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Eric Ray and Glenn Langenburg pay tribute to our colleague and friend. Kasey Wertheim passed away on March 7, 2025 and will be greatly missed. Please join us as we remember his legacy and contributions to the field of fingerprints and tell a few personal stories.

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back 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 Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

This week, Marianna sits down with John Faragon to discuss latent TB management and the Hepatitis B vaccine. Tune in to learn all about how the opportunistic infections guidelines have changed and how this affects people with HIV. -- Help us track the number of listeners our episode gets by filling out this brief form! (https://www.e2NECA.org/?r=PCS6722)--Want to chat? Email us at podcast@necaaetc.org with comments or ideas for new episodes. --Check out our free online courses: www.necaaetc.org/rise-courses--Download our HIV mobile apps:Google Play Store: https://play.google.com/store/apps/developer?id=John+Faragon&hl=en_US&gl=USApple App Store: https://apps.apple.com/us/developer/virologyed-consultants-llc/id1216837691

Forensically speaking, a latent footprint refers to an imprint left by a person's foot on a surface that is not immediately visible to the naked eye. These footprints are typically created when an individual transfers natural oils, dirt, or other substances from their feet onto a surface as they walk. These impressions are often faint and can only be revealed through specialized techniques like dusting, chemical treatments, or photography. Forensic experts use these methods to make latent footprints visible and then compare them to known footwear patterns to help identify or exclude potential suspects in criminal investigations.In the affidavit, investigators stated that they found a latent footprint at the scene of the crime that was consistent with the type of print you would find on a pair of Vans shoes. The problem with that? Just about everyone has Vans.In this episode, we take a look at the latent footprint as evidence and how it might be used by the prosecutors during the trial for Bryan Kohberger.to contact me:bobbycapucci@protonmail.com(commercial at 7:22)source:Clue in Idaho Murder Case Leaves Question About Bryan Kohberger Evidence (newsweek.com)Become a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

Forensically speaking, a latent footprint refers to an imprint left by a person's foot on a surface that is not immediately visible to the naked eye. These footprints are typically created when an individual transfers natural oils, dirt, or other substances from their feet onto a surface as they walk. These impressions are often faint and can only be revealed through specialized techniques like dusting, chemical treatments, or photography. Forensic experts use these methods to make latent footprints visible and then compare them to known footwear patterns to help identify or exclude potential suspects in criminal investigations.In the affidavit, investigators stated that they found a latent footprint at the scene of the crime that was consistent with the type of print you would find on a pair of Vans shoes. The problem with that? Just about everyone has Vans.In this episode, we take a look at the latent footprint as evidence and how it might be used by the prosecutors during the trial for Bryan Kohberger.to contact me:bobbycapucci@protonmail.com(commercial at 7:22)source:Clue in Idaho Murder Case Leaves Question About Bryan Kohberger Evidence (newsweek.com)

Eric Ray and Glenn Langenburg pay tribute to our mentor and friend. Pat Wertheim recently passed and will be greatly missed. Please join us as we remember his legacy and tell a few stories.

In this episode, we sit down with UNB also known as Mc Panda to discuss the world of hip-hop music, the challenges of being an independent music producer, and the struggles faced by Northeast Indian artists. UNB opens up about his journey, from starting as a rapper to navigating the hip-hop music industry, dealing with India's Got Talent controversy, and the ongoing India-Nepal relations debate. He shares insights into music-making software, the creative process behind his latest album collaborations 2025, and the reality of performing at live music events. We also dive deep into the Northeast India identity crisis, the struggles of artists trying to break into the mainstream, and life as an artist in Delhi. Beyond music, UNB reveals his fitness transformation story, going from overweight to fit, and how discipline plays a crucial role in both his health and career. Whether you're passionate about rap and hip-hop influences, the grind of independent music in India, or looking for inspiration in songwriting, this podcast has something for you. Don't forget to like, comment, and subscribe for more exclusive interviews and insights from the world of music and creativity!

Cada setmana Joana Serra de la Llibreria Catalana de Perpinyà ens tria un llibre. Avui ens ha portat el primer recull de poesia i relats breus de l'Íngrid Obiol. Un llibre editat per les Edicions Paraules, en la col·lecció Guergal.

Today, we're joined by Jonas Geiping, research group leader at Ellis Institute and the Max Planck Institute for Intelligent Systems to discuss his recent paper, “Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.” This paper proposes a novel language model architecture which uses recurrent depth to enable “thinking in latent space.” We dig into “internal reasoning” versus “verbalized reasoning”—analogous to non-verbalized and verbalized thinking in humans, and discuss how the model searches in latent space to predict the next token and dynamically allocates more compute based on token difficulty. We also explore how the recurrent depth architecture simplifies LLMs, the parallels to diffusion models, the model's performance on reasoning tasks, the challenges of comparing models with varying compute budgets, and architectural advantages such as zero-shot adaptive exits and natural speculative decoding. The complete show notes for this episode can be found at https://twimlai.com/go/723.

In this episode Glenn and Eric catch up after some hectic travel from recent weather issues. They do some news updates and also Eric had advice for new Patreon subscribers. Then they play an Oscar-themed round of “A Truth, a Lie, and a Mandela Effect” because it is the weekend of the 2025 Oscars. Then the guys tackle a topic which has come up many times in the past, but they've never actually dived into before: Simultaneous Impressions. They talk about different scenarios that can occur such as: some impressions stand alone, none stand alone, aggregation of features, and physical gaps or voids in an impression. They review the famous Mass v. Patterson case from 2005 that started the initial controversy in the field. They also have a chance to discuss John Black's JFI research article from 2006 and other source material on the subject. Reference: Black, J.P. Pilot Study: The Application of ACE-V to Simultaneous (Cluster) Impressions. Journal of Forensic Identification, 56(6) Dated: November/December 2006 Pages: 933-971.

