a data set from the internet, which is called ShareGPT data set, which is like a dialogue data set between user and chat GPT conversation. It turns out to be like pretty high quality data, dialogue data. So we fine tune on it and then we train it and release the model called V2. And people were very excited about it because it kind of like demonstrate open way model can reach this conversation capability similar to chat GPT. And then we basically release the model with and also build a demo website for the model. People were very excited about it. But during the development, the biggest challenge to us at the time was like, how do we even evaluate it? How do we even argue this model we trained is better than others? And then what's the gap between this open source model that other proprietary offering? At that time, it was like GPT-4 was just announced and it's like Cloud One. What's the difference between them? And then after that, like every week, there's a new model being fine tuned, released. So even until still now, right? And then we have that demo website for V2 now. And then we thought like, okay, maybe we can add a few more of the model as well, like API model as well. And then we quickly realized that people need a tool to compare between different models. So we have like a side by side UI implemented on the website to that people choose, you know, compare. And we quickly realized that maybe we can do something like, like a battle on top of ECLMs, like just anonymize it, anonymize the identity, and that people vote which one is better. So the community decides which one is better, not us, not us arguing, you know, our model is better or what. And that turns out to be like, people are very excited about this idea. And then we tweet, we launch, and that's, yeah, that's April, May. And then it was like first two, three weeks, like just a few hundred thousand views tweet on our launch tweets. And then we have regularly double update weekly, beginning at a time, adding new model GPT-4 as well. So it was like, that was the, you know, the initial.Anastasios [00:04:58]: Another pivotal moment, just to jump in, would be private models, like the GPT, I'm a little,Wei Lin [00:05:04]: I'm a little chatty. That was this year. That was this year.Anastasios [00:05:07]: Huge.Wei Lin [00:05:08]: That was also huge.Alessio [00:05:09]: In the beginning, I saw the initial release was May 3rd of the beta board. On April 6, we did a benchmarks 101 episode for a podcast, just kind of talking about, you know, how so much of the data is like in the pre-training corpus and blah, blah, blah. And like the benchmarks are really not what we need to evaluate whether or not a model is good. Why did you not make a benchmark? Maybe at the time, you know, it was just like, Hey, let's just put together a whole bunch of data again, run a, make a score that seems much easier than coming out with a whole website where like users need to vote. Any thoughts behind that?Wei Lin [00:05:41]: I think it's more like fundamentally, we don't know how to automate this kind of benchmarks when it's more like, you know, conversational, multi-turn, and more open-ended task that may not come with a ground truth. So let's say if you ask a model to help you write an email for you for whatever purpose, there's no ground truth. How do you score them? Or write a story or a creative story or many other things like how we use ChatterBee these days. It's more open-ended. You know, we need human in the loop to give us feedback, which one is better. And I think nuance here is like, sometimes it's also hard for human to give the absolute rating. So that's why we have this kind of pairwise comparison, easier for people to choose which one is better. So from that, we use these pairwise comparison, those to calculate the leaderboard. Yeah. You can add more about this methodology.Anastasios [00:06:40]: Yeah. I think the point is that, and you guys probably also talked about this at some point, but static benchmarks are intrinsically, to some extent, unable to measure generative model performance. And the reason is because you cannot pre-annotate all the outputs of a generative model. You change the model, it's like the distribution of your data is changing. New labels to deal with that. New labels are great automated labeling, right? Which is why people are pursuing both. And yeah, static benchmarks, they allow you to zoom in to particular types of information like factuality, historical facts. We can build the best benchmark of historical facts, and we will then know that the model is great at historical facts. But ultimately, that's not the only axis, right? And we can build 50 of them, and we can evaluate 50 axes. But it's just so, the problem of generative model evaluation is just so expansive, and it's so subjective, that it's just maybe non-intrinsically impossible, but at least we don't see a way. We didn't see a way of encoding that into a fixed benchmark.Wei Lin [00:07:47]: But on the other hand, I think there's a challenge where this kind of online dynamic benchmark is more expensive than static benchmark, offline benchmark, where people still need it. Like when they build models, they need static benchmark to track where they are.Anastasios [00:08:03]: It's not like our benchmark is uniformly better than all other benchmarks, right? It just measures a different kind of performance that has proved to be useful.Swyx [00:08:14]: You guys also published MTBench as well, which is a static version, let's say, of Chatbot Arena, right? That people can actually use in their development of models.Wei Lin [00:08:25]: Right. I think one of the reasons we still do this static benchmark, we still wanted to explore, experiment whether we can automate this, because people, eventually, model developers need it to fast iterate their model. So that's why we explored LM as a judge, and ArenaHard, trying to filter, select high-quality data we collected from Chatbot Arena, the high-quality subset, and use that as a question and then automate the judge pipeline, so that people can quickly get high-quality signal, benchmark signals, using this online benchmark.Swyx [00:09:03]: As a community builder, I'm curious about just the initial early days. Obviously when you offer effectively free A-B testing inference for people, people will come and use your arena. What do you think were the key unlocks for you? Was it funding for this arena? Was it marketing? When people came in, do you see a noticeable skew in the data? Which obviously now you have enough data sets, you can separate things out, like coding and hard prompts, but in the early days, it was just all sorts of things.Anastasios [00:09:31]: Yeah, maybe one thing to establish at first is that our philosophy has always been to maximize organic use. I think that really does speak to your point, which is, yeah, why do people come? They came to use free LLM inference, right? And also, a lot of users just come to the website to use direct chat, because you can chat with the model for free. And then you could think about it like, hey, let's just be kind of like more on the selfish or conservative or protectionist side and say, no, we're only giving credits for people that battle or so on and so forth. Strategy wouldn't work, right? Because what we're trying to build is like a big funnel, a big funnel that can direct people. And some people are passionate and interested and they battle. And yes, the distribution of the people that do that is different. It's like, as you're pointing out, it's like, that's not as they're enthusiastic.Wei Lin [00:10:24]: They're early adopters of this technology.Anastasios [00:10:27]: Or they like games, you know, people like this. And we've run a couple of surveys that indicate this as well, of our user base.Wei Lin [00:10:36]: We do see a lot of developers come to the site asking polling questions, 20-30%. Yeah, 20-30%.Anastasios [00:10:42]: It's obviously not reflective of the general population, but it's reflective of some corner of the world of people that really care. And to some extent, maybe that's all right, because those are like the power users. And you know, we're not trying to claim that we represent the world, right? We represent the people that come and vote.Swyx [00:11:02]: Did you have to do anything marketing-wise? Was anything effective? Did you struggle at all? Was it success from day one?Wei Lin [00:11:09]: At some point, almost done. Okay. Because as you can imagine, this leaderboard depends on community engagement participation. If no one comes to vote tomorrow, then no leaderboard.Anastasios [00:11:23]: So we had some period of time when the number of users was just, after the initial launch, it went lower. Yeah. And, you know, at some point, it did not look promising. Actually, I joined the project a couple months in to do the statistical aspects, right? As you can imagine, that's how it kind of hooked into my previous work. At that time, it wasn't like, you know, it definitely wasn't clear that this was like going to be the eval or something. It was just like, oh, this is a cool project. Like Wayland seems awesome, you know, and that's it.Wei Lin [00:11:56]: Definitely. There's in the beginning, because people don't know us, people don't know what this is for. So we had a hard time. But I think we were lucky enough that we have some initial momentum. And as well as the competition between model providers just becoming, you know, became very intense. Intense. And then that makes the eval onto us, right? Because always number one is number one.Anastasios [00:12:23]: There's also an element of trust. Our main priority in everything we do is trust. We want to make sure we're doing everything like all the I's are dotted and the T's are crossed and nobody gets unfair treatment and people can see from our profiles and from our previous work and from whatever, you know, we're trustworthy people. We're not like trying to make a buck and we're not trying to become famous off of this or that. It's just, we're trying to provide a great public leaderboard community venture project.Wei Lin [00:12:51]: Yeah.Swyx [00:12:52]: Yes. I mean, you are kind of famous now, you know, that's fine. Just to dive in more into biases and, you know, some of this is like statistical control. The classic one for human preference evaluation is humans demonstrably prefer longer contexts or longer outputs, which is actually something that we don't necessarily want. You guys, I think maybe two months ago put out some length control studies. Apart from that, there are just other documented biases. Like, I'd just be interested in your review of what you've learned about biases and maybe a little bit about how you've controlled for them.Anastasios [00:13:32]: At a very high level, yeah. Humans are biased. Totally agree. Like in various ways. It's not clear whether that's good or bad, you know, we try not to make value judgments about these things. We just try to describe them as they are. And our approach is always as follows. We collect organic data and then we take that data and we mine it to get whatever insights we can get. And, you know, we have many millions of data points that we can now use to extract insights from. Now, one of those insights is to ask the question, what is the effect of style, right? You have a bunch of data, you have votes, people are voting either which way. We have all the conversations. We can say what components of style contribute to human preference and how do they contribute? Now, that's an important question. Why is that an important question? It's important because some people want to see which model would be better if the lengths of the responses were the same, were to be the same, right? People want to see the causal effect of the model's identity controlled for length or controlled for markdown, number of headers, bulleted lists, is the text bold? Some people don't, they just don't care about that. The idea is not to impose the judgment that this is not important, but rather to say ex post facto, can we analyze our data in a way that decouples all the different factors that go into human preference? Now, the way we do this is via statistical regression. That is to say the arena score that we show on our leaderboard is a particular type of linear model, right? It's a linear model that takes, it's a logistic regression that takes model identities and fits them against human preference, right? So it regresses human preference against model identity. What you get at the end of that logistic regression is a parameter vector of coefficients. And when the coefficient is large, it tells you that GPT 4.0 or whatever, very large coefficient, that means it's strong. And that's exactly what we report in the table. It's just the predictive effect of the model identity on the vote. The other thing that you can do is you can take that vector, let's say we have M models, that is an M dimensional vector of coefficients. What you can do is you say, hey, I also want to understand what the effect of length is. So I'll add another entry to that vector, which is trying to predict the vote, right? That tells me the difference in length between two model responses. So we have that for all of our data. We can compute it ex post facto. We added it into the regression and we look at that predictive effect. And then the idea, and this is formally true under certain conditions, not always verifiable ones, but the idea is that adding that extra coefficient to this vector will kind of suck out the predictive power of length and put it into that M plus first coefficient and quote, unquote, de-bias the rest so that the effect of length is not included. And that's what we do in style control. Now we don't just do it for M plus one. We have, you know, five, six different style components that have to do with markdown headers and bulleted lists and so on that we add here. Now, where is this going? You guys see the idea. It's a general methodology. If you have something that's sort of like a nuisance parameter, something that exists and provides predictive value, but you really don't want to estimate that. You want to remove its effect. In causal inference, these things are called like confounders often. What you can do is you can model the effect. You can put them into your model and try to adjust for them. So another one of those things might be cost. You know, what if I want to look at the cost adjusted performance of my model, which models are punching above their weight, parameter count, which models are punching above their weight in terms of parameter count, we can ex post facto measure that. We can do it without introducing anything that compromises the organic nature of theWei Lin [00:17:17]: data that we collect.Anastasios [00:17:18]: Hopefully that answers the question.Wei Lin [00:17:20]: It does.Swyx [00:17:21]: So I guess with a background in econometrics, this is super familiar.Anastasios [00:17:25]: You're probably better at this than me for sure.Swyx [00:17:27]: Well, I mean, so I used to be, you know, a quantitative trader and so, you know, controlling for multiple effects on stock price is effectively the job. So it's interesting. Obviously the problem is proving causation, which is hard, but you don't have to do that.Anastasios [00:17:45]: Yes. Yes, that's right. And causal inference is a hard problem and it goes beyond statistics, right? It's like you have to build the right causal model and so on and so forth. But we think that this is a good first step and we're sort of looking forward to learning from more people. You know, there's some good people at Berkeley that work on causal inference for the learning from them on like, what are the really most contemporary techniques that we can use in order to estimate true causal effects if possible.Swyx [00:18:10]: Maybe we could take a step through the other categories. So style control is a category. It is not a default. I have thought that when you wrote that blog post, actually, I thought it would be the new default because it seems like the most obvious thing to control for. But you also have other categories, you have coding, you have hard prompts. We consider that.Anastasios [00:18:27]: We're still actively considering it. It's just, you know, once you make that step, once you take that step, you're introducing your opinion and I'm not, you know, why should our opinion be the one? That's kind of a community choice. We could put it to a vote.Wei Lin [00:18:39]: We could pass.Anastasios [00:18:40]: Yeah, maybe do a poll. Maybe do a poll.Swyx [00:18:42]: I don't know. No opinion is an opinion.Wei Lin [00:18:44]: You know what I mean?Swyx [00:18:45]: Yeah.Wei Lin [00:18:46]: There's no neutral choice here.Swyx [00:18:47]: Yeah. You have all these others. You have instruction following too. What are your favorite categories that you like to talk about? Maybe you tell a little bit of the stories, tell a little bit of like the hard choices that you had to make.Wei Lin [00:18:57]: Yeah. Yeah. Yeah. I think the, uh, initially the reason why we want to add these new categories is essentially to answer some of the questions from our community, which is we won't have a single leaderboard for everything. So these models behave very differently in different domains. Let's say this model is trend for coding, this model trend for more technical questions and so on. On the other hand, to answer people's question about like, okay, what if all these low quality, you know, because we crowdsource data from the internet, there will be noise. So how do we de-noise? How do we filter out these low quality data effectively? So that was like, you know, some questions we want to answer. So basically we spent a few months, like really diving into these questions to understand how do we filter all these data because these are like medias of data points. And then if you want to re-label yourself, it's possible, but we need to kind of like to automate this kind of data classification pipeline for us to effectively categorize them to different categories, say coding, math, structure, and also harder problems. So that was like, the hope is when we slice the data into these meaningful categories to give people more like better signals, more direct signals, and that's also to clarify what we are actually measuring for, because I think that's the core part of the benchmark. That was the initial motivation. Does that make sense?Anastasios [00:20:27]: Yeah. Also, I'll just say, this does like get back to the point that the philosophy is to like mine organic, to take organic data and then mine it x plus factor.Alessio [00:20:35]: Is the data cage-free too, or just organic?Anastasios [00:20:39]: It's cage-free.Wei Lin [00:20:40]: No GMO. Yeah. And all of these efforts are like open source, like we open source all of the data cleaning pipeline, filtering pipeline. Yeah.Swyx [00:20:50]: I love the notebooks you guys publish. Actually really good just for learning statistics.Wei Lin [00:20:54]: Yeah. I'll share this insights with everyone.Alessio [00:20:59]: I agree on the initial premise of, Hey, writing an email, writing a story, there's like no ground truth. But I think as you move into like coding and like red teaming, some of these things, there's like kind of like skill levels. So I'm curious how you think about the distribution of skill of the users. Like maybe the top 1% of red teamers is just not participating in the arena. So how do you guys think about adjusting for it? And like feels like this where there's kind of like big differences between the average and the top. Yeah.Anastasios [00:21:29]: Red teaming, of course, red teaming is quite challenging. So, okay. Moving back. There's definitely like some tasks that are not as subjective that like pairwise human preference feedback is not the only signal that you would want to measure. And to some extent, maybe it's useful, but it may be more useful if you give people better tools. For example, it'd be great if we could execute code with an arena, be fantastic.Wei Lin [00:21:52]: We want to do it.Anastasios [00:21:53]: There's also this idea of constructing a user leaderboard. What does that mean? That means some users are better than others. And how do we measure that? How do we quantify that? Hard in chatbot arena, but where it is easier is in red teaming, because in red teaming, there's an explicit game. You're trying to break the model, you either win or you lose. So what you can do is you can say, Hey, what's really happening here is that the models and humans are playing a game against one another. And then you can use the same sort of Bradley Terry methodology with some, some extensions that we came up with in one of you can read one of our recent blog posts for, for the sort of theoretical extensions. You can attribute like strength back to individual players and jointly attribute strength to like the models that are in this jailbreaking game, along with the target tasks, like what types of jailbreaks you want.Wei Lin [00:22:44]: So yeah.Anastasios [00:22:45]: And I think that this is, this is a hugely important and interesting avenue that we want to continue researching. We have some initial ideas, but you know, all thoughts are welcome.Wei Lin [00:22:54]: Yeah.Alessio [00:22:55]: So first of all, on the code execution, the E2B guys, I'm sure they'll be happy to helpWei Lin [00:22:59]: you.Alessio [00:23:00]: I'll please set that up. They're big fans. We're investors in a company called Dreadnought, which we do a lot in AI red teaming. I think to me, the most interesting thing has been, how do you do sure? Like the model jailbreak is one side. We also had Nicola Scarlini from DeepMind on the podcast, and he was talking about, for example, like, you know, context stealing and like a weight stealing. So there's kind of like a lot more that goes around it. I'm curious just how you think about the model and then maybe like the broader system, even with Red Team Arena, you're just focused on like jailbreaking of the model, right? You're not doing kind of like any testing on the more system level thing of the model where like, maybe you can get the training data back, you're going to exfiltrate some of the layers and the weights and things like that.Wei Lin [00:23:43]: So right now, as you can see, the Red Team Arena is at a very early stage and we are still exploring what could be the potential new games we can introduce to the platform. So the idea is still the same, right? And we build a community driven project platform for people. They can have fun with this website, for sure. That's one thing, and then help everyone to test these models. So one of the aspects you mentioned is stealing secrets, stealing training sets. That could be one, you know, it could be designed as a game. Say, can you still use their credential, you know, we hide, maybe we can hide the credential into system prompts and so on. So there are like a few potential ideas we want to explore for sure. Do you want to add more?Anastasios [00:24:28]: I think that this is great. This idea is a great one. There's a lot of great ideas in the Red Teaming space. You know, I'm not personally like a Red Teamer. I don't like go around and Red Team models, but there are people that do that and they're awesome. They're super skilled. When I think about the Red Team arena, I think those are really the people that we're building it for. Like, we want to make them excited and happy, build tools that they like. And just like chatbot arena, we'll trust that this will end up being useful for the world. And all these people are, you know, I won't say all these people in this community are actually good hearted, right? They're not doing it because they want to like see the world burn. They're doing it because they like, think it's fun and cool. And yeah. Okay. Maybe they want to see, maybe they want a little bit.Wei Lin [00:25:13]: I don't know. Majority.Anastasios [00:25:15]: Yeah.Wei Lin [00:25:16]: You know what I'm saying.Anastasios [00:25:17]: So, you know, trying to figure out how to serve them best, I think, I don't know where that fits. I just, I'm not expressing. And give them credits, right?Wei Lin [00:25:24]: And give them credit.Anastasios [00:25:25]: Yeah. Yeah. So I'm not trying to express any particular value judgment here as to whether that's the right next step. It's just, that's sort of the way that I think we would think about it.Swyx [00:25:35]: Yeah. We also talked to Sander Schulhoff of the HackerPrompt competition, and he's pretty interested in Red Teaming at scale. Let's just call it that. You guys maybe want to talk with him.Wei Lin [00:25:45]: Oh, nice.Swyx [00:25:46]: We wanted to cover a little, a few topical things and then go into the other stuff that your group is doing. You know, you're not just running Chatbot Arena. We can also talk about the new website and your future plans, but I just wanted to briefly focus on O1. It is the hottest, latest model. Obviously, you guys already have it on the leaderboard. What is the impact of O1 on your evals?Wei Lin [00:26:06]: Made our interface slower.Anastasios [00:26:07]: It made it slower.Swyx [00:26:08]: Yeah.Wei Lin [00:26:10]: Because it needs like 30, 60 seconds, sometimes even more to, the latency is like higher. So that's one. Sure. But I think we observe very interesting things from this model as well. Like we observe like significant improvement in certain categories, like more technical or math. Yeah.Anastasios [00:26:32]: I think actually like one takeaway that was encouraging is that I think a lot of people before the O1 release were thinking, oh, like this benchmark is saturated. And why were they thinking that? They were thinking that because there was a bunch of models that were kind of at the same level. They were just kind of like incrementally competing and it sort of wasn't immediately obvious that any of them were any better. Nobody, including any individual person, it's hard to tell. But what O1 did is it was, it's clearly a better model for certain tasks. I mean, I used it for like proving some theorems and you know, there's some theorems that like only I know because I still do a little bit of theory. Right. So it's like, I can go in there and ask like, oh, how would you prove this exact thing? Which I can tell you has never been in the public domain. It'll do it. It's like, what?Wei Lin [00:27:19]: Okay.Anastasios [00:27:20]: So there's this model and it crushed the benchmark. You know, it's just like really like a big gap. And what that's telling us is that it's not saturated yet. It's still measuring some signal. That was encouraging. The point, the takeaway is that the benchmark is comparative. There's no absolute number. There's no maximum ELO. It's just like, if you're better than the rest, then you win. I think that was actually quite helpful to us.Swyx [00:27:46]: I think people were criticizing, I saw some of the academics criticizing it as not apples to apples. Right. Like, because it can take more time to reason, it's basically doing some search, doing some chain of thought that if you actually let the other models do that same thing, they might do better.Wei Lin [00:28:03]: Absolutely.Anastasios [00:28:04]: To be clear, none of the leaderboard currently is apples to apples because you have like Gemini Flash, you have, you know, all sorts of tiny models like Lama 8B, like 8B and 405B are not apples to apples.Wei Lin [00:28:19]: Totally agree. They have different latencies.Anastasios [00:28:21]: Different latencies.Wei Lin [00:28:22]: Control for latency. Yeah.Anastasios [00:28:24]: Latency control. That's another thing. We can do style control, but latency control. You know, things like this are important if you want to understand the trade-offs involved in using AI.Swyx [00:28:34]: O1 is a developing story. We still haven't seen the full model yet, but it's definitely a very exciting new paradigm. I think one community controversy I just wanted to give you guys space to address is the collaboration between you and the large model labs. People have been suspicious, let's just say, about how they choose to A-B test on you. I'll state the argument and let you respond, which is basically they run like five anonymous models and basically argmax their Elo on LMSYS or chatbot arena, and they release the best one. Right? What has been your end of the controversy? How have you decided to clarify your policy going forward?Wei Lin [00:29:15]: On a high level, I think our goal here is to build a fast eval for everyone, and including everyone in the community can see the data board and understand, compare the models. More importantly, I think we want to build the best eval also for model builders, like all these frontier labs building models. They're also internally facing a challenge, which is how do they eval the model? That's the reason why we want to partner with all the frontier lab people, and then to help them testing. That's one of the... We want to solve this technical challenge, which is eval. Yeah.Anastasios [00:29:54]: I mean, ideally, it benefits everyone, right?Wei Lin [00:29:56]: Yeah.Anastasios [00:29:57]: And people also are interested in seeing the leading edge of the models. People in the community seem to like that. Oh, there's a new model up. Is this strawberry? People are excited. People are interested. Yeah. And then there's this question that you bring up of, is it actually causing harm?Wei Lin [00:30:15]: Right?Anastasios [00:30:16]: Is it causing harm to the benchmark that we are allowing this private testing to happen? Maybe stepping back, why do you have that instinct? The reason why you and others in the community have that instinct is because when you look at something like a benchmark, like an image net, a static benchmark, what happens is that if I give you a million different models that are all slightly different, and I pick the best one, there's something called selection bias that plays in, which is that the performance of the winning model is overstated. This is also sometimes called the winner's curse. And that's because statistical fluctuations in the evaluation, they're driving which model gets selected as the top. So this selection bias can be a problem. Now there's a couple of things that make this benchmark slightly different. So first of all, the selection bias that you include when you're only testing five models is normally empirically small.Wei Lin [00:31:12]: And that's why we have these confidence intervals constructed.Anastasios [00:31:16]: That's right. Yeah. Our confidence intervals are actually not multiplicity adjusted. One thing that we could do immediately tomorrow in order to address this concern is if a model provider is testing five models and they want to release one, and we're constructing the models at level one minus alpha, we can just construct the intervals instead at level one minus alpha divided by five. That's called Bonferroni correction. What that'll tell you is that the final performance of the model, the interval that gets constructed, is actually formally correct. We don't do that right now, partially because we know from simulations that the amount of selection bias you incur with these five things is just not huge. It's not huge in comparison to the variability that you get from just regular human voters. So that's one thing. But then the second thing is the benchmark is live, right? So what ends up happening is it'll be a small magnitude, but even if you suffer from the winner's curse after testing these five models, what'll happen is that over time, because we're getting new data, it'll get adjusted down. So if there's any bias that gets introduced at that stage, in the long run, it actually doesn't matter. Because asymptotically, basically in the long run, there's way more fresh data than there is data that was used to compare these five models against these private models.Swyx [00:32:35]: The announcement effect is only just the first phase and it has a long tail.Anastasios [00:32:39]: Yeah, that's right. And it sort of like automatically corrects itself for this selection adjustment.Swyx [00:32:45]: Every month, I do a little chart of Ellim's ELO versus cost, just to track the price per dollar, the amount of like, how much money do I have to pay for one incremental point in ELO? And so I actually observe an interesting stability in most of the ELO numbers, except for some of them. For example, GPT-4-O August has fallen from 12.90