We are working with Amplify on the 2025 State of AI Engineering Survey to be presented at the AIE World's Fair in SF! Join the survey to shape the future of AI Eng!We first met Snipd over a year ago, and were immediately impressed by the design, but were doubtful about the behavior of snipping as the title behavior:Podcast apps are enormously sticky - Spotify spent almost $1b in podcast acquisitions and exclusive content just to get an 8% bump in market share among normies.However, after a disappointing Overcast 2.0 rewrite with no AI features in the last 3 years, I finally bit the bullet and switched to Snipd. It's 2025, your podcast app should be able to let you search transcripts of your podcasts. Snipd is the best implementation of this so far.And yet they keep shipping:What impressed us wasn't just how this tiny team of 4 was able to bootstrap a consumer AI app against massive titans and do so well; but also how seriously they think about learning through podcasts and improving retention of knowledge over time, aka “Duolingo for podcasts”. As an educational AI podcast, that's a mission we can get behind.Full Video PodFind us on YouTube! This was the first pod we've ever shot outdoors!Show Notes* How does Shazam work?* Flutter/FlutterFlow* wav2vec paper* Perplexity Online LLM* Google Search Grounding* Comparing Snipd transcription with our Bee episode* NIPS 2017 Flo Rida* Gustav Söderström - Background AudioTimestamps* [00:00:03] Takeaways from AI Engineer NYC* [00:00:17] Weather in New York.* [00:00:26] Swyx and Snipd.* [00:01:01] Kevin's AI summit experience.* [00:01:31] Zurich and AI.* [00:03:25] SigLIP authors join OpenAI.* [00:03:39] Zurich is very costly.* [00:04:06] The Snipd origin story.* [00:05:24] Introduction to machine learning.* [00:09:28] Snipd and user knowledge extraction.* [00:13:48] App's tech stack, Flutter, Python.* [00:15:11] How speakers are identified.* [00:18:29] The concept of "backgroundable" video.* [00:29:05] Voice cloning technology.* [00:31:03] Using AI agents.* [00:34:32] Snipd's future is multi-modal AI.* [00:36:37] Snipd and existing user behaviour.* [00:42:10] The app, summary, and timestamps.* [00:55:25] The future of AI and podcasting.* [1:14:55] Voice AITranscriptswyx [00:00:03]: Hey, I'm here in New York with Kevin Ben-Smith of Snipd. Welcome.Kevin [00:00:07]: Hi. Hi. Amazing to be here.swyx [00:00:09]: Yeah. This is our first ever, I think, outdoors podcast recording.Kevin [00:00:14]: It's quite a location for the first time, I have to say.swyx [00:00:18]: I was actually unsure because, you know, it's cold. It's like, I checked the temperature. It's like kind of one degree Celsius, but it's not that bad with the sun. No, it's quite nice. Yeah. Especially with our beautiful tea. With the tea. Yeah. Perfect. We're going to talk about Snips. I'm a Snips user. I'm a Snips user. I had to basically, you know, apart from Twitter, it's like the number one use app on my phone. Nice. When I wake up in the morning, I open Snips and I, you know, see what's new. And I think in terms of time spent or usage on my phone, I think it's number one or number two. Nice. Nice. So I really had to talk about it also because I think people interested in AI want to think about like, how can we, we're an AI podcast, we have to talk about the AI podcast app. But before we get there, we just finished. We just finished the AI Engineer Summit and you came for the two days. How was it?Kevin [00:01:07]: It was quite incredible. I mean, for me, the most valuable was just being in the same room with like-minded people who are building the future and who are seeing the future. You know, especially when it comes to AI agents, it's so often I have conversations with friends who are not in the AI world. And it's like so quickly it happens that you, it sounds like you're talking in science fiction. And it's just crazy talk. It was, you know, it's so refreshing to talk with so many other people who already see these things and yeah, be inspired then by them and not always feel like, like, okay, I think I'm just crazy. And like, this will never happen. It really is happening. And for me, it was very valuable. So day two, more relevant, more relevant for you than day one. Yeah. Day two. So day two was the engineering track. Yeah. That was definitely the most valuable for me. Like also as a producer. Practitioner myself, especially there were one or two talks that had to do with voice AI and AI agents with voice. Okay. So that was quite fascinating. Also spoke with the speakers afterwards. Yeah. And yeah, they were also very open and, and, you know, this, this sharing attitudes that's, I think in general, quite prevalent in the AI community. I also learned a lot, like really practical things that I can now take away with me. Yeah.swyx [00:02:25]: I mean, on my side, I, I think I watched only like half of the talks. Cause I was running around and I think people saw me like towards the end, I was kind of collapsing. I was on the floor, like, uh, towards the end because I, I needed to get, to get a rest, but yeah, I'm excited to watch the voice AI talks myself.Kevin [00:02:43]: Yeah. Yeah. Do that. And I mean, from my side, thanks a lot for organizing this conference for bringing everyone together. Do you have anything like this in Switzerland? The short answer is no. Um, I mean, I have to say the AI community in, especially Zurich, where. Yeah. Where we're, where we're based. Yeah. It is quite good. And it's growing, uh, especially driven by ETH, the, the technical university there and all of the big companies, they have AI teams there. Google, like Google has the biggest tech hub outside of the U S in Zurich. Yeah. Facebook is doing a lot in reality labs. Uh, Apple has a secret AI team, open AI and then SwapBit just announced that they're coming to Zurich. Yeah. Um, so there's a lot happening. Yeah.swyx [00:03:23]: So, yeah, uh, I think the most recent notable move, I think the entire vision team from Google. Uh, Lucas buyer, um, and, and all the other authors of Siglip left Google to join open AI, which I thought was like, it's like a big move for a whole team to move all at once at the same time. So I've been to Zurich and it just feels expensive. Like it's a great city. Yeah. It's great university, but I don't see it as like a business hub. Is it a business hub? I guess it is. Right.Kevin [00:03:51]: Like it's kind of, well, historically it's, uh, it's a finance hub, finance hub. Yeah. I mean, there are some, some large banks there, right? Especially UBS, uh, the, the largest wealth manager in the world, but it's really becoming more of a tech hub now with all of the big, uh, tech companies there.swyx [00:04:08]: I guess. Yeah. Yeah. And, but we, and research wise, it's all ETH. Yeah. There's some other things. Yeah. Yeah. Yeah.Kevin [00:04:13]: It's all driven by ETH. And then, uh, it's sister university EPFL, which is in Lausanne. Okay. Um, which they're also doing a lot, but, uh, it's, it's, it's really ETH. Uh, and otherwise, no, I mean, it's a beautiful, really beautiful city. I can recommend. To anyone. To come, uh, visit Zurich, uh, uh, let me know, happy to show you around and of course, you know, you, you have the nature so close, you have the mountains so close, you have so, so beautiful lakes. Yeah. Um, I think that's what makes it such a livable city. Yeah.swyx [00:04:42]: Um, and the cost is not, it's not cheap, but I mean, we're in New York city right now and, uh, I don't know, I paid $8 for a coffee this morning, so, uh, the coffee is cheaper in