If you've listened to the podcast for a while, you might have heard our ElevenLabs-powered AI co-host Charlie a few times. Text-to-speech has made amazing progress in the last 18 months, with OpenAI's Advanced Voice Mode (aka “Her”) as a sneak peek of the future of AI interactions (see our “Building AGI in Real Time” recap). Yet, we had yet to see a real killer app for AI voice (not counting music).Today's guests, Raiza Martin and Usama Bin Shafqat, are the lead PM and AI engineer behind the NotebookLM feature flag that gave us the first viral AI voice experience, the “Deep Dive” podcast:The idea behind the “Audio Overviews” feature is simple: take a bunch of documents, websites, YouTube videos, etc, and generate a podcast out of them. This was one of the first demos that people built with voice models + RAG + GPT models, but it was always a glorified speech-to-text. Raiza and Usama took a very different approach:* Make it conversational: when you listen to a NotebookLM audio there are a ton of micro-interjections (Steven Johnson calls them disfluencies) like “Oh really?” or “Totally”, as well as pauses and “uh…”, like you would expect in a real conversation. These are not generated by the LLM in the transcript, but they are built into the the audio model. See ~28:00 in the pod for more details. * Listeners love tension: if two people are always in agreement on everything, it's not super interesting. They tuned the model to generate flowing conversations that mirror the tone and rhythm of human speech. They did not confirm this, but many suspect the 2 year old SoundStorm paper is related to this model.* Generating new insights: because the hosts' goal is not to summarize, but to entertain, it comes up with funny metaphors and comparisons that actually help expand on the content rather than just paraphrasing like most models do. We have had listeners make podcasts out of our podcasts, like this one.This is different than your average SOTA-chasing, MMLU-driven model buildooor. Putting product and AI engineering in the same room, having them build evals together, and understanding what the goal is lets you get these unique results. The 5 rules for AI PMsWe always focus on AI Engineers, but this episode had a ton of AI PM nuggets as well, which we wanted to collect as NotebookLM is one of the most successful products in the AI space:1. Less is more: the first version of the product had 0 customization options. All you could do is give it source documents, and then press a button to generate. Most users don't know what “temperature” or “top-k” are, so you're often taking the magic away by adding more options in the UI. Since recording they added a few, like a system prompt, but those were features that users were “hacking in”, as Simon Willison highlighted in his blog post.2. Use Real-Time Feedback: they built a community of 65,000 users on Discord that is constantly reporting issues and giving feedback; sometimes they noticed server downtime even before the Google internal monitoring did. Getting real time pings > aggregating user data when doing initial iterations. 3. Embrace Non-Determinism: AI outputs variability is a feature, not a bug. Rather than limiting the outputs from the get-go, build toggles that you can turn on/off with feature flags as the feedback starts to roll in.4. Curate with Taste: if you try your product and it sucks, you don't need more data to confirm it. Just scrap that and iterate again. This is even easier for a product like this; if you start listening to one of the podcasts and turn it off after 10 seconds, it's never a good sign. 5. Stay Hands-On: It's hard to build taste if you don't experiment. Trying out all your competitors products as well as unrelated tools really helps you understand what users are seeing in market, and how to improve on it.Chapters00:00 Introductions01:39 From Project Tailwind to NotebookLM09:25 Learning from 65,000 Discord members12:15 How NotebookLM works18:00 Working with Steven Johnson23:00 How to prioritize features25:13 Structuring the data pipelines29:50 How to eval34:34 Steering the podcast outputs37:51 Defining speakers personalities39:04 How do you make audio engaging?45:47 Humor is AGI51:38 Designing for non-determinism53:35 API when?55:05 Multilingual support and dialect considerations57:50 Managing system prompts and feature requests01:00:58 Future of NotebookLM01:04:59 Podcasts for your codebase01:07:16 Plans for real-time chat01:08:27 Wrap upShow Notes* Notebook LM* AI Test Kitchen* Nicholas Carlini* Steven Johnson* Wealth of Nations* Histories of Mysteries by Andrej Karpathy* chicken.pdf Threads* Area 120* Raiza Martin* Usama Bin ShafqatTranscriptNotebookLM [00:00:00]: Hey everyone, we're here today as guests on Latent Space. It's great to be here, I'm a long time listener and fan, they've had some great guests on this show before. Yeah, what an honor to have us, the hosts of another podcast, join as guests. I mean a huge thank you to Swyx and Alessio for the invite, thanks for having us on the show. Yeah really, it seems like they brought us here to talk a little bit about our show, our podcast. Yeah, I mean we've had lots of listeners ourselves, listeners at Deep Dive. Oh yeah, we've made a ton of audio overviews since we launched and we're learning a lot. There's probably a lot we can share around what we're building next, huh? Yeah, we'll share a little bit at least. The short version is we'll keep learning and getting better for you. We're glad you're along for the ride. So yeah, keep listening. Keep listening and stay curious. We promise to keep diving deep and bringing you even better options in the future. Stay curious.Alessio [00:00:52]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners. And I'm joined by my co-host, Swyx, founder of Smol.ai.Swyx [00:01:01]: Hey, and today we're back in the studio with our special guest, Raiza Martin. And Raiza, I forgot to get your last name, Shafqat.Raiza [00:01:10]: Yes.Swyx [00:01:10]: Okay, welcome.Raiza [00:01:12]: Hello, thank you for having us.Swyx [00:01:14]: So AI podcasters meet human podcasters, always fun. Congrats on the success of Notebook LM. I mean, how does it feel?Raiza [00:01:22]: It's been a lot of fun. A lot of it, honestly, was unexpected. But my favorite part is really listening to the audio overviews that people have been making.Swyx [00:01:29]: Maybe we should do a little bit of intros and tell the story. You know, what is your path into the sort of Google AI org? Or maybe, actually, I don't even know what org you guys are in.Raiza [00:01:39]: I can start. My name is Raisa. I lead the Notebook LM team inside of Google Labs. So specifically, that's the org that we're in. It's called Google Labs. It's only about two years old. And our whole mandate is really to build AI products. That's it. We work super closely with DeepMind. Our entire thing is just, like, try a bunch of things and see what's landing with users. And the background that I have is, really, I worked in payments before this, and I worked in ads right before, and then startups. I tell people, like, at every time that I changed orgs, I actually almost quit Google. Like, specifically, like, in between ads and payments, I was like, all right, I can't do this. Like, this is, like, super hard. I was like, it's not for me. I'm, like, a very zero-to-one person. But then I was like, okay, I'll try. I'll interview with other teams. And when I interviewed in payments, I was like, oh, these people are really cool. I don't know if I'm, like, a super good fit with this space, but I'll try it because the people are cool. And then I really enjoyed that, and then I worked on, like, zero-to-one features inside of payments, and I had a lot of fun. But then the time came again where I was like, oh, I don't know. It's like, it's time to leave. It's time to start my own thing. But then I interviewed inside of Google Labs, and I was like, oh, darn. Like, there's definitely, like—Alessio [00:02:48]: They got you again.Raiza [00:02:49]: They got me again. And so now I've been here for two years, and I'm happy that I stayed because especially with, you know, the recent success of Notebook LM, I'm like, dang, we did it. I actually got to do it. So that was really cool.Usama [00:03:02]: Kind of similar, honestly. I was at a big team at Google. We do sort of the data center supply chain planning stuff. Google has, like, the largest sort of footprint. Obviously, there's a lot of management stuff to do there. But then there was this thing called Area 120 at Google, which does not exist anymore. But I sort of wanted to do, like, more zero-to-one building and landed a role there. We were trying to build, like, a creator commerce platform called Kaya. It launched briefly a couple years ago. But then Area 120 sort of transitioned and morphed into Labs. And, like, over the last few years, like, the focus just got a lot clearer. Like, we were trying to build new AI products and do it in the wild and sort of co-create and all of that. So, you know, we've just been trying a bunch of different things. And this one really landed, which has felt pretty phenomenal. Really, really landed.Swyx [00:03:53]: Let's talk about the brief history of Notebook LM. You had a tweet, which is very helpful for doing research. May 2023, during Google I.O., you announced Project Tailwind.Raiza [00:04:03]: Yeah.Swyx [00:04:03]: So today is October 2024. So you joined October 2022?Raiza [00:04:09]: Actually, I used to lead AI Test Kitchen. And this was actually, I think, not I.O. 2023. I.O. 2022 is when we launched AI Test Kitchen, or announced it. And I don't know if you remember it.Swyx [00:04:23]: That's how you, like, had the basic prototype for Gemini.Raiza [00:04:26]: Yes, yes, exactly. Lambda.Swyx [00:04:28]: Gave beta access to people.Raiza [00:04:29]: Yeah, yeah, yeah. And I remember, I was like, wow, this is crazy. We're going to launch an LLM into the wild. And that was the first project that I was working on at Google. But at the same time, my manager at the time, Josh, he was like, hey, I want you to really think about, like, what real products would we build that are not just demos of the technology? That was in October of 2022. I was sitting next to an engineer that was working on a project called Talk to Small Corpus. His name was Adam. And the idea of Talk to Small Corpus is basically using LLM to talk to your data. And at the time, I was like, wait, there's some, like, really practical things that you can build here. And just a little bit of background, like, I was an adult learner. Like, I went to college while I was working a full-time job. And the first thing I thought was, like, this would have really helped me with my studying, right? Like, if I could just, like, talk to a textbook, especially, like, when I was tired after work, that would have been huge. We took a lot of, like, the Talk to Small Corpus prototypes, and I showed it to a lot of, like, college students, particularly, like, adult learners. They were like, yes, like, I get it, right? Like, I didn't even have to explain it to them. And we just continued to iterate the prototype from there to the point where we actually got a slot as part of the I.O. demo in 23.Swyx [00:05:42]: And Corpus, was it a textbook? Oh, my gosh.Raiza [00:05:45]: Yeah. It's funny. Actually, when he explained the project to me, he was like, talk to Small Corpus. It was like, talk to a small corpse?Swyx [00:05:51]: Yeah, nobody says Corpus.Raiza [00:06:00]: It was like, a small corpse? This is not AI. Yeah, yeah. And it really was just, like, a way for us to describe the amount of data that we thought, like, it could be good for.Swyx [00:06:02]: Yeah, but even then, you're still, like, doing rag stuff. Because, you know, the context length back then was probably, like, 2K, 4K.Raiza [00:06:08]: Yeah, it was basically rag.Raiza [00:06:09]: That was essentially what it was.Raiza [00:06:10]: And I remember, I was like, we were building the prototypes. And at the same time, I think, like, the rest of the world was. Right? We were seeing all of these, like, chat with PDF stuff come up. And I was like, come on, we gotta go. Like, we have to, like, push this out into the world. I think if there was anything, I wish we would have launched sooner because I wanted to learn faster. But I think, like, we netted out pretty well.Alessio [00:06:30]: Was the initial product just text-to-speech? Or were you also doing kind of, like, synthesizing of the content, refining it? Or were you just helping people read through it?Raiza [00:06:40]: Before we did the I.O. announcement in 23, we'd already done a lot of studies. And one of the first things that I realized was the first thing anybody ever typed was, summarize the thing. Right?Raiza [00:06:53]: Summarize the document.Raiza [00:06:54]: And it was, like, half like a test and half just like, oh, I know the content. I want to see how well it does this. So it was part of the first thing that we launched. It was called Project Tailwind back then. It was just Q&A, so you could chat with the doc just through text, and it would automatically generate a summary as well. I'm not sure if we had it back then.Raiza [00:07:12]: I think we did.Raiza [00:07:12]: It would also generate the key topics in your document, and it could support up to, like, 10 documents. So it wasn't just, like, a single doc.Alessio [00:07:20]: And then the I.O. demo went well, I guess. And then what was the discussion from there to where we are today? Is there any, maybe, intermediate step of the product that people missed between this was launch or?Raiza [00:07:33]: It was interesting because every step of the way, I think we hit, like, some pretty critical milestones. So I think from the initial demo, I think there was so much excitement of, like, wow, what is this thing that Google is launching? And so we capitalized on that. We built the wait list. That's actually when we also launched the Discord server, which has been huge for us because for us in particular, one of the things that I really wanted to do was to be able to launch features and get feedback ASAP. Like, the moment somebody tries it, like, I want to hear what they think right now, and I want to ask follow-up questions. And the Discord has just been so great for that. But then we basically took the feedback from I.O., we continued to refine the product.Raiza [00:08:12]: So we added more features.Raiza [00:08:13]: We added sort of, like, the ability to save notes, write notes. We generate follow-up questions. So there's a bunch of stuff in the product that shows, like, a lot of that research. But it was really the rolling out of things. Like, we removed the wait list, so rolled out to all of the United States. We rolled out to over 200 countries and territories. We started supporting more languages, both in the UI and, like, the actual source stuff. We experienced, like, in terms of milestones, there was, like, an explosion of, like, users in Japan. This was super interesting in terms of just, like, unexpected. Like, people would write to us and they would be like, this is amazing. I have to read all of these rules in English, but I can chat in Japanese. It's like, oh, wow. That's true, right? Like, with LLMs, you kind of get this natural, it translates the content for you. And you can ask in your sort of preferred mode. And I think that's not just, like, a language thing, too. I think there's, like, I do this test with Wealth of Nations all the time because it's, like, a pretty complicated text to read. The Evan Smith classic.Swyx [00:09:11]: It's, like, 400 pages or something.Raiza [00:09:12]: Yeah. But I like this test because I'm, like, asking, like, Normie, you know, plain speak. And then it summarizes really well for me. It sort of adapts to my tone.Swyx [00:09:22]: Very capitalist.Raiza [00:09:25]: Very on brand.Swyx [00:09:25]: I just checked in on a Notebook LM Discord. 65,000 people. Yeah.Raiza [00:09:29]: Crazy.Swyx [00:09:29]: Just, like, for one project within Google. It's not, like, it's not labs. It's just Notebook LM.Raiza [00:09:35]: Just Notebook LM.Swyx [00:09:36]: What do you learn from the community?Raiza [00:09:39]: I think that the Discord is really great for hearing about a couple of things.Raiza [00:09:43]: One, when things are going wrong. I think, honestly, like, our fastest way that we've been able to find out if, like, the servers are down or there's just an influx of people being, like, it saysRaiza [00:09:53]: system unable to answer.Raiza [00:09:54]: Anybody else getting this?Raiza [00:09:56]: And I'm, like, all right, let's go.Raiza [00:09:58]: And it actually catches it a lot faster than, like, our own monitoring does.Raiza [00:10:01]: It's, like, that's been really cool. So, thank you.Swyx [00:10:03]: Canceled eat a dog.Raiza [00:10:05]: So, thank you to everybody. Please keep reporting it. I think the second thing is really the use cases.Raiza [00:10:10]: I think when we put it out there, I was, like, hey, I have a hunch of how people will use it, but, like, to actually hear about, you know, not just the context of, like, the use of Notebook LM, but, like, what is this person's life like? Why do they care about using this tool?Raiza [00:10:23]: Especially people who actually have trouble using it, but they keep pushing.Raiza [00:10:27]: Like, that's just so critical to understand what was so motivating, right?Raiza [00:10:31]: Like, what was your problem that was, like, so worth solving? So, that's, like, a second thing.Raiza [00:10:34]: The third thing is also just hearing sort of, like, when we have wins and when we don't have wins because there's actually a lot of functionality where I'm, like, hmm, IRaiza [00:10:42]: don't know if that landed super well or if that was actually super critical.Raiza [00:10:45]: As part of having this sort of small project, right, I want to be able to unlaunch things, too. So, it's not just about just, like, rolling things out and testing it and being, like, wow, now we have, like, 99 features. Like, hopefully we get to a place where it's, like, there's just a really strong core feature set and the things that aren't as great, we can just unlaunch.Swyx [00:11:02]: What have you unlaunched? I have to ask.Raiza [00:11:04]: I'm in the process of unlaunching some stuff, but, for example, we had this idea that you could highlight the text in your source passage and then you could transform it. And nobody was really using it and it was, like, a very complicated piece of our architecture and it's very hard to continue supporting it in the context of new features. So, we were, like, okay, let's do a 50-50 sunset of this thing and see if anybody complains.Raiza [00:11:28]: And so far, nobody has.Swyx [00:11:29]: Is there, like, a feature flagging paradigm inside of your architecture that lets you feature flag these things easily?Raiza [00:11:36]: Yes, and actually...Raiza [00:11:37]: What is it called?Swyx [00:11:38]: Like, I love feature flagging.Raiza [00:11:40]: You mean, like, in terms of just, like, being able to expose things to users?Swyx [00:11:42]: Yeah, as a PM. Like, this is your number one tool, right?Raiza [00:11:44]: Yeah, yeah.Swyx [00:11:45]: Let's try this out. All right, if it works, roll it out. If it doesn't, roll it back, you know?Raiza [00:11:49]: Yeah, I mean, we just run Mendel experiments for the most part. And, actually, I don't know if you saw it, but on Twitter, somebody was able to get around our flags and they enabled all the experiments.Raiza [00:11:58]: They were, like, check out what the Notebook LM team is cooking.Raiza [00:12:02]: I was, like, oh!Raiza [00:12:03]: And I was at lunch with the rest of the team and I was, like, I was eating. I was, like, guys, guys, Magic Draft League!Raiza [00:12:10]: They were, like, oh, no!Raiza [00:12:12]: I was, like, okay, just finish eating and then let's go figure out what to do.Raiza [00:12:15]: Yeah.Alessio [00:12:15]: I think a post-mortem would be fun, but I don't think we need to do it on the podcast now. Can we just talk about what's behind the magic? So, I think everybody has questions, hypotheses about what models power it. I know you might not be able to share everything, but can you just get people very basic? How do you take the data and put it in the model? What text model you use? What's the text-to-speech kind of, like, jump between the two? Sure.Raiza [00:12:42]: Yeah.Raiza [00:12:42]: I was going to say, SRaiza, he manually does all the podcasts.Raiza [00:12:46]: Oh, thank you.Usama [00:12:46]: Really fast. You're very fast, yeah.Raiza [00:12:48]: Both of the voices at once.Usama [00:12:51]: Voice actor.Raiza [00:12:52]: Good, good.Usama [00:12:52]: Yeah, so, for a bit of background, we were building this thing sort of outside Notebook LM to begin with. Like, just the idea is, like, content transformation, right? Like, we can do different modalities. Like, everyone knows that. Everyone's been poking at it. But, like, how do you make it really useful? And, like, one of the ways we thought was, like, okay, like, you maybe, like, you know, people learn better when they're hearing things. But TTS exists, and you can, like, narrate whatever's on screen. But you want to absorb it the same way. So, like, that's where we sort of started out into the realm of, like, maybe we try, like, you know, two people are having a conversation kind of format. We didn't actually start out thinking this would live in Notebook, right? Like, Notebook was sort of, we built this demo out independently, tried out, like, a few different sort of sources. The main idea was, like, go from some sort of sources and transform it into a listenable, engaging audio format. And then through that process, we, like, unlocked a bunch more sort of learnings. Like, for example, in a sense, like, you're not prompting the model as much because, like, the information density is getting unrolled by the model prompting itself, in a sense. Because there's two speakers, and they're both technically, like, AI personas, right? That have different angles of looking at things. And, like, they'll have a discussion about it. And that sort of, we realized that's kind of what was making it riveting, in a sense. Like, you care about what comes next, even if you've read the material already. Because, like, people say they get new insights on their own journals or books or whatever. Like, anything that they've written themselves. So, yeah, from a modeling perspective, like, it's, like Reiza said earlier, like, we work with the DeepMind audio folks pretty closely. So, they're always cooking up new techniques to, like, get better, more human-like audio. And then Gemini 1.5 is really, really good at absorbing long context. So, we sort of, like, generally put those things together in a way that we could reliably produce the audio.Raiza [00:14:52]: I would add, like, there's something really nuanced, I think, about sort of the evolution of, like, the utility of text-to-speech. Where, if it's just reading an actual text response, and I've done this several times. I do it all the time with, like, reading my text messages. Or, like, sometimes I'm trying to read, like, a really dense paper, but I'm trying to do actual work. I'll have it, like, read out the screen. There is something really robotic about it that is not engaging. And it's really hard to consume content in that way. And it's never been really effective. Like, particularly for me, where I'm, like, hey, it's actually just, like, it's fine for, like, short stuff. Like, texting, but even that, it's, like, not that great. So, I think the frontier of experimentation here was really thinking about there is a transform that needs to happen in between whatever.Raiza [00:15:38]: Here's, like, my resume, right?Raiza [00:15:39]: Or here's, like, a 100-page slide deck or something. There is a transform that needs to happen that is inherently editorial. And I think this is where, like, that two-person persona, right, dialogue model, they have takes on the material that you've presented. That's where it really sort of, like, brings the content to life in a way that's, like, not robotic. And I think that's, like, where the magic is, is, like, you don't actually know what's going to happen when you press generate.Raiza [00:16:08]: You know, for better or for worse.Raiza [00:16:09]: Like, to the extent that, like, people are, like, no, I actually want it to be more predictable now. Like, I want to be able to tell them. But I think that initial, like, wow was because you didn't know, right? When you upload your resume, what's it about to say about you? And I think I've seen enough of these where I'm, like, oh, it gave you good vibes, right? Like, you knew it was going to say, like, something really cool. As we start to shape this product, I think we want to try to preserve as much of that wow as much as we can. Because I do think, like, exposing, like, all the knobs and, like, the dials, like, we've been thinking about this a lot. It's like, hey, is that, like, the actual thing?Raiza [00:16:43]: Is that the thing that people really want?Alessio [00:16:45]: Have you found differences in having one model just generate the conversation and then using text-to-speech to kind of fake two people? Or, like, are you actually using two different kind of system prompts to, like, have a conversation step-by-step? I'm always curious, like, if persona system prompts make a big difference? Or, like, you just put in one prompt and then you just let it run?Usama [00:17:05]: I guess, like, generally we use a lot of inference, as you can tell with, like, the spinning thing takes a while. So, yeah, there's definitely, like, a bunch of different things happening under the hood. We've tried both approaches and they have their, sort of, drawbacks and benefits. I think that that idea of, like, questioning, like, the two different personas, like, persists throughout, like, whatever approach we try. It's like, there's a bit of, like, imperfection in there. Like, we had to really lean into the fact that, like, to build something that's engaging, like, it needs to be somewhat human and it needs to be just not a chatbot. Like, that was sort of, like, what we need to diverge from. It's like, you know, most chatbots will just narrate the same kind of answer, like, given the same sources, for the most part, which is ridiculous. So, yeah, there's, like, experimentation there under the hood, like, with the model to, like, make sure that it's spitting out, like, different takes and different personas and different, sort of, prompting each other is, like, a good analogy, I guess.Swyx [00:18:00]: Yeah, I think Steven Johnson, I think he's on your team. I don't know what his role is. He seems like chief dreamer, writer.Raiza [00:18:08]: Yeah, I mean, I can comment on Steven. So, Steven joined, actually, in the very early days, I think before it was even a fully funded project. And I remember when he joined, I was like, Steven Johnson's going to be on my team? You know, and for folks who don't know him, Steven is a New York Times bestselling author of, like, 14 books. He has a PBS show. He's, like, incredibly smart, just, like, a true, sort of, celebrity by himself. And then he joined Google, and he was like, I want to come here, and I want to build the thing that I've always dreamed of, which is a tool to help me think. I was like, a what? Like, a tool to help you think? I was like, what do you need help with? Like, you seem to be doing great on your own. And, you know, he would describe this to me, and I would watch his flow. And aside from, like, providing a lot of inspiration, to be honest, like, when I watched Steven work, I was like, oh, nobody works like this, right? Like, this is what makes him special. Like, he is such a dedicated, like, researcher and journalist, and he's so thorough, he's so smart. And then I had this realization of, like, maybe Steven is the product. Maybe the work is to take Steven's expertise and bring it to, like, everyday people that could really benefit from this. Like, just watching him work, I was like, oh, I could definitely use, like, a mini-Steven, like, doing work for me. Like, that would make me a better PM. And then I thought very quickly about, like, the adjacent roles that could use sort of this, like, research and analysis tool. And so, aside from being, you know, chief dreamer, Steven also represents, like, a super workflow that I think all of us, like, if we had access to a tool like it, would just inherently, like, make us better.Swyx [00:19:46]: Did you make him express his thoughts while he worked, or you just silently watched him, or how does this work?Raiza [00:19:52]: Oh, now you're making me admit it. But yes, I did just silently watch him.Swyx [00:19:57]: This is a part of the PM toolkit, right? They give user interviews and all that.Raiza [00:20:00]: Yeah, I mean, I did interview him, but I noticed, like, if I interviewed him, it was different than if I just watched him. And I did the same thing with students all the time. Like, I followed a lot of students around. I watched them study. I would ask them, like, oh, how do you feel now, right?Raiza [00:20:15]: Or why did you do that? Like, what made you do that, actually?Raiza [00:20:18]: Or why are you upset about, like, this particular thing? Why are you cranky about this particular topic? And it was very similar, I think, for Steven, especially because he was describing, he was in the middle of writing a book. And he would describe, like, oh, you know, here's how I research things, and here's how I keep my notes. Oh, and here's how I do it. And it was really, he was doing this sort of, like, self-questioning, right? Like, now we talk about, like, chain of, you know, reasoning or thought, reflection.Raiza [00:20:44]: And I was like, oh, he's the OG.Raiza [00:20:46]: Like, I watched him do it in real time. I was like, that's, like, L-O-M right there. And to be able to bring sort of that expertise in a way that was, like, you know, maybe, like, costly inference-wise, but really have, like, that ability inside of a tool that was, like, for starters, free inside of NotebookLM, it was good to learn whether or not people really did find use out of it.Swyx [00:21:05]: So did he just commit to using NotebookLM for everything, or did you just model his existing workflow?Raiza [00:21:12]: Both, right?Raiza [00:21:12]: Like, in the beginning, there was no product for him to use. And so he just kept describing the thing that he wanted. And then eventually, like, we started building the thing. And then I would start watching him use it. One of the things that I love about Steven is he uses the product in ways where it kind of does it, but doesn't quite. Like, he's always using it at, like, the absolute max limit of this thing. But the way that he describes it is so full of promise, where he's like, I can see it going here. And all I have to do is sort of, like, meet him there and sort of pressure test whether or not, you know, everyday people want it. And we just have to build it.Swyx [00:21:47]: I would say OpenAI has a pretty similar person, Andrew Mason, I think his name is. It's very similar, like, just from the writing world and using it as a tool for thought to shape Chachabitty. I don't think that people who use AI tools to their limit are common. I'm looking at my NotebookLM now. I've got two sources. You have a little, like, source limit thing. And my bar is over here, you know, and it stretches across the whole thing. I'm like, did he fill it up?Raiza [00:22:09]: Yes, and he has, like, a higher limit than others, I think. He fills it up.Raiza [00:22:14]: Oh, yeah.Raiza [00:22:14]: Like, I don't think Steven even has a limit, actually.Swyx [00:22:17]: And he has Notes, Google Drive stuff, PDFs, MP3, whatever.Raiza [00:22:22]: Yes, and one of my favorite demos, he just did this recently, is he has actually PDFs of, like, handwritten Marie Curie notes. I see.Swyx [00:22:29]: So you're doing image recognition as well. Yeah, it does support it today.Raiza [00:22:32]: So if you have a PDF that's purely images, it will recognize it.Raiza [00:22:36]: But his demo is just, like, super powerful.Raiza [00:22:37]: He's like, okay, here's Marie Curie's notes. And it's like, here's how I'm using it to analyze it. And I'm using it for, like, this thing that I'm writing.Raiza [00:22:44]: And that's really compelling.Raiza [00:22:45]: It's like the everyday person doesn't think of these applications. And I think even, like, when I listen to Steven's demo, I see the gap. I see how Steven got there, but I don't see how I could without him. And so there's a lot of work still for us to build of, like, hey, how do I bring that magic down to, like, zero work? Because I look at all the steps that he had to take in order to do it, and I'm like, okay, that's product work for us, right? Like, that's just onboarding.Alessio [00:23:09]: And so from an engineering perspective, people come to you and it's like, hey, I need to use this handwritten notes from Marie Curie from hundreds of years ago. How do you think about adding support for, like, data sources and then maybe any fun stories and, like, supporting more esoteric types of inputs?Raiza [00:23:25]: So I think about the product in three ways, right? So there's the sources, the source input. There's, like, the capabilities of, like, what you could do with those sources. And then there's the third space, which is how do you output it into the world? Like, how do you put it back out there? There's a lot of really basic sources that we don't support still, right? I think there's sort of, like, the handwritten notes stuff is one, but even basic things like DocX or, like, PowerPoint, right? Like, these are the things that people, everyday people are like, hey, my professor actually gave me everything in DocX. Can you support that? And then just, like, basic stuff, like images and PDFs combined with text. Like, there's just a really long roadmap for sources that I think we just have to work on.Raiza [00:24:04]: So that's, like, a big piece of it.Raiza [00:24:05]: On the output side, and I think this is, like, one of the most interesting things that we learned really early on, is, sure, there's, like, the Q&A analysis stuff, which is like, hey, when did this thing launch? Okay, you found it in the slide deck. Here's the answer. But most of the time, the reason why people ask those questions is because they're trying to make something new. And so when, actually, when some of those early features leaked, like, a lot of the features we're experimenting with are the output types. And so you can imagine that people care a lot about the resources that they're putting into NotebookLM because they're trying to create something new. So I think equally as important as, like, the source inputs are the outputs that we're helping people to create. And really, like, you know, shortly on the roadmap, we're thinking about how do we help people use NotebookLM to distribute knowledge? And that's, like, one of the most compelling use cases is, like, shared notebooks. It's, like, a way to share knowledge. How do we help people take sources and, like, one-click new documents out of it, right? And I think that's something that people think is, like, oh, yeah, of course, right? Like, one push a document. But what does it mean to do it right? Like, to do it in your style, in your brand, right?Raiza [00:25:08]: To follow your guidelines, stuff like that.Raiza [00:25:09]: So I think there's a lot of work, like, on both sides of that equation.Raiza [00:25:13]: Interesting.Swyx [00:25:13]: Any comments on the engineering side of things?Usama [00:25:16]: So, yeah, like I said, I was mostly working on building the text to audio, which kind of lives as a separate engineering pipeline, almost, that we then put into NotebookLM. But I think there's probably tons of NotebookLM engineering war stories on dealing with sources. And so I don't work too closely with engineers directly. But I think a lot of it does come down to, like, Gemini's native understanding of images really well with the latest generation.Raiza [00:25:39]: Yeah, I think on the engineering and modeling side, I think we are a really good example of a team that's put a product out there, and we're getting a lot of feedback from the users, and we return the data to the modeling team, right? To the extent that we say, hey, actually, you know what people are uploading, but we can't really support super well?Raiza [00:25:56]: Text plus image, right?Raiza [00:25:57]: Especially to the extent that, like, NotebookLM can handle up to 50 sources, 500,000 words each. Like, you're not going to be able to jam all of that into, like, the context window. So how do we do multimodal embeddings with that? There's really, like, a lot of things that we have to solve that are almost there, but not quite there yet.Alessio [00:26:16]: On then turning it into audio, I think one of the best things is it has so many of the human... Does that happen in the text generation that then becomes audio? Or is that a part of, like, the audio model that transforms the text?Usama [00:26:27]: It's a bit of both, I would say. The audio model is definitely trying to mimic, like, certain human intonations and, like, sort of natural, like, breathing and pauses and, like, laughter and things like that. But yeah, in generating, like, the text, we also have to sort of give signals on, like, where those things maybe would make sense.Alessio [00:26:45]: And on the input side, instead of having a transcript versus having the audio, like, can you take some of the emotions out of it, too? If I'm giving, like, for example, when we did the recaps of our podcast, we can either give audio of the pod or we can give a diarized transcription of it. But, like, the transcription doesn't have some of the, you know, voice kind of, like, things.Raiza [00:27:05]: Yeah, yeah.Alessio [00:27:05]: Do you reconstruct that when people upload audio or how does that work?Raiza [00:27:09]: So when you upload audio today, we just transcribe it. So it is quite lossy in the sense that, like, we don't transcribe, like, the emotion from that as a source. But when you do upload a text file and it has a lot of, like, that annotation, I think that there is some ability for it to be reused in, like, the audio output, right? But I think it will still contextualize it in the deep dive format. So I think that's something that's, like, particularly important is, like, hey, today we only have one format.Raiza [00:27:37]: It's deep dive.Raiza [00:27:38]: It's meant to be a pretty general overview and it is pretty peppy.Raiza [00:27:42]: It's just very upbeat.Raiza [00:27:43]: It's very enthusiastic, yeah.Raiza [00:27:45]: Yeah, yeah.Raiza [00:27:45]: Even if you had, like, a sad topic, I think they would find a way to be, like, silver lining, though.Raiza [00:27:50]: Really?Raiza [00:27:51]: Yeah.Raiza [00:27:51]: We're having a good chat.Raiza [00:27:54]: Yeah, that's awesome.Swyx [00:27:54]: One of the ways, many, many, many ways that deep dive went viral is people saying, like, if you want to feel good about yourself, just drop in your LinkedIn. Any other, like, favorite use cases that you saw from people discovering things in social media?Raiza [00:28:08]: I mean, there's so many funny ones and I love the funny ones.Raiza [00:28:11]: I think because I'm always relieved when I watch them. I'm like, haha, that was funny and not scary. It's great.Raiza [00:28:17]: There was another one that was interesting, which was a startup founder putting their landing page and being like, all right, let's test whether or not, like, the value prop is coming through. And I was like, wow, that's right.Raiza [00:28:26]: That's smart.Usama [00:28:27]: Yeah.Raiza [00:28:28]: And then I saw a couple of other people following up on that, too.Raiza [00:28:32]: Yeah.Swyx [00:28:32]: I put my about page in there and, like, yeah, if there are things that I'm not comfortable with, I should remove it. You know, so that it can pick it up. Right.Usama [00:28:39]: I think that the personal hype machine was, like, a pretty viral one. I think, like, people uploaded their dreams and, like, some people, like, keep sort of dream journals and it, like, would sort of comment on those and, like, it was therapeutic. I didn't see those.Raiza [00:28:54]: Those are good. I hear from Googlers all the time, especially because we launched it internally first. And I think we launched it during the, you know, the Q3 sort of, like, check-in cycle. So all Googlers have to write notes about, like, hey, you know, what'd you do in Q3? And what Googlers were doing is they would write, you know, whatever they accomplished in Q3 and then they would create an audio overview. And these people they didn't know would just ping me and be like, wow, I feel really good, like, going into a meeting with my manager.Raiza [00:29:25]: And I was like, good, good, good, good. You really did that, right?Usama [00:29:29]: I think another cool one is just, like, any Wikipedia article. Yeah. Like, you drop it in and it's just, like, suddenly, like, the best sort of summary overview.Raiza [00:29:38]: I think that's what Karpathy did, right? Like, he has now a Spotify channel called Histories of Mysteries, which is basically, like, he just took, like, interesting stuff from Wikipedia and made audio overviews out of it.Swyx [00:29:50]: Yeah, he became a podcaster overnight.Raiza [00:29:52]: Yeah.Raiza [00:29:53]: I'm here for it. I fully support him.Raiza [00:29:55]: I'm racking up the listens for him.Swyx [00:29:58]: Honestly, it's useful even without the audio. You know, I feel like the audio does add an element to it, but I always want, you know, paired audio and text. And it's just amazing to see what people are organically discovering. I feel like it's because you laid the groundwork with NotebookLM and then you came in and added the sort of TTS portion and made it so good, so human, which is weird. Like, it's this engineering process of humans. Oh, one thing I wanted to ask. Do you have evals?Raiza [00:30:23]: Yeah.Swyx [00:30:23]: Yes.Raiza [00:30:24]: What? Potatoes for chefs.Swyx [00:30:27]: What is that? What do you mean, potatoes?Raiza [00:30:29]: Oh, sorry.Raiza [00:30:29]: Sorry. We were joking with this, like, a couple of weeks ago. We were doing, like, side-by-sides. But, like, Raiza sent me the file and it was literally called Potatoes for Chefs. And I was like, you know, my job is really serious, but you have to laugh a little bit. Like, the title of the file is, like, Potatoes for Chefs.Swyx [00:30:47]: Is it like a training document for chefs?Usama [00:30:50]: It's just a side-by-side for, like, two different kind of audio transcripts.Swyx [00:30:54]: The question is really, like, as you iterate, the typical engineering advice is you establish some kind of test or benchmark. You're at, like, 30 percent. You want to get it up to 90, right?Raiza [00:31:05]: Yeah.Swyx [00:31:05]: What does that look like for making something sound human and interesting and voice?Usama [00:31:11]: We have the sort of formal eval process as well. But I think, like, for this particular project, we maybe took a slightly different route to begin with. Like, there was a lot of just within the team listening sessions. A lot of, like, sort of, like... Dogfooding.Raiza [00:31:23]: Yeah.Usama [00:31:23]: Like, I think the bar that we tried to get to before even starting formal evals with raters and everything was much higher than I think other projects would. Like, because that's, as you said, like, the traditional advice, right? Like, get that ASAP. Like, what are you looking to improve on? Whatever benchmark it is. So there was a lot of just, like, critical listening. And I think a lot of making sure that those improvements actually could go into the model. And, like, we're happy with that human element of it. And then eventually we had to obviously distill those down into an eval set. But, like, still there's, like, the team is just, like, a very, very, like, avid user of the product at all stages.Raiza [00:32:02]: I think you just have to be really opinionated.Raiza [00:32:05]: I think that sometimes, if you are, your intuition is just sharper and you can move a lot faster on the product.Raiza [00:32:12]: Because it's like, if you hold that bar high, right?Raiza [00:32:15]: Like, if you think about, like, the iterative cycle, it's like, hey, we could take, like, six months to ship this thing. To get it to, like, mid where we were. Or we could just, like, listen to this and be like, yeah, that's not it, right? And I don't need a rater to tell me that. That's my preference, right? And collectively, like, if I have two other people listen to it, they'll probably agree. And it's just kind of this step of, like, just keep improving it to the point where you're like, okay, now I think this is really impressive. And then, like, do evals, right? And then validate that.Swyx [00:32:43]: Was the sound model done and frozen before you started doing all this? Or are you also saying, hey, we need to improve the sound model as well? Both.Usama [00:32:51]: Yeah, we were making improvements on the audio and just, like, generating the transcript as well. I think another weird thing here was, like, we needed to be entertaining. And that's much harder to quantify than some of the other benchmarks that you can make for, like, you know, Sweebench or get better at this math.Swyx [00:33:10]: Do you just have people rate one to five or, you know, or just thumbs up and down?Usama [00:33:14]: For the formal rater evals, we have sort of like a Likert scale and, like, a bunch of different dimensions there. But we had to sort of break down what makes it entertaining into, like, a bunch of different factors. But I think the team stage of that was more critical. It was like, we need to make sure that, like, what is making it fun and engaging? Like, we dialed that as far as it goes. And while we're making other changes that are necessary, like, obviously, they shouldn't make stuff up or, you know, be insensitive.Raiza [00:33:41]: Hallucinations. Safety.Swyx [00:33:42]: Other safety things.Raiza [00:33:43]: Right.Swyx [00:33:43]: Like a bunch of safety stuff.Raiza [00:33:45]: Yeah, exactly.Usama [00:33:45]: So, like, with all of that and, like, also just, you know, following sort of a coherent narrative and structure is really important. But, like, with all of this, we really had to make sure that that central tenet of being entertaining and engaging and something you actually want to listen to. It just doesn't go away, which takes, like, a lot of just active listening time because you're closest to the prompts, the model and everything.Swyx [00:34:07]: I think sometimes the difficulty is because we're dealing with non-deterministic models, sometimes you just got a bad roll of the dice and it's always on the distribution that you could get something bad. Basically, how many do you, like, do ten runs at a time? And then how do you get rid of the non-determinism?Raiza [00:34:23]: Right.Usama [00:34:23]: Yeah, that's bad luck.Raiza [00:34:25]: Yeah.Swyx [00:34:25]: Yeah.Usama [00:34:26]: I mean, there still will be, like, bad audio overviews. There's, like, a bunch of them that happens. Do you mean for, like, the raider? For raiders, right?Swyx [00:34:34]: Like, what if that one person just got, like, a really bad rating? You actually had a great prompt, you actually had a great model, great weights, whatever. And you just, you had a bad output.Usama [00:34:42]: Like, and that's okay, right?Raiza [00:34:44]: I actually think, like, the way that these are constructed, if you think about, like, the different types of controls that the user has, right? Like, what can the user do today to affect it?Usama [00:34:54]: We push a button.Raiza [00:34:55]: You just push a button.Swyx [00:34:56]: I have tried to prompt engineer by changing the title. Yeah, yeah, yeah.Raiza [00:34:59]: Changing the title, people have found out.Raiza [00:35:02]: Yeah.Raiza [00:35:02]: The title of the notebook, people have found out. You can add show notes, right? You can get them to think, like, the show has changed. Someone changed the language of the output. Changing the language of the output. Like, those are less well-tested because we focused on, like, this one aspect. So it did change the way that we sort of think about quality as well, right? So it's like, quality is on the dimensions of entertainment, of course, like, consistency, groundedness. But in general, does it follow the structure of the deep dive? And I think when we talk about, like, non-determinism, it's like, well, as long as it follows, like, the structure of the deep dive, right? It sort of inherently meets all those other qualities. And so it makes it a little bit easier for us to ship something with confidence to the extent that it's like, I know it's going to make a deep dive. It's going to make a good deep dive. Whether or not the person likes it, I don't know. But as we expand to new formats, as we open up controls, I think that's where it gets really much harder. Even with the show notes, right? Like, people don't know what they're going to get when they do that. And we see that already where it's like, this is going to be a lot harder to validate in terms of quality, where now we'll get a greater distribution. Whereas I don't think we really got, like, varied distribution because of, like, that pre-process that Raiza was talking about. And also because of the way that we'd constrain, like, what were we measuring for? Literally, just like, is it a deep dive?Swyx [00:36:18]: And you determine what a deep dive is. Yeah. Everything needs a PM. Yeah, I have, this is very similar to something I've been thinking about for AI products in general. There's always like a chief tastemaker. And for Notebook LM, it seems like it's a combination of you and Steven.Raiza [00:36:31]: Well, okay.Raiza [00:36:32]: I want to take a step back.Swyx [00:36:33]: And Raiza, I mean, presumably for the voice stuff.Raiza [00:36:35]: Raiza's like the head chef, right? Of, like, deep dive, I think. Potatoes.Raiza [00:36:40]: Of potatoes.Raiza [00:36:41]: And I say this because I think even though we are already a very opinionated team, and Steven, for sure, very opinionated, I think of the audio generations, like, Raiza was the most opinionated, right? And we all, like, would say, like, hey, I remember, like, one of the first ones he sent me.Raiza [00:36:57]: I was like, oh, I feel like they should introduce themselves. I feel like they should say a title. But then, like, we would catch things, like, maybe they shouldn't say their names.Raiza [00:37:04]: Yeah, they don't say their names.Usama [00:37:05]: That was a Steven catch, like, not give them names.Raiza [00:37:08]: So stuff like that is, like, we all injected, like, a little bit of just, like, hey, here's, like, my take on, like, how a podcast should be, right? And I think, like, if you're a person who, like, regularly listens to podcasts, there's probably some collective preference there that's generic enough that you can standardize into, like, the deep dive format. But, yeah, it's the new formats where I think, like, oh, that's the next test. Yeah.Swyx [00:37:30]: I've tried to make a clone, by the way. Of course, everyone did. Yeah. Everyone in AI was like, oh, no, this is so easy. I'll just take a TTS model. Obviously, our models are not as good as yours, but I tried to inject a consistent character backstory, like, age, identity, where they work, where they went to school, what their hobbies are. Then it just, the models try to bring it in too much.Raiza [00:37:49]: Yeah.Swyx [00:37:49]: I don't know if you tried this.Raiza [00:37:51]: Yeah.Swyx [00:37:51]: So then I'm like, okay, like, how do I define a personality? But it doesn't keep coming up every single time. Yeah.Raiza [00:37:58]: I mean, we have, like, a really, really good, like, character designer on our team.Raiza [00:38:02]: What?Swyx [00:38:03]: Like a D&D person?Raiza [00:38:05]: Just to say, like, we, just like we had to be opinionated about the format, we had to be opinionated about who are those two people talking.Raiza [00:38:11]: Okay.Raiza [00:38:12]: Right.Raiza [00:38:12]: And then to the extent that, like, you can design the format, you should be able to design the people as well.Raiza [00:38:18]: Yeah.Swyx [00:38:18]: I would love, like, a, you know, like when you play Baldur's Gate, like, you roll, you roll like 17 on Charisma and like, it's like what race they are. I don't know.Raiza [00:38:27]: I recently, actually, I was just talking about character select screens.Raiza [00:38:30]: Yeah. I was like, I love that, right.Raiza [00:38:32]: And I was like, maybe there's something to be learned there because, like, people have fallen in love with the deep dive as a, as a format, as a technology, but also as just like those two personas.Raiza [00:38:44]: Now, when you hear a deep dive and you've heard them, you're like, I know those two.Raiza [00:38:48]: Right.Raiza [00:38:48]: And people, it's so funny when I, when people are trying to find out their names, like, it's a, it's a worthy task.Raiza [00:38:54]: It's a worthy goal.Raiza [00:38:55]: I know what you're doing. But the next step here is to sort of introduce, like, is this like what people want?Raiza [00:39:00]: People want to sort of edit the personas or do they just want more of them?Swyx [00:39:04]: I'm sure you're getting a lot of opinions and they all, they all conflict with each other. Before we move on, I have to ask, because we're kind of on this topic. How do you make audio engaging? Because it's useful, not just for deep dive, but also for us as podcasters. What is, what does engaging mean? If you could break it down for us, that'd be great.Usama [00:39:22]: I mean, I can try. Like, don't, don't claim to be an expert at all.Swyx [00:39:26]: So I'll give you some, like variation in tone and speed. You know, there's this sort of writing advice where, you know, this sentence is five words. This sentence is three, that kind of advice where you, where you vary things, you have excitement, you have laughter, all that stuff. But I'd be curious how else you break down.Usama [00:39:42]: So there's the basics, like obviously structure that can't be meandering, right? Like there needs to be sort of a, an ultimate goal that the voices are trying to get to, human or artificial. I think one thing we find often is if there's just too much agreement between people, like that's not fun to listen to. So there needs to be some sort of tension and build up, you know, withholding information. For example, like as you listen to a story unfold, like you're going to learn more and more about it. And audio that maybe becomes even more important because like you actually don't have the ability to just like skim to the end of something. You're driving or something like you're going to be hooked because like there's, and that's how like, that's how a lot of podcasts work. Like maybe not interviews necessarily, but a lot of true crime, a lot of entertainment in general. There's just like a gradual unrolling of information. And that also like sort of goes back to the content transformation aspect of it. Like maybe you are going from, let's say the Wikipedia article of like one of the History of Mysteries, maybe episodes. Like the Wikipedia article is going to state out the information very differently. It's like, here's what happened would probably be in the very first paragraph. And one approach we could have done is like maybe a person's just narrating that thing. And maybe that would work for like a certain audience. Or I guess that's how I would picture like a standard history lesson to unfold. But like, because we're trying to put it in this two-person dialogue format, like there, we inject like the fact that, you know, there's, you don't give everything at first. And then you set up like differing opinions of the same topic or the same, like maybe you seize on a topic and go deeper into it and then try to bring yourself back out of it and go back to the main narrative. So that's, that's mostly from like the setting up the script perspective. And then the audio, I was saying earlier, it's trying to be as close to just human speech as possible. I think was the, what we found success with so far.Raiza [00:41:40]: Yeah. Like with interjections, right?Raiza [00:41:41]: Like I think like when you listen to two people talk, there's a lot of like, yeah, yeah, right. And then there's like a lot of like that questioning, like, oh yeah, really?Raiza [00:41:49]: What did you think?Swyx [00:41:50]: I noticed that. That's great.Raiza [00:41:52]: Totally.Usama [00:41:54]: Exactly.Swyx [00:41:55]: My question is, do you pull in speech experts to do this? Or did you just come up with it yourselves? You can be like, okay, talk to a whole bunch of fiction writers to, to make things engaging or comedy writers or whatever, stand up comedy, right? They have to make audio engaging, but audio as well. Like there's professional fields of studying where people do this for a living, but us as AI engineers are just making this up as we go.Raiza [00:42:19]: I mean, it's a great idea, but you definitely didn't.Raiza [00:42:22]: Yeah.Swyx [00:42:24]: My guess is you didn't.Raiza [00:42:25]: Yeah.Swyx [00:42:26]: There's a, there's a certain field of authority that people have. They're like, oh, like you can't do this because you don't have any experience like making engaging audio. But that's what you literally did.Raiza [00:42:35]: Right.Usama [00:42:35]: I mean, I was literally chatting with someone at Google earlier today about how some people think that like you need a linguistics person in the room for like making a good chatbot. But that's not actually true because like this person went to school for linguistics. And according to him, he's an engineer now. According to him, like most of his classmates were not actually good at language. Like they knew how to analyze language and like sort of the mathematical patterns and rhythms and language. But that doesn't necessarily mean they were going to be eloquent at like while speaking or writing. So I think, yeah, a lot of we haven't invested in specialists in audio format yet, but maybe that would.Raiza [00:43:13]: I think it's like super interesting because I think there is like a very human question of like what makes something interesting. And there's like a very deep question of like what is it, right? Like what is the quality that we are all looking for? Is it does somebody have to be funny? Does something have to be entertaining? Does something have to be straight to the point? And I think when you try to distill that, this is the interesting thing I think about our experiment, about this particular launch is first, we only launched one format. And so we sort of had to squeeze everything we believed about what an interesting thing is into one package. And as a result of it, I think we learned it's like, hey, interacting with a chatbot is sort of novel at first, but it's not interesting, right? It's like humans are what makes interacting with chatbots interesting.Raiza [00:43:59]: It's like, ha ha ha, I'm going to try to trick it. It's like, that's interesting.Raiza [00:44:02]: Spell strawberry, right?Raiza [00:44:04]: This is like the fun that like people have with it. But like that's not the LLM being interesting.Raiza [00:44:08]: That's you just like kind of giving it your own flavor. But it's like, what does it mean to sort of flip it on its head and say, no, you be interesting now, right? Like you give the chatbot the opportunity to do it. And this is not a chatbot per se. It is like just the audio. And it's like the texture, I think, that really brings it to life. And it's like the things that we've described here, which is like, okay, now I have to like lead you down a path of information about like this commercialization deck.Raiza [00:44:36]: It's like, how do you do that?Raiza [00:44:38]: To be able to successfully do it, I do think that you need experts. I think we'll engage with experts like down the road, but I think it will have to be in the context of, well, what's the next thing we're building, right? It's like, what am I trying to change here? What do I fundamentally believe needs to be improved? And I think there's still like a lot more studying that we have to do in terms of like, well, what are people actually using this for? And we're just in such early days. Like it hasn't even been a month. Two, three weeks.Usama [00:45:05]: Three weeks.Raiza [00:45:06]: Yeah, yeah.Usama [00:45:07]: I think one other element to that is the fact that you're bringing your own sources to it. Like it's your stuff. Like, you know this somewhat well, or you care to know about this. So like that, I think, changed the equation on its head as well. It's like your sources and someone's telling you about it. So like you care about how that dynamic is, but you just care for it to be good enough to be entertaining. Because ultimately they're talking about your mortgage deed or whatever.Swyx [00:45:33]: So it's interesting just from the topic itself. Even taking out all the agreements and the hiding of the slow reveal. I mean, there's a baseline, maybe.Usama [00:45:42]: Like if it was like too drab. Like if someone was reading it off, like, you know, that's like the absolute worst.Raiza [00:45:46]: But like...Swyx [00:45:47]: Do you prompt for humor? That's a tough one, right?Raiza [00:45:51]: I think it's more of a generic way to bring humor out if possible. I think humor is actually one of the hardest things. Yeah.Raiza [00:46:00]: But I don't know if you saw...Raiza [00:46:00]: That is AGI.Swyx [00:46:01]: Humor is AGI.Raiza [00:46:02]: Yeah, but did you see the chicken one?Raiza [00:46:03]: No.Raiza [00:46:04]: Okay. If you haven't heard it... We'll splice it in here.Swyx [00:46:06]: Okay.Raiza [00:46:07]: Yeah.Raiza [00:46:07]: There is a video on Threads. I think it was by Martino Wong. And it's a PDF.Raiza [00:46:16]: Welcome to your deep dive for today. Oh, yeah. Get ready for a fun one. Buckle up. Because we are diving into... Chicken, chicken, chicken. Chicken, chicken. You got that right. By Doug Zonker. Now. And yes, you heard that title correctly. Titles. Our listener today submitted this paper. Yeah, they're going to need our help. And I can totally see why. Absolutely. It's dense. It's baffling. It's a lot. And it's packed with more chicken than a KFC buffet. What? That's hilarious.Raiza [00:46:48]: That's so funny. So it's like stuff like that, that's like truly delightful, truly surprising.Raiza [00:46:53]: But it's like we didn't tell it to be funny.Usama [00:46:55]: Humor is contextual also. Like super contextual is what we're realizing. So we're not prompting for humor, but we're prompting for maybe a lot of other things that are bringing out that humor.Alessio [00:47:04]: I think the thing about ad-generated content, if we look at YouTube, like we do videos on YouTube and it's like, you know, a lot of people like screaming in the thumbnails to get clicks. There's like everybody, there's kind of like a meta of like what you need to do to get clicks. But I think in your product, there's no actual creator on the other side investing the time. So you can actually generate a type of content that is maybe not universally appealing, you know, at a much, yeah, exactly. I think that's the most interesting thing. It's like, well, is there a way for like, take Mr.Raiza [00:47:36]: Beast, right?Alessio [00:47:36]: It's like Mr. Beast optimizes videos to reach the biggest audience and like the most clicks. But what if every video could be kind of like regenerated to be closer to your taste, you know, when you watch it?Raiza [00:47:48]: I think that's kind of the promise of AI that I think we are just like touching on, which is, I think every time I've gotten information from somebody, they have delivered it to me in their preferred method, right?Raiza [00:47:59]: Like if somebody gives me a PDF, it's a PDF.Raiza [00:48:01]: Somebody gives me a hundred slide deck, that is the format in which I'm going to read it. But I think we are now living in the era where transformations are really possible, which is, look, like I don't want to read your hundred slide deck, but I'll listen to a 16 minute audio overview on the drive home. And that, that I think is, is really novel. And that is, is paving the way in a way that like maybe we wanted, but didn'tRaiza [00:48:24]: expect.Raiza [00:48:25]: Where I also think you're listening to a lot of content that normally wouldn't have had content made about it. Like I watched this TikTok where this woman uploaded her diary from 2004.Raiza [00:48:36]: For sure, right?Raiza [00:48:36]: Like nobody was goin

Greg crawls through a sewage pipe with Tank Dell to find fantasy freedom, examines the ledger of George Pickens' limited snaps, and smuggles some info out of the Coachspeak Index Discord.   Follow us online at https://x.com/CoachspeakIndex   Join our Discord at https://www.patreon.com/CoachspeakIndex   Get up to $1,000 in bonus cash, plus a free pick on Underdog: https://play.underdogfantasy.com/p-coachspeak-index

Shawn Wang (aka swyx) is the founder of smol.ai (AI news curation), and the cohost of Latent Space (popular AI Engineer podcast). Plus, Shawn started the AI Engineer movement with his essay Rise of the AI Engineer and organized two incredible AI engineer conferences in the past twelve months - AI Engineer World's Fair and AI Engineer SummitAnd Shawn has angel invested in DevTools like Airbyte, Railway, Supabase, Replay.io, Stackblitz, Flutterflow, Fireworks.ai while running the DevTools angels community. Besides this, Shawn curates DX.tips (DevTools magazine) and in a past life wrote the Coding Career handbook, championed learn in public, cofounded Svelte Society and was previously Head of Developer Experience at Temporal, and a Developer Advocate at AWS and Netlify.Also, before this, Shawn had a very successful career in investment banking, trading, building data pipelines and performing quantitate portfolio management. I think this brings him a very unique perspective - I've always admired his ability to zoom out and see the big picture and the trends. Even though Shawn is now all-in on AI, he's still one of the go-to authorities on DevTools go-to-market.As you can tell, Shawn is someone I deeply admire. So I'm glad he came back.What we discuss:Organizing the AI Engineer ConferencesRise of the AI EngineerIntentionality and principles (yes we even talk about Alcoholics Anonymous)The AI CEOInvisible deadlinesIlya believing in AGI more than most people at OpenAIAre developers going to be obsolete? Thor convinced swyx to invest in SupabaseBuilding DevTools that work well with LLMsAngel investing in DevTools - why and howIs DevRel dead?How to hire DevRelWhy DX.tips existsLinks:Rise of the AI Engineer https://www.latent.space/p/ai-engineerLatent Space Podcast https://www.latent.space/swyx's Twitter https://x.com/swyxswyx's website https://www.swyx.io/swyx's LinkedIn https://www.linkedin.com/in/shawnswyxwang/smol.ai https://smol.ai/DevTools Angels https://github.com/sw-yx/devtools-angelsDX.tips https://dx.tips/DevRel's Death as Zero Interest Rate Phenomenon https://dx.tips/zirp AI Engineer Summit https://www.ai.engineer/summit/2023AI Engineer World's Fair https://www.ai.engineer/worldsfairCoding Career Handbook https://www.learninpublic.org/Shawn's previous appearance on Scaling DevTools https://podcast.scalingdevtools.com/episodes/swyx Eisenhower Matrix https://asana.com/resources/eisenhower-matrixThor from Supabase https://x.com/thorwebdevSolaris AI coworking space in SF https://www.solarissf.com/Browserbase https://www.browserbase.com/Indent https://indent.com/ and Fouad https://x.com/fouadmatinHow to do hackathons https://dx.tips/hackathonsHow to do conferences https://dx.tips/conf-guideHow to hire DevRel https://dx.tips/mailbox-first-devrel-hiringClimbing the ladder of abstraction with Amelia Wattenberger https://www.youtube.com/watch?v=PAy_GHUAICwCheck out the Enterprise Ready Conf from WorkOS https://enterprise-ready.com/

The SMOL version of an ep about smol precious creatures, we're talkin' slimy little angels with bellies for feet... literally! That's what gastropod means! We've got Dr. Jann Vendetti of the Natural History Museum of LA County to talk with Alie about snail-based beauty products, escargot, urban snails, thousands and thousands of teeth, and whether or not you should adopt a rabbit-sized pet snail. Check out the SLIME project at NHM.orgFull-length (*not* G-rated) Malacology episode + tons of science linksMore kid-friendly Smologies episodes!Become a patron of Ologies for as little as a buck a monthOlogiesMerch.com has hats, shirts, masks, totes!Follow @Ologies on X and InstagramFollow @AlieWard on X and InstagramSound editing by Jarrett Sleeper of MindJam MediaMade possible by work from Mercedes Maitland, Noel Dilworth, Susan Hale, Jacob Chaffee, Kelly R. Dwyer, Emily White, & Erin TalbertSmologies theme song by Harold Malcolm

Limiting Process Priority in a FreeBSD Jail, Why You Should Use FreeBSD, The web fun fact that domains can end in dots and canonicalization failures, Replacing postfix with dma + auth, modern unix tool list, Smol KVM, The Computers of Voyager NOTES This episode of BSDNow is brought to you by Tarsnap (https://www.tarsnap.com/bsdnow) and the BSDNow Patreon (https://www.patreon.com/bsdnow) Headlines FreeBSD Tips and Tricks: Limiting Process Priority in a FreeBSD Jail (https://it-notes.dragas.net/2024/07/11/limiting-process-priority-in-freebsd-jail/) Why You Should Use FreeBSD (https://freebsdfoundation.org/blog/why-you-should-use-freebsd/) News Roundup The web fun fact that domains can end in dots and canonicalization failures (https://utcc.utoronto.ca/~cks/space/blog/web/DomainDotsAndCanonicalization) Replacing postfix with dma + auth (https://dan.langille.org/2024/08/02/replacing-postfix-with-dma-auth/) modern unix tool list (https://notes.billmill.org/computer_usage/cli_tips_and_tools/modern_unix_tool_list.html) Smol KVM (https://adventurist.me/posts/00324) The Computers of Voyager (https://hackaday.com/2024/05/06/the-computers-of-voyager/) Beastie Bits No unmodified files remain from original import of OpenBSD (https://www.undeadly.org/cgi?action=article;sid=20240824114631) The BSDCan 2024 Playlist is now complete (https://www.undeadly.org/cgi?action=article;sid=20240814053159) UDP parallel input committed to -current (http://undeadly.org/cgi?action=article;sid=20240727110501) Your browser is your Computer (https://www.exaequos.com) For the member-berries (https://defrag98.com) Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv (mailto:feedback@bsdnow.tv) Join us and other BSD Fans in our BSD Now Telegram channel (https://t.me/bsdnow)

Byl konec roku 2019. Evropou se pomalu začal plížit covid a Velká Británie se připravovala na vystoupení z EU. Právě v této době velkých změn i nejistot naházel Libor Smolík věci do auta a se svou partnerkou Míšou spěchal vstříc novému životu v Anglii. Navzdory těžkopádnému startu se tam zabydleli a založili rodinu, a po takřka čtyřech letech si život na největším evropském ostrově nemohou vynachválit. Nejen o britské povaze a stereotypech vypráví Smolík v nové epizodě podcastu Slepá mapa.

Voyage to the End of the Universe (1964)AIP Production #6439Jeff and Cheryl board an intergalactic flight to a very familiar destination in Voyage to the End of the Universe. Directed by Jack Pollack (Jindřich Polák) Screenplay by Paul Jurist (Pavel Juráček) and Jack Pollack (Jindřich Polák) Based on Oblok Magellana/The Magellanic Cloud by Stanislaw Lem Produced by Rudolph Wohl (Rudolf Wolf) for Filmové Studio Barrandov Starring:Dennis Stephens (Zdeněk Štěpánek) as the Captain Rodney Lucas (Radovan Lukavský) as Commander Byron MacDonald Dana Meredith (Dana Medřická) as Charlotte, the Sociologist Irene Kova (Irena Kacírková) as Brigit Frances Smolen (František Smolík) as Anthony Thompson Otto Lack (Otto Lackovič)as Coordinator Michael Jerry Tullis (Jiří Vršťala)as Pilot Erik Svenson Myron March (Miroslav Machácek) as Bernard Rudolph Dial (Rudolf Deyl) as Pilot Ervin Herold John Mares (Jaroslav Mares) as Mark Martin Tapin (Martin Tapák) as biologist Peter Hubert Marcella Martin (Marcela Martínková) as Mark's wife, Stephanie Joseph Adams (Jozef Adamovic) as Coordinator Laurence John Rose (Jaroslav Rozsíval) as the Ship's Doctor Produced for Filmové Studio BarrandovReleased by American International Pictures Stream Voyage to the End of the Universe on Pluto or the Roku Channel or rent on Prime Video or Fandango at HomeThe Criterion Channel (as Ikarie X-B1) or on YouTube Visit our website - https://aippod.com/ and follow the American International Podcast on Letterboxd, Instagram and Threads @aip_pod and on Facebook at facebook.com/AmericanInternationalPodcast View the Voyage to the End of the Universe trailer here. Our open and close includes clips from the following films/trailers: How to Make a Monster (1958), The Brain That Wouldn't Die (1962), I Was a Teenage Werewolf (1957), High School Hellcats (1958), Beach Blanket Bingo (1965), The Wild Angels (1966), It Conquered the World (1956), The Abominable Dr. Phibes (1971), and Female Jungle (1955)

Jakub Smolík brzy oslaví půlkulaté narozeniny a jak se na zpěváka sluší a patří, bude slavit hned několika koncerty v rámci svého Tour 65. „To číslo má opravdu spojitost s věkem. Byla doba, kdy jsem si to neuměl představit,“ směje se oblíbený zpěvák. „Netušil jsem, že v té době budu ještě hrát a mít radost z dětí. Konečně si všechno moc užívám.“

Jakub Smolík brzy oslaví půlkulaté narozeniny a jak se na zpěváka sluší a patří, bude slavit hned několika koncerty v rámci svého Tour 65. „To číslo má opravdu spojitost s věkem. Byla doba, kdy jsem si to neuměl představit,“ směje se oblíbený zpěvák. „Netušil jsem, že v té době budu ještě hrát a mít radost z dětí. Konečně si všechno moc užívám.“

Tiffany, Megan, Katie, & Kayla share a little about some of their favorite Cozy, witchy, spooky, books to help you get in the mood as the season changes and get more recs to build your Fall TBR! No Spoilers.

Stop what you're doing right now and go buy a ticket to Cats: The Jellicle Ball at PAC NYC. Then, go listen to Short N Sweet cover to cover, AND THEN we can have a conversation. This week, the EBF team reflects on the sheer ascension that occurred at Cats: The Jellicle Ball (Gus

Do you use a bookmark or do you dog-ear like a barbarian? Dust Jackets, Annotating, Genres - we discuss all of this and more about our book habits in our very first Smol Talk episode!

For more of my work visit me on instagram at http://www.instagram.com/davidminiatures or my website https://www.davidminiatures.com   Special thanks to my guest on this episode Carson Cox of https://www.youtube.com/@smolworldworkshop   Check his YouTube Channel here: https://www.youtube.com/@smolworldworkshop   Carson Patreon here: https://www.patreon.com/SmolWorldWorkshop   And his instagram here: https://www.instagram.com/smolworldworkshop/   All the tools and materials I use can be found on Amazon: USA: https://www.amazon.com/shop/davidminiatures Canada: https://www.amazon.ca/shop/davidminiatures   Shout out to Joe Gaudet for the Voice over for the logo sting! https://direct.me/joegaudet

In the 296th  episode of 40 and 20, The Watch Clicker Podcast, we discuss some of the things that have caught our attention in the last few weeks. Circula ProFlight Blue Omega Aqua Terra Black Code 41 UNIFY Vero Open Water 38 France Only Zenith Defy Skyline Edition Paris Divers 65 Release Beaucroft Seeker 37mm Nivada Grenchen Other Things: Andrew: GCI Outdoor Pod Rocker Everett: Presumed Innocent ***********************************  This Episode's Sponsors: Escapement Media: https://escapementmedia.com Foster Watch Co: https://fosterwatches.com Frank Affronti Photography: https://www.affrontography.com ***********************************  Check out all of Watch Clicker's content, including columns, reviews, and fantastic photography at: watchclicker.com Check out the Watch Clicker Shop with all your favorite gear, fully branded, here. Our full catalog of podcasts is at watchclicker.com/4020-the-watch-clicker-podcast/ On instagram: 40and20 (@40and20_watchclicker): https://www.instagram.com/40and20_watchclicker/ WatchClicker (@watchclicker): www.instagram.com/watchclicker/?hl=en You can support Watch Clicker and 40 and 20 here: Patreon Intro/Outro Music: Bummin on Tremelo, by Kevin MacLeod (incompetch.com) Licensed under Creative Commons: By Attribution 3.0 License Creativecommons.org/licenses/by.3.0/

A short little episode to tell you about a change we're making to Ologies that I am genuinely very pumped about, as well as some weird secrets I did not intend to tell. But my point is that you can now have a kid-safe show and feed that is safe for kids and classrooms and a road trip with your shy parents. Subscribe to Smologies on Apple Podcasts, Spotify, Overcast, Pocket Casts, Castbox, Podcast Addict, or wherever you get podcasts.Sponsors of OlogiesTranscripts and bleeped episodesBecome a patron of Ologies for as little as a buck a monthOlogiesMerch.com has hats, shirts, hoodies, totes!Follow @Ologies on Instagram and XFollow @AlieWard on Instagram and XEditing by Mercedes Maitland of Maitland Audio Productions, Jacob Chaffee, and Jarrett Sleeper of MindJam MediaManaging Director: Susan HaleScheduling producer: Noel DilworthTranscripts by Aveline Malek Website by Kelly R. DwyerTheme song by Nick Thorburn

Explore the strategic depths of ecommerce and subscription models with Mihaela Mateescu, Head of Performance Marketing at Smol. In this episode of Customers Who Click, Mihaela shares valuable insights on driving customer retention and long-term value through innovative marketing and product strategies. Learn about the significance of customer engagement post-purchase and how Smol leverages data and customer feedback to optimize their offerings. Whether you're looking to enhance your marketing strategies or interested in the dynamics of subscription services, this episode provides actionable insights and proven tactics for nurturing customer relationships and boosting retention.

Featuring: Belghast, Kodra, and Tamrielo   Hey Folks! We have a bit of a “Smol” show at least when it comes to cast.  Last week we had to take that off because Bel was in “duck and cover” mode do to the storm system tearing through the center of the country and producing over one hundred Tornados.  This week we had a bunch of folks with other commitments leaving it with just Bel, Kodra, and Tam.  We talk a bit about the Helldivers Review Bomb, Angry Tarkov Players, and a now-fixed outrageous bug in Path of Exile that allowed for 18 linking a skill. From there we dive into some of Tam's recent experiences in trying to get Fallout New Vegas up and running and how like Bel said a few weeks back…  the games really still hold up well once you get past the technical limitations. Kodra gives a bit of a round-up of Metroidvania titles that he has been playing recently and then talks a bit about considering Strawberry Jam “done” after beating the Expert Lobby Yellows.   Topics Discussed Helldivers Review Bomb Path of Exile Ridiculous Socket Bug The Effort of installing Fallout New Vegas Older Fallout Games Still Hold Up Fallout New California TCM Metroidvania Roundup Ender Lilies: Quietus of the Knights Tales of Kenzera: ZAU TEVI Celeste Strawberry Jam  Expert Lobby Yellows Completed

So many great pranks this year. Let's talk about them, some new outfits, the new music, and the return of a Minty fresh ghost. Oh, and of course some 3D lives. Each week we aim to bring together the biggest events in Vtubing and talk about what's been going on. Stop by, hang out, and let's catch up with us! Join this discord : https://discord.gg/wFMcTGHWGJ Follow here for updates: https://twitter.com/SuperChatsPod Shorts over here: https://www.tiktok.com/@superchatspod 00:00:00 Marker 2 00:01:44 Pekomama is here https://youtu.be/8RNfOikz3Is 00:08:15 Marine's Massage Trip https://youtu.be/CVrFKDrlggI 00:12:44 Haachama's ...a piggy? https://youtu.be/lclUrxJH-74 00:19:33 Peo's Never Gonna Give You Up https://youtu.be/OUge2paDje8 00:20:37 Botan's Smol https://youtu.be/ET5b4RISScg 00:22:21 Fauna's 2010 Minecraft Stream https://youtu.be/Jf2d2Of8Jto 00:26:38 Holocure Cancelled...Forever?! 00:27:24 Us Being April Fools 00:31:14 Nasa's LEWDS?! 00:33:15 Rie is Miku? 00:36:35 Phase Coffee Company? https://youtu.be/ZMQqqwCQO0U 00:37:56 Rinniko Bellerose https://youtu.be/Dn59am2Cmwk 00:41:09 Hololive's New Dating Games 00:42:47 Hololive's What If? Series https://youtu.be/tkZqlRTj8ko 00:45:56 Luna's 4th Anniversary 3D Live https://youtu.be/o8PZa10DymU 00:52:52 Flare's Birthday Live https://youtu.be/u8AeA1ucX2o 00:57:48 Kizuna Ai Left Her Company 01:02:52 Mozzu's Back https://youtu.be/TFyKM8A4hCM 01:06:12 Doki Gets Apex Feature https://x.com/PlayApex/status/1773032211249352774 01:07:23 A-Chan on Break 01:08:42 Ina's New Outfit https://youtu.be/7ON5t1BvD4A 01:12:21 GX Aura is Back from Japan https://www.twitch.tv/videos/2102998289 01:22:40 Filian and Sayu Teamed Up https://youtu.be/ME6TBt5g2JY 01:24:15 Phase Connect Offcollab! https://youtu.be/o2HwhI72hHM 01:27:12 Spice and Wolf Vtuber 01:28:05 New Original Song from Luna https://youtu.be/ysV01Wj-wHM 01:30:17 Kobo, Kaela, and Zeta's New Cover https://youtu.be/dhgfvQM57sA 01:31:04 Sora and Azki's Cover https://youtu.be/dmf34e1ivrk 01:31:38 Suisei and Miko's Fully Animated MV https://youtu.be/gtgME6MJpk4 01:32:39 Polka's New Cover https://youtu.be/qIVOf5jVRnw 01:33:29 Kobo's New Original Song https://youtu.be/50Ura_ZcSvY 01:34:50 Doki's New(?) Music Video https://youtu.be/Mgc_QUbNhLs 01:36:22 Advent's April Fool's Collab https://youtu.be/EowJuA2nT5o 01:43:10 Shiori64 Easter Special https://youtu.be/-pwzzYrR4us 01:44:57 Kiara's Speggtacular Eggstravaganza https://youtu.be/Tc2n8LoGLNY 01:48:53 Matara and Mint Podcasts Incoming https://youtu.be/CjN67re3BWI 01:52:40 Shiina's Goop Stream https://youtu.be/6pXwi9hpot4 01:56:11 Miori's Bayonetta Stream  01:58:15 Uruka's Birthday Stream https://youtu.be/UMmIf0ZSs88 01:59:58 Saya's Shinkansen 0 Stream https://youtu.be/WffHbSFbLio 02:01:39 Community and Shilling 02:05:29 Birfdays

Our next SF event is AI UX 2024 - let's see the new frontier for UX since last year! Last call: we are recording a preview of the AI Engineer World's Fair with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an “ex-technical co-founder type”. Reach out to him for more!David Luan has been at the center of the modern AI revolution: he was the ~30th hire at OpenAI, he led Google's LLM efforts and co-led Google Brain, and then started Adept in 2022, one of the leading companies in the AI agents space. In today's episode, we asked David for some war stories from his time in early OpenAI (including working with Alec Radford ahead of the GPT-2 demo with Sam Altman, that resulted in Microsoft's initial $1b investment), and how Adept is building agents that can “do anything a human does on a computer" — his definition of useful AGI.Why Google *couldn't* make GPT-3While we wanted to discuss Adept, we couldn't talk to a former VP Eng of OpenAI and former LLM tech lead at Google Brain and not ask about the elephant in the room. It's often asked how Google had such a huge lead in 2017 with Vaswani et al creating the Transformer and Noam Shazeer predicting trillion-parameter models and yet it was David's team at OpenAI who ended up making GPT 1/2/3. David has some interesting answers:“So I think the real story of GPT starts at Google, of course, right? Because that's where Transformers sort of came about. However, the number one shocking thing to me was that, and this is like a consequence of the way that Google is organized…what they (should) have done would be say, hey, Noam Shazeer, you're a brilliant guy. You know how to scale these things up. Here's half of all of our TPUs. And then I think they would have destroyed us. He clearly wanted it too…You know, every day we were scaling up GPT-3, I would wake up and just be stressed. And I was stressed because, you know, you just look at the facts, right? Google has all this compute. Google has all the people who invented all of these underlying technologies. There's a guy named Noam who's really smart, who's already gone and done this talk about how he wants a trillion parameter model. And I'm just like, we're probably just doing duplicative research to what he's doing. He's got this decoder only transformer that's probably going to get there before we do. And it turned out the whole time that they just couldn't get critical mass. So during my year where I led the Google LM effort and I was one of the brain leads, you know, it became really clear why. At the time, there was a thing called the Brain Credit Marketplace. Everyone's assigned a credit. So if you have a credit, you get to buy end chips according to supply and demand. So if you want to go do a giant job, you had to convince like 19 or 20 of your colleagues not to do work. And if that's how it works, it's really hard to get that bottom up critical mass to go scale these things. And the team at Google were fighting valiantly, but we were able to beat them simply because we took big swings and we focused.”Cloning HGI for AGIHuman intelligence got to where it is today through evolution. Some argue that to get to AGI, we will approximate all the “FLOPs” that went into that process, an approach most famously mapped out by Ajeya Cotra's Biological Anchors report:The early days of OpenAI were very reinforcement learning-driven with the Dota project, but that's a very inefficient way for these models to re-learn everything. (Kanjun from Imbue shared similar ideas in her episode).David argues that there's a shortcut. We can bootstrap from existing intelligence.“Years ago, I had a debate with a Berkeley professor as to what will it actually take to build AGI. And his view is basically that you have to reproduce all the flops that went into evolution in order to be able to get there… I think we are ignoring the fact that you have a giant shortcut, which is you can behaviorally clone everything humans already know. And that's what we solved with LLMs!”LLMs today basically model intelligence using all (good!) written knowledge (see our Datasets 101 episode), and have now expanded to non-verbal knowledge (see our HuggingFace episode on multimodality). The SOTA self-supervised pre-training process is surprisingly data-efficient in taking large amounts of unstructured data, and approximating reasoning without overfitting.But how do you cross the gap from the LLMs of today to building the AGI we all want? This is why David & friends left to start Adept.“We believe the clearest framing of general intelligence is a system that can do anything a human can do in front of a computer. A foundation model for actions, trained to use every software tool, API, and webapp that exists, is a practical path to this ambitious goal” — ACT-1 BlogpostCritical Path: Abstraction with ReliabilityThe AGI dream is fully autonomous agents, but there are levels to autonomy that we are comfortable giving our agents, based on how reliable they are. In David's word choice, we always want higher levels of “abstractions” (aka autonomy), but our need for “reliability” is the practical limit on how high of an abstraction we can use.“The critical path for Adept is we want to build agents that can do a higher and higher level abstraction things over time, all while keeping an insanely high reliability standard. Because that's what turns us from research into something that customers want. And if you build agents with really high reliability standard, but are continuing pushing a level of abstraction, you then learn from your users how to get that next level of abstraction faster. So that's how you actually build the data flow. That's the critical path for the company. Everything we do is in service of that.”We saw how Adept thinks about different levels of abstraction at the 2023 Summit:The highest abstraction is the “AI Employee”, but we'll get there with “AI enabled employees”. Alessio recently gave a talk about the future of work with “services as software” at this week's Nvidia GTC (slides).No APIsUnlike a lot of large research labs, Adept's framing of AGI as "being able to use your computer like a human" carries with it a useful environmental constraint:“Having a human robot lets you do things that humans do without changing everything along the way. It's the same thing for software, right? If you go itemize out the number of things you want to do on your computer for which every step has an API, those numbers of workflows add up pretty close to zero. And so then many points along the way, you need the ability to actually control your computer like a human. It also lets you learn from human usage of computers as a source of training data that you don't get if you have to somehow figure out how every particular step needs to be some particular custom private API thing. And so I think this is actually the most practical path (to economic value).”This realization and conviction means that multimodal modals are the way to go. Instead of using function calling to call APIs to build agents, which is what OpenAI and most of the open LLM industry have done to date, Adept wants to “drive by vision”, (aka see the screen as a human sees it) and pinpoint where to click and type as a human does. No APIs needed, because most software don't expose APIs.Extra context for readers: You can see the DeepMind SIMA model in the same light: One system that learned to play a diverse set of games (instead of one dedicated model per game) using only pixel inputs and keyboard-and-mouse action outputs!The OpenInterpreter team is working on a “Computer API” that also does the same.To do this, Adept had to double down on a special kind of multimodality for knowledge work:“A giant thing that was really necessary is really fast multimodal models that are really good at understanding knowledge work and really good at understanding screens. And that is needs to kind of be the base for some of these agents……I think one big hangover primarily academic focus for multimodal models is most multimodal models are primarily trained on like natural images, cat and dog photos, stuff that's come out of the camera… (but) where are they going to be the most useful? They're going to be most useful in knowledge work tasks. That's where the majority of economic value is going to be. It's not in cat and dogs. And so if that's what it is, what do you need to train? I need to train on like charts, graphs, tables, invoices, PDFs, receipts, unstructured data, UIs. That's just a totally different pre-training corpus. And so Adept spent a lot of time building that.”With this context, you can now understand the full path of Adept's public releases:* ACT-1 (Sept 2022): a large Transformers model optimized for browser interactions. It has a custom rendering of the browser viewport that allows it to better understand it and take actions.* Persimmon-8B (Sept 2023): a permissive open LLM (weights and code here)* Fuyu-8B (Oct 2023): a small version of the multimodal model that powers Adept. Vanilla decoder-only transformer with no specialized image encoder, which allows it to handle input images of varying resolutions without downsampling.* Adept Experiments (Nov 2023): A public tool to build automations in the browser. This is powered by Adept's core technology but it's just a piece of their enterprise platform. They use it as a way to try various design ideas.* Fuyu Heavy (Jan 2024) - a new multimodal model designed specifically for digital agents and the world's third-most-capable multimodal model (beating Gemini Pro on MMMU, AI2D, and ChartQA), “behind only GPT4-V and Gemini Ultra, which are 10-20 times bigger”The Fuyu-8B post in particular exhibits a great number of examples on knowledge work multimodality:Why Adept is NOT a Research LabWith OpenAI now worth >$90b and Anthropic >$18b, it is tempting to conclude that the AI startup metagame is to build a large research lab, and attract the brightest minds and highest capital to build AGI. Our past guests (see the Humanloop episode) and (from Imbue) combined to ask the most challenging questions of the pod - with David/Adept's deep research pedigree from Deepmind and OpenAI, why is Adept not building more general foundation models (like Persimmon) and playing the academic benchmarks game? Why is Adept so focused on commercial agents instead?“I feel super good that we're doing foundation models in service of agents and all of the reward within Adept is flowing from “Can we make a better agent”…… I think pure play foundation model companies are just going to be pinched by how good the next couple of (Meta Llama models) are going to be… And then seeing the really big players put ridiculous amounts of compute behind just training these base foundation models, I think is going to commoditize a lot of the regular LLMs and soon regular multimodal models. So I feel really good that we're just focused on agents.”and the commercial grounding is his answer to Kanjun too (whom we also asked the inverse question to compare with Adept):“… the second reason I work at Adept is if you believe that actually having customers and a reward signal from customers lets you build AGI faster, which we really believe, then you should come here. And I think the examples for why that's true is for example, our evaluations are not academic evals. They're not simulator evals. They're like, okay, we have a customer that really needs us to do these particular things. We can do some of them. These are the ones they want us to, we can't do them at all. We've turned those into evals.. I think that's a degree of practicality that really helps.”And his customers seem pretty happy, because David didn't need to come on to do a sales pitch:David: “One of the things we haven't shared before is we're completely sold out for Q1.”Swyx: “Sold out of what?”David: “Sold out of bandwidth to onboard more customers.”Well, that's a great problem to have.Show Notes* David Luan* Dextro at Data Driven NYC (2015)* Adept* ACT-1* Persimmon-8B* Adept Experiments* Fuyu-8B* $350M Series B announcement* Amelia Wattenberger talk at AI Engineer Summit* FigureChapters* [00:00:00] Introductions* [00:01:14] Being employee #30 at OpenAI and its early days* [00:13:38] What is Adept and how do you define AGI?* [00:21:00] Adept's critical path and research directions* [00:26:23] How AI agents should interact with software and impact product development* [00:30:37] Analogies between AI agents and self-driving car development* [00:32:42] Balancing reliability, cost, speed and generality in AI agents* [00:37:30] Potential of foundation models for robotics* [00:39:22] Core research questions and reasons to work at AdeptTranscriptsAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:15]: Hey, and today we have David Luan, CEO, co-founder of Adept in the studio. Welcome.David [00:00:20]: Yeah, thanks for having me.Swyx [00:00:21]: Been a while in the works. I've met you socially at one of those VC events and you said that you were interested in coming on and glad we finally were able to make this happen.David: Yeah, happy to be part of it.Swyx: So we like to introduce the speaker and then also just like have you talk a little bit about like what's not on your LinkedIn, what people should just generally know about you. You started a company in college, which was the first sort of real time video detection classification API that was Dextro, and that was your route to getting acquired into Axon where you're a director of AI. Then you were the 30th hire at OpenAI?David [00:00:53]: Yeah, 30, 35, something around there. Something like that.Swyx [00:00:56]: So you were VP of Eng for two and a half years to two years, briefly served as tech lead of large models at Google, and then in 2022 started Adept. So that's the sort of brief CV. Is there anything else you like want to fill in the blanks or like people should know more about?David [00:01:14]: I guess a broader story was I joined OpenAI fairly early and I did that for about two and a half to three years leading engineering there. It's really funny, I think second or third day of my time at OpenAI, Greg and Ilya pulled me in a room and we're like, you know, you should take over our directs and we'll go mostly do IC work. So that was fun, just coalescing a bunch of teams out of a couple of early initiatives that had already happened. The company, the Dota effort was going pretty hard and then more broadly trying to put bigger picture direction around what we were doing with basic research. So I spent a lot of time doing that. And then I led Google's LLM efforts, but also co-led Google Brain was one of the brain leads more broadly. You know, there's been a couple of different eras of AI research, right? If we count everything before 2012 as prehistory, which people hate it when I say that, kind of had this like you and your three best friends write a research paper that changes the world period from like 2012 to 2017. And I think the game changed in 2017 and like most labs didn't realize it, but we at OpenAI really did. I think in large part helped by like Ilya's constant beating of the drum that the world would be covered in data centers. And I think-Swyx [00:02:15]: It's causally neat.David [00:02:16]: Yeah. Well, like I think we had conviction in that, but it wasn't until we started seeing results that it became clear that that was where we had to go. But also part of it as well was for OpenAI, like when I first joined, I think one of the jobs that I had to do was how do I tell a differentiated vision for who we were technically compared to, you know, hey, we're just smaller Google Brain, or like you work at OpenAI if you live in SF and don't want to commute to Mountain View or don't want to live in London, right? That's like not enough to like hang your technical identity as a company. And so what we really did was, and I spent a lot of time pushing this, is just how do we get ourselves focused on a certain class of like giant swings and bets, right? Like how do you flip the script from you just do bottom-up research to more about how do you like leave some room for that, but really make it about like, what are the big scientific outcomes that you want to show? And then you just solve them at all costs, whether or not you care about novelty and all that stuff. And that became the dominant model for a couple of years, right? And then what's changed now is I think the number one driver of AI products over the next couple of years is going to be the deep co-design and co-evolution of product and users for feedback and actual technology. And I think labs, every tool to go do that are going to do really well. And that's a big part of why I started Adept.Alessio [00:03:20]: You mentioned Dota, any memories thinking from like the switch from RL to Transformers at the time and kind of how the industry was evolving more in the LLM side and leaving behind some of the more agent simulation work?David [00:03:33]: Like zooming way out, I think agents are just absolutely the correct long-term direction, right? You just go to find what AGI is, right? You're like, Hey, like, well, first off, actually, I don't love AGI definitions that involve human replacement because I don't think that's actually how it's going to happen. Even this definition of like, Hey, AGI is something that outperforms humans at economically valuable tasks is kind of implicit view of the world about what's going to be the role of people. I think what I'm more interested in is like a definition of AGI that's oriented around like a model that can do anything a human can do on a computer. If you go think about that, which is like super tractable, then agent is just a natural consequence of that definition. And so what did all the work we did on our own stuff like that get us was it got us a really clear formulation. Like you have a goal and you want to maximize the goal, you want to maximize reward, right? And the natural LLM formulation doesn't come with that out of the box, right? I think that we as a field got a lot right by thinking about, Hey, how do we solve problems of that caliber? And then the thing we forgot is the Novo RL is like a pretty terrible way to get there quickly. Why are we rediscovering all the knowledge about the world? Years ago, I had a debate with a Berkeley professor as to what will it actually take to build AGI. And his view is basically that you have to reproduce all the flops that went into evolution in order to be able to get there. Right.Swyx [00:04:44]: The biological basis theory. Right.David [00:04:46]: So I think we are ignoring the fact that you have a giant shortcut, which is you can behavioral clone everything humans already know. And that's what we solved with LLMs. We've solved behavioral cloning, everything that humans already know. Right. So like today, maybe LLMs is like behavioral cloning every word that gets written on the internet in the future, the multimodal models are becoming more of a thing where behavioral cloning the visual world. But really, what we're just going to have is like a universal byte model, right? Where tokens of data that have high signal come in, and then all of those patterns are like learned by the model. And then you can regurgitate any combination now. Right. So text into voice out, like image into other image out or video out or whatever, like these like mappings, right? Like all just going to be learned by this universal behavioral cloner. And so I'm glad we figured that out. And I think now we're back to the era of how do we combine this with all of the lessons we learned during the RL period. That's what's going to drive progress.Swyx [00:05:35]: I'm still going to pressure you for a few more early opening stories before we turn to the ADET stuff. On your personal site, which I love, because it's really nice, like personal, you know, story context around like your history. I need to update it. It's so old. Yeah, it's so out of date. But you mentioned GPT-2. Did you overlap with GPT-1? I think you did, right?David [00:05:53]: I actually don't quite remember. I think I was joining right around- Right around then?Swyx [00:05:57]: I was right around that, yeah. Yeah. So what I remember was Alec, you know, just kind of came in and was like very obsessed with Transformers and applying them to like Reddit sentiment analysis. Yeah, sentiment, that's right. Take us through-David [00:06:09]: Sentiment neuron, all this stuff.Swyx [00:06:10]: The history of GPT as far as you know, you know, according to you. Ah, okay.David [00:06:14]: History of GPT, according to me, that's a pretty good question. So I think the real story of GPT starts at Google, of course, right? Because that's where Transformers sort of came about. However, the number one shocking thing to me was that, and this is like a consequence of the way that Google is organized, where like, again, you and your three best friends write papers, right? Okay. So zooming way out, right? I think about my job when I was a full-time research leader as a little bit of a portfolio allocator, right? So I've got really, really smart people. My job is to convince people to coalesce around a small number of really good ideas and then run them over the finish line. My job is not actually to promote a million ideas and never have critical mass. And then as the ideas start coming together and some of them start working well, my job is to nudge resources towards the things that are really working and then start disbanding some of the things that are not working, right? That muscle did not exist during my time at Google. And I think had they had it, what they would have done would be say, hey, Noam Shazir, you're a brilliant guy. You know how to scale these things up. Here's half of all of our TPUs. And then I think they would have destroyed us. He clearly wanted it too.Swyx [00:07:17]: He's talking about trillion parameter models in 2017.David [00:07:20]: Yeah. So that's the core of the GPT story, right? Which is that, and I'm jumping around historically, right? But after GPT-2, we were all really excited about GPT-2. I can tell you more stories about that. It was the last paper that I even got to really touch before everything became more about building a research org. You know, every day we were scaling up GPT-3, I would wake up and just be stressed. And I was stressed because, you know, you just look at the facts, right? Google has all this compute. Google has all the people who invented all of these underlying technologies. There's a guy named Noam who's really smart, who's already gone and done this talk about how he wants a trillion parameter model. And I'm just like, we're probably just doing duplicative research to what he's doing, right? He's got this decoder only transformer that's probably going to get there before we do. And I was like, but like, please just like let this model finish, right? And it turned out the whole time that they just couldn't get critical mass. So during my year where I led the Google LM effort and I was one of the brain leads, you know, it became really clear why, right? At the time, there was a thing called the brain credit marketplace. And did you guys know the brain credit marketplace? No, I never heard of this. Oh, so it's actually, it's a, you can ask any Googler.Swyx [00:08:23]: It's like just like a thing that, that, I mean, look like, yeah, limited resources, you got to have some kind of marketplace, right? You know, sometimes it's explicit, sometimes it isn't, you know, just political favors.David [00:08:34]: You could. And so then basically everyone's assigned a credit, right? So if you have a credit, you get to buy end chips according to supply and demand. So if you want to go do a giant job, you had to convince like 19 or 20 of your colleagues not to do work. And if that's how it works, it's really hard to get that bottom up critical mass to go scale these things. And the team at Google were fighting valiantly, but we were able to beat them simply because we took big swings and we focused. And I think, again, that's like part of the narrative of like this phase one of AI, right? Of like this modern AI era to phase two. And I think in the same way, I think phase three company is going to out execute phase two companies because of the same asymmetry of success.Swyx [00:09:12]: Yeah. I think it's underrated how much NVIDIA works with you in the early days as well. I think maybe, I think it was Jensen. I'm not sure who circulated a recent photo of him delivering the first DGX to you guys.David [00:09:24]: I think Jensen has been a complete legend and a mastermind throughout. I have so much respect for NVIDIA. It is unreal.Swyx [00:09:34]: But like with OpenAI, like kind of give their requirements, like co-design it or just work of whatever NVIDIA gave them.David [00:09:40]: So we work really closely with them. There's, I'm not sure I can share all the stories, but examples of ones that I've found particularly interesting. So Scott Gray is amazing. I really like working with him. He was on one of my teams, the supercomputing team, which Chris Berner runs and Chris Berner still does a lot of stuff in that. As a result, like we had very close ties to NVIDIA. Actually, one of my co-founders at Adept, Eric Elson, was also one of the early GPGPU people. So he and Scott and Brian Catanzaro at NVIDIA and Jonah and Ian at NVIDIA, I think all were very close. And we're all sort of part of this group of how do we push these chips to the absolute limit? And I think that kind of collaboration helped quite a bit. I think one interesting set of stuff is knowing the A100 generation, that like quad sparsity was going to be a thing. Is that something that we want to go look into, right? And figure out if that's something that we could actually use for model training. Really what it boils down to is that, and I think more and more people realize this, six years ago, people, even three years ago, people refused to accept it. This era of AI is really a story of compute. It's really the story of how do you more efficiently map actual usable model flops to compute,Swyx [00:10:38]: Is there another GPT 2, 3 story that you love to get out there that you think is underappreciated for the amount of work that people put into it?David [00:10:48]: So two interesting GPT 2 stories. One of them was I spent a good bit of time just sprinting to help Alec get the paper out. And I remember one of the most entertaining moments was we were writing the modeling section. And I'm pretty sure the modeling section was the shortest modeling section of any ML, reasonably legitimate ML paper to that moment. It was like section three model. This is a standard vanilla decoder only transformer with like these particular things, those paragraph long if I remember correctly. And both of us were just looking at the same being like, man, the OGs in the field are going to hate this. They're going to say no novelty. Why did you guys do this work? So now it's funny to look at in hindsight that it was pivotal kind of paper, but I think it was one of the early ones where we just leaned fully into all we care about is solving problems in AI and not about, hey, is there like four different really simple ideas that are cloaked in mathematical language that doesn't actually help move the field forward?Swyx [00:11:42]: Right. And it's like you innovate on maybe like data set and scaling and not so much the architecture.David [00:11:48]: We all know how it works now, right? Which is that there's a collection of really hard won knowledge that you get only by being at the frontiers of scale. And that hard won knowledge, a lot of it's not published. A lot of it is stuff that's actually not even easily reducible to what looks like a typical academic paper. But yet that's the stuff that helps differentiate one scaling program from another. You had a second one? So the second one is, there's like some details here that I probably shouldn't fully share, but hilariously enough for the last meeting we did with Microsoft before Microsoft invested in OpenAI, Sam Altman, myself and our CFO flew up to Seattle to do the final pitch meeting. And I'd been a founder before. So I always had a tremendous amount of anxiety about partner meetings, which this basically this is what it was. I had Kevin Scott and Satya and Amy Hood, and it was my job to give the technical slides about what's the path to AGI, what's our research portfolio, all of this stuff, but it was also my job to give the GPT-2 demo. We had a slightly bigger version of GPT-2 that we had just cut maybe a day or two before this flight up. And as we all know now, model behaviors you find predictable at one checkpoint are not predictable in another checkpoint. And so I'd spent all this time trying to figure out how to keep this thing on rails. I had my canned demos, but I knew I had to go turn it around over to Satya and Kevin and let them type anything in. And that just, that really kept me up all night.Swyx [00:13:06]: Nice. Yeah.Alessio [00:13:08]: I mean, that must have helped you talking about partners meeting. You raised $420 million for Adept. The last round was a $350 million Series B, so I'm sure you do great in partner meetings.Swyx [00:13:18]: Pitchers meetings. Nice.David [00:13:20]: No, that's a high compliment coming from a VC.Alessio [00:13:22]: Yeah, no, I mean, you're doing great already for us. Let's talk about Adept. And we were doing pre-prep and you mentioned that maybe a lot of people don't understand what Adept is. So usually we try and introduce the product and then have the founders fill in the blanks, but maybe let's do the reverse. Like what is Adept? Yeah.David [00:13:38]: So I think Adept is the least understood company in the broader space of foundational models plus agents. So I'll give some color and I'll explain what it is and I'll explain also why it's actually pretty different from what people would have guessed. So the goal for Adept is we basically want to build an AI agent that can do, that can basically help humans do anything a human does on a computer. And so what that really means is we want this thing to be super good at turning natural language like goal specifications right into the correct set of end steps and then also have all the correct sensors and actuators to go get that thing done for you across any software tool that you already use. And so the end vision of this is effectively like I think in a couple of years everyone's going to have access to like an AI teammate that they can delegate arbitrary tasks to and then also be able to, you know, use it as a sounding board and just be way, way, way more productive. Right. And just changes the shape of every job from something where you're mostly doing execution to something where you're mostly actually doing like these core liberal arts skills of what should I be doing and why. Right. And I find this like really exciting and motivating because I think it's actually a pretty different vision for how AGI will play out. I think systems like Adept are the most likely systems to be proto-AGIs. But I think the ways in which we are really counterintuitive to everybody is that we've actually been really quiet because we are not a developer company. We don't sell APIs. We don't sell open source models. We also don't sell bottom up products. We're not a thing that you go and click and download the extension and like we want more users signing up for that thing. We're actually an enterprise company. So what we do is we work with a range of different companies, some like late stage multi-thousand people startups, some fortune 500s, et cetera. And what we do for them is we basically give them an out of the box solution where big complex workflows that their employees do every day could be delegated to the model. And so we look a little different from other companies in that in order to go build this full agent thing, the most important thing you got to get right is reliability. So initially zooming way back when, one of the first things that DEP did was we released this demo called Act One, right? Act One was like pretty cool. It's like kind of become a hello world thing for people to show agent demos by going to Redfin and asking to buy a house somewhere because like we did that in the original Act One demo and like showed that, showed like Google Sheets, all this other stuff. Over the last like year since that has come out, there's been a lot of really cool demos and you go play with them and you realize they work 60% of the time. But since we've always been focused on how do we build an amazing enterprise product, enterprises can't use anything that isn't in the nines of reliability. And so we've actually had to go down a slightly different tech tree than what you might find in the prompt engineering sort of plays in the agent space to get that reliability. And we've decided to prioritize reliability over all else. So like one of our use cases is crazy enough that it actually ends with a physical truck being sent to a place as the result of the agent workflow. And if you're like, if that works like 60% of the time, you're just blowing money and poor truck drivers going places.Alessio [00:16:30]: Interesting. One of the, our investment teams has this idea of services as software. I'm actually giving a talk at NVIDIA GTC about this, but basically software as a service, you're wrapping user productivity in software with agents and services as software is replacing things that, you know, you would ask somebody to do and the software just does it for you. When you think about these use cases, do the users still go in and look at the agent kind of like doing the things and can intervene or like are they totally removed from them? Like the truck thing is like, does the truck just show up or are there people in the middle checking in?David [00:17:04]: I think there's two current flaws in the framing for services as software, or I think what you just said. I think that one of them is like in our experience, as we've been rolling out Adept, the people who actually do the jobs are the most excited about it because they don't go from, I do this job to, I don't do this job. They go from, I do this job for everything, including the shitty rote stuff to I'm a supervisor. And I literally like, it's pretty magical when you watch the thing being used because now it parallelizes a bunch of the things that you had to do sequentially by hand as a human. And you can just click into any one of them and be like, Hey, I want to watch the trajectory that the agent went through to go solve this. And the nice thing about agent execution as opposed to like LLM generations is that a good chunk of the time when the agent fails to execute, it doesn't give you the wrong result. It just fails to execute. And the whole trajectory is just broken and dead and the agent knows it, right? So then those are the ones that the human then goes and solves. And so then they become a troubleshooter. They work on the more challenging stuff. They get way, way more stuff done and they're really excited about it. I think the second piece of it that we've found is our strategy as a company is to always be an augmentation company. And I think one out of principle, that's something we really care about. But two, actually, if you're framing yourself as an augmentation company, you're always going to live in a world where you're solving tasks that are a little too hard for what the model can do today and still needs a human to provide oversight, provide clarifications, provide human feedback. And that's how you build a data flywheel. That's how you actually learn from the smartest humans how to solve things models can't do today. And so I actually think that being an augmentation company forces you to go develop your core AI capabilities faster than someone who's saying, ah, okay, my job is to deliver you a lights off solution for X.Alessio [00:18:42]: Yeah. It's interesting because we've seen two parts of the market. One is we have one company that does agents for SOC analysts. People just don't have them, you know, and just they cannot attract the talent to do it. And similarly, in a software development, you have Copilot, which is the augmentation product, and then you have sweep.dev and you have these products, which they just do the whole thing. I'm really curious to see how that evolves. I agree that today the reliability is so important in the enterprise that they just don't use most of them. Yeah. Yeah. No, that's cool. But it's great to hear the story because I think from the outside, people are like, oh, a dev, they do Act One, they do Persimon, they do Fuyu, they do all this stuff. Yeah, it's just the public stuff.Swyx [00:19:20]: It's just public stuff.David [00:19:21]: So one of the things we haven't shared before is we're completely sold out for Q1. And so I think...Swyx [00:19:26]: Sold out of what?David [00:19:27]: Sold out of bandwidth to go on board more customers. And so we're like working really hard to go make that less of a bottleneck, but our expectation is that I think we're going to be significantly more public about the broader product shape and the new types of customers we want to attract later this year. So I think that clarification will happen by default.Swyx [00:19:43]: Why have you become more public? You know, if the whole push has... You're sold out, you're my enterprise, but you're also clearly putting effort towards being more open or releasing more things.David [00:19:53]: I think we just flipped over that way fairly recently. That's a good question. I think it actually boils down to two things. One, I think that, frankly, a big part of it is that the public narrative is really forming around agents as being the most important thing. And I'm really glad that's happening because when we started the company in January 2022, everybody in the field knew about the agents thing from RL, but the general public had no conception of what it was. They were still hanging their narrative hat on the tree of everything's a chatbot. And so I think now one of the things that I really care about is that when people think agent, they actually think the right thing. All sorts of different things are being called agents. Chatbots are being called agents. Things that make a function call are being called agents. To me, an agent is something that you can give a goal and get an end step workflow done correctly in the minimum number of steps. And so that's a big part of why. And I think the other part is because I think it's always good for people to be more aware of Redept as they think about what the next thing they want to do in their careers. The field is quickly pivoting in a world where foundation models are looking more and more commodity. And I think a huge amount of gain is going to happen from how do you use foundation models as the well-learned behavioral cloner to go solve agents. And I think people who want to do agents research should really come to Redept.Swyx [00:21:00]: When you say agents have become more part of the public narrative, are there specific things that you point to? I'll name a few. Bill Gates in his blog post mentioning that agents are the future. I'm the guy who made OSes, and I think agents are the next thing. So Bill Gates, I'll call that out. And then maybe Sam Altman also saying that agents are the future for open AI.David [00:21:17]: I think before that even, I think there was something like the New York Times, Cade Metz wrote a New York Times piece about it. Right now, in a bit to differentiate, I'm seeing AI startups that used to just brand themselves as an AI company, but now brand themselves as an AI agent company. It's just like, it's a term I just feel like people really want.Swyx [00:21:31]: From the VC side, it's a bit mixed. Is it? As in like, I think there are a lot of VCs where like, I would not touch any agent startups because like- Why is that? Well, you tell me.Alessio [00:21:41]: I think a lot of VCs that are maybe less technical don't understand the limitations of the-Swyx [00:21:46]: No, that's not fair.Alessio [00:21:47]: No, no, no, no. I think like- You think so? No, no. I think like the, what is possible today and like what is worth investing in, you know? And I think like, I mean, people look at you and say, well, these guys are building agents. They needed 400 million to do it. So a lot of VCs are maybe like, oh, I would rather invest in something that is tacking on AI to an existing thing, which is like easier to get the market and kind of get some of the flywheel going. But I'm also surprised a lot of funders just don't want to do agents. It's not even the funding. Sometimes we look around and it's like, why is nobody doing agents for X? Wow.David [00:22:17]: That's good to know actually. I never knew that before. My sense from my limited perspective is there's a new agent company popping up every day.Swyx [00:22:24]: So maybe I'm- They are. They are. But like I have advised people to take agents off of their title because it's so diluted.David [00:22:31]: It's now so diluted.Swyx [00:22:32]: Yeah. So then it doesn't stand for anything. Yeah.David [00:22:35]: That's a really good point.Swyx [00:22:36]: So like, you know, you're a portfolio allocator. You have people know about Persimmon, people know about Fuyu and Fuyu Heavy. Can you take us through like how you think about that evolution of that and what people should think about what that means for adepts and sort of research directions? Kind of take us through the stuff you shipped recently and how people should think about the trajectory of what you're doing.David [00:22:56]: The critical path for adepts is we want to build agents that can do a higher and higher level abstraction things over time, all while keeping an insanely high reliability standard. Because that's what turns us from research into something that customers want. And if you build agents with really high reliability standard, but are continuing pushing a level of abstraction, you then learn from your users how to get that next level of abstraction faster. So that's how you actually build the data flow. That's the critical path for the company. Everything we do is in service of that. So if you go zoom way, way back to Act One days, right? Like the core thing behind Act One is can we teach large model basically how to even actuate your computer? And I think we're one of the first places to have solved that and shown it and shown the generalization that you get when you give it various different workflows and texts. But I think from there on out, we really realized was that in order to get reliability, companies just do things in various different ways. You actually want these models to be able to get a lot better at having some specification of some guardrails for what it actually should be doing. And I think in conjunction with that, a giant thing that was really necessary is really fast multimodal models that are really good at understanding knowledge work and really good at understanding screens. And that is needs to kind of be the base for some of these agents. Back then we had to do a ton of research basically on how do we actually make that possible? Well, first off, like back in forgot exactly one month to 23, like there were no multimodal models really that you could use for things like this. And so we pushed really hard on stuff like the Fuyu architecture. I think one big hangover primarily academic focus for multimodal models is most multimodal models are primarily trained on like natural images, cat and dog photos, stuff that's come out of the camera. Coco. Yeah, right. And the Coco is awesome. Like I love Coco. I love TY. Like it's really helped the field. Right. But like that's the build one thing. I actually think it's really clear today. Multimodal models are the default foundation model, right? It's just going to supplant LLMs. Like you just train a giant multimodal model. And so for that though, like where are they going to be the most useful? They're going to be most useful in knowledge work tasks. That's where the majority of economic value is going to be. It's not in cat and dogs. Right. And so if that's what it is, what do you need to train? I need to train on like charts, graphs, tables, invoices, PDFs, receipts, unstructured data, UIs. That's just a totally different pre-training corpus. And so a depth spent a lot of time building that. And so the public for use and stuff aren't trained on our actual corpus, it's trained on some other stuff. But you take a lot of that data and then you make it really fast and make it really good at things like dense OCR on screens. And then now you have the right like raw putty to go make a good agent. So that's kind of like some of the modeling side, we've kind of only announced some of that stuff. We haven't really announced much of the agent's work, but that if you put those together with the correct product form factor, and I think the product form factor also really matters. I think we're seeing, and you guys probably see this a little bit more than I do, but we're seeing like a little bit of a pushback against the tyranny of chatbots as form factor. And I think that the reason why the form factor matters is the form factor changes what data you collect in the human feedback loop. And so I think we've spent a lot of time doing full vertical integration of all these bits in order to get to where we are.Swyx [00:25:44]: Yeah. I'll plug Amelia Wattenberger's talk at our conference, where she gave a little bit of the thinking behind like what else exists other than chatbots that if you could delegate to reliable agents, you could do. I was kind of excited at Adept experiments or Adept workflows, I don't know what the official name for it is. I was like, okay, like this is something I can use, but it seems like it's just an experiment for now. It's not your product.David [00:26:06]: So you basically just use experiments as like a way to go push various ideas on the design side to some people and just be like, yeah, we'll play with it. Actually the experiments code base underpins the actual product, but it's just the code base itself is kind of like a skeleton for us to go deploy arbitrary cards on the side.Swyx [00:26:22]: Yeah.Alessio [00:26:23]: Makes sense. I was going to say, I would love to talk about the interaction layer. So you train a model to see UI, but then there's the question of how do you actually act on the UI? I think there was some rumors about open app building agents that are kind of like, they manage the end point. So the whole computer, you're more at the browser level. I read in one of your papers, you have like a different representation, kind of like you don't just take the dome and act on it. You do a lot more stuff. How do you think about the best way the models will interact with the software and like how the development of products is going to change with that in mind as more and more of the work is done by agents instead of people?David [00:26:58]: This is, there's so much surface area here and it's actually one of the things I'm really excited about. And it's funny because I've spent most of my time doing research stuff, but there's like a whole new ball game that I've been learning about and I find it really cool. So I would say the best analogy I have to why Adept is pursuing a path of being able to use your computer like a human, plus of course being able to call APIs and being able to call APIs is the easy part, like being able to use your computer like a human is a hard part. It's in the same way why people are excited about humanoid robotics, right? In a world where you had T equals infinity, right? You're probably going to have various different form factors that robots could just be in and like all the specialization. But the fact is that humans live in a human environment. So having a human robot lets you do things that humans do without changing everything along the way. It's the same thing for software, right? If you go itemize out the number of things you want to do on your computer for which every step has an API, those numbers of workflows add up pretty close to zero. And so then many points along the way, you need the ability to actually control your computer like a human. It also lets you learn from human usage of computers as a source of training data that you don't get if you have to somehow figure out how every particular step needs to be some particular custom private API thing. And so I think this is actually the most practical path. I think because it's the most practical path, I think a lot of success will come from going down this path. I kind of think about this early days of the agent interaction layer level is a little bit like, do you all remember Windows 3.1? Like those days? Okay, this might be, I might be, I might be too old for you guys on this. But back in the day, Windows 3.1, we had this transition period between pure command line, right? Being the default into this new world where the GUI is the default and then you drop into the command line for like programmer things, right? The old way was you booted your computer up, DOS booted, and then it would give you the C colon slash thing. And you typed Windows and you hit enter, and then you got put into Windows. And then the GUI kind of became a layer above the command line. The same thing is going to happen with agent interfaces is like today we'll be having the GUI is like the base layer. And then the agent just controls the current GUI layer plus APIs. And in the future, as more and more trust is built towards agents and more and more things can be done by agents, if more UIs for agents are actually generative in and of themselves, then that just becomes a standard interaction layer. And if that becomes a standard interaction layer, what changes for software is that a lot of software is going to be either systems or record or like certain customized workflow execution engines. And a lot of how you actually do stuff will be controlled at the agent layer.Alessio [00:29:19]: And you think the rabbit interface is more like it would like you're not actually seeing the app that the model interacts with. You're just saying, hey, I need to log this call on Salesforce. And you're never actually going on salesforce.com directly as the user. I can see that being a model.David [00:29:33]: I think I don't know enough about what using rabbit in real life will actually be like to comment on that particular thing. But I think the broader idea that, you know, you have a goal, right? The agent knows how to break your goal down into steps. The agent knows how to use the underlying software and systems or record to achieve that goal for you. The agent maybe presents you information in a custom way that's only relevant to your particular goal, all just really leads to a world where you don't really need to ever interface with the apps underneath unless you're a power user for some niche thing.Swyx [00:30:03]: General question. So first of all, I think like the sort of input mode conversation. I wonder if you have any analogies that you like with self-driving, because I do think like there's a little bit of how the model should perceive the world. And you know, the primary split in self-driving is LiDAR versus camera. And I feel like most agent companies that I'm tracking are all moving towards camera approach, which is like the multimodal approach, you know, multimodal vision, very heavy vision, all the Fuyu stuff that you're doing. You're focusing on that, including charts and tables. And do you find that inspiration there from like the self-driving world? That's a good question.David [00:30:37]: I think sometimes the most useful inspiration I've found from self-driving is the levels analogy. I think that's awesome. But I think that our number one goal is for agents not to look like self-driving. We want to minimize the chances that agents are sort of a thing that you just have to bang your head at for a long time to get to like two discontinuous milestones, which is basically what's happened in self-driving. We want to be living in a world where you have the data flywheel immediately, and that takes you all the way up to the top. But similarly, I mean, compared to self-driving, like two things that people really undervalue is like really easy to driving a car down highway 101 in a sunny day demo. That actually doesn't prove anything anymore. And I think the second thing is that as a non-self-driving expert, I think one of the things that we believe really strongly is that everyone undervalues the importance of really good sensors and actuators. And actually a lot of what's helped us get a lot of reliability is a really strong focus on actually why does the model not do this thing? And the non-trivial amount of time, the time the model doesn't actually do the thing is because if you're a wizard of ozzing it yourself, or if you have unreliable actuators, you can't do the thing. And so we've had to fix a lot of those problems.Swyx [00:31:43]: I was slightly surprised just because I do generally consider the way most that we see all around San Francisco as the most, I guess, real case of agents that we have in very material ways.David [00:31:55]: Oh, that's absolutely true. I think they've done an awesome job, but it has taken a long time for self-driving to mature from when it entered the consciousness and the driving down 101 on a sunny day moment happened to now. Right. So I want to see that more compressed.Swyx [00:32:07]: And I mean, you know, cruise, you know, RIP. And then one more thing on just like, just going back on this reliability thing, something I have been holding in my head that I'm curious to get your commentary on is I think there's a trade-off between reliability and generality, or I want to broaden reliability into just general like sort of production readiness and enterprise readiness scale. Because you have reliability, you also have cost, you have speed, speed is a huge emphasis for a debt. The tendency or the temptation is to reduce generality to improve reliability and to improve cost, improve speed. Do you perceive a trade-off? Do you have any insights that solve those trade-offs for you guys?David [00:32:42]: There's definitely a trade-off. If you're at the Pareto frontier, I think a lot of folks aren't actually at the Pareto frontier. I think the way you get there is basically how do you frame the fundamental agent problem in a way that just continues to benefit from data? I think one of the main ways of being able to solve that particular trade-off is you basically just want to formulate the problem such that every particular use case just looks like you collecting more data to go make that use case possible. I think that's how you really solve. Then you get into the other problems like, okay, are you overfitting on these end use cases? You're not doing a thing where you're being super prescriptive for the end steps that the model can only do, for example.Swyx [00:33:17]: Then the question becomes, do you have one house model that you can then customize for each customer and you're fine-tuning them on each customer's specific use case?David [00:33:25]: Yeah.Swyx [00:33:26]: We're not sharing that. You're not sharing that. It's tempting, but that doesn't look like AGI to me. You know what I mean? That is just you have a good base model and then you fine-tune it.David [00:33:35]: For what it's worth, I think there's two paths to a lot more capability coming out of the models that we all are training these days. I think one path is you figure out how to spend, compute, and turn it into data. In that path, I consider search, RL, all the things that we all love in this era as part of that path, like self-play, all that stuff. The second path is how do you get super competent, high intelligence demonstrations from humans? I think the right way to move forward is you kind of want to combine the two. The first one gives you maximum sample efficiency for a little second, but I think that it's going to be hard to be running at max speed towards AGI without actually solving a bit of both.Swyx [00:34:16]: You haven't talked much about synthetic data, as far as I can tell. Probably this is a bit too much of a trend right now, but any insights on using synthetic data to augment the expensive human data?David [00:34:26]: The best part about framing AGI as being able to help people do things on computers is you have an environment.Swyx [00:34:31]: Yes. So you can simulate all of it.David [00:34:35]: You can do a lot of stuff when you have an environment.Alessio [00:34:37]: We were having dinner for our one-year anniversary. Congrats. Yeah. Thank you. Raza from HumanLoop was there, and we mentioned you were coming on the pod. This is our first-Swyx [00:34:45]: So he submitted a question.Alessio [00:34:46]: Yeah, this is our first, I guess, like mailbag question. He asked, when you started GPD 4 Data and Exist, now you have a GPD 4 vision and help you building a lot of those things. How do you think about the things that are unique to you as Adept, and like going back to like the maybe research direction that you want to take the team and what you want people to come work on at Adept, versus what is maybe now become commoditized that you didn't expect everybody would have access to?David [00:35:11]: Yeah, that's a really good question. I think implicit in that question, and I wish he were tier two so he can push back on my assumption about his question, but I think implicit in that question is calculus of where does advantage accrue in the overall ML stack. And maybe part of the assumption is that advantage accrues solely to base model scaling. But I actually believe pretty strongly that the way that you really win is that you have to go build an agent stack that is much more than that of the base model itself. And so I think like that is always going to be a giant advantage of vertical integration. I think like it lets us do things like have a really, really fast base model, is really good at agent things, but is bad at cat and dog photos. It's pretty good at cat and dog photos. It's not like soda at cat and dog photos, right? So like we're allocating our capacity wisely, right? That's like one thing that you really get to do. I also think that the other thing that is pretty important now in the broader foundation modeling space is I feel despite any potential concerns about how good is agents as like a startup area, right? Like we were talking about earlier, I feel super good that we're doing foundation models in service of agents and all of the reward within Adept is flowing from can we make a better agent? Because right now I think we all see that, you know, if you're training on publicly available web data, you put in the flops and you do reasonable things, then you get decent results. And if you just double the amount of compute, then you get predictably better results. And so I think pure play foundation model companies are just going to be pinched by how good the next couple of llamas are going to be and the next what good open source thing. And then seeing the really big players put ridiculous amounts of compute behind just training these base foundation models, I think is going to commoditize a lot of the regular LLMs and soon regular multimodal models. So I feel really good that we're just focused on agents.Swyx [00:36:56]: So you don't consider yourself a pure play foundation model company?David [00:36:59]: No, because if we were a pure play foundation model company, we would be training general foundation models that do summarization and all this other...Swyx [00:37:06]: You're dedicated towards the agent. Yeah.David [00:37:09]: And our business is an agent business. We're not here to sell you tokens, right? And I think like selling tokens, unless there's like a...Swyx [00:37:14]: Not here to sell you tokens. I love it.David [00:37:16]: It's like if you have a particular area of specialty, right? Then you won't get caught in the fact that everyone's just scaling to ridiculous levels of compute. But if you don't have a specialty, I find that, I think it's going to be a little tougher.Swyx [00:37:27]: Interesting. Are you interested in robotics at all? Just a...David [00:37:30]: I'm personally fascinated by robotics. I've always loved robotics.Swyx [00:37:33]: Embodied agents as a business, you know, Figure is like a big, also sort of open AI affiliated company that raises a lot of money.David [00:37:39]: I think it's cool. I think, I mean, I don't know exactly what they're doing, but...Swyx [00:37:44]: Robots. Yeah.David [00:37:46]: Well, I mean, that's a...Swyx [00:37:47]: Yeah. What question would you ask? If we had them on, what would you ask them?David [00:37:50]: Oh, I just want to understand what their overall strategy is going to be between now and when there's reliable stuff to be deployed. But honestly, I just don't know enough about it.Swyx [00:37:57]: And if I told you, hey, fire your entire warehouse workforce and, you know, put robots in there, isn't that a strategy? Oh yeah.David [00:38:04]: Yeah. Sorry. I'm not questioning whether they're doing smart things. I genuinely don't know what they're doing as much, but I think there's two things. One, I'm so excited for someone to train a foundation model of robots. It's just, I think it's just going to work. Like I will die on this hill, but I mean, like again, this whole time, like we've been on this podcast, we're just going to continually saying these models are basically behavioral cloners. Right. So let's go behavioral clone all this like robot behavior. Right. And then you figure out everything else you have to do in order to teach you how to solve a new problem. That's going to work. I'm super stoked for that. I think unlike what we're doing with helping humans with knowledge work, it just sounds like a more zero sum job replacement play. Right. And I'm personally less excited about that.Alessio [00:38:46]: We had a Ken June from InBoo on the podcast. We asked her why people should go work there and not at Adept.Swyx [00:38:52]: Oh, that's so funny.Alessio [00:38:54]: Well, she said, you know, there's space for everybody in this market. We're all doing interesting work. And she said, they're really excited about building an operating system for agent. And for her, the biggest research thing was like getting models, better reasoning and planning for these agents. The reverse question to you, you know, why should people be excited to come work at Adept instead of InBoo? And maybe what are like the core research questions that people should be passionate about to have fun at Adept? Yeah.David [00:39:22]: First off, I think that I'm sure you guys believe this too. The AI space to the extent there's an AI space and the AI agent space are both exactly as she likely said, I think colossal opportunities and people are just going to end up winning in different areas and a lot of companies are going to do well. So I really don't feel that zero something at all. I would say to like change the zero sum framing is why should you be at Adept? I think there's two huge reasons to be at Adept. I think one of them is everything we do is in the service of like useful agents. We're not a research lab. We do a lot of research in service of that goal, but we don't think about ourselves as like a classic research lab at all. And I think the second reason I work at Adept is if you believe that actually having customers and a reward signal from customers lets you build a GI faster, which we really believe, then you should come here. And I think the examples for why that's true is for example, our evaluations, they're not academic evals. They're not simulator evals. They're like, okay, we have a customer that really needs us to do these particular things. We can do some of them. These are the ones they want us to, we can't do them at all. We've turned those into evals, solve it, right? I think that's really cool. Like everybody knows a lot of these evals are like pretty saturated and the new ones that even are not saturated. You look at someone and you're like, is this actually useful? Right? I think that's a degree of practicality that really helps. Like we're equally excited about the same problems around reasoning and planning and generalization and all of this stuff. They're very grounded in actual needs right now, which is really cool.Swyx [00:40:45]: Yeah. This has been a wonderful dive. You know, I wish we had more time, but I would just leave it kind of open to you. I think you have broad thoughts, you know, just about