Zurich than the New York city. Okay. Okay. Let's talk about Snipt. What is Snipt and, you know, then we'll talk about your origin story, but I just, let's, let's get a crisp, what is Snipt? Yeah.Kevin [00:05:03]: I always see two definitions of Snipt, so I'll give you one really simple, straightforward one, and then a second more nuanced, um, which I think will be valuable for the rest of our conversation. So the most simple one is just to say, look, we're an AI powered podcast app. So if you listen to podcasts, we're now providing this AI enhanced experience. But if you look at the more nuanced, uh, podcast. Uh, perspective, it's actually, we, we've have a very big focus on people who like your audience who listened to podcasts to learn something new. Like your audience, you want, they want to learn about AI, what's happening, what's, what's, what's the latest research, what's going on. And we want to provide a, a spoken audio platform where you can do that most effectively. And AI is basically the way that we can achieve that. Yeah.swyx [00:05:53]: Means to an end. Yeah, exactly. When you started. Was it always meant to be AI or is it, was it more about the social sharing?Kevin [00:05:59]: So the first version that we ever released was like three and a half years ago. Okay. Yeah. So this was before ChatGPT. Before Whisper. Yeah. Before Whisper. Yeah. So I think a lot of the features that we now have in the app, they weren't really possible yet back then. But we already from the beginning, we always had the focus on knowledge. That's the reason why, you know, we in our team, why we listen to podcasts, but we did have a bit of a different approach. Like the idea in the very beginning was, so the name is Snips and you can create these, what we call Snips, which is basically a small snippet, like a clip from a, from a podcast. And we did envision sort of like a, like a social TikTok platform where some people would listen to full episodes and they would snip certain, like the best parts of it. And they would post that in a feed and other users would consume this feed of Snips. And use that as a discovery tool or just as a means to an end. And yeah, so you would have both people who create Snips and people who listen to Snips. So our big hypothesis in the beginning was, you know, it will be easy to get people to listen to these Snips, but super difficult to actually get them to create them. So we focused a lot of, a lot of our effort on making it as seamless and easy as possible to create a Snip. Yeah.swyx [00:07:17]: It's similar to TikTok. You need CapCut for there to be videos on TikTok. Exactly.Kevin [00:07:23]: And so for, for Snips, basically whenever you hear an amazing insight, a great moment, you can just triple tap your headphones. And our AI actually then saves the moment that you just listened to and summarizes it to create a note. And this is then basically a Snip. So yeah, we built, we built all of this, launched it. And what we found out was basically the exact opposite. So we saw that people use the Snips to discover podcasts, but they really, you know, they don't. You know, really love listening to long form podcasts, but they were creating Snips like crazy. And this was, this was definitely one of these aha moments when we realized like, hey, we should be really doubling down on the knowledge of learning of, yeah, helping you learn most effectively and helping you capture the knowledge that you listen to and actually do something with it. Because this is in general, you know, we, we live in this world where there's so much content and we consume and consume and consume. And it's so easy to just at the end of the podcast. You just start listening to the next podcast. And five minutes later, you've forgotten everything. 90%, 99% of what you've actually just learned. Yeah.swyx [00:08:31]: You don't know this, but, and most people don't know this, but this is my fourth podcast. My third podcast was a personal mixtape podcast where I Snipped manually sections of podcasts that I liked and added my own commentary on top of them and published them as small episodes. Nice. So those would be maybe five to 10 minute Snips. Yeah. And then I added something that I thought was a good story or like a good insight. And then I added my own commentary and published it as a separate podcast. It's cool. Is that still live? It's still live, but it's not active, but you can go back and find it. If you're, if, if you're curious enough, you'll see it. Nice. Yeah. You have to show me later. It was so manual because basically what my process would be, I hear something interesting. I note down the timestamp and I note down the URL of the podcast. I used to use Overcast. So it would just link to the Overcast page. And then. Put in my note taking app, go home. Whenever I feel like publishing, I will take one of those things and then download the MP3, clip out the MP3 and record my intro, outro and then publish it as a, as a podcast. But now Snips, I mean, I can just kind of double click or triple tap.Kevin [00:09:39]: I mean, those are very similar stories to what we hear from our users. You know, it's, it's normal that you're doing, you're doing something else while you're listening to a podcast. Yeah. A lot of our users, they're driving, they're working out, walking their dog. So in those moments when you hear something amazing, it's difficult to just write them down or, you know, you have to take out your phone. Some people take a screenshot, write down a timestamp, and then later on you have to go back and try to find it again. Of course you can't find it anymore because there's no search. There's no command F. And, um, these, these were all of the issues that, that, that we encountered also ourselves as users. And given that our background was in AI, we realized like, wait, hey, this is. This should not be the case. Like podcast apps today, they're still, they're basically repurposed music players, but we actually look at podcasts as one of the largest sources of knowledge in the world. And once you have that different angle of looking at it together with everything that AI is now enabling, you realize like, hey, this is not the way that we, that podcast apps should be. Yeah.swyx [00:10:41]: Yeah. I agree. You mentioned something that you said your background is in AI. Well, first of all, who's the team and what do you mean your background is in AI?Kevin [00:10:48]: Those are two very different things. I'm going to ask some questions. Yeah. Um, maybe starting with, with my backstory. Yeah. My backstory actually goes back, like, let's say 12 years ago or something like that. I moved to Zurich to study at ETH and actually I studied something completely different. I studied mathematics and economics basically with this specialization for quant finance. Same. Okay. Wow. All right. So yeah. And then as you know, all of these mathematical models for, um, asset pricing, derivative pricing, quantitative trading. And for me, the thing that, that fascinates me the most was the mathematical modeling behind it. Uh, mathematics, uh, statistics, but I was never really that passionate about the finance side of things.swyx [00:11:32]: Oh really? Oh, okay. Yeah. I mean, we're different there.Kevin [00:11:36]: I mean, one just, let's say symptom that I noticed now, like, like looking back during that time. Yeah. I think I never read an academic paper about the subject in my free time. And then it was towards the end of my studies. I was already working for a big bank. One of my best friends, he comes to me and says, Hey, I just took this course. You have to, you have to do this. You have to take this lecture. Okay. And I'm like, what, what, what is it about? It's called machine learning and I'm like, what, what, what kind of stupid name is that? Uh, so you sent me the slides and like over a weekend I went through all of the slides and I just, I just knew like freaking hell. Like this is it. I'm, I'm in love. Wow. Yeah. Okay. And that was then over the course of the next, I think like 12 months, I just really got into it. Started reading all about it, like reading blog posts, starting building my own models.swyx [00:12:26]: Was this course by a famous person, famous university? Was it like the Andrew Wayne Coursera thing? No.Kevin [00:12:31]: So this was a ETH course. So a professor at ETH. Did he teach in English by the way? Yeah. Okay.swyx [00:12:37]: So these slides are somewhere available. Yeah. Definitely. I mean, now they're quite outdated. Yeah. Sure. Well, I think, you know, reflecting on the finance thing for a bit. So I, I was, used to be a trader, uh, sell side and buy side. I was options trader first and then I was more like a quantitative hedge fund analyst. We never really use machine learning. It was more like a little bit of statistical modeling, but really like you, you fit, you know, your regression.Kevin [00:13:03]: No, I mean, that's, that's what it is. And, uh, or you, you solve partial differential equations and have then numerical methods to, to, to solve these. That's, that's for you. That's your degree. And that's, that's not really what you do at work. Right. Unless, well, I don't know what you do at work. In my job. No, no, we weren't solving the partial differential. Yeah.swyx [00:13:18]: You learn all this in school and then you don't use it.Kevin [00:13:20]: I mean, we, we, well, let's put it like that. Um, in some things, yeah, I mean, I did code algorithms that would do it, but it was basically like, it was the most basic algorithms and then you just like slightly improve them a little bit. Like you just tweak them here and there. Yeah. It wasn't like starting from scratch, like, Oh, here's this new partial differential equation. How do we know?swyx [00:13:43]: Yeah. Yeah. I mean, that's, that's real life, right? Most, most of it's kind of boring or you're, you're using established things because they're established because, uh, they tackle the most important topics. Um, yeah. Portfolio management was more interesting for me. Um, and, uh, we, we were sort of the first to combine like social data with, with quantitative trading. And I think, uh, I think now it's very common, but, um, yeah. Anyway, then you, you went, you went deep on machine learning and then what? You quit your job? Yeah. Yeah. Wow.Kevin [00:14:12]: I quit my job because, uh, um, I mean, I started using it at the bank as well. Like try, like, you know, I like desperately tried to find any kind of excuse to like use it here or there, but it just was clear to me, like, no, if I want to do this, um, like I just have to like make a real cut. So I quit my job and joined an early stage, uh, tech startup in Zurich where then built up the AI team over five years. Wow. Yeah. So yeah, we built various machine learning, uh, things for, for banks from like models for, for sales teams to identify which clients like which product to sell to them and with what reasons all the way to, we did a lot, a lot with bank transactions. One of the actually most fun projects for me was we had an, an NLP model that would take the booking text of a transaction, like a credit card transaction and pretty fired. Yeah. Because it had all of these, you know, like numbers in there and abbreviations and whatnot. And sometimes you look at it like, what, what is this? And it was just, you know, it would just change it to, I don't know, CVS. Yeah.swyx [00:15:15]: Yeah. But I mean, would you have hallucinations?Kevin [00:15:17]: No, no, no. The way that everything was set up, it wasn't like, it wasn't yet fully end to end generative, uh, neural network as what you would use today. Okay.swyx [00:15:30]: Awesome. And then when did you go like full time on Snips? Yeah.Kevin [00:15:33]: So basically that was, that was afterwards. I mean, how that started was the friend of mine who got me into machine learning, uh, him and I, uh, like he also got me interested into startups. He's had a big impact on my life. And the two of us were just a jam on, on like ideas for startups every now and then. And his background was also in AI data science. And we had a couple of ideas, but given that we were working full times, we were thinking about, uh, so we participated in Hack Zurich. That's, uh, Europe's biggest hackathon, um, or at least was at the time. And we said, Hey, this is just a weekend. Let's just try out an idea, like hack something together and see how it works. And the idea was that we'd be able to search through podcast episodes, like within a podcast. Yeah. So we did that. Long story short, uh, we managed to do it like to build something that we realized, Hey, this actually works. You can, you can find things again in podcasts. We had like a natural language search and we pitched it on stage. And we actually won the hackathon, which was cool. I mean, we, we also, I think we had a good, um, like a good, good pitch or a good example. So we, we used the famous Joe Rogan episode with Elon Musk where Elon Musk smokes a joint. Okay. Um, it's like a two and a half hour episode. So we were on stage and then we just searched for like smoking weed and it would find that exact moment. It will play it. And it just like, come on with Elon Musk, just like smoking. Oh, so it was video as well? No, it was actually completely based on audio. But we did have the video for the presentation. Yeah. Which had a, had of course an amazing effect. Yeah. Like this gave us a lot of activation energy, but it wasn't actually about winning the hackathon. Yeah. But the interesting thing that happened was after we pitched on stage, several of the other participants, like a lot of them came up to us and started saying like, Hey, can I use this? Like I have this issue. And like some also came up and told us about other problems that they have, like very adjacent to this with a podcast. Where's like, like this. Like, could, could I use this for that as well? And that was basically the, the moment where I realized, Hey, it's actually not just us who are having these issues with, with podcasts and getting to the, making the most out of this knowledge. Yeah. The other people. Yeah. That was now, I guess like four years ago or something like that. And then, yeah, we decided to quit our jobs and start, start this whole snip thing. Yeah. How big is the team now? We're just four people. Yeah. Just four people. Yeah. Like four. We're all technical. Yeah. Basically two on the, the backend side. So one of my co-founders is this person who got me into machine learning and startups. And we won the hackathon together. So we have two people for the backend side with the AI and all of the other backend things. And two for the front end side, building the app.swyx [00:18:18]: Which is mostly Android and iOS. Yeah.Kevin [00:18:21]: It's iOS and Android. We also have a watch app for, for Apple, but yeah, it's mostly iOS. Yeah.swyx [00:18:27]: The watch thing, it was very funny because in the, in the Latent Space discord, you know, most of us have been slowly adopting snips. You came to me like a year ago and you introduced snip to me. I was like, I