Giving computers a voice has always been at the center of sci-fi movies; “I'm sorry Dave, I'm afraid I can't do that” wouldn't hit as hard if it just appeared on screen as a terminal output, after all. The first electronic speech synthesizer, the Voder, was built at Bell Labs 85 years ago (1939!), and it's…. something:We will not cover the history of Text To Speech (TTS), but the evolution of the underlying architecture has generally been Formant Synthesis → Concatenative Synthesis → Neural Networks. Nowadays, state of the art TTS is just one API call away with models like Eleven Labs and OpenAI's TTS, or products like Descript. Latency is minimal, they have very good intonation, and can mimic a variety of accents. You can hack together your own voice AI therapist in a day!But once you have a computer that can communicate via voice, what comes next? Singing

Cette semaine : Skull and Bones, Suicide Squad: Kill The Justice League, Rainbow Six: SMOL, Banishers: Ghosts of New Eden, Balatro, Last Epoch, DLC Elden Ring annoncé, Capcom Pro Tour, Joey Valence & Brae - Punk Tactics, Constellation, et l'histoire de Faker. Lisez plutôt Torréfaction #286 : Balatro, Banishers: Ghosts of New Eden, Last Epoch, R6 SMOL, Skull and Bones et de la culture qu'elle est bonne avec sa vraie mise en page sur Geekzone. Pensez à vos rétines.

My guests today are Draper and Facu, co-founders of Smol and contributors to Yearn and Ajna. Smol is a set of practical EVM tools born out of Draper and Facu's experience building frontends and marketing material for Yearn Finance. On this episode, Facu and Draper walk us through the variety of tools and projects they've worked on together, and give us insights into how DeFi protocols do successful go to market. We also get a sneak peek at upcoming projects like Smol v2, and a new interface to Yearn Finance. It was great learning about dapp development and GTM from Facu and Draper, and to hear more about their unique collaborative relationship. I hope you enjoy the show. This episode is brought to you by Speedrun Ethereum. Speedrun Ethereum is a hands-on crash course in Web3 development that'll teach you how to build an NFT contract, a DEX, a multisig wallet, and more, all through hands-on step-by-step project based learning. If you've always wanted to get more technical, or to expand beyond your comfort zone in Web3 dev, Speedrun Ethereum is the place to go. Get started at speedrunethereum.com As always, this show is provided as entertainment and does not constitute legal, financial, or tax advice or any form of endorsement or suggestion. Crypto has risks and you alone are responsible for doing your research and making your own decisions. Links Hosted by @nicholas Sponsored by Speedrun Ethereum Smol Yearn Ajna

Welcome back to another episode of Let's Go Hunt! – the everyman's hunting and outdoor podcast.. We aren't experts, but we're out there doing it. Join us on our journey!  As always we have: Mike Gonçalves, chef of merginal quality Vince Hall, who is getting loaded but not in the good Las Vegas way Sam […] The post Let's Go Hunt 065 – Washyosister Sauce: Athlon BTR Gen 2 2-12×42 scopes, merginal meals, and smol boats. appeared first on Firearms Radio Network.