don't know. I'm, you know, I'm very sticky to overcast and then slowly we switch. Why watch?Kevin [00:18:43]: So it goes back to a lot of our users, they do something else while, while listening to a podcast, right? Yeah. And one of the, us giving them the ability to then capture this knowledge, even though they're doing something else at the same time is one of the killer features. Yeah. Maybe I can actually, maybe at some point I should maybe give a bit more of an overview of what the, all of the features that we have. Sure. So this is one of the killer features and for one big use case that people use this for is for running. Yeah. So if you're a big runner, a big jogger or cycling, like really, really cycling competitively and a lot of the people, they don't want to take their phone with them when they go running. So you load everything onto the watch. So you can download episodes. I mean, if you, if you have an Apple watch that has internet access, like with a SIM card, you can also directly stream. That's also possible. Yeah. So of course it's a, it's basically very limited to just listening and snipping. And then you can see all of your snips later on your phone. Let me tell you this error I just got.swyx [00:19:47]: Error playing episode. Substack, the host of this podcast, does not allow this podcast to be played on an Apple watch. Yeah.Kevin [00:19:52]: That's a very beautiful thing. So we found out that all of the podcasts hosted on Substack, you cannot play them on an Apple watch. Why is this restriction? What? Like, don't ask me. We try to reach out to Substack. We try to reach out to some of the bigger podcasters who are hosting the podcast on Substack to also let them know. Substack doesn't seem to care. This is not specific to our app. You can also check out the Apple podcast app. Yeah. It's the same problem. It's just that we actually have identified it. And we tell the user what's going on.swyx [00:20:25]: I would say we host our podcast on Substack, but they're not very serious about their podcasting tools. I've told them before, I've been very upfront with them. So I don't feel like I'm shitting on them in any way. And it's kind of sad because otherwise it's a perfect creative platform. But the way that they treat podcasting as an afterthought, I think it's really disappointing.Kevin [00:20:45]: Maybe given that you mentioned all these features, maybe I can give a bit of a better overview of the features that we have. Let's do that. Let's do that. So I think we're mostly in our minds. Maybe for some of the listeners.swyx [00:20:55]: I mean, I'll tell you my version. Yeah. They can correct me, right? So first of all, I think the main job is for it to be a podcast listening app. It should be basically a complete superset of what you normally get on Overcast or Apple Podcasts or anything like that. You pull your show list from ListenNotes. How do you find shows? You've got to type in anything and you find them, right?Kevin [00:21:18]: Yeah. We have a search engine that is powered by ListenNotes. Yeah. But I mean, in the meantime, we have a huge database of like 99% of all podcasts out there ourselves. Yeah.swyx [00:21:27]: What I noticed, the default experience is you do not auto-download shows. And that's one very big difference for you guys versus other apps, where like, you know, if I'm subscribed to a thing, it auto-downloads and I already have the MP3 downloaded overnight. For me, I have to actively put it onto my queue, then it auto-downloads. And actually, I initially didn't like that. I think I maybe told you that I was like, oh, it's like a feature that I don't like. Like, because it means that I have to choose to listen to it in order to download and not to... It's like opt-in. There's a difference between opt-in and opt-out. So I opt-in to every episode that I listen to. And then, like, you know, you open it and depends on whether or not you have the AI stuff enabled. But the default experience is no AI stuff enabled. You can listen to it. You can see the snips, the number of snips and where people snip during the episode, which roughly correlates to interest level. And obviously, you can snip there. I think that's the default experience. I think snipping is really cool. Like, I use it to share a lot on Discord. I think we have tons and tons of just people sharing snips and stuff. Tweeting stuff is also like a nice, pleasant experience. But like the real features come when you actually turn on the AI stuff. And so the reason I got snipped, because I got fed up with Overcast not implementing any AI features at all. Instead, they spent two years rewriting their app to be a little bit faster. And I'm like, like, it's 2025. I should have a podcast that has transcripts that I can search. Very, very basic thing. Overcast will basically never have it.Kevin [00:22:49]: Yeah, I think that was a good, like, basic overview. Maybe I can add a bit to it with the AI features that we have. So one thing that we do every time a new podcast comes out, we transcribe the episode. We do speaker diarization. We identify the speaker names. Each guest, we extract a mini bio of the guest, try to find a picture of the guest online, add it. We break the podcast down into chapters, as in AI generated chapters. That one. That one's very handy. With a quick description per title and quick description per each chapter. We identify all books that get mentioned on a podcast. You can tell I don't use that one. It depends on the podcast. There are some podcasts where the guests often recommend like an amazing book. So later on, you can you can find that again.swyx [00:23:42]: So you literally search for the word book or I just read blah, blah, blah.Kevin [00:23:46]: No, I mean, it's all LLM based. Yeah. So basically, we have we have an LLM that goes through the entire transcript and identifies if a user mentions a book, then we use perplexity API together with various other LLM orchestration to go out there on the Internet, find everything that there is to know about the book, find the cover, find who or what the author is, get a quick description of it for the author. We then check on which other episodes the author appeared on.swyx [00:24:15]: Yeah, that is killer.Kevin [00:24:17]: Because that for me, if. If there's an interesting book, the first thing I do is I actually listen to a podcast episode with a with a writer because he usually gives a really great overview already on a podcast.swyx [00:24:28]: Sometimes the podcast is with the person as a guest. Sometimes his podcast is about the person without him there. Do you pick up both?Kevin [00:24:37]: So, yes, we pick up both in like our latest models. But actually what we show you in the app, the goal is to currently only show you the guest to separate that. In the future, we want to show the other things more.swyx [00:24:47]: For what it's worth, I don't mind. Yeah, I don't think like if I like if I like somebody, I'll just learn about them regardless of whether they're there or not.Kevin [00:24:55]: Yeah, I mean, yes and no. We we we have seen there are some personalities where this can break down. So, for example, the first version that we released with this feature, it picked up much more often a person, even if it was not a guest. Yeah. For example, the best examples for me is Sam Altman and Elon Musk. Like they're just mentioned on every second podcast and it has like they're not on there. And if you're interested in it, you can go to Elon Musk. And actually like learning from them. Yeah, I see. And yeah, we updated our our algorithms, improved that a lot. And now it's gotten much better to only pick it up if they're a guest. And yeah, so this this is maybe to come back to the features, two more important features like we have the ability to chat with an episode. Yes. Of course, you can do the old style of searching through a transcript with a keyword search. But I think for me, this is this is how you used to do search and extracting knowledge in the in the past. Old school. And the A.I. Web. Way is is basically an LLM. So you can ask the LLM, hey, when do they talk about topic X? If you're interested in only a certain part of the episode, you can ask them for four to give a quick overview of the episode. Key takeaways afterwards also to create a note for you. So this is really like very open, open ended. And yeah. And then finally, the snipping feature that we mentioned just to reiterate. Yeah. I mean, here the the feature is that whenever you hear an amazing idea, you can trip. It's up your headphones or click a button in the app and the A.I. summarizes the insight you just heard and saves that together with the original transcript and audio in your knowledge library. I also noticed that you you skip dynamic content. So dynamic content, we do not skip it automatically. Oh, sorry. You detect. But we detect it. Yeah. I mean, that's one of the thing that most people don't don't actually know that like the way that ads get inserted into podcasts or into most podcasts is actually that every time you listen. To a podcast, you actually get access to a different audio file and on the server, a different ad is inserted into the MP3 file automatically. Yeah. Based on IP. Exactly. And that's what that means is if we transcribe an episode and have a transcript with timestamps like words, word specific timestamps, if you suddenly get a different audio file, like the whole time says I messed up and that's like a huge issue. And for that, we actually had to build another algorithm that would dynamically on the floor. I re sync the audio that you're listening to the transcript that we have. Yeah. Which is a fascinating problem in and of itself.swyx [00:27:24]: You sync by matching up the sound waves? Or like, or do you sync by matching up words like you basically do partial transcription?Kevin [00:27:33]: We are not matching up words. It's happening on the basically a bytes level matching. Yeah. Okay.swyx [00:27:40]: It relies on this. It relies on the exact match at some point.Kevin [00:27:46]: So it's actually. We're actually not doing exact matches, but we're doing fuzzy matches to identify the moment. It's basically, we basically built Shazam for podcasts. Just as a little side project to solve this issue.swyx [00:28:02]: Actually, fun fact, apparently the Shazam algorithm is open. They published the paper, it's talked about it. I haven't really dived into the paper. I thought it was kind of interesting that basically no one else has built Shazam.Kevin [00:28:16]: Yeah, I mean, well, the one thing is the algorithm. If you now talk about Shazam, the other thing is also having the database behind it and having the user mindset that if they have this problem, they come to you, right?swyx [00:28:29]: Yeah, I'm very interested in the tech stack. There's a big data pipeline. Could you share what is the tech stack?Kevin [00:28:35]: What are the most interesting or challenging pieces of it? So the general tech stack is our entire backend is, or 90% of our backend is written in Python. Okay. Hosting everything on Google Cloud Platform. And our front end is written with, well, we're using the Flutter framework. So it's written in Dart and then compiled natively. So we have one code base that handles both Android and iOS. You think that was a good decision? It's something that a lot of people are exploring. So up until now, yes. Okay. Look, it has its pros and cons. Some of the, you know, for example, earlier, I mentioned we have a Apple Watch app. Yeah. I mean, there's no Flutter for that, right? So that you build native. And then of course you have to sort of like sync these things together. I mean, I'm not the front end engineer, so I'm not just relaying this information, but our front end engineers are very happy with it. It's enabled us to be quite fast and be on both platforms from the very beginning. And when I talk with people and they hear that we are using Flutter, usually they think like, ah, it's not performant. It's super junk, janky and everything. And then they use it. They use our app and they're always super surprised. Or if they've already used our app, I couldn't tell them. They're like, what? Yeah. Um, so there is actually a lot that you can do with it.swyx [00:29:51]: The danger, the concern, there's a few concerns, right? One, it's Google. So when were they, when are they going to abandon it? Two, you know, they're optimized for Android first. So iOS is like a second, second thought, or like you can feel that it is not a native iOS app. Uh, but you guys put a lot of care into it. And then maybe three, from my point of view, JavaScript, as a JavaScript guy, React Native was supposed to be there. And I think that it hasn't really fulfilled that dream. Um, maybe Expo is trying to do that, but, um, again, it is not, does not feel as productive as Flutter. And I've, I spent a week on Flutter and dot, and I'm an investor in Flutter flow, which is the local, uh, Flutter, Flutter startup. That's doing very, very well. I think a lot of people are still Flutter skeptics. Yeah. Wait. So are you moving away from Flutter?Kevin [00:30:41]: I don't know. We don't have plans to do that. Yeah.swyx [00:30:43]: You're just saying about that. What? Yeah. Watch out. Okay. Let's go back to the stack.Kevin [00:30:47]: You know, that was just to give you a bit of an overview. I think the more interesting things are, of course, on the AI side. So we, like, as I mentioned earlier, when we started out, it was before chat GPT for the chat GPT moment before there was the GPT 3.5 turbo, uh, API. So in the beginning, we actually were running everything ourselves, open source models, try to fine tune them. They worked. There was us, but let's, let's be honest. They weren't. What was the sort of? Before Whisper, the transcription. Yeah, we were using wave to work like, um, there was a Google one, right? No, it was a Facebook, Facebook one. That was actually one of the papers. Like when that came out for me, that was one of the reasons why I said we, we should try something to start a startup in the audio space. For me, it was a bit like before that I had been following the NLP space, uh, quite closely. And as, as I mentioned earlier, we, we did some stuff at the startup as well, that I was working up. But before, and wave to work was the first paper that I had at least seen where the whole transformer architecture moved over to audio and bit more general way of saying it is like, it was the first time that I saw the transformer architecture being applied to continuous data instead of discrete tokens. Okay. And it worked amazingly. Ah, and like the transformer architecture plus self-supervised learning, like these two things moved over. And then for me, it was like, Hey, this is now going to take off similarly. It's the text space has taken off. And with these two things in place, even if some features that we want to build are not possible yet, they will be possible in the near term, uh, with this, uh, trajectory. So that was a little side, side note. No, it's in the meantime. Yeah. We're using