Our first ever demo day aimed for 15-20 people and ended up ballooning to >200 and covered in the news. We are now running the 2024 edition in SF on Feb 23: Latent Space Final Frontiers, a startup and research competition in “The Autonomous Workforce”, ​”Beyond Transformers & GPUs”, and “​Embodied AI”. RSVP here! You can find all LS online/IRL events on our new calendar. Super Early Bird tickets have just gone on sale for AI Engineer World's Fair, June 25-27!Today we have the honor of hosting two of Together AI's co-founders: Ce Zhang (CTO) and Vipul Ved Prakash (CEO). This is a rare opportunity to recap the history of the company since our last check-in with Tri Dao (Chief Scientist), some of their big releases, and do a deep dive into the state of the AI inference market. Together has emerged as one of the most consequential new startups in the new AI summer, last announcing a ~$100m Series A raise in November (at a ~$360-565m valuation). But there are at least three Togethers - Together the Research Lab, Together the Fine Tuning & Inference platform, and Together the custom models service. As we clarify on the pod, the overarching philosophy of Together is the ability to improve on all these fronts simultaneously by being “full stack”, from the lowest level kernel and systems programming to the highest level mathematical abstractions driving new model architectures and inference algorithms.Bringing Research and Industry TogetherIn just one year, Together has been behind some of the most exciting research in AI:* RedPajama, a fully open source dataset for model pre-training which mirrored the Llama1 recipe. Then followed by RedPajama2, a 30T tokens dataset of filtered and de-duplicated tokens. * RedPajama-INCITE-3B and 7B, which were SOTA in a few benchmarks at the time of release. * FlashAttention-2, developed by Together's Chief Scientist Tri Dao. We covered FA-2 in a previous episode with him.* Mamba-3B, the most promising transformer-alternative model that they released in collaboration with Cartesia. * StripedHyena, a SOTA graft of Hyena state space models and transformer models together* Medusa, an alternative to speculative decoding that lets you use multiple decoding heads instead of a draft model. * MonarchMixer, which was one of the most popular orals at NeurIPS 2023. It's an approach to transformers that replaces many of its core parts with Monarch matrices for better computational efficiency. And I'm sure we missed something! As Vipul reveals, almost 50% of Together staff is researchers, and two of their co-founders (Chris Ré and Percy Liang) are professors at Stanford, so we can expect a lot more here.Bringing “Disaggregated” GPUs TogetherOn their cloud, they offer inference as a service, fine-tuning, pre-training, etc, but unlike other providers they think of themselves as a disaggregated cloud. Today, they have ~8,000 A100 and H100 GPUs on their platform (an exclusive revealed on the pod!) totaling over 20 exaflops of compute, but instead of just buying more and putting them in a cluster and then exposing a `us-east-1` option for customers, they are taking heterogenous compute sources and adding a unified layer on top of it for developers to consume. Building on Ce's research, Together's GPU Clusters are taking on comparable AWS and GCP offerings in both cost and speed:Take the Hessian AI center in Germany or the DoE's INCITE; they have GPUs that they want to share with researchers, but they lack the cloud layer over it. Similarly, there's starting to be more and more differentiation amongst types of GPUs: H100s, A100s, MI3000s, etc. Each of them has different availability and performance based on task, and the end user shouldn't have to be an hardware expert to run inference on a model, so Together abstracts a lot of that away.A big theme of the Together inference stack, a “bag of 50 tricks” that we discuss on the pod, is also “hardware-aware” algorithms like FlashAttention and Mamba, which further emphasize the benefits of co-developing everything together:Special Focus: Transformer AlternativesAs we mentioned above, they are also funding a lot of research in Transformer alternatives. To reiterate a few points on why they matter:* Longer context is not the motivation for sub-quadratic architectures: Transformers don't inherently have hard limitations on context size, but they just get extremely expensive. When developing sub-quadratic alternatives, you easily enable very long context, but that's now how you should compare them. Even at same context size, inference and training is much cheaper on sub-quadratic architectures like Hyena.* Emergence of hybrid architectures: a lot of early conversations have been around the “post-Transformers” era, but it might be more like “half-Transformers”. Hybrid architectures could have split layers with some transformer-based and some state-space ones. One of the challenges is that a lot of hardware kernels are optimized for transformer operations, so you'd lose a lot by moving away completely.* Higher speed = higher GPU throughput: if we could reach the same benchmark performance on subquadratic architectures, it'd solve a lot of the GPU crunch. Today we peak at ~170 tok/s on inference in some open models; if we could reach 5,000 tok/s on the same card, you'd be able to serve 30x more customers on the same hardware. As a cloud provider, you're obviously incentivized to get there.We had a lot of fun chatting with the Together guys and we covered a lot of ground, so enjoy the conversation!Note: This is the first episode of a “cloud providers mini-series”. We have Erik from Modal and Ben from Replicate coming up next!Video PodcastJoin us to watching the video version of this pod on our snazzy YouTube!Show Notes* Together AI* RedPajama Dataset v1 Announcement* RedPajama Models v1 Announcement* Together Embeddings* StripedHyena-7B* Mamba-3B-SlimPJ* Vipul's X thread on Anyscale* Vipul's Razor* SemiAnalysis' "Inference Race to the Bottom" post* Chris Ré* Mike Conover's episode* Slim Pajama by Cerebras* Dolma by AI2* Jina AI* Tengyu's Voyage AITimestamps* [00:00:00] Introductions* [00:00:43] Origin and current state of Together.ai* [00:02:15] Transition from Apple to Together and the vision for open AI* [00:04:54] How Chris Ré introduced Ce and Vipul* [00:08:43] How RedPajama came to be* [00:13:34] Model training and Transformer alternatives* [00:15:37] DSIR and the importance of data in LLMs* [00:21:19] Inference vs Fine-tuning vs Pre-training usage on Together* [00:23:20] Together's GPU stash* [00:27:02] Why standardization of inference metrics is important* [00:29:26] Building moats in AI inference* [00:31:49] Federated vs disaggregated cloud computing* [00:34:57] Opportunities for improvement in the inference stack* [00:36:13] Anyscale benchmarking drama* [00:41:27] Not just an inference platform* [00:43:50] Together Embeddings and the future of embedding models* [00:45:53] State space models and hybrid architectures* [00:53:52] The need for 5,000 tokens/s speed in AI inference* [01:00:23] What's the most interesting unsolved question in AI?TranscriptAlessio [00:00:00]: Hey, everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:14]: Hey, and today we're together with Together. Welcome to the studio, guys.Ce / Vipul [00:00:20]: Thank you.Swyx [00:00:21]: I don't know how you typically give self intros, but does anyone want to go first? How do we get our audience acquainted, especially to who's speaking, because it's unusual for us to do a four-person pod. Yeah.Ce [00:00:33]: Hi, everyone. I'm Ce. I'm one of the co-founders of Together and the CTO, working with the team on technical things.Vipul [00:00:40]: I'm Vipul Ved Prakash, co-founder and CEO of Together.Swyx [00:00:43]: I always consider you guys as one of the sort of all-in-one companies. I always want to say labs, but I feel like you're not a lab. What is the sort of origin of Together, and then what is it today? I feel like it used to be Together.xyz, and then now you're Together.ai.Vipul [00:01:00]: I think fundamentally, Together is about open and independent AI systems. We think this is one of the most consequential technologies of our time, and when we started the company in June 2022, our focus was to build a platform for open source, independent, user-owned AI systems. One way to think about it is big labs, frontier model labs, have built their own platforms for developer platforms for their models. We think of Together as a platform for everything else, whether these are open models, whether these are models being built by companies that are owned by them. Our sort of XYZ roots, we have a fairly deep decentralization and open ethos that kind of reflects in all our platform and strategy and business. And we also, the way we structure our cloud is by combining data centers around the world instead of, you know, we are today not located in hyperscalers, we have built a footprint of AI supercomputers in this sort of very disaggregated, decentralized manner.Alessio [00:02:15]: I know before Together, you were at Apple, so you go from like the most walled garden, private, we don't say anything company, to we want everything to be open and everybody to know somebody. What maybe did you learn from like the Apple way of being super close and polished and maybe what are you taking now to Together to make it open, but also a very nice developer experience?Vipul [00:02:37]: Yeah, I would say, you know, one sort of my, you know, background has been in open source for a long time. One of the first things I created was a collaborative spam filter, you know, this was back in the day. It's called Vipul's Razor. And it became quite popular. And the first company I founded called CloudMark was built around, you know, taking open source and building both an open side of it and a commercial product around it. I think Apple is sort of very focused on providing this amazing experience to its customers with, you know, most of the technology sort of hidden behind the product. And certainly the focus on fluidity and applying complex technology to make everyday things simple is something that Apple does really well. And, you know, that's been a sort of big part of how we think about our developer platforms. I think it informs it. The other thing is that during my years at Apple, we, you know, worked a lot on deep learning. And one of the things that was sort of very viscerally accessible to me was how well these systems worked. We, you know, we built an open domain Q&A system. This was based on Facebook's LSTM paper in 2016. And it was remarkable because we had a parallel system based on sort of information retrieval techniques, which is extremely complicated, didn't work that well. And you know, this thing we wrote in a week was just incredible performance. So I think some of those experiences, at least for me personally, sort of were creating this roadmap of how important and powerful this technology is. And you know, when the scaling loss paper was published, I was very clear, like it was in some ways something very profound. We've never had algorithms that improve in capabilities with scale out. So this is almost a new era of computing. So that's been, I think, the influence of Apple, my years at Apple, really for me, like crystallized the value of what we are doing together.Alessio [00:04:54]: And how did you decide to join forces? Because you did a postdoc with Chris Ré at Stanford. You know, we already had Tri Dao from Together and we talked about Hazy. What was like the meeting of the mind of, hey, I come from like the more technical postdoc assistant professor background and we've got yet a more product thing. What got you excited to like build this now?Ce [00:05:15]: So we have been working on this together, Chris, in the essentially last like 10 years, right? So it was like a machine learning system 10 years ago was like Power BI's graphic model, right? And then convolutional neural network and then all the foundation model that we see today. But if you look at this, I think that fundamentally the thing we are actually optimizing is actually not that different. It's always about data movement across essentially all the stacks, right? So when you do distributed like computing, it's about communication across different machines. When you do, for example, flash attention, it's about data movement at a different essentially memory hierarchy, right? So we have been doing this in the last 10 years and seeing the field start grow, grow, grow. So we kind of feel the current kind of this like wave of technology is actually the perfect time to actually bring all the research essentially into something real. And we are super lucky that we got introduced to Weibo, right? And then we hope to join forces and bring this to real world.Swyx [00:06:10]: It's an unusual team of like sort of research and industry. Like you've been like a third or fourth time founder now. Third time founder, yeah. And so like what is your first order of business when you like set up together? Like how do you sort of put something like this together? Oh my God, I'm going to use this word so much.Vipul [00:06:27]: I feel AI companies are really kind of driven by research. And Chris and I had been talking about how to reduce the cost of building models. We felt that there aren't really big data modes around foundation models. They are built from a subset of the web. What is difficult is the cost of capital to build these. And one of the ways in which you can reduce this cost is by making more efficient systems. With that, it was really about finding the right set of co-founders and team. In fact, when Chris introduced me to Ce, and I think within the first five minutes of talking to Ce, I was like, we are starting this company. And our early focus was thinking about this more sort of disparate set of resources, you know, GPUs around the internet. Can we use those to build? And we really have to compress communication for, you know, when we do gradient averaging, there's just a lot of traffic. And if you can reduce that somehow, you sort of open up the possibility of using cheaper compute, you know, across the network. And Ce's research for a decade has been in that subject. You know, and from there, finding, you know, other folks in the network, I think there is generally a lot of excitement and philosophical alignment around what we are doing, which, you know, we publish papers, we publish open source libraries and code, we build open models. And I think the people in academia in, you know, machine learning and NLP, that's really what they want to do. So I think that's been really a kind of kernel for, you know, composition of the company. And we're lucky to have, you know, at this point, attracted some of the best researchers in the field. So I think that's the most important thing. And, you know, the rest of it is sort of driven by us. A couple of these philosophies around independent systems and decentralization and good developer interfaces, you want to make it accessible. That's, you know, just as important. And the rest follows from there, I think.Alessio [00:08:43]: I want to try and fill in some of the blanks in the history of Together. I think people come on your website today and they say, you raised a hundred million dollars Series A. They're like, wow, these guys are like super legit company. But it feels like Red Pajama just came out a year ago. I remember we had Mike Conover in the studio, who had built Dolly at Databricks. And you announced it literally the morning we were recording. So we're like in the studio on our phones, looking at it. And it's like, wow, this is like the first time now there's like a good curated dataset to do open pre-training. So maybe let's start from there. Like, what was the motivation behind it? Why did you decide to do that? It's, datasets are one of the things that most people don't want to work on. They just want to do models, not datasets.Ce [00:09:27]: Yeah. So, yeah, first one is not the first, right? So I think it's actually built on a whole bunch of amazing effort the community already have. For example, Eleuther have the pile, right? There's a whole bunch of amazing datasets they have, like C4, right, from Google, right? So I think really get inspired by the impact those like datasets have on the community, right? So I think when we did Red Pajama, it was a time that people are really fascinated by Lama, the model, like Lama 1, right? Which I feel like decades ago, right? But it's kind of, people are really excited about the quality, right? So that's really like a big shift in people how to think about open model. People start to see hope, right? So, but the one problem of Lama is the data recipe is being described in a pretty detailed way in the paper, but the data is actually not there. So, and our original thinking is how about we take the recipe and we try to do our best effort reproduction and try to put it out, such that we can learn from our mistakes in the reproduction together, right? So that's essentially the original thinking behind Red Pajama. And we have been pretty happy and excited about what community have been kind of build on it. For example, there's a dataset called Slim Pajama, right? Which do deduplication over our data, right?Swyx [00:10:38]: From Cerebras, did they talk to you before?Ce [00:10:39]: Oh, yeah, yeah, yeah, yeah. So, yeah, so we are very good friends so we can discuss about technical perspective. We are pretty excited because I think it's kind of why we do Red Pajama in the first place is that people can actually build not only models, but also datasets essentially over that piece of artifact, right? So that's actually what inspired us to do the first version of Red Pajama dataset.Swyx [00:11:01]: Yeah, and then you released V2 maybe two months ago.Ce [00:11:04]: Yeah.Swyx [00:11:05]: 30 trillion tokens.Ce [00:11:06]: Yeah, 30 trillion tokens. So I think what's exciting about Red Pajama V2 is not only the number of tokens, but we start to kind of learn from Red Pajama V1. So one thing that we learned was that data quality is really the core, right? So you want to take this couple trillion token dataset and try to bring them down maybe to one trillion or two trillion, right? The way that you actually filter them, deduplicate them is not something that kind of pre-decided before you see the application, right? So you kind of want to have a modular framework to think about data quality, right? So like given application, let's automatically or maybe semi-automatically try to come up with a way to filter it down. So that's why in Red Pajama V2, we kind of overlay the dataset with like 40 different pre-computed quality signal, right? If you want to reproduce your best effort, like C4 filter, it's kind of like 20 lines of code, right? And this open up this opportunity you can actually put different filter together, learn the combination of filter. We are very excited to see what community actually come up with using Red Pajama V2.Swyx [00:12:11]: It was retrospectively so obvious that this is a good idea that I wonder how come more datasets don't do this. You release the dataset with all these toggles that you can turn on and off, right? And you can sort of tune up and down the quality in ways that you believe is important to you. Yeah, I just, it makes so much sense now in retrospect. Because everyone just publishes like their pipeline and then the end result. But what about all the intermediate stages? Yeah.Ce [00:12:35]: Yeah, so I think, so there are multiple things there. I don't think we are the only one like doing that. For example, like Doma from AI2, right? They have this very flexible format to actually put in those quality signals, right? Think like, we are actually calling them some, right? So you can actually load Red Pajama using their tool. That whole thing should work, right? So I think one fundamental thing that changed in the last year, essentially, in the beginning when people think about data, it's always like a byproduct of the model, right? You release the model, you also release the data, right? The data side is there essentially to show people, ah, if you train on this data, you'll get a good model. But I think what started to change is when people started building more and more of those models, people started to realize like different subset of data side is kind of valuable for different applications, right? The data becomes something to play with, right? So I think we are kind of lucky that we happen to release Red Pajama right at that point that we get this opportunity to actually learn from that.Alessio [00:13:34]: And you guys have a custom model training platform on Together 2. You have a bunch of stuff in there for data selection, like the DSIR and things like that. How did you decide to work on that versus, because you first started with like some of the fine tunes on LLAMA. Do you see a lot of interest there? And I know you've been doing a lot of research on state space models and other transformer alternatives. Like, do you also see that as something you'll keep working on this year and push more people towards?Vipul [00:14:02]: Yeah, I mean, we, you know, we think of how to make training more efficient and building models more efficient. Part of that is being able to select the right dataset. This is why you have signals, DSIR. You can start with a small dataset and find similar documents, build models with that. So we think it's an important part of the kind of model build tooling that, you know, sort of widely useful for people building different kinds of models. Similarly, you know, we are running into the limits of how fast you can make transformers. And we want inference at 5,000 tokens per second. I don't think we will get there with transformers and we need to learn longer sequences. Data, again, becomes very, very expensive with transformers. So I work on space state models and all the research that we are doing there. And hopefully other labs will pick up on this and make it a kind of important target for optimization. But we think that, you know, open source is a great place for this. We can provide these recipes for data and for training to our customers who are building, you know, custom models themselves. And, you know, we are quite excited about the sort of progress we are seeing there.Alessio [00:15:18]: Do you have some of these models available for inference on Together? Can people play around with a strictly, you know?Swyx [00:15:25]: Yeah.Vipul [00:15:25]: Yeah, they're available for inference on our serverless platform.Swyx [00:15:29]: I always try to be the person who asks about acronyms in case, you know, people want to understand. Should we explain importance resampling, you know, that kind of stuff?Ce [00:15:37]: Oh, yeah. So DSIR essentially, it's a fundamental idea. So it's one of the paper from Percy, right? So essentially, if you know what you are doing, you can actually use that as a very strong signal about what data to put in to insert training process, right? So that's essentially the fundamental idea, right? So, and then more concretely, right? So there are actually different versions of DSIR, right? So one version is like if you have a validation site, right? You can actually somehow measure the similarity between the validation site and also your pre-trained corpus and essentially subset, like the subset. And often there's actually like less targeted version of DSIR where you'll say, yeah, maybe Wikipedia is actually a very good corpus. Let's try to find more Wikipedia, right? And you can think about it in two ways, either as a way to come up with different weights for different data slices. Yeah, so as like filter type of step. Yeah, for a data set, or think about that as like data augmentation. So that's how, yeah, that's how we think about DSIR.Swyx [00:16:33]: That makes sense. I will have to read the paper to understand a little bit more. Because when you say things like, we have to know in advance what we were trying to do with the model, then we do importance resampling. That is against the principle of general intelligence, right? Like the point is to train AGI.Ce [00:16:48]: Yeah, so it depends on what do you mean by being general or generic, right? So I think, I mean, you can always take a meta-learning perspective that we know the distribution of tasks that we care about, right? So you can always go kind of up in the ladder of how general the whole thing is, right? But also for many of the customers that we are actually talking to, right, they have kind of very targeted application, right? The benefit you can get out of that is you could build a better open model, often smaller, often easier to do inference, if you know what you want, right? So I think the whole trade-off would be, and the x-axis would be how generic the whole thing will be. The y-axis would be not only the top accuracy, but also a whole bunch of the deployment cost, right? The size of the model, right? The robustness of the model. So I think different people will navigate the space in different way. And we want to be the platform, essentially, whatever point that you want, we have a solution for you.Swyx [00:17:43]: One more thing on data before we go deeper on state-space models. Are we running out of data? Can we go in order of magnitude? Can we go five orders of magnitude? How do both of you think about how much data we have and how much we need?Ce [00:17:55]: Yeah, so I think that's a very, very good question. So I don't think we are running out of data on Earth.Swyx [00:18:02]: Right, so think about it globally. Training data, training class data.Ce [00:18:05]: Yeah, yeah, so I think, I mean, some of them are not accessible, right? But I do think there are many organizations in the world have enough data to actually train very, very good models, right? So, I mean, they are not publicly available, right? But there are people who actually have access to those, right? So I think in general, right? So if you think about the data in the open space, right? So I guess that was specifically that you actually mean whether we are running out of data. I do think there need to be some way, right? That people who are training open models get connected with essentially data that's not internet data. So I think that channel need to be opened up for the open model to get more data, right? But I'm kind of on the optimistic side that the society will figure out a way that we can train open models that's beyond this internet data.Swyx [00:18:57]: Beyond internet, meaning books?Ce [00:19:00]: I mean, there are a lot of those, right?Swyx [00:19:02]: Books, right?Ce [00:19:02]: Transcripts, right? Videos, audios, right? So there are a whole bunch of data sources that we are not integrating into open data side, right? So, and maybe they shouldn't be open, right? So I think the community need to figure out a way, yeah, like the best balance, yeah? Such that we can have open models, but on the other hand, also have a reasonable collection of data that we can actually use.Swyx [00:19:29]: I think a lot of people think that, there's a theory that Whisper was released so that you could transcribe YouTube and then use that as a source of tokens. Then I talked to other researchers who are like, you know, YouTube has very low quality tokens. You know, do you want your model to talk like a live streamer from YouTube? Because that's what they're going to do. So it's not clear, like what the quality of this data could be.Ce [00:19:53]: Yeah, I guess that depends on your application, right? So I think as a platform, right? So our goal is whatever application that you have, yeah, so we have a platform that you can actually achieve your goal, right? So there are definitely applications that kind of make sense to speak like YouTube, right? So, but there are probably also other application that kind of more on the formal side, right? So I think there are going to be a diverse collection of models, both open and closed, right? So, and we kind of want to be the engine that powers that.Swyx [00:20:21]: There's a lot of people who own data sources who are doing the locally optimal thing and humanity as a whole is losing out. So like New York Times is swinging open AI, you know, Stack Overflow shut down their API, Reddit shut down their API, X, you know, made their own model, right? On Twitter data. We're just going to have all these like tiny little gardens of data that it would be useful in a general model, but everyone's just trying to make their own model. And it seems like globally suboptimal.Vipul [00:20:47]: I think you need to have some kind of a marketplace for figuring out how to get this, you know, data into models and have, I think we'll increasingly see more of that. You know, I think there's a positive aspect to it too. There is a incentive for creators to participate in a system, which is sort of more fair relative to, you know, the capture of value by an AI company that's taking their data. But I agree. I think this is a big open problem that needs to be solved. And I hope there will be, you know, serious efforts around it.Alessio [00:21:19]: Let's talk about the most precious resource on planet earth, GPUs. You have a lot of compute obviously, but you also have a lot of product pieces. You have inference, you have fine tuning, you have pre-training. What's the split in terms of usage? Do you see most people are just running inference on off the shelf models? Do you see maybe some last mile fine tuning?Vipul [00:21:40]: I would say right now, the top five models on our inference stack are probably all fine-tuned versions of open models. And we've seen- Who fine-tuned them?Swyx [00:21:51]: You fine-tuned them?Vipul [00:21:52]: They were fine-tuned by our customers.Swyx [00:21:54]: By your customers.Vipul [00:21:55]: You know, either on our platform or off our platform. And we are generally seeing that, you know, that is the sort of trend where you can get better quality on your task by sort of now easily adapting these models to your data. We also have, I would say, over 20 big model builds happening on the platform, which are customer. We see a lot of training and it's also somewhat surprisingly a more continuous kind of workload. We sort of imagine that this would be more episodic. You train a model and then you do inference. But what we find is, you know, we train a model and then they train the next version and then the next version, which sort of grows in scale. I would say training is still the bigger portion. Some ways inference is super linear to model quality. And as the models are getting better, there's more and more inference.Swyx [00:22:48]: Oh, because they're more useful. Yeah, they're more useful, yeah. So, okay, so training is bigger. This is actually consistent with what we've heard from Mosaic, that, you know, people think that training is sort of like a one-time deal. You do one big run and then you're done. It's never true. And so I'm interested in, like, putting some numbers and I don't know what you have disclosed or what you want to disclose, but, like, how many GPUs do you have? What is the equivalent amount of compute that you have? Because I understand that your GPU setup is different than what people typically think of, like, a giant data center somewhere, right?Vipul [00:23:20]: I don't think we have shared this number publicly. It's, you know, so this will be the first time, I guess. Like, we have close to 7,000 to 8,000 GPUs today. It's growing monthly.Swyx [00:23:31]: What class of GPU are they?Vipul [00:23:32]: They're mostly A100s and H100s.Swyx [00:23:35]: Okay.Vipul [00:23:36]: And probably more, I think, split towards H100s now. You know, we'll be sort of building this best-of-class hardware. So as there are other versions of these coming out later this year, we plan to have those in the fleet as well.Alessio [00:23:53]: I know when we talked last year, you were also using some of the supercomputers by the Department of Energy. There was kind of like a lot of random GPU compute in the world. Have you seen that kind of getting timed out? I think maybe a year ago, people were like, oh, yeah, you can use this GPU computer that is going to be end-of-life. Has the bar changed to give access to those resources?Ce [00:24:13]: From our perspective, it's actually getting better. Yeah, so from the community perspective, because many of the institutions in the world, they're actually investing in hardware, right? So for example, we are working with one of the institutes in Germany called Hessian AI, right, which gives us a lot of help on the compute side. So they start to have this very big GPU cluster, and they're actually sharing that with the community, right? And it's not super big, right, but also not a small one, right? So you start to see this, like, different lives that start to pop up, right? And because of the power of the community, they start to actually share that. So we actually find as a researcher today, it's probably easier for them to actually get a GPU than last year.Swyx [00:24:56]: Interesting.Alessio [00:24:56]: And then for you to buy them, what's the state of the market right now? Is it still extremely hard to get any? Do you have Jensen's phone number? Do you have like GM phone number? Do you guys get like the SDR because you're like under 10,000?Vipul [00:25:12]: NVIDIA is obviously motivated to help us, both as an investor and we are their customers. I would say the market is very tight still, and it's likely going to be this way for a while, is my sense that the demand for AI computing is just kind of ramped up very, very quickly, and it will take a while for supply to catch up.Swyx [00:25:37]: So how tight it is, and let's say compared to like a year ago, two years ago, what do you mean when you say tight? The things you want, you can't get?Vipul [00:25:42]: You can't get them immediately. They're sort of, you know, minimally like two to three months out. Any inventory that shows up tends to clear very, very rapidly. And, you know, we obviously sort of look at this in a very detailed and analytic. There is four to 5 million GPUs that will be sold this year from NVIDIA and others buying. And if you think about 512 to 1,000 GPU cluster for a company, that's 4,000 to 8,000 companies, right? So it's in some ways a very small number. In other ways, the cost of GPUs will be, you know, 80 to $100 billion, and then you layer servers and data center space and electricity on top of that, and that's, you know, close to $250 billion worth of kind of compute, which when you compare it to the cloud computing of today, you know, AWS's last year was $88 billion in revenue. So this is really kind of a build-out happening of AI hyperscalers. It is much more disaggregated, and it's very, very global. So, you know, we think that GPUs are going to be sort of a precious resource for a long time, and using them optimally is very valuable.Swyx [00:27:02]: Yeah.Alessio [00:27:02]: Our friend, Dylan Patel from Semianalysis, he wrote a post about the inference market recently and obviously mentioned you guys. In his post, he said, our model indicates that Together is better off using two A180 gig system rather than a H100-based system. The temperature and performance testing also point to Together utilizing speculative decoding. Any thoughts? Is Dylan right? I don't know, what's-Swyx [00:27:26]: What is his model, man? What does he know that they don't know? Yeah, exactly.Alessio [00:27:30]: I wanna know, I guess like from the outside, and sometimes we even do it, we try and speculate on what people are actually doing. So for the first time, now we have a former guest writing about a current guest. So we wanna know what you guys thought and maybe what are some of the misconceptions that people from the outside have on what it takes to run like a GPU cloud today?Vipul [00:27:50]: Yeah, big fan of Dylan's, by the way. I religiously read Semianalysis. I think there were some errors in that analysis. In particular, we were trying to decode it and one of the things we noticed is that it assumed that input tokens weren't being priced. So I think that may have been an error in the model. I also don't think that there's this assumption that people are running this at a loss. I think it's very expensive. You can't do that for very long. And there are trade-offs in terms of batch sizes you use and the kind of tokens per second performance that are kind of system trade-offs. We've done a lot of work. This is one of the key areas of research for us. So our inference stack is a combination of 50 different sort of tricks and techniques and we think there's a lot of room for optimization here. So whichever hardware provides better performance, whether it's H100 or A100s or L40s, we can sort of measure price performance on particular hardware and we tend to use that for that model or in some cases, certain customers have data streams which can be then optimized for a particular configuration regime. So we do fairly detailed work on how to make this more efficient and so it's hard to, from the outside, looking at memory bandwidth and estimating what's actually happening.Alessio [00:29:26]: How much of these 50 tricks are you giving to yourself and how many are you gonna open? Because we have three now, obviously Flash Attention 2 is open source. He mentioned he'd love to come work together because of how much you care about open source. Yeah, how do you weigh that as a CEO and CTO?Vipul [00:29:43]: A lot of it is open, right? Flash Attention, Flash Decoding, et cetera, and we publish something that's very generally universally useful. It's going to produce better open source AI. We tend to publish as open source. I think on the inference stack, there are open source inference stacks which are pretty good and definitely today, it gives us a competitive advantage to have the best one. So we are not sort of rushing out to release everything about it. It's not overall that additive to open source out there and it is particularly useful as a business for us to provide best price performance. Yeah, we make these decisions. We have discussions. Anything that we keep closed, we generally talk about it quite a bit and decide like this is the piece that is closed for today and it may not be the case six months from now. It may not matter as much.Ce [00:30:40]: Yeah, so I think being open is kind of very important, right? So I think the whole company actually built on this idea that there's going to be ecosystem built on our open models, right? And that's also how we are really lucky to attract this top group of talents to actually join us because of the dream and the mission that we have on our side to really facilitate the open ecosystem, right? So I think in general, it's like I think all the ideas should be open. So that's why we publish papers, right? We actually talk about ideas, right? So I don't think it makes any sense to keep idea like close, right? So there are some software artifact that are kind of really deeply embedded into our kind of own kind of like stack. It kind of only useful when you're trying to build a disaggregated cloud, right? Maybe at some point that we're going to be open as people said, right? But at this moment, right? So we are kind of busy actually building it, right? So that's probably kind of getting to the picture about when that piece is going to be open, right? But I think on the research side, the ideas and for our people to publish things, I think that's really, really important, right? So I think that's how we get talent. That's how I think we as a company going to move the field forward.Swyx [00:31:49]: I noticed that you never used the word federated learning or inference. Is there a distinction that you draw?Ce [00:31:55]: So, I mean, it's definitely not intentional, but I think federated learning is, have been used in so many different ways by so many different people. It starts to lose a very precise meaning about what that really mean, right? If you go back to the original Google paper of federated learning, I think that's very different from what people are talking about today when they say federated. Yeah, we kind of want to be really precise about it.Swyx [00:32:18]: And so your term is disaggregated.Ce [00:32:19]: Yeah, so as an infrastructure, right? So that's disaggregated.Swyx [00:32:22]: Aren't most clouds disaggregated? Like what's different about it?Ce [00:32:27]: So one way is that most of the cloud are disaggregated, but some of that is actually being exposed to the user, right? If you go to AWS, you do know which region you are in, right? So I think one thing that we are trying to do is you have this disaggregated cloud, not only about location or geographically where they are, but about this reliability and also this diversity of this infrastructure. So, and if we want to build a reliable, high-quality layer over that, the user actually don't know, right? What's actually happening under the cover, right? So I think that's one of the difference of the way that we are thinking about infrastructure.Swyx [00:33:06]: Yeah, a bit closer to Cloudflare than AWS. Yeah. Yeah. We have one question here, which we'll just throw out, it's kind of fun. So going back to this sort of inference stack piece, maybe if you had to pull out like a call for researcher or just like point out interesting areas of work that you're interested in, what pieces of the stack have the most opportunity for improvement?Ce [00:33:27]: Yeah, so I think the way we are thinking about the inference stack is, so there are multiple things that can happen, right? So you can do better algorithms, like speckle decoding, you can change the model architecture, you can go really crazy on the system side, right? And you can also code it on the hardware, right? So it's not really clear innovation on a single dimension will get you there. So the key thesis on our side is, if you only push on one direction, you are going to reach diminishing return really, really quickly. Yeah, there's only that much you can do on the system side, only that much you can do on the algorithm side. I think the only big thing that's going to happen is when you ask all those dimensions to actually compound, right? So to have algorithm, model, and system all come together, so I think that's how we reach the next 10 times improvement on inference, right? So I don't think there's a single dimension that is particularly important, but looking at this space in a joint way, right? Try to co-optimize jointly multiple dimensions, I think that's going to be really important for the community to look at.Vipul [00:34:28]: Yeah, we often see, I see numbers from the team and you have these multiple methods, not all of them compound. So you mix these together, it's still similar results and some combination of them will have this incredible effect that is really, really super interesting. So it's very systems, you know, a kind of broad systems approach to it that's the most effective.Swyx [00:34:51]: I think I finally get the name of the company, like- Bring it together, yeah. Everything needs to be automated together.Alessio [00:34:57]: All right, just quickly, how does all this work change, just like some of the architectures change? I know a mixture of experts like speculative decoding is a little less efficient because of memory bandwidth. How much of it do you invest when it's a maybe model-specific improvement versus more horizontal thing? Also, you're researching different architectures, so how much do you want to spend time optimizing what state of the art today versus what's coming next?Vipul [00:35:24]: We do spend time on what state of the art today as well as what's next. You know, the value we get from doing specific optimization, even for, you know, what works well for a particular model on A100s with a particular bus versus H100s, it's a worthwhile investment for us. So we will go down fairly deep into a specific architecture and specific hardware. It does also inform what works better where, and you don't have to take the same approach for, you know, every model and every sort of hardware setup. We can take these different approaches and we do have these multiple systems now. We know that this, you know, system B is better for mixed role and system C is going to be better for stripe tying or Mamba.Alessio [00:36:13]: Before we move on from inference, we need to talk about any scale of drama. So we're actually having Sumit on the podcast tomorrow, who also talked about, kind of came to your guys' support about how, yeah, how important it's not just like, oh, together saying this benchmark's not good because they look bad in it. How, I guess like, it's a hard question to ask, but like, why did you decide to just come out and say it? And how maybe does that also reflect the values that you guys have about open source and openness and kind of like being transparent about what's real and maybe hopes for standardizing some of these benchmarks to make it more clear?Ce [00:36:56]: So it's a great service and skills doing for the community, right? I mean, it's very hard to do benchmark. The moment you do benchmark comparing N players, right, N minus one will be unhappy. You have two tables, then maybe N of them will be unhappy, right? So it's a very great thing that they're doing. And in some of the work that we are doing, we actually use RMOperf, right? So it's a great thing that they're actually doing. So I think one thing about benchmark is, and probably the professor part of me are talking, is a good benchmark should think about how it's going to incentivize the field to actually move forward, right? So if the benchmark really become a kind of standard, how are people going to over-optimize to the benchmark if you are going to do that? And when people are doing that, what are we actually trying to incentivize, right? Will that move the world to a better place? Or will that essentially have every single player focus on marketing or spending time or money on something that actually do not matter on technical side, right? It's very hard to actually strike a balance, right? So I think the reason we kind of try to give feedback on the benchmark is kind of want to open up the discussion about how does the industry should come together and define maybe a common way that we compare with each other, right? So like how database people doing TPC, right? Maybe you should have something actually similar, right? So we are trying to start some of the conversation. So it's not really that we jump out to say it's not good because there's no way we can have a perfect benchmark. That doesn't really exist, right? So just try to kickstart a conversation that maybe we should come together and do something that the community agree and align with the benefit a user going to get, right? So just get the conversation started.Vipul [00:38:42]: I've spoken to the AnyScale team after that, and I think they had really great intentions. And partly, I think it felt very objective and everyone sort of had a reaction to it because it just didn't match their benchmarks that we've all run internally against different services. I think a common industry benchmark run by an independent party versus one of the vendors.Swyx [00:39:04]: Is there one that you appoint to?Vipul [00:39:06]: I don't think one exists today. I think there should be. We're having some conversations about someone setting one up. And there's lots of interesting aspects of this. Time to first token is a function of where the test was run from. There is different load on these services at different times of the day and weekday or weekend. So you have to measure that well. And I think if all of that were done very well by an independent source, that will be a very useful service to customers and in the services themselves.Swyx [00:39:39]: Yeah, I'll point people to artificialanalysis.ai, which is a new one that recently emerged. I don't know if they've done it right. It looks like a side project of a couple people. But I think it's in all the provider's interest to work with them. And ensure that there's an independent third party that's measuring these things, right? At least on the baseline. For me, what's worrying is more about what Toa was saying, which is, do these benchmarks skew things in ways that customers might not be mindful of? Like, what are these things overemphasizing that we might be missing? And I don't really know. It seems like a lot of these services bundled together, they're a version of quantization as well. So that means there's performance trade-offs, right? You're not comparing apples to apples, the same model itself, even though it's like a llama variant or whatever. So what do people trade off? They trade off latency, they trade off price. Obviously, those are the first two. But what else, right? What factors matter in an inference business?Ce [00:40:33]: Yeah, so I think there's also the throughput, right? So there's the time to first token, right? So, and then there are things that users do not often see, for example, the reliability, right? The capacity, right? So that also have impact on user experience at a global scale. Maybe not a single query, right? But in aggregation, you can also see a whole bunch of, like, whether you are emphasizing P50, P95, right? So the whole bunch of things that you can actually play with. And of course, there's also quality. So there are different ways to actually make the whole thing faster, specification, quantization, or combination of those, right? So yeah, so there are so many things to actually play with. So they probably need a benchmark that the protocol is transparent to make sure, like, it's very clear what we are doing and a whole bunch of check on the quality to make sure we are putting the right group of stories in the same table. So I think then essentially the user can actually navigate the space. So I think that's going to be good for everyone.Swyx [00:41:27]: Yeah, makes sense. It's a very important field and I think hopefully there's a good third party that emerges from this. So I just want to touch on one more piece, which is I think I'm appreciating from this discussion that fine tuning is a bigger part of your business than I thought. The other big player in fine tuning is Mosaic. Well, Mosaic is more training, but like there's a bunch of other players in the fine tuning space. If I was a prospective fine tuning customer, what do I come to you with? Do I come to you with my custom data and that's it? Do I also have to write the fine tuning code? What level of engagement do you do with your customers?Vipul [00:42:01]: I think across the spectrum, our customers are training models, pre-training models from scratch and many of them will bring their data sets, you know, user infrastructure and training stack to train their models. There are others who have trained smaller models and want to scale up, scale up across infrastructure, scale up across data. So we'll sort of help them do that. We will have customers who are sort of initially started a little bit more consultative. They have a particular task and idea in mind and we will help them get from there to the data set and the right model to achieve that task. So it's a spectrum and, you know, our goal is to, we're trying to productize as much of this as possible. So that the whole process can be fast and scalable. I would say there is a lot more understanding around fine tuning now, like even the last six months, there are, you know, source tools, recipes, literature, podcasts, discord channels where people are figuring out and it really is in many ways, one of the successes of open source is you have small collectives of, you know, engineers who have created, who are now creating the top models on open source leaderboards. And I have tried out all sorts of different sort of, you know, data recipes, creating synthetic data. Merging models. Merging models. So it's, that's really fun to see. And I think that sort of agency that exists now is exciting. And that is, we see a lot of that sort of being applied into products and, you know, more commercial models that people are deploying in their applications.Alessio [00:43:50]: And then just to, I guess, wrap up the together, it's almost becoming like a platform as a service, because now you release together embeddings. How did you get 92.5 accuracy on 32K retrieval? And do you think we're kind of like getting to embeddings or just like, we did everything that we could, you know, we're getting to like the most optimized it's gonna get and then we should just focus on models and inference or do you think there's still room there to improve?Ce [00:44:17]: Oh, I don't think we haven't even got started on embedding. Yeah. So I think there are so many things. So like embedding is really fundamental for many things, for example, rack, right? So deep in application. So that's how people bring knowledge in. That's also the fundamental piece when you want to build a better model, right? So that's give you this understanding about what actually get into the model. You can actually use that to actually build a better data set, get a better model, then get better embedding, you'll start this loop, right? Without the good embedding, the loop is not closed, right? So I think both on the quality side, how to embed more like dedicated semantics, like into those vectors, how to deal with negation, for example, right? So, and how can you make the whole thing really, really fast? So I think for the next couple years, yeah, we will see a whole bunch of new embeddings maybe of different size and much, much faster than today. Yeah, so I think it's a very active research area. I think people should invest more, yeah.Swyx [00:45:14]: I was surprised to see, I think Jina or, yeah, there's Jina AI, and then there's another guy, Tengyu's Voyage. They are coming out as startups purely focused on embeddings.Ce [00:45:25]: Yeah. Yeah, so I think it's a very, very important piece of the system, right? So you people haven't focused on a lot on them before, and they should definitely start to do that.Swyx [00:45:36]: Yeah. Why are the Chinese universities so good at embeddings? You know what I mean, right? Like the BGE and- Yeah, yeah, yeah.Ce [00:45:44]: So I don't know. We just released our first embedded model, so we still try to learn how to build an embedded model. Yeah, so ask me again in six months.Swyx [00:45:53]: I'll probably have more insight about how to build a better one. I just noticed that you saw 8002 was used to be at the top of the MTB chart, and then it's just like sliding down and down and down, and all the new models are coming out of China for some reason. And I'm like, I don't know what's going on there. So we cannot leave this discussion without talking about state space models. But first of all, how much of the company is dedicated to research? Like it's obviously like not production quality yet, but-Vipul [00:46:17]: I would say it's like 40, 45% I was counting this morning. That's huge.Swyx [00:46:22]: Yeah, so that's the biggest- It's a big investment. Yeah. Okay, well, I mean, it looks like it's paying off, so. And then high level, I will confess or admit or mention for the listeners who are also similarly skeptical, I did not used to care about long contexts because I was like, you know, 30K is enough, 100K is enough, right? I'm not, you know, modeling DNA sequences or anything like that. Why do I need long context? And I mean, first of all, I'll throw that open to you. But second of all, I think what Mamba did for me was change that perception of that. It's only about a long context. The only reason you want sub-quadratic architectures is for long context. Actually, that's not true. And it's also just more efficient to train, period. Right? I'll just leave that open to you. Like what's the motivation that people should keep in their heads? There are multiple things, right?Ce [00:47:09]: So one thing is that, I mean, the moment a model can do for long context well, so it often means that it's kind of cheaper. Yeah, so I mean, that's why it's kind of long. I mean, in principle, transformer can do long context. It's just very expensive. So I think what those like state-based models trying to do is try to push the size of the state, right? Like as small as possible. That's why it's kind of long context, right? And try to kind of like decouple this like quadratical dependency, right? To make sure you can have a much better execution pattern.One direct consequence of those is you can do long context really cheaply, but on the other hand, also introduce a whole bunch of benefit even you are not doing long context. Right? So I think that's actually probably equally important. Because data gets smaller, you can do really large batch size, right? You can actually be very faster. Right? So yeah. And another thing is like, one of the hypothesis that we have is, like in Stripe Hyena, it start to have a hybrid architecture, right? It has part of it has like state-based model and part of it is still the transformer. So different component probably deal with different things kind of better. So maybe by putting them together, by thinking about how information propagate, over this whole horizon of this context, you can probably get an even better quality model than transformer. Right? So I think that's why we are kind of invest a lot of things, on those models. Not only for the context, which is very important, but also for a whole bunch of benefit it could get.Swyx [00:48:42]: Yeah. How should people treat the distinction between Mamba and Stripe Hyena? Like what's the point of releasing these two as separate models? Is one like sort of the together proprietary one and then the other is like the more open research one?Ce [00:48:53]: Yeah. So I think it's pretty much a different stage of exploration. So they kind of have different hypothesis when we try to build those. Yeah. Like for instance, there are different view about state-based model. One is Hyena, another is like Mamba, right? They're actually different architecture. So when we build Stripe Hyena, right? So the curiosity that we have is how good can we... So what is the highest quality non-transformer model we can ever build? The goal of Stripe Hyena is try to see whether we can match Mistral. And by fine-tuning well, whether we can outperform that in some way, right? So it has a very, very strong baseline that we are trying to beat. So that's why there's hybrid scene, like getting the picture, right? And for Mamba, it's kind of more... The curiosity was how far can we push for pure architecture? Then we start from this very system make from small to large, right? All the way to 3 billion, right? So the baseline was essentially the best 3 billion model. So I guess at a different stage of exploration, at some point, I think they are going to converge. We actually learn different things, like when building different models. I think they are just like this intermediate stage in the exploration at different points.Alessio [00:50:02]: You mentioned the hybrid architecture. Is that the model grafting that you mentioned in the Stripe Hyena post where I mentioned you can have transformers and not together? Like this is a concept that I hadn't heard before reading about this. So I think most people's mental models, like transformers or something else, it's not transformers AND something else. How do you train a model that is hybrid? Is there any difference in like how you construct your datasets? Is there any difference in then how you run inference on it? How should people think about starting research in this field?Ce [00:50:36]: Yeah, so we were also very surprised. Yeah, so when we come up with this hybrid architecture. So the way to think about it is like you have different layers in the neural network, right? So like the stateless model for some layer will already give you the benefit. For the other layer, they could be transformers, right? They could give you this more global view of the sequence, but for me, for other layer, don't have to have that, right? I still can have all the other things that kick in, right? So we don't know what is the optimal mixture between different architectures. I mean, in principle, we can have a mamba, hyena, and transformer, all those things that come together, right? And then you can see what makes sense. We have no idea what is optimal doing that. So what we are excited about is now the community have a whole bunch of building blocks that they can actually like playing like a Lego, right? So just put together and see what happen, right? So we are kind of very excited about that. Yeah, we are in the process of trying to learn more like about this architecture. And when we know what we are talking about, we will definitely share with the community about how to do that in a systematic way.Swyx [00:51:41]: Cool. What are we still unsure about? Like, why don't we just, you know, put all the money in the world and training these things now? Like what is left to figure out before we scale this thing?Ce [00:51:53]: So like if you look at how transformer like it's been developed, right? In the last like five to 10 years, right? So people don't start from like, you have this attention to all you need the paper and then let's put all the money in, right? Always start from this very systematic understanding about the scaling, about data quality, about essentially the limits, right? I think for a state-based model from the labs to the real world, you kind of need to go through the same process. But of course, the second time doing that is kind of easier, right? But I think there's no way we can get rid of this systematic step of studying scaling law, study what data to put in, right? So what's the impact of different data slices to the data, yeah, to the final model quality.Swyx [00:52:33]: Do you expect that the data inputs will be different?Ce [00:52:37]: I don't know, but I wouldn't take that for granted that they should be the same, right? So that's one of the hypothesis that, so we have no opinion on that because I think that's the result of the study, not the assumption. Yeah, we do not need to assume that.Swyx [00:52:51]: Okay, scaling laws and data, anything else like architectural that we are not sure about? Because now you have this selection mechanism that you're pretty happy with.Ce [00:52:59]: Yeah, so, I mean, first of all, how to mix them, right? So, and second is what is the architecture? So if you look at transformer, right? So one very interesting piece there is people optimize also the hardware, yeah, to make sure that things run very fast, right?They're very efficient kernel, they're very efficient hardware. And then that's add another boost, right, for the transformer architecture, right? So that's something that should happen for state-based model. Which architecture is kind of easier kind of to run on the hardware, right? So, hosting going kind of faster, you can put more data, it add another dimension in the scaling law. So I think we just need to plow the whole space and just be really systematic from small model to 1 billion, 3 billion, 7 billion, just go all the way up, right? So I wouldn't jump around in the space. I would just like be patient and just like be systematic. Yeah, I think we'll get there, yeah.Swyx [00:53:52]: Yeah, well, I'm looking forward for more research from you guys to figure that out. So one dimension, which we didn't talk about, we talked about long context, we talked about efficiency, but speed is very, speed is also very important. A good inference provider provides, let's say 70 tokens per second, and then maybe that's faster than less good inference providers that are more like 30 tokens per second. But that's the rough range, right? State-of-the-art today. That's around the human speaking speed, human reading speed is about 200 words per minute. Why do we need 5,000 tokens per second is my question back to Vipul. And maybe is this something that is an emphasis for research as well, or is this more just an inference only thing?Vipul [00:54:29]: There are applications that are consuming the tokens that are produced from unmodeled, so they're not necessarily being read or heard by humans. That's a place where we see that level of requirement today that really nobody can quite satisfy. There is, can I think about, as intelligence grows, how do you sort of increase the bandwidth of, you know, how do you reduce the latency of it? If we can do 5,000 tokens a second, the same card can produce, the throughput of that card goes up significantly and can support more applications. So I think it's important from that perspective. And then there are, it opens up new UX possibilities. Once you can get sort of an immediate answer

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Edy Smol el Gurú de la moda nos cuenta como es su vida sin quimioterapias, como es que los hombres lo buscan para tener encuentros con el, su remedio natural para poder sobrellevar el cáncer, la libertad que deben tener las parejas de hoy para ser felices, la gente que nunca tuvo dinero y se comporta de manera maleducada, mis preferencias por los hombres aun teniendo novia, como le doy asesoría en moda a esposas de políticos y funcionarios, porque los hombres prefieren tener relaciones con otros hombres, la razón por la que no usa desodorante. --- Support this podcast: https://podcasters.spotify.com/pod/show/gusgripodcast/support

Becca is joined by Corporate Gossip startup correspondent Aaron Cohn to dip our tube-socked toes into the FTX trial. We meet Sam Bankman-Fried (SBF), a rumpled 28-year-old-grown-adult-boy-genius who built a $40 billion crypto empire in less than three years. But his meteoric rise to crypto kingpin was followed quickly by his fall to the bottom of shit-coin mountain.  And now, SBF is facing up to 115 years in federal prison for fraud.  Check "build a website" off your to-do list with realnice website builder!  Pictures & links on our substack Support the pod by buying us a coffee Check out our reading list  Follow us on youtube, instagram, and tiktok Hosts: Becca Platsky (Becca@nitetoast.com) Adam Platsky (Adam@nitetoast.com)  Produced by: Michael Albanese @bigmanmike Timestamps:  17:40 - Corporate Gossip #1: Sam Bankman Fried raises $420.69m from 69 founders in a “meme” funding round and raises zero red flags 33:45 - Corporate Gossip #2: This guy can't be a criminal… I mean, come on, have you ever seen a criminal in wrinkly cargo shorts??? 45:00 -  Corporate Gossip #3:  To the moon!  50:00 - Corporate Gossip #4: FTX: Fraud on top of shit mountain  1:09:40 - Corproate Gossip #5: When people stop being polite, and start getting indicted 1:13:30 - Corporate Gossip #6: Will the Smol bean / Widdle baby defense work for Sam?