whisper. We're still hosting some of the models ourselves. So for example, the whole transcription speaker diarization pipeline, uh,swyx [00:32:38]: You need it to be as cheap as possible.Kevin [00:32:40]: Yeah, exactly. I mean, we're doing this at scale where we have a lot of audio.swyx [00:32:44]: We're what numbers can you disclose? Like what, what are just to give people an idea because it's a lot. So we have more than a million podcasts that we've already processed when you say a million. So processing is basically, you have some kind of list of podcasts that you will auto process and others where a paying pay member can choose to press the button and transcribe it. Right. Is that the rough idea? Yeah, exactly.Kevin [00:33:08]: Yeah. And if, when you press that button or we also transcribe it. Yeah. So first we do the, we do the transcription. We do the. The, the speaker diarization. So basically you identify speech blocks that belong to the same speaker. This is then all orchestrated within, within LLM to identify which speech speech block belongs to which speaker together with, you know, we identify, as I mentioned earlier, we identify the guest name and the bio. So all of that comes together with an LLM to actually then assign assigned speaker names to, to each block. Yeah. And then most of the rest of the, the pipeline we've now used, we've now migrated to LLM. So we use mainly open AI, Google models, so the Gemini models and the open AI models, and we use some perplexity basically for those things where we need, where we need web search. Yeah. That's something I'm still hoping, especially open AI will also provide us an API. Oh, why? Well, basically for us as a consumer, the more providers there are.swyx [00:34:07]: The more downtime.Kevin [00:34:08]: The more competition and it will lead to better, better results. And, um, lower costs over time. I don't, I don't see perplexity as expensive. If you use the web search, the price is like $5 per a thousand queries. Okay. Which is affordable. But, uh, if you compare that to just a normal LLM call, um, it's, it's, uh, much more expensive. Have you tried Exa? We've, uh, looked into it, but we haven't really tried it. Um, I mean, we, we started with perplexity and, uh, it works, it works well. And if I remember. Correctly, Exa is also a bit more expensive.swyx [00:34:45]: I don't know. I don't know. They seem to focus on the search thing as a search API, whereas perplexity, maybe more consumer-y business that is higher, higher margin. Like I'll put it like perplexity is trying to be a product, Exa is trying to be infrastructure. Yeah. So that, that'll be my distinction there. And then the other thing I will mention is Google has a search grounding feature. Yeah. Which you, which you might want. Yeah.Kevin [00:35:07]: Yeah. We've, uh, we've also tried that out. Um, not as good. So we, we didn't, we didn't go into. Too much detail in like really comparing it, like quality wise, because we actually already had the perplexity one and it, and it's, and it's working. Yeah. Um, I think also there, the price is actually higher than perplexity. Yeah. Really? Yeah.swyx [00:35:26]: Google should cut their prices.Kevin [00:35:29]: Maybe it was the same price. I don't want to say something incorrect, but it wasn't cheaper. It wasn't like compelling. And then, then there was no reason to switch. So, I mean, maybe like in general, like for us, given that we do work with a lot of content, price is actually something that we do look at. Like for us, it's not just about taking the best model for every task, but it's really getting the best, like identifying what kind of intelligence level you need and then getting the best price for that to be able to really scale this and, and provide us, um, yeah, let our users use these features with as many podcasts as possible. Yeah.swyx [00:36:03]: I wanted to double, double click on diarization. Yeah. Uh, it's something that I don't think people do very well. So you know, I'm, I'm a, I'm a B user. I don't have it right now. And, and they were supposed to speak, but they dropped out last minute. Um, but, uh, we've had them on the podcast before and it's not great yet. Do you use just PI Anode, the default stuff, or do you find any tricks for diarization?Kevin [00:36:27]: So we do use the, the open source packages, but we have tweaked it a bit here and there. For example, if you mentioned the BAI guys, I actually listened to the podcast episode was super nice. Thank you. And when you started talking about speaker diarization, and I just have to think about, uh, I don't know.Kevin [00:36:49]: Is it possible? I don't know. I don't know. F**k this. Yeah, no, I don't know.Kevin [00:36:55]: Yeah. We are the best. This is a.swyx [00:37:07]: I don't know. This is the best. I don't know. This is the best. Yeah. Yeah. Yeah. You're doing good.Kevin [00:37:12]: So, so yeah. This is great. This is good. Yeah. No, so that of course helps us. Another thing that helps us is that we know certain structural aspects of the podcast. For example, how often does someone speak? Like if someone, like let's say there's a one hour episode and someone speaks for 30 seconds, that person is most probably not the guest and not the host. It's probably some ad, like some speaker from an ad. So we have like certain of these heuristics that we can use and we leverage to improve things. And in the past, we've also changed the clustering algorithm. So basically how a lot of the speaker diarization works is you basically create an embedding for the speech that's happening. And then you try to somehow cluster these embeddings and then find out this is all one speaker. This is all another speaker. And there we've also tweaked a couple of things where we again used heuristics that we could apply from knowing how podcasts function. And that's also actually why I was feeling so much with the BAI guys, because like all of these heuristics, like for them, it's probably almost impossible to use any heuristics because it can just be any situation, anything.Kevin [00:38:34]: So that's one thing that we do. Yeah, another thing is that we actually combine it with LLM. So the transcript, LLMs and the speaker diarization, like bringing all of these together to recalibrate some of the switching points. Like when does the speaker stop? When does the next one start?swyx [00:38:51]: The LLMs can add errors as well. You know, I wouldn't feel safe using them to be so precise.Kevin [00:38:58]: I mean, at the end of the day, like also just to not give a wrong impression, like the speaker diarization is also not perfect that we're doing, right? I basically don't really notice it.swyx [00:39:08]: Like I use it for search.Kevin [00:39:09]: Yeah, it's not perfect yet, but it's gotten quite good. Like, especially if you compare, if you look at some of the, like if you take a latest episode and you compare it to an episode that came out a year ago, we've improved it quite a bit.swyx [00:39:23]: Well, it's beautifully presented. Oh, I love that I can click on the transcript and it goes to the timestamp. So simple, but you know, it should exist. Yeah, I agree. I agree. So this, I'm loading a two hour episode of Detect Me Right Home, where there's a lot of different guests calling in and you've identified the guest name. And yeah, so these are all LLM based. Yeah, it's really nice.Kevin [00:39:49]: Yeah, like the speaker names.swyx [00:39:50]: I would say that, you know, obviously I'm a power user of all these tools. You have done a better job than