The first workshops and talks from the AI Engineer Summit are now up! Join the >20k viewers on YouTube, find clips on Twitter (we're also clipping @latentspacepod), and chat with us on Discord!Text-to-SQL was one of the first applications of NLP. Thoughtspot offered “Ask your data questions” as their core differentiation compared to traditional dashboarding tools. In a way, they provide a much friendlier interface with your own structured (aka “tabular”, as in “SQL tables”) data, the same way that RLHF and Instruction Tuning helped turn the GPT-3 of 2020 into the ChatGPT of 2022.Today, natural language queries on your databases are a commodity. There are 4 different ChatGPT plugins that offer this, as well as a bunch of startups like one of our previous guests, Seek.ai. Perplexity originally started with a similar product in 2022: In March 2023 LangChain wrote a blog post on LLMs and SQL highlighting why they don't consistently work:* “LLMs can write SQL, but they are often prone to making up tables, making up field”* “LLMs have some context window which limits the amount of text they can operate over”* “The SQL it writes may be incorrect for whatever reason, or it could be correct but just return an unexpected result.”For example, if you ask a model to “return all active users in the last 7 days” it might hallucinate a `is_active` column, join to an `activity` table that doesn't exist, or potentially get the wrong date (especially in leap years!).We previously talked to Shreya Rajpal at Guardrails AI, which also supports Text2SQL enforcement. Their approach was to run the actual SQL against your database and then use the error messages to improve the query: Semantic Layers to the rescueCube is an open source semantic layer which recently integrated with LangChain to solve these issues in a different way. You can use YAML, Javascript, or Python to create definitions of different metrics, measures and dimensions for your data: Creating these metrics and passing them in the model context limits the possibility for errors as the model just needs to query the `active_users` view, and Cube will then expand that into the full SQL in a reliable way. The downside of this approach compared to the Guardrails one for example is that it requires more upfront work to define metrics, but on the other hand it leads to more reliable and predictable outputs. The promise of adding a great semantic layer to your LLM app is irresistible - you greatly minimize hallucinations, make much more token efficient prompts, and your data stays up to date without any retraining or re-indexing. However, there are also difficulties with implementing semantic layers well, so we were glad to go deep on the topic with Artem as one of the leading players in this space!Timestamps* [00:00:00] Introductions* [00:01:28] Statsbot and limitations of natural language processing in 2017* [00:04:27] Building Cube as the infrastructure for Statsbot* [00:08:01] Open sourcing Cube in 2019* [00:09:09] Explaining the concept of a semantic layer/Cube* [00:11:01] Using semantic layers to provide context for AI models working with tabular data* [00:14:47] Workflow of generating queries from natural language via semantic layer* [00:21:07] Using Cube to power customer-facing analytics and natural language interfaces* [00:22:38] Building data-driven AI applications and agents* [00:25:59] The future of the modern data stack* [00:29:43] Example use cases of Slack bots powered by Cube* [00:30:59] Using GPT models and limitations around math* [00:32:44] Tips for building data-driven AI apps* [00:35:20] Challenges around monetizing embedded analytics* [00:36:27] Lightning RoundTranscriptSwyx: Hey everyone, welcome to the Latent Space podcast. This is Swyx, writer, editor of Latent Space and founder of Smol.ai and Alessio, partner and CTO in residence at Decibel Partners. [00:00:15]Alessio: Hey everyone, and today we have Artem Keydunov on the podcast, co-founder of Cube. Hey Artem. [00:00:21]Artem: Hey Alessio, hi Swyx. Good to be here today, thank you for inviting me. [00:00:25]Alessio: Yeah, thanks for joining. For people that don't know, I've known Artem for a long time, ever since he started Cube. And Cube is actually a spin-out of his previous company, which is Statsbot. And this kind of feels like going both backward and forward in time. So the premise of Statsbot was having a Slack bot that you can ask, basically like text to SQL in Slack, and this was six, seven years ago, something like that. A lot ahead of its time, and you see startups trying to do that today. And then Cube came out of that as a part of the infrastructure that was powering Statsbot. And Cube then evolved from an embedded analytics product to the semantic layer and just an awesome open source evolution. I think you have over 16,000 stars on GitHub today, you have a very active open source community. But maybe for people at home, just give a quick like lay of the land of the original Statsbot product. You know, what got you interested in like text to SQL and what were some of the limitations that you saw then, the limitations that you're also seeing today in the new landscape? [00:01:28]Artem: I started Statsbot in 2016. The original idea was to just make sort of a side project based off my initial project that I did at a company that I was working for back then. And I was working for a company that was building software for schools, and we were using Slack a lot. And Slack was growing really fast, a lot of people were talking about Slack, you know, like Slack apps, chatbots in general. So I think it was, you know, like another wave of, you know, bots and all that. We have one more wave right now, but it always comes in waves. So we were like living through one of those waves. And I wanted to build a bot that would give me information from different places where like a data lives to Slack. So it was like developer data, like New Relic, maybe some marketing data, Google Analytics, and then some just regular data, like a production database, so it sells for sometimes. And I wanted to bring it all into Slack, because we were always chatting, you know, like in Slack, and I wanted to see some stats in Slack. So that was the idea of Statsbot, right, like bring stats to Slack. I built that as a, you know, like a first sort of a side project, and I published it on Reddit. And people started to use it even before Slack came up with that Slack application directory. So it was a little, you know, like a hackish way to install it, but people are still installing it. So it was a lot of fun. And then Slack kind of came up with that application directory, and they reached out to me and they wanted to feature Statsbot, because it was one of the already being kind of widely used bots on Slack. So they featured me on this application directory front page, and I just got a lot of, you know, like new users signing up for that. It was a lot of fun, I think, you know, like, but it was sort of a big limitation in terms of how you can process natural language, because the original idea was to let people ask questions directly in Slack, right, hey, show me my, you know, like opportunities closed last week or something like that. My co founder, who kind of started helping me with this Slack application, him and I were trying to build a system to recognize that natural language. But it was, you know, we didn't have LLMs right back then and all of that technology. So it was really hard to build the system, especially the systems that can kind of, you know, like keep talking to you, like maintain some sort of a dialogue. It was a lot of like one off requests, and like, it was a lot of hit and miss, right? If you know how to construct a query in natural language, you will get a result back. But you know, like, it was not a system that was capable of, you know, like asking follow up questions to try to understand what you actually want. And then kind of finally, you know, like, bring this all context and go to generate a SQL query, get the result back and all of that. So that was a really missing part. And I think right now, that's, you know, like, what is the difference? So right now, I kind of bullish that if I would start Statsbot again, probably would have a much better shot at it. But back then, that was a big limitation. We kind of build a queue, right, as we were working on Statsbot, because we needed it. [00:04:27]Alessio: What was the ML stack at the time? Were you building, trying to build your own natural language understanding models, like were there open source models that were good that you were trying to leverage? [00:04:38]Artem: I think it was mostly combination of a bunch of things. And we tried a lot of different approaches. The first version, which I built, like was Regex. They were working well. [00:04:47]Swyx: It's the same as I did, I did option pricing when I was in finance, and I had a natural language pricing tool thing. And it was Regex. It was just a lot of Regex. [00:04:59]Artem: Yeah. [00:05:00]Artem: And my co-founder, Pavel, he's much smarter than I am. He's like PhD in math, all of that. And he started to do some stuff. I was like, no, you just do that stuff. I don't know. I can do Regex. And he started to do some models and trying to either look at what we had on the market back then, or try to build a different sort of models. Again, we didn't have any foundation back in place, right? We wanted to try to use existing math, obviously, right? But it was not something that we can take the model and try and run it. I think in 2019, we started to see more of stuff, like ecosystem being built, and then it eventually kind of resulted in all this LLM, like what we have right now. But back then in 2016, it was not much available for just the people to build on top. It was some academic research, right, kind of been happening. But it was very, very early for something to actually be able to use. [00:05:58]Alessio: And then that became Cube, which started just as an open source project. And I think I remember going on a walk with you in San Mateo in 2020, something like that. And you had people reaching out to you who were like, hey, we use Cube in production. I just need to give you some money, even though you guys are not a company. What's the story of Cube then from Statsbot to where you are today? [00:06:21]Artem: We built a Cube at Statsbot because we needed it. It was like, the whole Statsbot stack was that we first tried to translate the initial sort of language query into some sort of multidimensional query. It's like we were trying to understand, okay, people wanted to get active opportunities, right? What does it mean? Is it a metric? Is it what a dimension here? Because usually in analytics, you always, you know, like, try to reduce everything down to the sort of, you know, like a multidimensional framework. So that was the first step. And that's where, you know, like it didn't really work well because all this limitation of us not having foundational technologies. But then from the multidimensional query, we wanted to go to SQL. And that's what was SemanticLayer and what was Cube essentially. So we built a framework where you would be able to map your data into this concept, into this metrics. Because when people were coming to Statsbot, they were bringing their own datasets, right? And the big question was, how do we tell the system what is active opportunities for that specific users? How we kind of, you know, like provide that context, how we do the training. So that's why we came up with the idea of building the SemanticLayer so people can actually define their metrics and then kind of use them as a Statsbot. So that's how we built a Cube. At some point, we saw people started to see more value in the Cube itself, you know, like kind of building the SemanticLayer and then using it to power different types of the application. So in 2019, we decided, okay, it feels like it might be a standalone product and a lot of people want to use it. Let's just try to open source it. So we took it out of Statsbot and open-sourced. [00:08:01]Swyx: Can I make sure that everyone has the same foundational knowledge? The concept of a cube is not something that you invented. I think, you know, not everyone has the same background in analytics and data that all three of us do. Maybe you want to explain like OLAP Cube, HyperCube, the brief history of cubes. Right. [00:08:17]Artem: I'll try, you know, like a lot of like Wikipedia pages and like a lot of like a blog post trying to go into academics of it. So I'm trying to like... [00:08:25]Swyx: Cube's according to you. Yeah. [00:08:27]Artem: So when we think about just a table in a database, the problem with the table, it's not a multidimensional, meaning that in many cases, if we want to slice the data, we kind of need to result with a different table, right? Like think about when you're writing a SQL query to answer one question, SQL query always ends up with a table, right? So you write one SQL, you got one. And then you write to answer a different question, you write a second query. So you're kind of getting a bunch of tables. So now let's imagine that we can kind of bring all these tables together into multidimensional table. And that's essentially Cube. So it's just like the way that we can have measures and dimension that can potentially be used at the same time from a different angles. [00:09:09]Alessio: So initially, a lot of your use cases were more BI related, but you recently released a LangChain integration. There's obviously more and more interest in, again, using these models to answer data questions. So you've seen the chat GPT code interpreter, which is renamed as like advanced data analysis. What's kind of like the future of like the semantic layer in AI? You know, what are like some of the use cases that you're seeing and why do you think it's a good strategy to make it easier to do now the text to SQL you wanted to do seven years ago? [00:09:39]Artem: Yeah. So, I mean, you know, when it started to happen, I was just like, oh my God, people are now building Statsbot with Cube. They just have a better technology for, you know, like natural language. So it kind of, it made sense to me, you know, like from the first moment I saw it. So I think it's something that, you know, like happening right now and chat bot is one of the use cases. I think, you know, like if you try to generalize it, the use case would be how do we use structured or tabular data with, you know, like AI models, right? Like how do we turn the data and give the context as a data and then bring it to the model and then model can, you know, like give you answers, make a questions, do whatever you want. But the question is like how we go from just the data in your data warehouse, database, whatever, which is usually just a tabular data, right? Like in a SQL based warehouses to some sort of, you know, like a context that system can do. And if you're building this application, you have to do it. It's like no way you can get away around not doing this. You either map it manually or you come up with some framework or something else. So our take is that and my take is that semantic layer is just really good place for this context to leave because you need to give this context to the humans. You need to give that context to the AI system anyway, right? So that's why you define metric once and then, you know, like you teach your AI system what this metric is about. [00:11:01]Alessio: What are some of the challenges of using tabular versus language data and some of the ways that having the semantic layer kind of makes that easier maybe? [00:11:09]Artem: Imagine you're a human, right? And you're going into like your new data analyst at a company and just people give you a warehouse with a bunch of tables and they tell you, okay, just try to make sense of this data. And you're going through all of these tables and you're really like trying to make sense without any, you know, like additional context or like some columns. In many cases, they might have a weird names. Sometimes, you know, if they follow some kind of like a star schema or, you know, like a Kimball style dimensions, maybe that would be easier because you would have facts and dimensions column, but it's still, it's hard to understand and kind of make sense because it doesn't have descriptions, right? And then there is like a whole like industry of like a data catalogs exist because the whole purpose of that to give context to the data so people can understand that. And I think the same applies to the AI, right? Like, and the same challenge is that if you give it pure tabular data, it doesn't have this sort of context that it can read. So you sort of needed to write a book or like essay about your data and give that book to the system so it can understand it. [00:12:12]Alessio: Can you run through the steps of how that works today? So the initial part is like the natural language query, like what are the steps that happen in between to do model, to semantic layer, semantic layer, to SQL and all that flow? [00:12:26]Artem: The first key step is to do some sort of indexing. That's what I was referring to, like write a book about your data, right? Describe in a text format what your data is about, right? Like what metrics it has, dimensions, what is the structures of that, what a relationship between those metrics, what are potential values of the dimensions. So sort of, you know, like build a really good index as a text representation and then turn it into embeddings into your, you know, like a vector storage. Once you have that, then you can provide that as a context to the model. I mean, there are like a lot of options, like either fine tune or, you know, like sort of in context learning, but somehow kind of give that as a context to the model, right? And then once this model has this context, it can create a query. Now the query I believe should be created against semantic layer because it reduces the room for the error. Because what usually happens is that your query to semantic layer would be very simple. It would be like, give me that metric group by that dimension and maybe that filter should be applied. And then your real query for the warehouse, it might have like a five joins, a lot of different techniques, like how to avoid fan out, fan traps, chasm traps, all of that stuff. And the bigger query, the more room that the model can make an error, right? Like even sometimes it could be a small error and then, you know, like your numbers is going to be off. But making a query against semantic layer, that sort of reduces the error. So the model generates a SQL query and then it executes us again, semantic layer. And semantic layer executes us against your warehouse and then sends result all the way back to the application. And then can be done multiple times because what we were missing was both this ability to have a conversation, right? With the model. You can ask question and then system can do a follow-up questions, you know, like then do a query to get some additional information based on this information, do a query again. And sort of, you know, like it can keep doing this stuff and then eventually maybe give you a big report that consists of a lot of like data points. But the whole flow is that it knows the system, it knows your data because you already kind of did the indexing and then it queries semantic layer instead of a data warehouse directly. [00:14:47]Alessio: Maybe just to make it a little clearer for people that haven't used a semantic layer before, you can add definitions like revenue, where revenue is like select from customers and like join orders and then sum of the amount of orders. But in the semantic layer, you're kind of hiding all of that away. So when you do natural language to queue, it just select revenue from last week and then it turns into a bigger query. [00:15:12]Swyx: One of the biggest difficulties around semantic layer for people who've never thought about this concept before, this all sounds super neat until you have multiple stakeholders within a single company who all have different concepts of what a revenue is. They all have different concepts of what active user is. And then they'll have like, you know, revenue revision one by the sales team, you know, and then revenue revision one, accounting team or tax team, I don't know. I feel like I always want semantic layer discussions to talk about the not so pretty parts of the semantic layer, because this is where effectively you ship your org chart in the semantic layer. [00:15:47]Artem: I think the way I think about it is that at the end of the day, semantic layer is a code base. And in Qubit, it's essentially a code base, right? It's not just a set of YAML files with pythons. I think code is never perfect, right? It's never going to be perfect. It will have a lot of, you know, like revisions of code. We have a version control, which helps it's easier with revisions. So I think we should treat our metrics and semantic layer as a code, right? And then collaboration is a big part of it. You know, like if there are like multiple teams that sort of have a different opinions, let them collaborate on the pull request, you know, they can discuss that, like why they think that should be calculated differently, have an open conversation about it, you know, like when everyone can just discuss it, like an open source community, right? Like you go on a GitHub and you talk about why that code is written the way it's written, right? It should be written differently. And then hopefully at some point you can come up, you know, like to some definition. Now if you still should have multiple versions, right? It's a code, right? You can still manage it. But I think the big part of that is that like, we really need to treat it as a code base. Then it makes a lot of things easier, not as spreadsheets, you know, like a hidden Excel files. [00:16:53]Alessio: The other thing is like then having the definition spread in the organization, like versus everybody trying to come up with their own thing. But yeah, I'm sure that when you talk to customers, there's people that have issues with the product and it's really like two people trying to define the same thing. One in sales that wants to look good, the other is like the finance team that wants to be conservative and they all have different definitions. How important is the natural language to people? Obviously you guys both work in modern data stack companies either now or before. There's going to be the whole wave of empowering data professionals. I think now a big part of the wave is removing the need for data professionals to always be in the loop and having non-technical folks do more of the work. Are you seeing that as a big push too with these models, like allowing everybody to interact with the data? [00:17:42]Artem: I think it's a multidimensional question. That's an example of, you know, like where you have a lot of inside the question. In terms of examples, I think a lot of people building different, you know, like agents or chatbots. You have a company that built an internal Slack bot that sort of answers questions, you know, like based on the data in a warehouse. And then like a lot of people kind of go in and like ask that chatbot this question. Is it like a real big use case? Maybe. Is it still like a toy pet project? Maybe too right now. I think it's really hard to tell them apart at this point because there is a lot of like a hype, you know, and just people building LLM stuff because it's cool and everyone wants to build something, you know, like even at least a pet project. So that's what happened in Krizawa community as well. We see a lot of like people building a lot of cool stuff and it probably will take some time for that stuff to mature and kind of to see like what are real, the best use cases. But I think what I saw so far, one use case was building this chatbot and we have even one company that are building it as a service. So they essentially connect into Q semantic layer and then offering their like chatbot So you can do it in a web, in a slack, so it can, you know, like answer questions based on data in your semantic layer, but also see a lot of things like they're just being built in house. And there are other use cases, sort of automation, you know, like that agent checks on the data and then kind of perform some actions based, you know, like on changes in data. But other dimension of your question is like, will it replace people or not? I think, you know, like what I see so far in data specifically, you know, like a few use cases of LLM, I don't see Q being part of that use case, but it's more like a copilot for data analyst, a copilot for data engineer, where you develop something, you develop a model and it can help you to write a SQL or something like that. So you know, it can create a boilerplate SQL, and then you can edit this SQL, which is fine because you know how to edit SQL, right? So you're not going to make a mistake, but it will help you to just generate, you know, like a bunch of SQL that you write again and again, right? Like boilerplate code. So sort of a copilot use case. I think that's great. And we'll see more of it. I think every platform that is building for data engineers will have some sort of a copilot capabilities and Cubectl, we're building this copilot capabilities to help people build semantic layers easier. I think that just a baseline for every engineering product right now to have some sort of, you know, like a copilot capabilities. Then the other use case is a little bit more where Cube is being involved is like, how do we enable access to data for non-technical people through the natural language as an interface to data, right? Like visual dashboards, charts, it's always has been an interface to data in every BI. Now I think we will see just a second interface as a just kind of a natural language. So I think at this point, many BI's will add it as a commodity feature is like Tableau will probably have a search bar at some point saying like, Hey, ask me a question. I know that some of the, you know, like AWS Squeak site, they're about to announce features like this in their like BI. And I think Power BI will do that, especially with their deal with open AI. So every company, every BI will have this some sort of a search capabilities built in inside their BI. So I think that's just going to be a baseline feature for them as well. But that's where Cube can help because we can provide that context, right? [00:21:07]Alessio: Do you know how, or do you have an idea for how these products will differentiate once you get the same interface? So right now there's like, you know, Tableau is like the super complicated and it's like super sad. It's like easier. Yeah. Do you just see everything will look the same and then how do people differentiate? [00:21:24]Artem: It's like they all have line chart, right? And they all have bar chart. I feel like it pretty much the same and it's going to be fragmented as well. And every major vendor and most of the vendors will try to have some sort of natural language capabilities and they might be a little bit different. Some of them will try to position the whole product around it. Some of them will just have them as a checkbox, right? So we'll see, but I don't think it's going to be something that will change the BI market, you know, like something that will can take the BI market and make it more consolidated rather than, you know, like what we have right now. I think it's still will remain fragmented. [00:22:04]Alessio: Let's talk a bit more about application use cases. So people also use Q for kind of like analytics in their product, like dashboards and things like that. How do you see that changing and more, especially like when it comes to like agents, you know, so there's like a lot of people trying to build agents for reporting, building agents for sales. If you're building a sales agent, you need to know everything about the purchasing history of the customer. All of these things. Yeah. Any thoughts there? What should all the AI engineers listening think about when implementing data into agents? [00:22:38]Artem: Yeah, I think kind of, you know, like trying to solve for two problems. One is how to make sure that agents or LLM model, right, has enough context about, you know, like a tabular data and also, you know, like how do we deliver updates to the context, which is also important because data is changing, right? So every time we change something upstream, we need to surely update that context in our vector database or something. And how do you make sure that the queries are correct? You know, I think it's obviously a big pain and that's all, you know, like AI kind of, you know, like a space right now, how do we make sure that we don't, you know, provide our own cancers, but I think, you know, like be able to reduce the room for error as much as possible that what I would look for, you know, like to try to like minimize potential damage. And then our use case for Qube, it's been using a lot to power sort of customer facing analytics. So I don't think much is going to change is that I feel like again, more and more products will adopt natural language interfaces as sort of a part of that product as well. So we would be able to power this business to not only, you know, like a chart, visuals, but also some sort of, you know, like a summaries, probably in the future, you're going to open the page with some surface stats and you will have a smart summary kind of generated by AI. And that summary can be powered by Qube, right, like, because the rest is already being powered by Qube. [00:24:04]Alessio: You know, we had Linus from Notion on the pod and one of the ideas he had that I really like is kind of like thumbnails of text, kind of like how do you like compress knowledge and then start to expand it. A lot of that comes into dashboards, you know, where like you have a lot of data, you have like a lot of charts and sometimes you just want to know, hey, this is like the three lines summary of it. [00:24:25]Artem: Exactly. [00:24:26]Alessio: Makes sense that you want to power that. How are you thinking about, yeah, the evolution of like the modern data stack in quotes, whatever that means today. What's like the future of what people are going to do? What's the future of like what models and agents are going to do for them? Do you have any, any thoughts? [00:24:42]Artem: I feel like modern data stack sometimes is not very, I mean, it's obviously big crossover between AI, you know, like ecosystem, AI infrastructure, ecosystem, and then sort of a data. But I don't think it's a full overlap. So I feel like when we know, like I'm looking at a lot of like what's happening in a modern data stack where like we use warehouses, we use BI's, you know, different like transformation tools, catalogs, like data quality tools, ETLs, all of that. I don't see a lot of being compacted by AI specifically. I think, you know, that space is being compacted as much as any other space in terms of, yes, we'll have all this copilot capabilities, some of AI capabilities here and there, but I don't see anything sort of dramatically, you know, being sort of, you know, a change or shifted because of, you know, like AI wave. In terms of just in general data space, I think in the last two, three years, we saw an explosion, right? Like we got like a lot of tools, every vendor for every problem. I feel like right now we should go through the cycle of consolidation. If Fivetran and DBT merge, they can be Alteryx of a new generation or something like that. And you know, probably some ETL tool there. I feel it might happen. I mean, it's just natural waves, you know, like in cycles. [00:25:59]Alessio: I wonder if everybody is going to have their own copilot. The other thing I think about these models is like Swyx was at Airbyte and yeah, there's Fivetran. [00:26:08]Swyx: Fivetran versus AirByte, I don't think it'll mix very well. [00:26:10]Alessio: A lot of times these companies are doing the syntax work for you of like building the integration between your data store and like the app or another data store. I feel like now these models are pretty good at coming up with the integration themselves and like using the docs to then connect the two. So I'm really curious, like in the future, what that will look like. And same with data transformation. I mean, you think about DBT and some of these tools and right now you have to create rules to normalize and transform data. In the future, I could see you explaining the model, how you want the data to be, and then the model figuring out how to do the transformation. I think it all needs a semantic layer as far as like figuring out what to do with it. You know, what's the data for and where it goes. [00:26:53]Artem: Yeah, I think many of this, you know, like workflows will be augmented by, you know, like some sort of a copilot. You know, you can describe what transformation you want to see and it can generate a boilerplate right, of transformation for you, or even, you know, like kind of generate a boilerplate of specific ETL driver or ETL integration. I think we're still not at the point where this code can be fully automated. So we still need a human and a loop, right, like who can be, who can use this copilot. But in general, I think, yeah, data work and software engineering work can be augmented quite significantly with all that stuff. [00:27:31]Alessio: You know, the big thing with machine learning before was like, well, all of your data is bad. You know, the data is not good for anything. And I think like now, at least with these models, they have some knowledge of their own and they can also tell you if your data is bad, which I think is like something that before you didn't have. Any cool apps that you've seen being built on Qube, like any kind of like AI native things that people should think about, new experiences, anything like that? [00:27:54]Artem: Well, I see a lot of Slack bots. They all remind me of Statsbot, but I know like I played with a few of them. They're much, much better than Statsbot. It feels like it's on the surface, right? It's just that use case that you really want, you know, think about you, a data engineer in your company, like everyone is like, and you're asking, hey, can you pull that data for me? And you would be like, can I build a bot to replace myself? You know, like, so they can both ping that bot instead. So it's like, that's why a lot of people doing that. So I think it's a first use case that actually people are playing with. But I think inside that use case, people get creative. So I see bots that can actually have a dialogue with you. So, you know, like you would come to that bot and say, hey, show me metrics. And the bot would be like, what kind of metrics? What do you want to look at? You will be like active users. And then it would be like, how do you define active users? You want to see active users sort of cohort, you want to see active users kind of changing behavior over time, like a lot of like a follow up questions. So it tries to sort of, you know, like understand what exactly you want. And that's how many data analysts work, right? When people started to ask you something, you always try to understand what exactly do you mean? Because many people don't know how to ask correct questions about your data. It's a sort of an interesting specter. On one side of the specter, you know, nothing is like, hey, show me metrics. And the other side of specter, you know how to write SQL, and you can write exact query to your data warehouse, right? So many people like a little bit in the middle. And the data analysts, they usually have the knowledge about your data. And that's why they can ask follow up questions and to understand what exactly you want. And I saw people building bots who can do that. That part is amazing. I mean, like generating SQL, all that stuff, it's okay, it's good. But when the bot can actually act like they know that your data and they can ask follow up questions. I think that's great. [00:29:43]Swyx: Yeah. [00:29:44]Alessio: Are there any issues with the models and the way they understand numbers? One of the big complaints people have is like GPT, at least 3.5, cannot do math. Have you seen any limitations and improvement? And also when it comes to what model to use, do you see most people use like GPT-4? Because it's like the best at this kind of analysis. [00:30:03]Artem: I think I saw people use all kinds of models. To be honest, it's usually GPT. So inside GPT, it could be 3.5 or 4, right? But it's not like I see a lot of something else, to be honest, like, I mean, maybe some open source alternatives, but it feels like the market is being dominated by just chat GPT. In terms of the problems, I think chatting about it with a few people. So if math is required to do math, you know, like outside of, you know, like chat GPT itself, so it would be like some additional Python scripts or something. When we're talking about production level use cases, it's quite a lot of Python code around, you know, like your model to make it work. To be honest, it's like, it's not that magic that you just throw the model in and like it can give you all these answers. For like a toy use cases, the one we have on a, you know, like our demo page or something, it works fine. But, you know, like if you want to do like a lot of post-processing, do a mass on URL, you probably need to code it in Python anyway. That's what I see people doing. [00:30:59]Alessio: We heard the same from Harrison and LangChain that most people just use OpenAI. We did a OpenAI has no moat emergency podcast, and it was funny to like just see the reaction that people had to that and how hard it actually is to break down some of the monopoly. What else should people keep in mind, Artem? You're kind of like at the cutting edge of this. You know, if I'm looking to build a data-driven AI application, I'm trying to build data into my AI workflows. Any mistakes people should avoid? Any tips on the best stack to use? What tools to use? [00:31:32]Artem: I would just recommend going through to warehouse as soon as possible. I think a lot of people feel that MySQL can be a warehouse, which can be maybe on like a lower scale, but definitely not from a performance perspective. So just kind of starting with a good warehouse, a query engine, Lakehouse, that's probably like something I would recommend starting from a day zero. And there are good ways to do it, very cheap, with open source technologies too, especially in the Lakehouse architecture. I think, you know, I'm biased, obviously, but using a semantic layer, preferably Cube, and for, you know, like a context. And other than that, I just feel it's a very interesting space in terms of AI ecosystem. I see a lot of people using link chain right now, which is great, you know, like, and we build an integration. But I'm sure the space will continue to evolve and, you know, like we'll see a lot of interesting tools and maybe, you know, like some tools would be a better fit for a job. I'm not aware of any right now, but it's always interesting to see how it evolves. Also it's a little unclear, you know, like how all the infrastructure around actually developing, testing, documenting, all that stuff will kind of evolve too. But yeah, again, it's just like really interesting to see and observe, you know, what's happening in this space. [00:32:44]Swyx: So before we go to the lightning round, I wanted to ask you on your thoughts on embedded analytics and in a sense, the kind of chatbots that people are inserting on their websites and building with LLMs is very much sort of end user programming or end user interaction with their own data. I love seeing embedded analytics, and for those who don't know, embedded analytics is basically user facing dashboards where you can see your own data, right? Instead of the company seeing data across all their customers, it's an individual user seeing their own data as a slice of the overall data that is owned by the platform that they're using. So I love embedded analytics. Well, actually, overwhelmingly, the observation that I've had is that people who try to build in this market fail to monetize. And I was wondering your insights on why. [00:33:31]Artem: I think overall, the statement is true. It's really hard to monetize, you know, like in embedded analytics. That's why at Qube we're excited more about our internal kind of BI use case, or like a company's a building, you know, like a chatbots for their internal data consumption or like internal workflows. Embedded analytics is hard to monetize because it's historically been dominated by the BI vendors. And we still see a lot of organizations are using BI tools as vendors. And what I was talking about, BI vendors adding natural language interfaces, they will probably add that to the embedded analytics capabilities as well, right? So they would be able to embed that too. So I think that's part of it. Also, you know, if you look at the embedded analytics market, the bigger organizations are big GADs, they're really more custom, you know, like it becomes and at some point I see many organizations, they just stop using any vendor, and they just kind of build most of the stuff from scratch, which probably, you know, like the right way to do. So it's sort of, you know, like you got a market that is very kept at the top. And then you also in that middle and small segment, you got a lot of vendors trying to, you know, like to compete for the buyers. And because again, the BI is very fragmented, embedded analytics, therefore is fragmented also. So you're really going after the mid market slice, and then with a lot of other vendors competing for that. So that's why it's historically been hard to monetize, right? I don't think AI really going to change that just because it's using model, you just pay to open AI. And that's it, like everyone can do that, right? So it's not much of a competitive advantage. So it's going to be more like a commodity features that a lot of vendors would be able to leverage. [00:35:20]Alessio: This is great, Artem. As usual, we got our lightning round. So it's three questions. One is about acceleration, one on exploration, and then take away. The acceleration thing is what's something that already happened in AI or maybe, you know, in data that you thought would take much longer, but it's already happening today. [00:35:38]Artem: To be honest, all this foundational models, I thought that we had a lot of models that been in production for like, you know, maybe decade or so. And it was like a very niche use cases, very vertical use cases, it's just like in very customized models. And even when we're building Statsbot back then in 2016, right, even back then, we had some natural language models being deployed, like a Google Translate or something that was still was a sort of a model, right, but it was very customized with a specific use case. So I thought that would continue for like, many years, we will use AI, we'll have all these customized niche models. But there is like foundational model, they like very generic now, they can serve many, many different use cases. So I think that is a big change. And I didn't expect that, to be honest. [00:36:27]Swyx: The next question is about exploration. What is one thing that you think is the most interesting unsolved question in AI? [00:36:33]Artem: I think AI is a subset of software engineering in general. And it's sort of connected to the data as well. Because software engineering as a discipline, it has quite a history. We build a lot of processes, you know, like toolkits and methodologies, how we prod that, [00:36:50]Swyx: right. [00:36:51]Artem: But AI, I don't think it's completely different. But it has some unique traits, you know, like, it's quite not idempotent, right, and kind of from many dimensions and like other traits. So which kind of may require a different methodologies may require different approaches and a different toolkit. I don't think how much is going to deviate from a standard software engineering, I think many tools and practices that we develop our software engineering can be applied to AI. And some of the data best practices can be applied as well. But it's like we got a DevOps, right, like it's just a bunch of tools, like ecosystem. So now like AI is kind of feels like it's shaping into that with a lot of its own, you know, like methodologies, practices and toolkits. So I'm really excited about it. And I think it's a lot of unsolved still question again, how do we develop that? How do we test you know, like, what is the best practices? How what is a methodologist? So I think that would be an interesting to see. [00:37:44]Alessio: Awesome. Yeah. Our final message, you know, you have a big audience of engineers and technical folks, what's something you want everybody to remember to think about to explore? [00:37:55]Artem: I mean, it says being hooked to try to build a chatbot, you know, like for analytics, back then and kind of, you know, like looking at what people do right now, I think, yeah, just do that. I mean, it's working right now, with foundational models, it's actually now it's possible to build all those cool applications. I'm so excited to see, you know, like, how much changed in the last six years or so that we actually now can build a smart agents. So I think that sort of, you know, like a takeaways and yeah, we are, as humans in general, we like we really move technology forward. And it's fun to see, you know, like, it's just a first hand. [00:38:30]Alessio: Well, thank you so much for coming on Artem. [00:38:32]Swyx: This was great. [00:38:32] Get full access to Latent Space at www.latent.space/subscribe

Thanks to the over 11,000 people who joined us for the first AI Engineer Summit! A full recap is coming, but you can 1) catch up on the fun and videos on Twitter and YouTube, 2) help us reach 1000 people for the first comprehensive State of AI Engineering survey and 3) submit projects for the new AI Engineer Foundation.See our Community page for upcoming meetups in SF, Paris, NYC, and Singapore. This episode had good interest on Twitter.Last month, Imbue was crowned as AI's newest unicorn foundation model lab, raising a $200m Series B at a >$1 billion valuation. As “stealth” foundation model companies go, Imbue (f.k.a. Generally Intelligent) has stood as an enigmatic group given they have no publicly released models to try out. However, ever since their $20m Series A last year their goal has been to “develop generally capable AI agents with human-like intelligence in order to solve problems in the real world”.From RL to Reasoning LLMsAlong with their Series A, they announced Avalon, “A Benchmark for RL Generalization Using Procedurally Generated Worlds”. Avalon is built on top of the open source Godot game engine, and is ~100x faster than Minecraft to enable fast RL benchmarking and a clear reward with adjustable game difficulty.After a while, they realized that pure RL isn't a good path to teach reasoning and planning. The agents were able to learn mechanical things like opening complex doors, climbing, but couldn't go to higher level tasks. A pure RL world also doesn't include a language explanation of the agent reasoning, which made it hard to understand why it made certain decisions. That pushed the team more towards the “models for reasoning” path:“The second thing we learned is that pure reinforcement learning is not a good vehicle for planning and reasoning. So these agents were able to learn all sorts of crazy things: They could learn to climb like hand over hand in VR climbing, they could learn to open doors like very complicated, like multiple switches and a lever open the door, but they couldn't do any higher level things. And they couldn't do those lower level things consistently necessarily. And as a user, I do not want to interact with a pure reinforcement learning end to end RL agent. As a user, like I need much more control over what that agent is doing.”Inspired by Chelsea Finn's work on SayCan at Stanford, the team pivoted to have their agents do the reasoning in natural language instead. This development parallels the large leaps in reasoning that humans have developed as the scientific method:“We are better at reasoning now than we were 3000 years ago. An example of a reasoning strategy is noticing you're confused. Then when I notice I'm confused, I should ask:* What was the original claim that was made? * What evidence is there for this claim? * Does the evidence support the claim? * Is the claim correct? This is like a reasoning strategy that was developed in like the 1600s, you know, with like the advent of science. So that's an example of a reasoning strategy. There are tons of them. We employ all the time, lots of heuristics that help us be better at reasoning. And we can generate data that's much more specific to them.“The Full Stack Model LabOne year later, it would seem that the pivot to reasoning has had tremendous success, and Imbue has now reached a >$1B valuation, with participation from Astera Institute, NVIDIA, Cruise CEO Kyle Vogt, Notion co-founder Simon Last, and others. Imbue tackles their work with a “full stack” approach:* Models. Pretraining very large (>100B parameter) models, optimized to perform well on internal reasoning benchmarks, with a ~10,000 Nvidia H100 GPU cluster lets us iterate rapidly on everything from training data to architecture and reasoning mechanisms.* Tools and Agents. Building internal productivity tools from coding agents for fixing type checking and linting errors, to sophisticated systems like CARBS (for hyperparameter tuning and network architecture search).* Interface Invention. Solving agent trust and collaboration (not merely communication) with humans by creating better abstractions and interfaces — IDEs for users to program computers in natural language.* Theory. Publishing research about the theoretical underpinnings of self-supervised learning, as well as scaling laws for machine learning research.Kanjun believes we are still in the “bare metal phase” of agent development, and they want to take a holistic approach to building the “operating system for agents”. We loved diving deep into the Imbue approach toward solving the AI Holy Grail of reliable agents, and are excited to share our conversation with you today!Timestamps* [00:00:00] Introductions* [00:06:07] The origin story of Imbue* [00:09:39] Imbue's approach to training large foundation models optimized for reasoning* [00:12:18] Imbue's goals to build an "operating system" for reliable, inspectable AI agents* [00:15:37] Imbue's process of developing internal tools and interfaces to collaborate with AI agents* [00:17:27] Imbue's focus on improving reasoning capabilities in models, using code and other data* [00:19:50] The value of using both public benchmarks and internal metrics to evaluate progress* [00:21:43] Lessons learned from developing the Avalon research environment* [00:23:31] The limitations of pure reinforcement learning for general intelligence* [00:28:36] Imbue's vision for building better abstractions and interfaces for reliable agents* [00:31:36] Interface design for collaborating with, rather than just communicating with, AI agents* [00:37:40] The future potential of an agent-to-agent protocol* [00:39:29] Leveraging approaches like critiquing between models and chain of thought* [00:45:49] Kanjun's philosophy on enabling team members as creative agents at Imbue* [00:53:51] Kanjun's experience co-founding the communal co-living space The Archive* [01:00:22] Lightning RoundShow Notes* Imbue* Avalon* CARBS (hyperparameter optimizer)* Series B announcement* Kanjun/Imbue's Podcast* MIT Media Lab* Research mentioned:* Momentum Contrast* SimClr* Chelsea Finn - SayCan* Agent Protocol - part of the AI Engineer Foundation* Xerox PARC* Michael Nielsen* Jason Benn* Outset Capital* Scenius - Kevin Kelly* South Park Commons* The Archive* Thursday Nights in AITranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, Partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai. [00:00:19]Swyx: Hey, and today in the studio we have Kanjun from Imbue. Welcome. So you and I have, I guess, crossed paths a number of times. You're formerly named Generally Intelligent and you've just announced your rename, rebrand in huge, humongous ways. So congrats on all of that. And we're here to dive in into deeper detail on Imbue. We like to introduce you on a high level basis, but then have you go into a little bit more of your personal side. So you graduated your BS at MIT and you also spent some time at the MIT Media Lab, one of the most famous, I guess, computer hacking labs in the world. Then you graduated MIT and you went straight into BizOps at Dropbox, where you're eventually chief of staff, which is a pretty interesting role we can dive into later. And then it seems like the founder bug hit you. You were basically a three times founder at Ember, Sorceress, and now at Generally Intelligent slash Imbue. What should people know about you on the personal side that's not on your LinkedIn? That's something you're very passionate about outside of work. [00:01:12]Kanjun: Yeah. I think if you ask any of my friends, they would tell you that I'm obsessed with agency, like human agency and human potential. [00:01:19]Swyx: That's work. Come on.Kanjun: It's not work. What are you talking about?Swyx: So what's an example of human agency that you try to promote? [00:01:27]Kanjun: With all of my friends, I have a lot of conversations with them that's kind of helping figure out what's blocking them. I guess I do this with a team kind of automatically too. And I think about it for myself often, like building systems. I have a lot of systems to help myself be more effective. At Dropbox, I used to give this onboarding talk called How to Be Effective, which people liked. I think like a thousand people heard this onboarding talk, and I think maybe Dropbox was more effective. I think I just really believe that as humans, we can be a lot more than we are. And it's what drives everything. I guess completely outside of work, I do dance. I do partner dance. [00:02:03]Swyx: Yeah. Lots of interest in that stuff, especially in the sort of group living houses in San Francisco, which I've been a little bit part of, and you've also run one of those. [00:02:12]Kanjun: That's right. Yeah. I started the archive with two friends, with Josh, my co-founder, and a couple of other folks in 2015. That's right. And GPT-3, our housemates built. [00:02:22]Swyx: Was that the, I guess, the precursor to Generally Intelligent, that you started doing more things with Josh? Is that how that relationship started? Yeah. [00:02:30]Kanjun: This is our third company together. Our first company, Josh poached me from Dropbox for Ember. And there we built a really interesting technology, laser raster projector, VR headset. And then we were like, VR is not the thing we're most passionate about. And actually it was kind of early days when we both realized we really do believe that in our lifetimes, like computers that are intelligent are going to be able to allow us to do much more than we can do today as people and be much more as people than we can be today. And at that time, we actually, after Ember, we were like, work on AI research or start an AI lab. A bunch of our housemates were joining OpenAI, and we actually decided to do something more pragmatic to apply AI to recruiting and to try to understand like, okay, if we are actually trying to deploy these systems in the real world, what's required? And that was Sorceress. That taught us so much about maybe an AI agent in a lot of ways, like what does it actually take to make a product that people can trust and rely on? I think we never really fully got there. And it's taught me a lot about what's required. And it's kind of like, I think informed some of our approach and some of the way that we think about how these systems will actually get used by people in the real world. [00:03:42]Swyx: Just to go one step deeper on that, you're building AI agents in 2016 before it was cool. You got some muscle and you raised $30 million. Something was working. What do you think you succeeded in doing and then what did you try to do that did not pan out? [00:03:56]Kanjun: Yeah. So the product worked quite well. So Sorceress was an AI system that basically looked for candidates that could be a good fit and then helped you reach out to them. And this was a little bit early. We didn't have language models to help you reach out. So we actually had a team of writers that like, you know, customized emails and we automated a lot of the customization. But the product was pretty magical. Like candidates would just be interested and land in your inbox and then you can talk to them. As a hiring manager, that's such a good experience. I think there were a lot of learnings, both on the product and market side. On the market side, recruiting is a market that is endogenously high churn, which means because people start hiring and then we hire the role for them and they stop hiring. So the more we succeed, the more they... [00:04:39]Swyx: It's like the whole dating business. [00:04:40]Kanjun: It's the dating business. Exactly. Exactly. And I think that's the same problem as the dating business. And I was really passionate about like, can we help people find work that is more exciting for them? A lot of people are not excited about their jobs and a lot of companies are doing exciting things and the matching could be a lot better. But the dating business phenomenon like put a damper on that, like it's actually a pretty good business. But as with any business with like relatively high churn, the bigger it gets, the more revenue we have, the slower growth becomes because if 30% of that revenue you lose year over year, then it becomes a worse business. So that was the dynamic we noticed quite early on after our Series A. I think the other really interesting thing about it is we realized what was required for people to trust that these candidates were like well vetted and had been selected for a reason. And it's what actually led us, you know, a lot of what we do at Imbue is working on interfaces to figure out how do we get to a situation where when you're building and using agents, these agents are trustworthy to the end user. That's actually one of the biggest issues with agents that, you know, go off and do longer range goals is that I have to trust, like, did they actually think through this situation? And that really informed a lot of our work today. [00:05:52]Alessio: Let's jump into GI now, Imbue. When did you decide recruiting was done for you and you were ready for the next challenge? And how did you pick the agent space? I feel like in 2021, it wasn't as mainstream. Yeah. [00:06:07]Kanjun: So the LinkedIn says that it started in 2021, but actually we started thinking very seriously about it in early 2020, late 2019, early 2020. So what we were seeing is that scale is starting to work and language models probably will actually get to a point where like with hacks, they're actually going to be quite powerful. And it was hard to see that at the time, actually, because GPT-3, the early versions of it, there are all sorts of issues. We're like, oh, that's not that useful, but we could kind of see like, okay, you keep improving it in all of these different ways and it'll get better. What Josh and I were really interested in is how can we get computers that help us do bigger things? Like, you know, there's this kind of future where I think a lot about, you know, if I were born in 1900 as a woman, like my life would not be that fun. I'd spend most of my time like carrying water and literally like getting wood to put in the stove to cook food and like cleaning and scrubbing the dishes and, you know, getting food every day because there's no refrigerator, like all of these things, very physical labor. And what's happened over the last 150 years since the industrial revolution is we've kind of gotten free energy, like energy is way more free than it was 150 years ago. And so as a result, we've built all these technologies like the stove and the dishwasher and the refrigerator, and we have electricity and we have infrastructure, running water, all of these things that have totally freed me up to do what I can do now. And I think the same thing is true for intellectual energy. We don't really see it today, but because we're so in it, but our computers have to be micromanaged. You know, part of why people are like, oh, you're stuck to your screen all day. Well, we're stuck to our screen all day because literally nothing happens unless I'm doing something in front of my screen. I don't, you know, I can't send my computer off to do a bunch of stuff for me. And there is a future where that's not the case, where, you know, I can actually go off and do stuff and trust that my computer will pay my bills and figure out my travel plans and do the detailed work that I am not that excited to do so that I can like be much more creative and able to do things that I as a human, I'm very excited about and collaborate with other people. And there are things that people are uniquely suited for. So that's kind of always been the thing that has been really exciting to me. Like Josh and I have known for a long time, I think that, you know, whatever AI is, it would happen in our lifetimes. And the personal computer kind of started giving us a bit of free intellectual energy. And this is like really the explosion of free intellectual energy. So in early 2020, we were thinking about this and what happened was self-supervised learning basically started working across everything. So worked in language, SimClear came out, I think MoCo had come out, Momentum Contrast had come out earlier in 2019, SimClear came out in early 2020. And we're like, okay, for the first time, self-supervised learning is working really well across images and text and suspect that like, okay, actually it's the case that machines can learn things the way that humans do. And if that's true, if they can learn things in a fully self-supervised way, because like as people, we are not supervised. We like go Google things and try to figure things out. So if that's true, then like what the computer could be is much bigger than what it is today. And so we started exploring ideas around like, how do we actually go? We didn't think about the fact that we could actually just build a research lab. So we were like, okay, what kind of startup could we build to like leverage self-supervised learning? So that eventually becomes something that allows computers to become much more able to do bigger things for us. But that became General Intelligence, which started as a research lab. [00:09:39]Alessio: So your mission is you aim to rekindle the dream of the personal computer. So when did it go wrong and what are like your first products and user facing things that you're building to rekindle it? [00:09:53]Kanjun: Yeah. So what we do at Imbue is we train large foundation models optimized for reasoning. And the reason for that is because reasoning is actually, we believe the biggest blocker to agents or systems that can do these larger goals. If we think about something that writes an essay, like when we write an essay, we like write it. We put it and then we're done. We like write it and then we look at it and we're like, oh, I need to do more research on that area. I'm going to go do some research and figure it out and come back and, oh, actually it's not quite right. The structure of the outline. So I'm going to rearrange the outline, rewrite it. It's this very iterative process and it requires thinking through like, okay, what am I trying to do? Is the goal correct? Also like, has the goal changed as I've learned more? So as a tool, like when should I ask the user questions? I shouldn't ask them questions all the time, but I should ask them questions in higher risk situations. How certain am I about the like flight I'm about to book? There are all of these notions of like risk certainty, playing out scenarios, figuring out how to make a plan that makes sense, how to change the plan, what the goal should be. That are things that we lump under the bucket of reasoning and models today, they're not optimized for reasoning. It turns out that there's not actually that much explicit reasoning data on the internet as you would expect. And so we get a lot of mileage out of optimizing our models for reasoning in pre-training. And then on top of that, we build agents ourselves and we, I can get into, we really believe in serious use, like really seriously using the systems and trying to get to an agent that we can use every single day, tons of agents that we can use every single day. And then we experiment with interfaces that help us better interact with the agents. So those are some set of things that we do on the kind of model training and agent side. And then the initial agents that we build, a lot of them are trying to help us write code better because code is most of what we do every day. And then on the infrastructure and theory side, we actually do a fair amount of theory work to understand like, how do these systems learn? And then also like, what are the right abstractions for us to build good agents with, which we can get more into. And if you look at our website, we build a lot of tools internally. We have a like really nice automated hyperparameter optimizer. We have a lot of really nice infrastructure and it's all part of the belief of like, okay, let's try to make it so that the humans are doing the things humans are good at as much as possible. So out of our very small team, we get a lot of leverage. [00:12:18]Swyx: And so would you still categorize yourself as a research lab now, or are you now in startup mode? Is that a transition that is conscious at all? [00:12:26]Kanjun: That's a really interesting question. I think we've always intended to build, you know, to try to build the next version of the computer, enable the next version of the computer. The way I think about it is there's a right time to bring a technology to market. So Apple does this really well. Actually, iPhone was under development for 10 years, AirPods for five years. And Apple has a story where iPhone, the first multi-touch screen was created. They actually were like, oh wow, this is cool. Let's like productionize iPhone. They actually brought, they like did some work trying to productionize it and realized this is not good enough. And they put it back into research to try to figure out like, how do we make it better? What are the interface pieces that are needed? And then they brought it back into production. So I think of production and research as kind of like these two separate phases. And internally we have that concept as well, where like things need to be done in order to get to something that's usable. And then when it's usable, like eventually we figure out how to productize it. [00:13:20]Alessio: What's the culture like to make that happen, to have both like kind of like product oriented, research oriented. And as you think about building the team, I mean, you just raised 200 million. I'm sure you want to hire more people. What are like the right archetypes of people that work at Imbue? [00:13:35]Kanjun: I would say we have a very unique culture in a lot of ways. I think a lot about social process design. So how do you design social processes that enable people to be effective? I like to think about team members as creative agents, because most companies, they think of their people as assets and they're very proud of this. And I think about like, okay, what is an asset? It's something you own that provides you value that you can discard at any time. This is a very low bar for people. This is not what people are. And so we try to enable everyone to be a creative agent and to really unlock their superpowers. So a lot of the work I do, you know, I was mentioning earlier, I'm like obsessed with agency. A lot of the work I do with team members is try to figure out like, you know, what are you really good at? What really gives you energy and where can we put you such that, how can I help you unlock that and grow that? So much of our work, you know, in terms of team structure, like much of our work actually comes from people. Carbs, our hyperparameter optimizer came from Abe trying to automate his own research process doing hyperparameter optimization. And he actually pulled some ideas from plasma physics. He's a plasma physicist to make the local search work. A lot of our work on evaluations comes from a couple of members of our team who are like obsessed with evaluations. We do a lot of work trying to figure out like, how do you actually evaluate if the model is getting better? Is the model making better agents? Is the agent actually reliable? A lot of things kind of like, I think of people as making the like them shaped blob inside imbue and I think, you know, yeah, that's the kind of person that we're, we're hiring for. We're hiring product engineers and data engineers and research engineers and all these roles. We have projects, not teams. We have a project around data, data collection and data engineering. That's actually one of the key things that improve the model performance. We have a pre-training kind of project with some fine tuning as part of that. And then we have an agent's project that's like trying to build on top of our models as well as use other models in the outside world to try to make agents then we actually use as programmers every day. So all sorts of different, different projects. [00:15:37]Swyx: As a founder, you're now sort of a capital allocator among all of these different investments effectively at different projects. And I was interested in how you mentioned that you were optimizing for improving reasoning and specifically inside of your pre-training, which I assume is just a lot of data collection. [00:15:55]Kanjun: We are optimizing reasoning inside of our pre-trained models. And a lot of that is about data. And I can talk more about like what, you know, what exactly does it involve? But actually big, maybe 50% plus of the work is figuring out even if you do have models that reason well, like the models are still stochastic. The way you prompt them still makes, is kind of random, like makes them do random things. And so how do we get to something that is actually robust and reliable as a user? How can I, as a user, trust it? We have all sorts of cool things on the, like, you know, I was mentioning earlier when I talked to other people building agents, they have to do so much work, like to try to get to something that they can actually productize and it takes a long time and agents haven't been productized yet for, partly for this reason is that like the abstractions are very leaky. We can get like 80% of the way there, but like self-driving cars, like the remaining 20% is actually really difficult. We believe that, and we have internally, I think some things that like an interface, for example, that lets me really easily like see what the agent execution is, fork it, try out different things, modify the prompt, modify like the plan that it is making. This type of interface, it makes it so that I feel more like I'm collaborating with the agent as it's executing, as opposed to it's just like doing something as a black box. That's an example of a type of thing that's like beyond just the model pre-training, but on the model pre-training side, like reasoning is a thing that we optimize for. And a lot of that is about what data do we put in. [00:17:27]Swyx: It's interesting just because I always think like, you know, out of the levers that you have, the resources that you have, I think a lot of people think that running foundation model company or a research lab is going to be primarily compute. And I think the share of compute has gone down a lot over the past three years. It used to be the main story, like the main way you scale is you just throw more compute at it. And now it's like, Flops is not all you need. You need better data, you need better algorithms. And I wonder where that shift has gone. This is a very vague question, but is it like 30-30-30 now? Is it like maybe even higher? So one way I'll put this is people estimate that Llama2 maybe took about three to $4 million of compute, but probably 20 to $25 million worth of labeling data. And I'm like, okay, well that's a very different story than all these other foundation model labs raising hundreds of millions of dollars and spending it on GPUs. [00:18:20]Kanjun: Data is really expensive. We generate a lot of data. And so that does help. The generated data is close to actually good, as good as human labeled data. [00:18:34]Swyx: So generated data from other models? [00:18:36]Kanjun: From our own models. From your own models. Or other models, yeah. [00:18:39]Swyx: Do you feel like there's certain variations of this? There's the sort of the constitutional AI approach from Anthropic and basically models sampling training on data from other models. I feel like there's a little bit of like contamination in there, or to put it in a statistical form, you're resampling a distribution that you already have that you already know doesn't match human distributions. How do you feel about that basically, just philosophically? [00:19:04]Kanjun: So when we're optimizing models for reasoning, we are actually trying to like make a part of the distribution really spiky. So in a sense, like that's actually what we want. We want to, because the internet is a sample of the human distribution that's also skewed in all sorts of ways. That is not the data that we necessarily want these models to be trained on. And so when we're generating data, we're not really randomly generating data. We generate very specific things that are like reasoning traces and that help optimize reasoning. Code also is a big piece of improving reasoning. So generated code is not that much worse than like regular human written code. You might even say it can be better in a lot of ways. So yeah. So we are trying to already do that. [00:19:50]Alessio: What are some of the tools that you thought were not a good fit? So you built Avalon, which is your own simulated world. And when you first started, the metagame was like using games to simulate things using, you know, Minecraft and then OpenAI is like the gym thing and all these things. And I think in one of your other podcasts, you mentioned like Minecraft is like way too slow to actually do any serious work. Is that true? Yeah. I didn't say it. [00:20:17]Swyx: I don't know. [00:20:18]Alessio: That's above my pay grade. But Avalon is like a hundred times faster than Minecraft for simulation. When did you figure that out that you needed to just like build your own thing? Was it kind of like your engineering team was like, Hey, this is too slow. Was it more a long-term investment? [00:20:34]Kanjun: Yeah. At that time we built Avalon as a research environment to help us learn particular things. And one thing we were trying to learn is like, how do you get an agent that is able to do many different tasks? Like RL agents at that time and environments at that time. What we heard from other RL researchers was the like biggest thing keeping holding the field back is lack of benchmarks that let us explore things like planning and curiosity and things like that and have the agent actually perform better if the agent has curiosity. And so we were trying to figure out in a situation where, how can we have agents that are able to handle lots of different types of tasks without the reward being pretty handcrafted? That's a lot of what we had seen is that like these very handcrafted rewards. And so Avalon has like a single reward it's across all tasks. And it also allowed us to create a curriculum so we could make the level more or less difficult. And it taught us a lot, maybe two primary things. One is with no curriculum, RL algorithms don't work at all. So that's actually really interesting. [00:21:43]Swyx: For the non RL specialists, what is a curriculum in your terminology? [00:21:46]Kanjun: So a curriculum in this particular case is basically the environment Avalon lets us generate simpler environments and harder environments for a given tasks. What's interesting is that the simpler environments, what you'd expect is the agent succeeds more often. So it gets more reward. And so, you know, kind of my intuitive way of thinking about it is, okay, the reason why it learns much faster with a curriculum is it's just getting a lot more signal. And that's actually an interesting general intuition to have about training these things as like, what kind of signal are they getting? And like, how can you help it get a lot more signal? The second thing we learned is that reinforcement learning is not a good vehicle, like pure reinforcement learning is not a good vehicle for planning and reasoning. So these agents were not able to, they were able to learn all sorts of crazy things. They could learn to climb like hand over hand in VR climbing, they could learn to open doors like very complicated, like multiple switches and a lever open the door, but they couldn't do any higher level things. And they couldn't do those lower level things consistently necessarily. And as a user, I do not want to interact with a pure reinforcement learning end to end RL agent. As a user, like I need much more control over what that agent is doing. And so that actually started to get us on the track of thinking about, okay, how do we do the reasoning part in language? And we were pretty inspired by our friend Chelsea Finn at Stanford was I think working on SACAN at the time where it's basically an experiment where they have robots kind of trying to do different tasks and actually do the reasoning for the robot in natural language. And it worked quite well. And that led us to start experimenting very seriously with reasoning. [00:23:31]Alessio: How important is the language part for the agent versus for you to inspect the agent? You know, like is it the interface to kind of the human on the loop really important or? [00:23:43]Kanjun: Yeah, I personally think of it as it's much more important for us, the human user. So I think you probably could get end to end agents that work and are fairly general at some point in the future. But I think you don't want that. Like we actually want agents that we can like perturb while they're trying to figure out what to do. Because, you know, even a very simple example, internally we have like a type error fixing agent and we have like a test generation agent. Test generation agent goes off rails all the time. I want to know, like, why did it generate this particular test? [00:24:19]Swyx: What was it thinking? [00:24:20]Kanjun: Did it consider, you know, the fact that this is calling out to this other function? And the formatter agent, if it ever comes up with anything weird, I want to be able to debug like what happened with RL end to end stuff. Like we couldn't do that. Yeah. [00:24:36]Swyx: It sounds like you have a bunch of agents operating internally within the company. What's your most, I guess, successful agent and what's your least successful one? [00:24:44]Kanjun: The agents don't work. All of them? I think the only successful agents are the ones that do really small things. So very specific, small things like fix the color of this button on the website or like change the color of this button. [00:24:57]Swyx: Which is now sweep.dev is doing that. Exactly. [00:25:00]Kanjun: Perfect. Okay. [00:25:02]Swyx: Well, we should just use sweep.dev. Well, I mean, okay. I don't know how often you have to fix the color of a button, right? Because all of them raise money on the idea that they can go further. And my fear when encountering something like that is that there's some kind of unknown asymptote ceiling that's going to prevent them, that they're going to run head on into that you've already run into. [00:25:21]Kanjun: We've definitely run into such a ceiling. But what is the ceiling? [00:25:24]Swyx: Is there a name for it? Like what? [00:25:26]Kanjun: I mean, for us, we think of it as reasoning plus these tools. So reasoning plus abstractions, basically. I think actually you can get really far with current models and that's why it's so compelling. Like we can pile debugging tools on top of these current models, have them critique each other and critique themselves and do all of these, like spend more computer inference time, context hack, retrieve augmented generation, et cetera, et cetera, et cetera. Like the pile of hacks actually does get us really far. And a way to think about it is like the underlying language model is kind of like a noisy channel. Actually I don't want to use this analogy. It's actually a really bad analogy, but you kind of like trying to get more signal out of the channel. We don't like to think about it that way. It's what the default approach is, is like trying to get more signal out of this noising channel. But the issue with agents is as a user, I want it to be mostly reliable. It's kind of like self-driving in that way. Like it's not as bad as self-driving, like in self-driving, you know, you're like hurtling at 70 miles an hour. It's like the hardest agent problem. But one thing we learned from Sorceress and one thing we learned by using these things internally is we actually have a pretty high bar for these agents to work. You know, it's actually really annoying if they only work 50% of the time and we can make interfaces to make it slightly less annoying. But yeah, there's a ceiling that we've encountered so far and we need to make the models better. We also need to make the kind of like interface to the user better. And also a lot of the like critiquing. I hope what we can do is help people who are building agents actually like be able to deploy them. I think, you know, that's the gap that we see a lot of today is everyone who's trying to build agents to get to the point where it's robust enough to be deployable. It just, it's like an unknown amount of time. Okay. [00:27:12]Swyx: So this goes back into what Embu is going to offer as a product or a platform. How are you going to actually help people deploy those agents? Yeah. [00:27:21]Kanjun: So our current hypothesis, I don't know if this is actually going to end up being the case. We've built a lot of tools for ourselves internally around like debugging, around abstractions or techniques after the model generation happens. Like after the language model generates the text and like interfaces for the user and the underlying model itself, like models talking to each other, maybe some set of those things kind of like an operating system. Some set of those things will be helpful for other people. And we'll figure out what set of those things is helpful for us to make our agents. Like what we want to do is get to a point where we can like start making an agent, deploy it, it's reliable, like very quickly. And there's a similar analog to software engineering, like in the early days, in the seventies and the sixties, like to program a computer, like you have to go all the way down to the registers and write things and eventually we had assembly. That was like an improvement. But then we wrote programming languages with these higher levels of abstraction and that allowed a lot more people to do this and much faster. And the software created is much less expensive. And I think it's basically a similar route here where we're like in the like bare metal phase of agent building. And we will eventually get to something with much nicer abstractions. [00:28:36]Alessio: We had this conversation with George Hotz and we were like, there's not a lot of reasoning data out there. And can the models really understand? And his take was like, look, with enough compute, you're not that complicated as a human. Like the model can figure out eventually why certain decisions are made. What's been your experience? Like as you think about reasoning data, like do you have to do a lot of like manual work or like is there a way to prompt models to extract the reasoning from actions that they [00:29:03]Swyx: see? [00:29:03]Kanjun: So we don't think of it as, oh, throw enough data at it and then it will figure out what the plan should be. I think we're much more explicit. You know, a way to think about it is as humans, we've learned a lot of reasoning strategies over time. We are better at reasoning now than we were 3000 years ago. An example of a reasoning strategy is noticing you're confused. Then when I notice I'm confused, I should ask like, huh, what was the original claim that was made? What evidence is there for this claim? Does the evidence support the claim? Is the claim correct? This is like a reasoning strategy that was developed in like the 1600s, you know, with like the advent of science. So that's an example of a reasoning strategy. There are tons of them. We employ all the time, lots of heuristics that help us be better at reasoning. And we didn't always have them. And because they're invented, like we can generate data that's much more specific to them. So I think internally, yeah, we have a lot of thoughts on what reasoning is and we generate a lot more specific data. We're not just like, oh, it'll figure out reasoning from this black box or like it'll figure out reasoning from the data that exists. Yeah. [00:30:04]Alessio: I mean, the scientific method is like a good example. If you think about hallucination, right, people are thinking, how do we use these models to do net new, like scientific research? And if you go back in time and the model is like, well, the earth revolves around the sun and people are like, man, this model is crap. It's like, what are you talking about? Like the sun revolves around the earth. It's like, how do you see the future? Like if the models are actually good enough, but we don't believe them, it's like, how do we make the two live together? So you're like, you use Inbu as a scientist to do a lot of your research and Inbu tells you, hey, I think this is like a serious path you should go down. And you're like, no, that sounds impossible. Like how is that trust going to be built? And like, what are some of the tools that maybe are going to be there to inspect it? [00:30:51]Kanjun: Really there are two answers to this. One element of it is as a person, like I need to basically get information out of the model such that I can try to understand what's going on with the model. Then the second question is like, okay, how do you do that? And that's kind of some of our debugging tools, they're not necessarily just for debugging. They're also for like interfacing with and interacting with the model. So like if I go back in this reasoning trace and like change a bunch of things, what's going to happen? Like, what does it conclude instead? So that kind of helps me understand like, what are its assumptions? And, you know, we think of these things as tools. And so it's really about like, as a user, how do I use this tool effectively? I need to be willing to be convinced as well. It's like, how do I use this tool effectively? And what can it help me with? [00:31:36]Swyx: And what can it tell me? There's a lot of mention of code in your process. And I was hoping to dive in even deeper. I think we might run the risk of giving people the impression that you view code or you use code just as like a tool within InView just for coding assistance. But I think you actually train code models. And I think there's a lot of informal understanding about how adding code to language models improves their reasoning capabilities. I wonder if there's any research or findings that you have to share that talks about the intersection of code and reasoning. Hmm. Yeah. [00:32:08]Kanjun: So the way I think about it intuitively is like code is the most explicit example of reasoning data on the internet. [00:32:15]Swyx: Yeah. [00:32:15]Kanjun: And it's not only structured, it's actually very explicit, which is nice. You know, it says this variable means this, and then it uses this variable. And then the function does this. As people, when we talk in language, it takes a lot more to extract that explicit structure out of our language. And so that's one thing that's really nice about code is I see it as almost like a curriculum for reasoning. I think we use code in all sorts of ways. The coding agents are really helpful for us to understand what are the limitations of the agents. The code is really helpful for the reasoning itself. But also code is a way for models to act. So by generating code, it can act on my computer. And, you know, when we talk about rekindling the dream of the personal computer, kind of where I see computers going is, you know, like computers will eventually become these much more malleable things where I, as a user today, I have to know how to write software code, like in order to make my computer do exactly what I want it to do. But in the future, if the computer is able to generate its own code, then I can actually interface with it in natural language. And so one way we think about agents is kind of like a natural language programming language. It's a way to program my computer in natural language that's much more intuitive to me as a user. And these interfaces that we're building are essentially IDEs for users to program our computers in natural language. Maybe I should say what we're doing that way. Maybe it's clearer. [00:33:47]Swyx: I don't know. [00:33:47]Alessio: That's a good pitch. What do you think about the different approaches people have, kind of like text first, browser first, like multi-on? What do you think the best interface will be? Or like, what is your, you know, thinking today? [00:33:59]Kanjun: In a lot of ways, like chat as an interface, I think Linus, Linus Lee, you had on this. I really like how he put it. Chat as an interface is skeuomorphic. So in the early days, when we made word processors on our computers, they had notepad lines because that's what we understood these like objects to be. Chat, like texting someone is something we understand. So texting our AI is something that we understand. But today's word documents don't have notepad lines. And similarly, the way we want to interact with agents, like chat is a very primitive way of interacting with agents. What we want is to be able to inspect their state and to be able to modify them and fork them and all of these other things. And we internally have, think about what are the right representations for that? Like architecturally, like what are the right representations? What kind of abstractions do we need to build? And how do we build abstractions that are not leaky? Because if the abstractions are leaky, which they are today, like, you know, this stochastic generation of text is like a leaky abstraction. I cannot depend on it. And that means it's actually really hard to build on top of. But our experience and belief is actually by building better abstractions and better tooling, we can actually make these things non-leaky. And now you can build like whole things on top of them. So these other interfaces, because of where we are, we don't think that much about them. [00:35:17]Swyx: Yeah. [00:35:17]Alessio: I mean, you mentioned, this is kind of like the Xerox Spark moment for AI. And we had a lot of stuff come out of Parc, like the, what you see is what you got editors and like MVC and all this stuff. But yeah, but then we didn't have the iPhone at Parc. We didn't have all these like higher things. What do you think it's reasonable to expect in like this era of AI, you know, call it like five years or so? Like what are like the things we'll build today and what are things that maybe we'll see in kind of like the second wave of products? [00:35:46]Kanjun: That's interesting. I think the waves will be much faster than before. Like what we're seeing right now is basically like a continuous wave. Let me zoom a little bit earlier. So people like the Xerox Parc analogy I give, but I think there are many different analogies. Like one is the like analog to digital computer is kind of an example, like another analogy to where we are today. The analog computer Vannevar Bush built in the 1930s, I think, and it's like a system of pulleys and it can only calculate one function. Like it can calculate like an integral. And that was so magical at the time because you actually did need to calculate this integral bunch, but it had a bunch of issues like in analog errors compound. And so there was actually a set of breakthroughs necessary in order to get to the digital computer, like Turing's decidability, Shannon. I think the like whole like relay circuits can be thought of as can be mapped to Boolean operators and a set of other like theoretical breakthroughs, which essentially were abstractions. They were like creating abstractions for these like very like lossy circuits. They were creating abstractions for these like very analog circuits and digital had this nice property of like being error correcting. And so when I talk about like less leaky abstractions, that's what I mean. That's what I'm kind of pointing a little bit to. It's not going to look exactly the same way. And then the Xerox PARC piece, a lot of that is about like, how do we get to computers that as a person, I can actually use well. And the interface actually helps it unlock so much more power. So the sets of things we're working on, like the sets of abstractions and the interfaces, like hopefully that like help us unlock a lot more power in these systems. Like hopefully that'll come not too far in the future. I could see a next version, maybe a little bit farther out. It's like an agent protocol. So a way for different agents to talk to each other and call each other. Kind of like HTTP. [00:37:40]Swyx: Do you know it exists already? [00:37:41]Kanjun: Yeah, there is a nonprofit that's working on one. I think it's a bit early, but it's interesting to think about right now. Part of why I think it's early is because the issue with agents, it's not quite like the internet where you could like make a website and the website would appear. The issue with agents is that they don't work. And so it may be a bit early to figure out what the protocol is before we really understand how these agents get constructed. But, you know, I think that's, I think it's a really interesting question. [00:38:09]Swyx: While we're talking on this agent to agent thing, there's been a bit of research recently on some of these approaches. I tend to just call them extremely complicated chain of thoughting, but any perspectives on kind of meta-GPT, I think it's the name of the paper. I don't know if you care about at the level of individual papers coming out, but I did read that recently and TLDR, it beat GPT-4 and human eval by role-playing software agent development agency, instead of having sort of single shot or single role, you have multiple roles and how having all of them criticize each other as agents communicating with other agents. [00:38:45]Kanjun: Yeah, I think this is an example of an interesting abstraction of like, okay, can I just plop in this like multi-role critiquing and see how it improves my agent? And can I just plop in chain of thought, tree of thought, plop in these other things and see how they improve my agent? One issue with this kind of prompting is that it's still not very reliable. It's like, there's one lens, which is like, okay, if you do enough of these techniques, you'll get to high reliability. And I think actually that's a pretty reasonable lens. We take that lens often. And then there's another lens that's like, okay, but it's starting to get really messy what's in the prompt and like, how do we deal with that messiness? And so maybe you need like cleaner ways of thinking about and constructing these systems. And we also take that lens. So yeah, I think both are necessary. Yeah. [00:39:29]Swyx: Side question, because I feel like this also brought up another question I had for you. I noticed that you work a lot with your own benchmarks, your own evaluations of what is valuable. I would say I would contrast your approach with OpenAI as OpenAI tends to just lean on, hey, we played StarCraft or hey, we ran it on the SAT or the, you know, the AP bio test and that did results. Basically, is benchmark culture ruining AI? [00:39:55]Swyx: Or is that actually a good thing? Because everyone knows what an SAT is and that's fine. [00:40:04]Kanjun: I think it's important to use both public and internal benchmarks. Part of why we build our own benchmarks is that there are not very many good benchmarks for agents, actually. And to evaluate these things, you actually need to think about it in a slightly different way. But we also do use a lot of public benchmarks for like, is the reasoning capability in this particular way improving? So yeah, it's good to use both. [00:40:26]Swyx: So for example, the Voyager paper coming out of NVIDIA played Minecraft and set their own benchmarks on getting the Diamond X or whatever and exploring as much of the territory as possible. And I don't know how that's received. That's obviously fun and novel for the rest of the engineer, the people who are new to the scene. But for people like yourselves, you build Avalon just because you already found deficiencies with using Minecraft. Is that valuable as an approach? Oh, yeah. I love Voyager. [00:40:57]Kanjun: I mean, Jim, I think is awesome. And I really like the Voyager paper and I think it has a lot of really interesting ideas, which is like the agent can create tools for itself and then use those tools. [00:41:06]Swyx: He had the idea of the curriculum as well, which is something that we talked about earlier. Exactly. [00:41:09]Kanjun: And that's like a lot of what we do. We built Avalon mostly because we couldn't use Minecraft very well to like learn the things we wanted. And so it's like not that much work to build our own. [00:41:19]Swyx: It took us, I don't know. [00:41:22]Kanjun: We had like eight engineers at the time, took about eight weeks. So six weeks. [00:41:27]Swyx: And OpenAI built their own as well, right? Yeah, exactly. [00:41:30]Kanjun: It's just nice to have control over our environment. But if you're doing our own sandbox to really trying to inspect our own research questions. But if you're doing something like experimenting with agents and trying to get them to do things like Minecraft is a really interesting environment. And so Voyager has a lot of really interesting ideas in it. [00:41:47]Swyx: Yeah. Cool. One more element that we had on this list, which is context and memory. I think that's kind of like the foundational, quote unquote, RAM of our era. I think Andrej Karpathy has already made this comparison. So there's nothing new here. And that's just the amount of working knowledge that we can fit into one of these agents. And it's not a lot, right? Especially if you need to get them to do long running tasks. If they need to self-correct from errors that they observe while operating in their environment. Do you see this as a problem? Do you think we're going to just trend to infinite context and that'll go away? Or how do you think we're going to deal with it? [00:42:22]Kanjun: I think when you talked about what's going to happen in the first wave and then in the second wave, I think what we'll see is we'll get like relatively simplistic agents pretty soon. And they will get more and more complex. And there's like a future wave in which they are able to do these like really difficult, really long running tasks. And the blocker to that future, one of the blockers is memory. And that was true of computers too. You know, I think when von Neumann made the von Neumann architecture, he was like, the biggest blocker will be like, we need this amount of memory, which is like, I don't remember exactly like 32 kilobytes or something to store programs. And that will allow us to write software. He didn't say it this way because he didn't have these terms, but that only really was like happened in the seventies with the microchip revolution. It may be the case that we're waiting for some research breakthroughs or some other breakthroughs in order for us to have like really good long running memory. And then in the meantime, agents will be able to do all sorts of things that are a little bit smaller than that. I do think with the pace of the field, we'll probably come up with all sorts of interesting things like, you know, RAG is already very helpful. [00:43:26]Swyx: Good enough, you think? [00:43:27]Kanjun: Maybe good enough for some things. [00:43:29]Swyx: How is it not good enough? I don't know. [00:43:31]Kanjun: I just think about a situation where you want something that's like an AI scientist. As a scientist, I have learned so much about my fields and a lot of that data is maybe hard to fine tune or on, or maybe hard to like put into pre-training. Like a lot of that data, I don't have a lot of like repeats of the data that I'm seeing. You know, like if I'm a scientist, I've like accumulated so many little data points. And ideally I'd want to store those somehow, or like use those to fine tune myself as a model somehow, or like have better memory somehow. I don't think RAG is enough for that kind of thing. But RAG is certainly enough for like user preferences and things like that. Like what should I do in this situation? What should I do in that situation? That's a lot of tasks. We don't have to be a scientist right away. Awesome. [00:44:21]Swyx: I have a hard question, if you don't mind me being bold. Yeah. I think the most comparable lab to InView is Adept. You know, a research lab with like some amount of product situation on the horizon, but not just yet, right? Why should people work for InView over Adept? And we can cut this if it's too like... Yeah. [00:44:40]Kanjun: The way I think about it is I believe in our approach. The type of thing that we're doing is we're trying to like build something that enables other people to build agents and build something that really can be maybe something like an operating system for agents. I know that that's what we're doing. I don't really know what everyone else is doing. You know, I can kind of like talk to people and have some sense of what they're doing. And I think it's a mistake to focus too much on what other people are doing, because extremely focused execution on the right thing is what matters. To the question of like, why us? I think like strong focus on reasoning, which we believe is the biggest blocker, on inspectability, which we believe is really important for user experience and also for the power and capability of these systems. Building non-leaky, good abstractions, which we believe is solving the core issue of agents, which is around reliability and being able to make them deployable. And then really seriously trying to use these things ourselves, like every single day, and getting to something that we can actually ship to other people that becomes something that is a platform. Like, it feels like it could be Mac or Windows. I love the dogfooding approach. [00:45:49]Swyx: That's extremely important. And you will not be surprised how many agent companies I talk to that don't use their own agent. Oh no, that's not good. That's a big surprise. [00:45:59]Kanjun: Yeah, I think if we didn't use our own agents, then we would have all of these beliefs about how good they are. Wait, did you have any other hard questions you wanted to ask? [00:46:08]Swyx: Yeah, mine was just the only other follow-up that you had based on the answer you just gave was, do you see yourself releasing models or do you see yourself, what is the artifacts that you want to produce that lead up to the general operating system that you want to have people use, right? And so a lot of people just as a byproduct of their work, just to say like, hey, I'm still shipping, is like, here's a model along the way. Adept took, I don't know, three years, but they released Persimmon recently, right? Like, do you think that kind of approach is something on your horizon? Or do you think there's something else that you can release that can show people, here's kind of the idea, not the end products, but here's the byproducts of what we're doing? [00:46:51]Kanjun: Yeah, I don't really believe in releasing things to show people like, oh, here's what we're doing that much. I think as a philosophy, we believe in releasing things that will be helpful to other people. [00:47:02]Swyx: Yeah. [00:47:02]Kanjun: And so I think we may release models or we may release tools that we think will help agent builders. Ideally, we would be able to do something like that, but I'm not sure exactly what they look like yet. [00:47:14]Swyx: I think more companies should get into the releasing evals and benchmarks game. Yeah. [00:47:20]Kanjun: Something that we have been talking to agent builders about is co-building evals. So we build a lot of our own evals and every agent builder tells me, basically evals are their biggest issue. And so, yeah, we're exploring right now. And if you are building agents, please reach out to me because I would love to, like, figure out how we can be helpful based on what we've seen. Cool. [00:47:40]Swyx: That's a good call to action. I know a bunch of people that I can send your way. Cool. Great. [00:47:43]Kanjun: Awesome. [00:47:44]Swyx: Yeah. We can zoom out to other interests now. [00:47:46]Alessio: We got a lot of stuff. So we have Sherif from Lexicon, the podcast. He had a lot of interesting questions on his website. You similarly have a lot of them. Yeah. [00:47:55]Swyx: I need to do this. I'm very jealous of people with personal websites right there. Like, here's the high level questions of goals of humanity that I want to set people on. And I don't have that. [00:48:04]Alessio: It's never too late, Sean. [00:48:05]Swyx: Yeah. [00:48:05]Alessio: It's never too late. [00:48:06]Kanjun: Exactly. [00:48:07]Alessio: There were a few that stuck out as related to your work that maybe you're kind of learning [00:48:12]Swyx: more about it. [00:48:12]Alessio: So one is why are curiosity and goal orientation often at odds? And from a human perspective, I get it. It's like, you know, would you want to like go explore things or kind of like focus on your career? How do you think about that from like an agent perspective? Where it's like, should you just stick to the task and try and solve it as in the guardrails as possible? Or like, should you look for alternative solutions? [00:48:34]Swyx: Yeah. [00:48:34]Kanjun: I think one thing that's really interesting about agents actually is that they can be forked. Like, you know, we can take an agent that's executed to a certain place and said, okay, here, like fork this and do a bunch of different things. I try a bunch of different things. Some of those agents can be goal oriented and some of them can be like more curiosity driven. You can prompt them in slightly different ways. And something I'm really curious about, like what would happen if in the future, you know, we were able to actually go down both paths. As a person, why I have this question on my website is I really find that like I really can only take one mode at a time and I don't understand why. And like, is it inherent in like the kind of context that needs to be held? That's why I think from an agent perspective, like forking it is really interesting. Like I can't fork myself to do both, but I maybe could fork an agent to like add a certain point in a task. [00:49:26]Swyx: Yeah. Explore both. Yeah. [00:49:28]Alessio: How has the thinking changed for you as the funding of the company changed? That's one thing that I think a lot of people in the space think is like, oh, should I raise venture capital? Like, how should I get money? How do you feel your options to be curious versus like goal oriented has changed as you raise more money and kind of like the company has grown? [00:49:50]Kanjun: Oh, that's really funny. Actually, things have not changed that much. So we raised our Series A $20 million in late 2021. And our entire philosophy at that time was, and still kind of is, is like, how do we figure out the stepping stones, like collect stepping stones that eventually let us build agents, kind of these new computers that help us do bigger things. And there was a lot of curiosity in that. And there was a lot of goal orientation in that. Like the curiosity led us to build CARBS, for example, this hyperparameter optimizer. Great name, by the way. [00:50:28]Swyx: Thank you. [00:50:29]Kanjun: Is there a story behind that name? [00:50:30]Swyx: Yeah. [00:50:31]Kanjun: Abe loves CARBS. It's also cost aware. So as soon as he came up with cost aware, he was like, I need to figure out how to make this work. But the cost awareness of it was really important. So that curiosity led us to this really cool hyperparameter optimizer. That's actually a big part of how we do our research. It lets us experiment on smaller models. And for those experiment results to carry to larger ones. [00:50:56]Swyx: Which you also published a scaling laws, which is great. I think the scaling laws paper from OpenAI was like the biggest. And from Google, I think, was the greatest public service to machine learning that any research lab can do. Yeah, totally. [00:51:10]Kanjun: What was nice about CARBS is it gave us scaling laws for all sorts of hyperparameters. So yeah, that's cool. It basically hasn't changed very much. So there's some curiosity. And then there's some goal oriented parts. Like Avalon, it was like a six to eight week sprint for all of us. And we got this thing out. And then now different projects do like more curiosity or more goal orientation at different times. Cool. [00:51:36]Swyx: Another one of your questions that we highlighted was, how can we enable artificial agents to permanently learn new abstractions and processes? I think this is might be called online learning. [00:51:45]Kanjun: Yeah. So I struggle with this because, you know, that scientist example I gave. As a scientist, I've like permanently learned a lot of new things. And I've updated and created new abstractions and learned them pretty reliably. And you were talking about like, okay, we have this RAM that we can store learnings in. But how well does online learning actually work? And the answer right now seems to be like, as models get bigger, they fine tune faster. So they're more sample efficient as they get bigger. [00

Want to help define the AI Engineer stack? Have opinions on the top tools, communities and builders? We're collaborating with friends at Amplify to launch the first State of AI Engineering survey! Please fill it out (and tell your friends)!If AI is so important, why is its software so bad?This was the motivating question for Chris Lattner as he reconnected with his product counterpart on Tensorflow, Tim Davis, and started working on a modular solution to the problem of sprawling, monolithic, fragmented platforms in AI development. They announced a $30m seed in 2022 and, following their successful double launch of Modular/Mojo

As alluded to on the pod, LangChain has just launched LangChain Hub: “the go-to place for developers to discover new use cases and polished prompts.” It's available to everyone with a LangSmith account, no invite code necessary. Check it out!In 2023, LangChain has speedrun the race from 2:00 to 4:00 to 7:00 Silicon Valley Time. From the back to back $10m Benchmark seed and (rumored) $20-25m Sequoia Series A in April, to back to back critiques of “LangChain is Pointless” and “The Problem with LangChain” in July, to teaching with Andrew Ng and keynoting at basically every AI conference this fall (including ours), it has been an extreme rollercoaster for Harrison and his growing team creating one of the most popular (>60k stars at time of writing) building blocks for AI Engineers.LangChain's OriginsThe first commit to LangChain shows its humble origins as a light wrapper around Python's formatter.format for prompt templating. But as Harrison tells the story, even his first experience with text-davinci-002 in early 2022 was focused on chatting with data from their internal company Notion and Slack, what is now known as Retrieval Augmented Generation (RAG). As the Generative AI meetup scene came to life post Stable Diffusion, Harrison saw a need for common abstractions for what people were building with text LLMs at the time:* LLM Math, aka Riley Goodside's “You Can't Do Math” REPL-in-the-loop (PR #8)* Self-Ask With Search, Ofir Press' agent pattern (PR #9) (later ReAct, PR #24)* NatBot, Nat Friedman's browser controlling agent (PR #18)* Adapters for OpenAI, Cohere, and HuggingFaceHubAll this was built and launched in a few days from Oct 16-25, 2022. Turning research ideas/exciting usecases into software quickly and often has been in the LangChain DNA from Day 1 and likely a big driver of LangChain's success, to date amassing the largest community of AI Engineers and being the default launch framework for every big name from Nvidia to OpenAI:Dancing with GiantsBut AI Engineering is built atop of constantly moving tectonic shifts: * ChatGPT launched in November (“The Day the AGI Was Born”) and the API released in March. Before the ChatGPT API, OpenAI did not have a chat endpoint. In order to build a chatbot with history, you had to make sure to chain all messages and prompt for completion. LangChain made it easy to do that out of the box, which was a huge driver of usage. * Today, OpenAI has gone all-in on the chat API and is deprecating the old completions models, essentially baking in the chat pattern as the default way most engineers should interact with LLMs… and reducing (but not eliminating) the value of ConversationChains.* And there have been more updates since: Plugins released in API form as Functions in June (one of our top pods ever… reducing but not eliminating the value of OutputParsers) and Finetuning in August (arguably reducing some need for Retrieval and Prompt tooling). With each update, OpenAI and other frontier model labs realign the roadmaps of this nascent industry, and Harrison credits the modular design of LangChain in staying relevant. LangChain has not been merely responsive either: LangChain added Agents in November, well before they became the hottest topic of the AI Summer, and now Agents feature as one of LangChain's top two usecases. LangChain's problem for podcasters and newcomers alike is its sheer scope - it is the world's most complete AI framework, but it also has a sprawling surface area that is difficult to fully grasp or document in one sitting. This means it's time for the trademark Latent Space move (ChatGPT, GPT4, Auto-GPT, and Code Interpreter Advanced Data Analysis GPT4.5): the executive summary!What is LangChain?As Harrison explains, LangChain is an open source framework for building context-aware reasoning applications, available in Python and JS/TS.It launched in Oct 2022 with the central value proposition of “composability”, aka the idea that every AI engineer will want to switch LLMs, and combine LLMs with other things into “chains”, using a flexible interface that can be saved via a schema.Today, LangChain's principal offerings can be grouped as:* Components: isolated modules/abstractions* Model I/O* Models (for LLM/Chat/Embeddings, from OpenAI, Anthropic, Cohere, etc)* Prompts (Templates, ExampleSelectors, OutputParsers)* Retrieval (revised and reintroduced in March)* Document Loaders (eg from CSV, JSON, Markdown, PDF)* Text Splitters (15+ various strategies for chunking text to fit token limits)* Retrievers (generic interface for turning an unstructed query into a set of documents - for self-querying, contextual compression, ensembling)* Vector Stores (retrievers that search by similarity of embeddings)* Indexers (sync documents from any source into a vector store without duplication)* Memory (for long running chats, whether a simple Buffer, Knowledge Graph, Summary, or Vector Store)* Use-Cases: compositions of Components* Chains: combining a PromptTemplate, LLM Model and optional OutputParser* with Router, Sequential, and Transform Chains for advanced usecases* savable, sharable schemas that can be loaded from LangChainHub* Agents: a chain that has access to a suite of tools, of nondeterministic length because the LLM is used as a reasoning engine to determine which actions to take and in which order. Notable 100LOC explainer here.* Tools (interfaces that an agent can use to interact with the world - preset list here. Includes things like ChatGPT plugins, Google Search, WolframAlpha. Groups of tools are bundled up as toolkits)* AgentExecutor (the agent runtime, basically the while loop, with support for controls, timeouts, memory sharing, etc)* LangChain has also added a Callbacks system for instrumenting each stage of LLM, Chain, and Agent calls (which enables LangSmith, LangChain's first cloud product), and most recently an Expression Language, a declarative way to compose chains.LangChain the company incorporated in January 2023, announced their seed round in April, and launched LangSmith in July. At time of writing, the company has 93k followers, their Discord has 31k members and their weekly webinars are attended by thousands of people live.The full-featuredness of LangChain means it is often the first starting point for building any mainstream LLM use case, because they are most likely to have working guides for the new developer. Logan (our first guest!) from OpenAI has been a notable fan of both LangChain and LangSmith (they will be running the first LangChain + OpenAI workshop at AI Eng Summit). However, LangChain is not without its critics, with Aravind Srinivas, Jim Fan, Max Woolf, Mckay Wrigley and the general Reddit/HN community describing frustrations with the value of their abstractions, and many are attempting to write their own (the common experience of adding and then removing LangChain is something we covered in our Agents writeup). Harrison compares this with the timeless ORM debate on the value of abstractions.LangSmithLast month, Harrison launched LangSmith, their LLM observability tool and first cloud product. LangSmith makes it easy to monitor all the different primitives that LangChain offers (agents, chains, LLMs) as well as making it easy to share and evaluate them both through heuristics (i.e. manually written ones) and “LLM evaluating LLM” flows. The top HN comment in the “LangChain is Pointless” thread observed that orchestration is the smallest part of the work, and the bulk of it is prompt tuning and data serialization. When asked this directly our pod, Harrison agreed:“I agree that those are big pain points that get exacerbated when you have these complex chains and agents where you can't really see what's going on inside of them. And I think that's partially why we built Langsmith…” (48min mark)You can watch the full launch on the LangChain YouTube:It's clear that the target audience for LangChain is expanding to folks who are building complex, production applications rather than focusing on the simpler “Q&A your docs” use cases that made it popular in the first place. As the AI Engineer space matures, there will be more and more tools graduating from supporting “hobby” projects to more enterprise-y use cases. In this episode we run through some of the history of LangChain, how it's growing from an open source project to one of the highest valued AI startups out there, and its future. We hope you enjoy it!Show Notes* LangChain* LangChain's Berkshire Hathaway Homepage* Abstractions tweet* LangSmith* LangSmith Cookbooks repo* LangChain Retrieval blog* Evaluating CSV Question/Answering blog and YouTube* MultiOn Partner blog* Harvard Sports Analytics Collective* Evaluating RAG Webinar* awesome-langchain:* LLM Math Chain* Self-Ask* LangChain Hub UI* “LangChain is Pointless”* Harrison's links* sports - estimating player compatibility in the NBA* early interest in prompt injections* GitHub* TwitterTimestamps* [00:00:00] Introduction* [00:00:48] Harrison's background and how sports led him into ML* [00:04:54] The inspiration for creating LangChain - abstracting common patterns seen in other GPT-3 projects* [00:05:51] Overview of LangChain - a framework for building context-aware reasoning applications* [00:10:09] Components of LangChain - modules, chains, agents, etc.* [00:14:39] Underappreciated parts of LangChain - text splitters, retrieval algorithms like self-query* [00:18:46] Hiring at LangChain* [00:20:27] Designing the LangChain architecture - balancing flexibility and structure* [00:24:09] The difference between chains and agents in LangChain* [00:25:08] Prompt engineering and LangChain* [00:26:16] Announcing LangSmith* [00:30:50] Writing custom evaluators in LangSmith* [00:33:19] Reducing hallucinations - fixing retrieval vs generation issues* [00:38:17] The challenges of long context windows* [00:40:01] LangChain's multi-programming language strategy* [00:45:55] Most popular LangChain blog posts - deep dives into specific topics* [00:50:25] Responding to LangChain criticisms* [00:54:11] Harrison's advice to AI engineers* [00:55:43] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai. [00:00:19]Swyx: Welcome. Today we have Harrison Chase in the studio with us. Welcome Harrison. [00:00:23]Harrison: Thank you guys for having me. I'm excited to be here. [00:00:25]Swyx: It's been a long time coming. We've been asking you for a little bit and we're really glad that you got some time to join us in the studio. Yeah. [00:00:32]Harrison: I've been dodging you guys for a while. [00:00:34]Swyx: About seven months. You pulled me in here. [00:00:37]Alessio: About seven months. But it's all good. I totally understand. [00:00:38]Swyx: We like to introduce people through the official backgrounds and then ask you a little bit about your personal side. So you went to Harvard, class of 2017. You don't list what you did in Harvard. Was it CS? [00:00:48]Harrison: Stats and CS. [00:00:50]Swyx: That's awesome. I love me some good stats. [00:00:52]Harrison: I got into it through stats, through doing sports analytics. And then there was so much overlap between stats and CS that I found myself doing more and more of that. [00:00:59]Swyx: And it's interesting that a lot of the math that you learn in stats actually comes over into machine learning which you applied at Kensho as a machine learning engineer and Robust Intelligence, which seems to be the home of a lot of AI founders.Harrison: It does. Yeah. Swyx: And you started LangChain, I think around November 2022 and incorporated in January. Yeah. [00:01:19]Harrison: I was looking it up for the podcast and the first tweet was on, I think October 24th. So just before the end of November or end of October. [00:01:26]Swyx: Yeah. So that's your LinkedIn. What should people know about you on the personal side that's not obvious on LinkedIn? [00:01:33]Harrison: A lot of how I got into this is all through sports actually. Like I'm a big sports fan, played a lot of soccer growing up and then really big fan of the NBA and NFL. And so freshman year at college showed up and I knew I liked math. I knew I liked sports. One of the clubs that was there was the Sports Analytics Collective. And so I joined that freshman year, I was doing a lot of stuff in like Excel, just like basic stats, but then like wanted to do more advanced stuff. So learn to code, learn kind of like data science and machine learning through that way. Kind of like just kept on going down that path. I think sports is a great entryway to data science and machine learning. There's a lot of like numbers out there. People like really care. Like I remember, I think sophomore, junior year, I was in the Sports Collective and the main thing we had was a blog. And so we wrote a blog. It wasn't me. One of the other people in the club wrote a blog predicting the NFL season. I think they made some kind of like with stats and I think their stats showed that like the Dolphins would end up beating the Patriots and New England got like pissed about it, of course. So people like really care and they'll give you feedback about whether you're like models doing well or poorly. And so you get that. And then you also get like instantaneous kind of like, well, not instantaneous, but really quick feedback. Like if you predict a game, the game happens that night. Like you don't have to wait a year to see what happens. So I think sports is a great kind of like entryway for kind of like data science. [00:02:43]Alessio: There was actually my first article on the Twilio blog with a Python script to like predict pricing of like Daily Fantasy players based on my past week performance. Yeah, I don't know. It's a good getaway drug. [00:02:56]Swyx: And on my end, the way I got into finance was through sports betting. So maybe we all have some ties in there. Was like Moneyball a big inspiration? The movie? [00:03:06]Harrison: Honestly, not really. I don't really like baseball. That's like the big thing. [00:03:10]Swyx: Let's call it a lot of stats. Cool. Well, we can dive right into LangChain, which is what everyone is excited about. But feel free to make all the sports analogies you want. That really drives home a lot of points. What was your GPT aha moment? When did you start working on GPT itself? Maybe not LangChain, just anything to do with the GPT API? [00:03:29]Harrison: I think it probably started around the time we had a company hackathon. I think that was before I launched LangChain. I'm trying to remember the exact sequence of events, but I do remember that at the hackathon I worked with Will, who's now actually at LangChain as well, and then two other members of Robust. And we made basically a bot where you could ask questions of Notion and Slack. And so I think, yeah, RAG, basically. And I think I wanted to try that out because I'd heard that it was getting good. I'm trying to remember if I did anything before that to realize that it was good. So then I would focus on that on the hackathon. I can't remember or not, but that was one of the first times that I built something [00:04:06]Swyx: with GPT-3. There wasn't that much opportunity before because the API access wasn't that widespread. You had to get into some kind of program to get that. [00:04:16]Harrison: DaVinci-002 was not terrible, but they did an upgrade to get it to there, and they didn't really publicize that as much. And so I think I remember playing around with it when the first DaVinci model came out. I was like, this is cool, but it's not amazing. You'd have to do a lot of work to get it to do something. But then I think that February or something, I think of 2022, they upgraded it and it was it got better, but I think they made less of an announcement around it. And so I just, yeah, it kind of slipped under the radar for me, at least. [00:04:45]Alessio: And what was the step into LangChain? So you did the hackathon, and then as you were building the kind of RAG product, you felt like the developer experience wasn't that great? Or what was the inspiration? [00:04:54]Harrison: No, honestly, so around that time, I knew I was going to leave my previous job. I was trying to figure out what I was going to do next. I went to a bunch of meetups and other events. This was like the September, August, September of that year. So after Stable Diffusion, but before ChatGPT. So there was interest in generative AI as a space, but not a lot of people hacking on language models yet. But there were definitely some. And so I would go to these meetups and just chat with people and basically saw some common abstractions in terms of what they were building, and then thought it would be a cool side project to factor out some of those common abstractions. And that became kind of like LangChain. I looked up again before this, because I remember I did a tweet thread on Twitter to announce LangChain. And we can talk about what LangChain is. It's a series of components. And then there's some end-to-end modules. And there was three end-to-end modules that were in the initial release. One was NatBot. So this was the web agent by Nat Friedman. Another was LLM Math Chain. So it would construct- [00:05:51]Swyx: GPT-3 cannot do math. [00:05:53]Harrison: Yeah, exactly. And then the third was Self-Ask. So some type of RAG search, similar to React style agent. So those were some of the patterns in terms of what I was seeing. And those all came from open source or academic examples, because the people who were actually working on this were building startups. And they were doing things like question answering over your databases, question answering over SQL, things like that. But I couldn't use their code as kind of like inspiration to factor things out. [00:06:18]Swyx: I talked to you a little bit, actually, roundabout, right after you announced LangChain. I'm honored. I think I'm one of many. This is your first open source project. [00:06:26]Harrison: No, that's not actually true. I released, because I like sports stats. And so I remember I did release some really small, random Python package for scraping data from basketball reference or something. I'm pretty sure I released that. So first project to get a star on GitHub, let's say that. [00:06:45]Swyx: Did you reference anything? What was the inspirations, like other frameworks that you look to when open sourcing LangChain or announcing it or anything like that? [00:06:53]Harrison: I mean, the only main thing that I looked for... I remember reading a Hacker News post a little bit before about how a readme on the project goes a long way. [00:07:02]Swyx: Readme's help. [00:07:03]Harrison: Yeah. And so I looked at it and was like, put some status checks at the top and have the title and then one or two lines and then just right into installation. And so that's the main thing that I looked at in terms of how to structure it. Because yeah, I hadn't done open source before. I didn't really know how to communicate that aspect of the marketing or getting people to use it. I think I had some trouble finding it, but I finally found it and used that as a lot [00:07:25]Swyx: of the inspiration there. Yeah. It was one of the subjects of my write-up how it was surprising to me that significant open source experience actually didn't seem to matter in the new wave of AI tooling. Most like auto-GPTs, Torrents, that was his first open source project ever. And that became auto-GPT. Yeah. I don't know. To me, it's just interesting how open source experience is kind of fungible or not necessary. Or you can kind of learn it on the job. [00:07:49]Alessio: Overvalued. [00:07:50]Swyx: Overvalued. Okay. You said it, not me. [00:07:53]Alessio: What's your description of LangChain today? I think when I built the LangChain Hub UI in January, there were a few things. And I think you were one of the first people to talk about agents that were already in there before it got hot now. And it's obviously evolved into a much bigger framework today. Run people through what LangChain is today, how they should think about it, and all of that. [00:08:14]Harrison: The way that we describe it or think about it internally is that LangChain is basically... I started off saying LangChain's a framework for building LLM applications, but that's really vague and not really specific. And I think part of the issue is LangChain does do a lot, so it's hard to be somewhat specific. But I think the way that we think about it internally, in terms of prioritization, what to focus on, is basically LangChain's a framework for building context-aware reasoning applications. And so that's a bit of a mouthful, but I think that speaks to a lot of the core parts of what's in LangChain. And so what concretely that means in LangChain, there's really two things. One is a set of components and modules. And these would be the prompt template abstraction, the LLM abstraction, chat model abstraction, vector store abstraction, text splitters, document loaders. And so these are combinations of things that we build and we implement, or we just have integrations with. So we don't have any language models ourselves. We don't have any vector stores ourselves, but we integrate with a lot of them. And then the text splitters, we have our own logic for that. The document loaders, we have our own logic for that. And so those are the individual modules. But then I think another big part of LangChain, and probably the part that got people using it the most, is the end-to-end chains or applications. So we have a lot of chains for getting started with question answering over your documents, chat question answering, question answering over SQL databases, agent stuff that you can plug in off the box. And that basically combines these components in a series of specific ways to do this. So if you think about a question answering app, you need a lot of different components kind of stacked. And there's a bunch of different ways to do question answering apps. So this is a bit of an overgeneralization, but basically, you know, you have some component that looks up an embedding from a vector store, and then you put that into the prompt template with the question and the context, and maybe you have the chat history as well. And then that generates an answer, and then maybe you parse that out, or you do something with the answer there. And so there's just this sequence of things that you basically stack in a particular way. And so we just provide a bunch of those assembled chains off the shelf to make it really easy to get started in a few lines of code. [00:10:09]Alessio: And just to give people context, when you first released LangChain, OpenAI did not have a chat API. It was a completion-only API. So you had to do all the human assistant, like prompting and whatnot. So you abstracted a lot of that away. I think the most interesting thing to me is you're kind of the Switzerland of this developer land. There's a bunch of vector databases that are killing each other out there to get people to embed data in them, and you're like, I love you all. You all are great. How do you think about being an opinionated framework versus leaving a lot of choice to the user? I mean, in terms of spending time into this integration, it's like you only have 10 people on the team. Obviously that takes time. Yeah. What's that process like for you all? [00:10:50]Harrison: I think right off the bat, having different options for language models. I mean, language models is the main one that right off the bat we knew we wanted to support a bunch of different options for. There's a lot to discuss there. People want optionality between different language models. They want to try it out. They want to maybe change to ones that are cheaper as new ones kind of emerge. They don't want to get stuck into one particular one if a better one comes out. There's some challenges there as well. Prompts don't really transfer. And so there's a lot of nuance there. But from the bat, having this optionality between the language model providers was a big important part because I think that was just something we felt really strongly about. We believe there's not just going to be one model that rules them all. There's going to be a bunch of different models that are good for a bunch of different use cases. I did not anticipate the number of vector stores that would emerge. I don't know how many we supported in the initial release. It probably wasn't as big of a focus as language models was. But I think it kind of quickly became so, especially when Postgres and Elastic and Redis started building their vector store implementations. We saw that some people might not want to use a dedicated vector store. Maybe they want to use traditional databases. I think to your point around what we're opinionated about, I think the thing that we believe most strongly is it's super early in the space and super fast moving. And so there's a lot of uncertainty about how things will shake out in terms of what role will vector databases play? How many will there be? And so I think a lot of it has always kind of been this optionality and ability to switch and not getting locked in. [00:12:19]Swyx: There's other pieces of LangChain which maybe don't get as much attention sometimes. And the way that you explained LangChain is somewhat different from the docs. I don't know how to square this. So for example, you have at the top level in your docs, you have, we mentioned ModelIO, we mentioned Retrieval, we mentioned Chains. Then you have a concept called Agents, which I don't know if exactly matches what other people call Agents. And we also talked about Memory. And then finally there's Callbacks. Are there any of the less understood concepts in LangChain that you want to give some air to? [00:12:53]Harrison: I mean, I think buried in ModelIO is some stuff around like few-shot example selectors that I think is really powerful. That's a workhorse. [00:13:01]Swyx: Yeah. I think that's where I start with LangChain. [00:13:04]Harrison: It's one of those things that you probably don't, if you're building an application, you probably don't start with it. You probably start with like a zero-shot prompt. But I think that's a really powerful one that's probably just talked about less because you don't need it right off the bat. And for those of you who don't know, that basically selects from a bunch of examples the ones that are maybe most relevant to the input at hand. So you can do some nice kind of like in-context learning there. I think that's, we've had that for a while. I don't think enough people use that, basically. Output parsers also used to be kind of important, but then function calling. There's this interesting thing where like the space is just like progressing so rapidly that a lot of things that were really important have kind of diminished a bit, to be honest. Output parsers definitely used to be an understated and underappreciated part. And I think if you're working with non-OpenAI models, they still are, but a lot of people are working with OpenAI models. But even within there, there's different things you can do with kind of like the function calling ability. Sometimes you want to have the option of having the text or the application you're building, it could return either. Sometimes you know that it wants to return in a structured format, and so you just want to take that structured format. Other times you're extracting things that are maybe a key in that structured format, and so you want to like pluck that key. And so there's just like some like annoying kind of like parsing of that to do. Agents, memory, and retrieval, we haven't talked at all. Retrieval, there's like five different subcomponents. You could also probably talk about all of those in depth. You've got the document loaders, the text splitters, the embedding models, the vector stores. Embedding models and vector stores, we don't really have, or sorry, we don't build, we integrate with those. Text splitters, I think we have like 15 or so. Like I think there's an under kind of like appreciated amount of those. [00:14:39]Swyx: And then... Well, it's actually, honestly, it's overwhelming. Nobody knows what to choose. [00:14:43]Harrison: Yeah, there is a lot. [00:14:44]Swyx: Yeah. Do you have personal favorites that you want to shout out? [00:14:47]Harrison: The one that we have in the docs is the default is like the recursive text splitter. We added a playground for text splitters the other week because, yeah, we heard a lot that like, you know, and like these affect things like the chunk overlap and the chunks, they affect things in really subtle ways. And so like I think we added a playground where people could just like choose different options. We have like, and a lot of the ideas are really similar. You split on different characters, depending on kind of like the type of text that you have marked down, you might want to split on differently than HTML. And so we added a playground where you can kind of like choose between those. I don't know if those are like underappreciated though, because I think a lot of people talk about text splitting as being a hard part, and it is a really important part of creating these retrieval applications. But I think we have a lot of really cool retrieval algorithms as well. So like self query is maybe one of my favorite things in LangChain, which is basically this idea of when you have a user question, the typical kind of like thing to do is you embed that question and then find the document that's most similar to that question. But oftentimes questions have things that just, you don't really want to look up semantically, they have some other meaning. So like in the example that I use, the example in the docs is like movies about aliens in the year 1980. 1980, I guess there's some semantic meaning for that, but it's a very particular thing that you care about. And so what the self query retriever does is it splits out the metadata filter and most vector stores support like a metadata filter. So it splits out this metadata filter, and then it splits out the semantic bit. And that's actually like kind of tricky to do because there's a lot of different filters that you can have like greater than, less than, equal to, you can have and things if you have multiple filters. So we have like a pretty complicated like prompt that does all that. That might be one of my favorite things in LangChain, period. Like I think that's, yeah, I think that's really cool. [00:16:26]Alessio: How do you think about speed of development versus support of existing things? So we mentioned retrieval, like you got, or, you know, text splitting, you got like different options for all of them. As you get building LangChain, how do you decide which ones are not going to keep supporting, you know, which ones are going to leave behind? I think right now, as you said, the space moves so quickly that like you don't even know who's using what. What's that like for you? [00:16:50]Harrison: Yeah. I mean, we have, you know, we don't really have telemetry on what people are using in terms of what parts of LangChain, the telemetry we have is like, you know, anecdotal stuff when people ask or have issues with things. A lot of it also is like, I think we definitely prioritize kind of like keeping up with the stuff that comes out. I think we added function calling, like the day it came out or the day after it came out, we added chat model support, like the day after it came out or something like that. That's probably, I think I'm really proud of how the team has kind of like kept up with that because this space is like exhausting sometimes. And so that's probably, that's a big focus of ours. The support, I think we've like, to be honest, we've had to get kind of creative with how we do that. Cause we have like, I think, I don't know how many open issues we have, but we have like 3000, somewhere between 2000 and 3000, like open GitHub issues. We've experimented with a lot of startups that are doing kind of like question answering over your docs and stuff like that. And so we've got them on the website and in the discord and there's a really good one, dosu on the GitHub that's like answering issues and stuff like that. And that's actually something we want to start leaning into more heavily as a company as well as kind of like building out an AI dev rel because we're 10 people now, 10, 11 people now. And like two months ago we were like six or something like that. Right. So like, and to have like 2,500 open issues or something like that, and like 300 or 400 PRs as well. Cause like one of the amazing things is that like, and you kind of alluded to this earlier, everyone's building in the space. There's so many different like touch points. LangChain is lucky enough to kind of like be a lot of the glue that connects it. And so we get to work with a lot of awesome companies, but that's also a lot of like work to keep up with as well. And so I don't really have an amazing answer, but I think like the, I think prioritize kind of like new things that, that come out. And then we've gotten creative with some of kind of like the support functions and, and luckily there's, you know, there's a lot of awesome people working on all those support coding, question answering things that we've been able to work with. [00:18:46]Swyx: I think there is your daily rhythm, which I've seen you, you work like a, like a beast man, like mad impressive. And then there's sometimes where you step back and do a little bit of high level, like 50,000 foot stuff. So we mentioned, we mentioned retrieval. You did a refactor in March and there's, there's other abstractions that you've sort of changed your mind on. When do you do that? When do you do like the, the step back from the day to day and go, where are we going and change the direction of the ship? [00:19:11]Harrison: It's a good question so far. It's probably been, you know, we see three or four or five things pop up that are enough to make us think about it. And then kind of like when it reaches that level, you know, we don't have like a monthly meeting where we sit down and do like a monthly plan or something. [00:19:27]Swyx: Maybe we should. I've thought about this. Yeah. I'd love to host that meeting. [00:19:32]Harrison: It's really been a lot of, you know, one of the amazing things is we get to interact with so many different people. So it's been a lot of kind of like just pattern matching on what people are doing and trying to see those patterns before they punch us in the face or something like that. So for retrieval, it was the pattern of seeing like, Hey, yeah, like a lot of people are using vector sort of stuff. But there's also just like other methods and people are offering like hosted solutions and we want our abstractions to work with that as well. So we shouldn't bake in this paradigm of doing like semantic search too heavily, which sounds like basic now, but I think like, you know, to start a lot of it was people needed help doing these things. But then there was like managed things that did them, hybrid retrieval mechanisms, all of that. I think another example of this, I mean, Langsmith, which we can maybe talk about was like very kind of like, I think we worked on that for like three or four months before announcing it kind of like publicly, two months maybe before giving it to kind of like anyone in beta. But this was a lot of debugging these applications as a pain point. We hear that like just understanding what's going on is a pain point. [00:20:27]Alessio: I mean, you two did a webinar on this, which is called Agents vs. Chains. It was fun, baby. [00:20:32]Swyx: Thanks for having me on. [00:20:33]Harrison: No, thanks for coming. [00:20:34]Alessio: That was a good one. And on the website, you list like RAG, which is retrieval of bank debt generation and agents as two of the main goals of LangChain. The difference I think at the Databricks keynote, you said chains are like predetermined steps and agents is models reasoning to figure out what steps to take and what actions to take. How should people think about when to use the two and how do you transition from one to the other with LangChain? Like is it a path that you support or like do people usually re-implement from an agent to a chain or vice versa? [00:21:05]Swyx: Yeah. [00:21:06]Harrison: You know, I know agent is probably an overloaded term at this point, and so there's probably a lot of different definitions out there. But yeah, as you said, kind of like the way that I think about an agent is basically like in a chain, you have a sequence of steps. You do this and then you do this and then you do this and then you do this. And with an agent, there's some aspect of it where the LLM is kind of like deciding what to do and what steps to do in what order. And you know, there's probably some like gray area in the middle, but you know, don't fight me on this. And so if we think about those, like the benefits of the chains are that they're like, you can say do this and you just have like a more rigid kind of like order and the way that things are done. They have more control and they don't go off the rails and basically everything that's bad about agents in terms of being uncontrollable and expensive, you can control more finely. The benefit of agents is that I think they handle like the long tail of things that can happen really well. And so for an example of this, let's maybe think about like interacting with a SQL database. So you can have like a SQL chain and you know, the first kind of like naive approach at a SQL chain would be like, okay, you have the user question. And then you like write the SQL query, you do some rag, you pull in the relevant tables and schemas, you write a SQL query, you execute that against the SQL database. And then you like return that as the answer, or you like summarize that with an LLM and return that to the answer. And that's basically the SQL chain that we have in LangChain. But there's a lot of things that can go wrong in that process. Starting from the beginning, you may like not want to even query the SQL database at all. Maybe they're saying like, hi, or something, or they're misusing the application. Then like what happens if you have some step, like a big part of the application that people with LangChain is like the context aware part. So there's generally some part of bringing in context to the language model. So if you bring in the wrong context to the language model, so it doesn't know which tables to query, what do you do then? If you write a SQL query, it's like syntactically wrong and it can't run. And then if it can run, like what if it returns an unexpected result or something? And so basically what we do with the SQL agent is we give it access to all these different tools. So it has another tool, it can run the SQL query as another, and then it can respond to the user. But then if it kind of like, it can decide which order to do these. And so it gives it flexibility to handle all these edge cases. And there's like, obviously downsides to that as well. And so there's probably like some safeguards you want to put in place around agents in terms of like not letting them run forever, having some observability in there. But I do think there's this benefit of, you know, like, again, to the other part of what LangChain is like the reasoning part, like each of those steps individually involves some aspect of reasoning, for sure. Like you need to reason about what the SQL query is, you need to reason about what to return. But there's then there's also reasoning about the order of operations. And so I think to me, the key is kind of like giving it an appropriate amount to reason about while still keeping it within checks. And so to the point, like, I would probably recommend that most people get started with chains and then when they get to the point where they're hitting these edge cases, then they think about, okay, I'm hitting a bunch of edge cases where the SQL query is just not returning like the relevant things. Maybe I should add in some step there and let it maybe make multiple queries or something like that. Basically, like start with chain, figure out when you're hitting these edge cases, add in the reasoning step to that to handle those edge cases appropriately. That would be kind of like my recommendation, right? [00:24:09]Swyx: If I were to rephrase it, in my words, an agent would be a reasoning node in a chain, right? Like you start with a chain, then you just add a reasoning node, now it's an agent. [00:24:17]Harrison: Yeah, the architecture for your application doesn't have to be just a chain or just an agent. It can be an agent that calls chains, it can be a chain that has an agent in different parts of them. And this is another part as well. Like the chains in LangChain are largely intended as kind of like a way to get started and take you some amount of the way. But for your specific use case, in order to kind of like eke out the most performance, you're probably going to want to do some customization at the very basic level, like probably around the prompt or something like that. And so one of the things that we've focused on recently is like making it easier to customize these bits of existing architectures. But you probably also want to customize your architectures as well. [00:24:52]Swyx: You mentioned a bit of prompt engineering for self-ask and then for this stuff. There's a bunch of, I just talked to a prompt engineering company today, PromptOps or LLMOps. Do you have any advice or thoughts on that field in general? Like are you going to compete with them? Do you have internal tooling that you've built? [00:25:08]Harrison: A lot of what we do is like where we see kind of like a lot of the pain points being like we can talk about LangSmith and that was a big motivation for that. And like, I don't know, would you categorize LangSmith as PromptOps? [00:25:18]Swyx: I don't know. It's whatever you want it to be. Do you want to call it? [00:25:22]Harrison: I don't know either. Like I think like there's... [00:25:24]Swyx: I think about it as like a prompt registry and you store them and you A-B test them and you do that. LangSmith, I feel like doesn't quite go there yet. Yeah. It's obviously the next step. [00:25:34]Harrison: Yeah, we'll probably go. And yeah, we'll do more of that because I think that's definitely part of the application of a chain or agent is you start with a default one, then you improve it over time. And like, I think a lot of the main new thing that we're dealing with here is like language models. And the main new way to control language models is prompts. And so like a lot of the chains and agents are powered by this combination of like prompt language model and then some output parser or something doing something with the output. And so like, yeah, we want to make that core thing as good as possible. And so we'll do stuff all around that for sure. [00:26:05]Swyx: Awesome. We might as well go into LangSmith because we're bringing it up so much. So you announced LangSmith I think last month. What are your visions for it? Is this the future of LangChain and the company? [00:26:16]Harrison: It's definitely part of the future. So LangSmith is basically a control center for kind of like your LLM application. So the main features that it kind of has is like debugging, logging, monitoring, and then like testing and evaluation. And so debugging, logging, monitoring, basically you set three environment variables and it kind of like logs all the runs that are happening in your LangChain chains or agents. And it logs kind of like the inputs and outputs at each step. And so the main use case we see for this is in debugging. And that's probably the main reason that we started down this path of building it is I think like as you have these more complex things, debugging what's actually going on becomes really painful whether you're using LangChain or not. And so like adding this type of observability and debuggability was really important. Yeah. There's a debugging aspect. You can see the inputs, outputs at each step. You can then quickly enter into like a playground experience where you can fiddle around with it. The first version didn't have that playground and then we'd see people copy, go to open AI playground, paste in there. Okay. Well, that's a little annoying. And then there's kind of like the monitoring, logging experience. And we recently added some analytics on like, you know, how many requests are you getting per hour, minute, day? What's the feedback like over time? And then there's like a testing debugging, sorry, testing and evaluation component as well where basically you can create datasets and then test and evaluate these datasets. And I think importantly, all these things are tied to each other and then also into LangChain, the framework. So what I mean by that is like we've tried to make it as easy as possible to go from logs to adding a data point to a dataset. And because we think a really powerful flow is you don't really get started with a dataset. You can accumulate a dataset over time. And so being able to find points that have gotten like a thumbs up or a thumbs down from a user can be really powerful in terms of creating a good dataset. And so that's maybe like a connection between the two. And then the connection in the other way is like all the runs that you have when you test or evaluate something, they're logged in the same way. So you can debug what exactly is going on and you don't just have like a final score. You have like this nice trace and thing where you can jump in. And then we also want to do more things to hook this into a LangChain proper, the framework. So I think like some of like the managing the prompts will tie in here already. Like we talked about example selectors using datasets as a few short examples is a path that we support in a somewhat janky way right now, but we're going to like make better over time. And so there's this connection between everything. Yeah. [00:28:42]Alessio: And you mentioned the dataset in the announcement blog post, you touched on heuristic evaluation versus LLMs evaluating LLMs. I think there's a lot of talk and confusion about this online. How should people prioritize the two, especially when they might start with like not a good set of evals or like any data at all? [00:29:01]Harrison: I think it's really use case specific in the distinction that I draw between heuristic and LLM. LLMs, you're using an LLM to evaluate the output heuristics, you have some common heuristic that you can use. And so some of these can be like really simple. So we were doing some kind of like measuring of an extraction chain where we wanted it to output JSON. Okay. One evaluation can be, can you use JSON.loads to load it? And like, right. And that works perfectly. You don't need an LLM to do that. But then for like a lot of like the question answering, like, is this factually accurate? And you have some ground truth fact that you know it should be answering with. I think, you know, LLMs aren't perfect. And I think there's a lot of discussion around the pitfalls of using LLMs to evaluate themselves. And I'm not saying they're perfect by any means, but I do think they're, we've found them to be kind of like better than blue or any of those metrics. And the way that I also like to use those is also just like guide my eye about where to look. So like, you know, I might not trust the score of like 0.82, like exactly correct, but like I can look to see like which data points are like flagged as passing or failing. And sometimes the evaluators messing up, but it's like good to like, you know, I don't have to look at like a hundred data points. I can focus on like 10 or something like that. [00:30:10]Alessio: And then can you create a heuristic once in Langsmith? Like what's like your connection to that? [00:30:16]Harrison: Yeah. So right now, all the evaluation, we actually do client side. And part of this is basically due to the fact that a lot of the evaluation is really application specific. So we thought about having evaluators, you could just click off and run in a server side or something like that. But we still think it's really early on in evaluation. We still think there's, it's just really application specific. So we prioritized instead, making it easy for people to write custom evaluators and then run them client side and then upload the results so that they can manually inspect them because I think manual inspection is still a pretty big part of evaluation for better or worse. [00:30:50]Swyx: We have this sort of components of observability. We have cost, latency, accuracy, and then planning. Is that listed in there? [00:30:57]Alessio: Well, planning more in the terms of like, if you're an agent, how to pick the right tool and whether or not you are picking the right tool. [00:31:02]Swyx: So when you talk to customers, how would you stack rank those needs? Are they cost sensitive? Are they latency sensitive? I imagine accuracy is pretty high up there. [00:31:13]Harrison: I think accuracy is definitely the top that we're seeing right now. I think a lot of the applications, people are, especially the ones that we're working with, people are still struggling to get them to work at a level where they're reliable [00:31:24]Swyx: enough. [00:31:25]Harrison: So that's definitely the first. Then I think probably cost becomes the next one. I think a few places where we've started to see this be like one of the main things is the AI simulation that came out. [00:31:36]Swyx: Generative agents. Yeah, exactly. [00:31:38]Harrison: Which is really fun to run, but it costs a lot of money. And so one of our team members, Lance, did an awesome job hooking up like a local model to it. You know, it's not as perfect, but I think it helps with that. Another really big place for this, we believe, is in like extraction of structured data from unstructured data. And the reason that I think it's so important there is that usually you do extraction of some type of like pre-processing or indexing process over your documents. I mean, there's a bunch of different use cases, but one use case is for that. And generally that's over a lot of documents. And so that starts to rack up a bill kind of quickly. And I think extraction is also like a simpler task than like reasoning about which tools to call next in an agent. And so I think it's better suited for that. Yeah. [00:32:15]Swyx: On one of the heuristics I wanted to get your thoughts on, hallucination is one of the big problems there. Do you have any recommendations on how people should reduce hallucinations? [00:32:25]Harrison: To reduce hallucinations, we did a webinar on like evaluating RAG this past week. And I think there's this great project called RAGOS that evaluates four different things across two different spectrums. So the two different spectrums are like, is the retrieval part right? Or is the generation, or sorry, like, is it messing up in retrieval or is it messing up in generation? And so I think to fix hallucination, it probably depends on where it's messing up. If it's messing up in generation, then you're getting the right information, but it's still hallucinating. Or you're getting like partially right information and hallucinating some bits, a lot of that's prompt engineering. And so that's what we would recommend kind of like focusing on the prompt engineering part. And then if you're getting it wrong in the, if you're just not retrieving the right stuff, then there's a lot of different things that you can probably do, or you should look at on the retrieval bit. And honestly, that's where it starts to become a bit like application specific as well. Maybe there's some temporal stuff going on. Maybe you're not parsing things correctly. Yeah. [00:33:19]Swyx: Okay. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:35]Harrison: Yeah. Yeah. [00:33:37]Swyx: Yeah. [00:33:38]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:56]Swyx: Yeah. Yeah. [00:33:58]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:04]Swyx: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:17]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah, I mean, there's probably a larger discussion around that, but openAI definitely had a huge headstart, right? And that's... Clawds not even publicly available yet, I don't think. [00:34:28]Swyx: The API? Yeah. Oh, well, you can just basically ask any of the business reps and they'll give it to you. [00:34:33]Harrison: You can. But it's still a different signup process. I think there's... I'm bullish that other ones will catch up especially like Anthropic and Google. The local ones are really interesting. I think we're seeing a big... [00:34:46]Swyx: Lama Two? Yeah, we're doing the fine-tuning hackathon tomorrow. Thanks for promoting that. [00:34:50]Harrison: No, thanks for it. I'm really excited about that stuff. I mean, that's something that like we've been, you know, because like, as I said, like the only thing we know is that the space is moving so fast and changing so rapidly. And like, local models are, have always been one of those things that people have been bullish on. And it seems like it's getting closer and closer to kind of like being viable. So I'm excited to see what we can do with some fine-tuning. [00:35:10]Swyx: Yeah. I have to confess, I did not know that you cared. It's not like a judgment on Langchain. I was just like, you know, you write an adapter for it and you're done, right? Like how much further does it go for Langchain? In terms of like, for you, it's one of the, you know, the model IO modules and that's it. But like, you seem very personally, very passionate about it, but I don't know what the Langchain specific angle for this is, for fine-tuning local models, basically. Like you're just passionate about local models and privacy and all that, right? And open source. [00:35:41]Harrison: Well, I think there's a few different things. Like one, like, you know, if we think about what it takes to build a really reliable, like context-aware reasoning application, there's probably a bunch of different nodes that are doing a bunch of different things. And I think it is like a really complex system. And so if you're relying on open AI for every part of that, like, I think that starts to get really expensive. Also like, probably just like not good to have that much reliability on any one thing. And so I do think that like, I'm hoping that for like, you know, specific parts at the end, you can like fine-tune a model and kind of have a more specific thing for a specific task. Also, to be clear, like, I think like, I also, at the same time, I think open AI is by far the easiest way to get started. And if I was building anything, I would absolutely start with open AI. So. [00:36:27]Swyx: It's something I think a lot of people are wrestling with. But like, as a person building apps, why take five vendors when I can take one vendor, right? Like, as long as I trust Azure, I'm just entrusting all my data to Azure and that's it. So I'm still trying to figure out the real case for local models in production. And I don't know, but fine-tuning, I think, is a good one. That's why I guess open AI worked on fine-tuning. [00:36:49]Harrison: I think there's also like, you know, like if there is, if there's just more options available, like prices are going to go down. So I'm happy about that. So like very selfishly, there's that aspect as well. [00:37:01]Alessio: And in the Lancsmith announcement, I saw in the product screenshot, you have like chain, tool and LLM as like the three core atoms. Is that how people should think about observability in this space? Like first you go through the chain and then you start dig down between like the model itself and like the tool it's using? [00:37:19]Harrison: We've added more. We've added like a retriever logging so that you can see like what query is going in and what are the documents you're getting out. Those are like the three that we started with. I definitely think probably the main ones, like basically the LLM. So the reason I think the debugging in Lancsmith and debugging in general is so needed for these LLM apps is that if you're building, like, again, let's think about like what we want people to build in with LangChain. These like context aware reasoning applications. Context aware. There's a lot of stuff in the prompt. There's like the instructions. There's any previous messages. There's any input this time. There's any documents you retrieve. And so there's a lot of like data engineering that goes into like putting it into that prompt. This sounds silly, but just like making sure the data shows up in the right format is like really important. And then for the reasoning part of it, like that's obviously also all in the prompt. And so being able to like, and there's like, you know, the state of the world right now, like if you have the instructions at the beginning or at the end can actually make like a big difference in terms of whether it forgets it or not. And so being able to kind of like. [00:38:17]Swyx: Yeah. And it takes on that one, by the way, this is the U curve in context, right? Yeah. [00:38:21]Harrison: I think it's real. Basically I've found long context windows really good for when I want to extract like a single piece of information about something basically. But if I want to do reasoning over perhaps multiple pieces of information that are somewhere in like the retrieved documents, I found it not to be that great. [00:38:36]Swyx: Yeah. I have said that that piece of research is the best bull case for Lang chain and all the vector companies, because it means you should do chains. It means you should do retrieval instead of long context, right? People are trying to extend long context to like 100K, 1 million tokens, 5 million tokens. It doesn't matter. You're going to forget. You can't trust it. [00:38:54]Harrison: I expect that it will probably get better over time as everything in this field. But I do also think there'll always be a need for kind of like vector stores and retrieval in some fashions. [00:39:03]Alessio: How should people get started with Langsmith Cookbooks? Wanna talk maybe a bit about that? [00:39:08]Swyx: Yeah. [00:39:08]Harrison: Again, like I think the main thing that even I find valuable about Langsmith is just like the debugging aspect of it. And so for that, it's very simple. You can kind of like turn on three environment variables and it just logs everything. And you don't look at it 95% of the time, but that 5% you do when something goes wrong, it's quite handy to have there. And so that's probably the easiest way to get started. And we're still in a closed beta, but we're letting people off the wait list every day. And if you really need access, just DM me and we're happy to give you access there. And then yeah, there's a lot that you can do with Langsmith that we've been talking about. And so Will on our team has been leading the charge on a really great like Langsmith Cookbooks repo that covers everything from collecting feedback, whether it's thumbs up, thumbs down, or like multi-scale or comments as well, to doing evaluation, doing testing. You can also use Langsmith without Langchain. And so we've got some notebooks on that in there. But we have Python and JavaScript SDKs that aren't dependent on Langchain in any way. [00:40:01]Swyx: And so you can use those. [00:40:01]Harrison: And then we'll also be publishing a notebook on how to do that just with the REST APIs themselves. So yeah, definitely check out that repo. That's a great resource that Will's put together. [00:40:10]Swyx: Yeah, awesome. So we'll zoom out a little bit from Langsmith and talk about Langchain, the company. You're also a first-time founder. Yes. And you've just hired your 10th employee, Julia, who I know from my data engineering days. You mentioned Will Nuno, I think, who maintains Langchain.js. I'm very interested in like your multi-language strategy, by the way. Ankush, your co-founder, Lance, who did AutoEval. What are you staffing up for? And maybe who are you hiring? [00:40:34]Harrison: Yeah, so 10 employees, 12 total. We've got three more joining over the next three weeks. We've got Julia, who's awesome leading a lot of the product, go-to-market, customer success stuff. And then we've got Bri, who's also awesome leading a lot of the marketing and ops aspects. And then other than that, all engineers. We've staffed up a lot on kind of like full stack infra DevOps, kind of like as we've started going into the hosted platform. So internally, we're split about 50-50 between the open source and then the platform stuff. And yeah, we're looking to hire particularly on kind of like the things, we're actually looking to hire across most fronts, to be honest. But in particular, we probably need one or two more people on like open source, both Python and JavaScript and happy to dive into the multi-language kind of like strategy there. But again, like strong focus there on engineering, actually, as opposed to maybe like, we're not a research lab, we're not a research shop. [00:41:48]Swyx: And then on the platform side, [00:41:49]Harrison: like we definitely need some more people on the infra and DevOps side. So I'm using this as an opportunity to tell people that we're hiring and that you should reach out if that sounds like you. [00:41:58]Swyx: Something like that, jobs, whatever. I don't actually know if we have an official job. [00:42:02]Harrison: RIP, what happened to your landing page? [00:42:04]Swyx: It used to be so based. The Berkshire Hathaway one? Yeah, so what was the story, the quick story behind that? Yeah, the quick story behind that is we needed a website [00:42:12]Harrison: and I'm terrible at design. [00:42:14]Swyx: And I knew that we couldn't do a good job. [00:42:15]Harrison: So if you can't do a good job, might as well do the worst job possible. Yeah, and like lean into it. And have some fun with it, yeah. [00:42:21]Swyx: Do you admire Warren Buffett? Yeah, I admire Warren Buffett and admire his website. And actually you can still find a link to it [00:42:26]Harrison: from our current website if you look hard enough. So there's a little Easter egg. Before we dive into more of the open source community things, [00:42:33]Alessio: let's dive into the language thing. How do you think about parity between the Python and JavaScript? Obviously, they're very different ecosystems. So when you're working on a LangChain, is it we need to have the same abstraction in both language or are you to the needs? The core stuff, we want to have the same abstractions [00:42:50]Harrison: because we basically want to be able to do serialize prompts, chains, agents, all the core stuff as tightly as possible and then use that between languages. Like even, yeah, like even right now when we log things to LangChain, we have a playground experience where you can run things that runs in JavaScript because it's kind of like in the browser. But a lot of what's logged is like Python. And so we need that core equivalence for a lot of the core things. Then there's like the incredibly long tail of like integrations, more researchy things. So we want to be able to do that. Python's probably ahead on a lot of like the integrations front. There's more researchy things that we're able to include quickly because a lot of people release some of their code in Python and stuff like that. And so we can use that. And there's just more of an ecosystem around the Python project. But the core stuff will have kind of like the same abstractions and be translatable. That didn't go exactly where I was thinking. So like the LangChain of Ruby, the LangChain of C-sharp, [00:43:44]Swyx: you know, there's demand for that. I mean, I think that's a big part of it. But you are giving up some real estate by not doing it. Yeah, it comes down to kind of like, you know, ROI and focus. And I think like we do think [00:43:58]Harrison: there's a strong JavaScript community and we wanted to lean into that. And I think a lot of the people that we brought on early, like Nuno and Jacob have a lot of experience building JavaScript tooling in that community. And so I think that's a big part of it. And then there's also like, you know, building JavaScript tooling in that community. Will we do another language? Never say never, but like... [00:44:21]Swyx: Python JS for now. Yeah. Awesome. [00:44:23]Alessio: You got 83 articles, which I think might be a record for such a young company. What are like the hottest hits, the most popular ones? [00:44:32]Harrison: I think the most popular ones are generally the ones where we do a deep dive on something. So we did something a few weeks ago around evaluating CSV q