Descript. Okay, wow. Descript is so much funding. They had their open AI invested in them and they still suck. So I don't know, like, you know, keep going. You're doing great. Yeah, thanks. Thanks.Kevin [00:40:12]: I mean, I would, I would say that, especially for anyone listening who's interested in building a consumer app with AI, I think the, like, especially if your background is in AI and you love working with AI and doing all of that, I think the most important thing is just to keep reminding yourself of what's actually the job to be done here. Like, what does actually the consumer want? Like, for example, you now were just delighted by the ability to click on this word and it jumps there. Yeah. Like, this is not, this is not rocket science. This is, like, you don't have to be, like, I don't know, Android Kapathi to come up with that and build that, right? And I think that's, that's something that's super important to keep in mind.swyx [00:40:52]: Yeah, yeah. Amazing. I mean, there's so many features, right? It's, it's so packed. There's quotes that you pick up. There's summarization. Oh, by the way, I'm going to use this as my official feature request. I want to customize what, how it's summarized. I want to, I want to have a custom prompt. Yeah. Because your summarization is good, but, you know, I have different preferences, right? Like, you know.Kevin [00:41:14]: So one thing that you can already do today, I completely get your feature request. And I think it just.swyx [00:41:18]: I'm sure people have asked it.Kevin [00:41:19]: I mean, maybe just in general as a, as a, how I see the future, you know, like in the future, I think all, everything will be personalized. Yeah, yeah. Like, not, this is not specific to us. Yeah. And today we're still in a, in a phase where the cost of LLMs, at least if you're working with, like, such long context windows. As us, I mean, there's a lot of tokens in, if you take an entire podcast, so you still have to take that cost into consideration. So if for every single user, we regenerate it entirely, it gets expensive. But in the future, this, you know, cost will continue to go down and then it will just be personalized. So that being said, you can already today, if you go to the player screen. Okay. And open up the chat. Yeah. You can go to the, to the chat. Yes. And just ask for a summary in your style.swyx [00:42:13]: Yeah. Okay. I mean, I, I listen to consume, you know? Yeah. Yeah. I, I've never really used this feature. I don't know. I think that's, that's me being a slow adopter. No, no. I mean, that's. It has, when does the conversation start? Okay.Kevin [00:42:26]: I mean, you can just type anything. I think what you're, what you're describing, I mean, maybe that is also an interesting topic to talk about. Yes. Where, like, basically I told you, like, look, we have this chat. You can just ask for it. Yeah. And this is, this is how ChatGPT works today. But if you're building a consumer app, you have to move beyond the chat box. People do not want to always type out what they want. So your feature request was, even though theoretically it's already possible, what you are actually asking for is, hey, I just want to open up the app and it should just be there in a nicely formatted way. Beautiful way such that I can read it or consume it without any issues. Interesting. And I think that's in general where a lot of the, the. Opportunities lie currently in the market. If you want to build a consumer app, taking the capability and the intelligence, but finding out what the actual user interface is the best way how a user can engage with this intelligence in a natural way.swyx [00:43:24]: Is this something I've been thinking about as kind of like AI that's not in your face? Because right now, you know, we like to say like, oh, use Notion has Notion AI. And we have the little thing there. And there's, or like some other. Any other platform has like the sparkle magic wand emoji, like that's our AI feature. Use this. And it's like really in your face. A lot of people don't like it. You know, it should just kind of become invisible, kind of like an invisible AI.Kevin [00:43:49]: 100%. I mean, the, the way I see it as AI is, is the electricity of, of the future. And like no one, like, like we don't talk about, I don't know, this, this microphone uses electricity, this phone, you don't think about it that way. It's just in there, right? It's not an electricity enabled product. No, it's just a product. Yeah. It will be the same with AI. I mean, now. It's still a, something that you use to market your product. I mean, we do, we do the same, right? Because it's still something that people realize, ah, they're doing something new, but at some point, no, it'll just be a podcast app and it will be normal that it has all of this AI in there.swyx [00:44:24]: I noticed you do something interesting in your chat where you source the timestamps. Yeah. Is that part of this prompt? Is there a separate pipeline that adds source sources?Kevin [00:44:33]: This is, uh, actually part of the prompt. Um, so this is all prompt engine. Engineering, um, uh, you should be able to click on it. Yeah, I clicked on it. Um, this is all prompt engineering with how to provide the, the context, you know, we, because we provide all of the transcript, how to provide the context and then, yeah, I get them all to respond in a correct way with a certain format and then rendering that on the front end. This is one of the examples where I would say it's so easy to create like a quick demo of this. I mean, you can just go to chat to be deep, paste this thing in and say like, yeah, do this. Okay. Like 15 minutes and you're done. Yeah. But getting this to like then production level that it actually works 99% of the time. Okay. This is then where, where the difference lies. Yeah. So, um, for this specific feature, like we actually also have like countless regexes that they're just there to correct certain things that the LLM is doing because it doesn't always adhere to the format correctly. And then it looks super ugly on the front end. So yeah, we have certain regexes that correct that. And maybe you'd ask like, why don't you use an LLM for that? Because that's sort of the, again, the AI native way, like who uses regexes anymore. But with the chat for user experience, it's very important that you have the streaming because otherwise you need to wait so long until your message has arrived. So we're streaming live the, like, just like ChatGPT, right? You get the answer and it's streaming the text. So if you're streaming the text and something is like incorrect. It's currently not easy to just like pipe, like stream this into another stream, stream this into another stream and get the stream back, which corrects it, that would be amazing. I don't know, maybe you can answer that. Do you know of any?swyx [00:46:19]: There's no API that does this. Yeah. Like you cannot stream in. If you own the models, you can, uh, you know, whatever token sequence has, has been emitted, start loading that into the next one. If you fully own the models, uh, I don't, it's probably not worth it. That's what you do. It's better. Yeah. I think. Yeah. Most engineers who are new to AI research and benchmarking actually don't know how much regexing there is that goes on in normal benchmarks. It's just like this ugly list of like a hundred different, you know, matches for some criteria that you're looking for. No, it's very cool. I think it's, it's, it's an example of like real world engineering. Yeah. Do you have a tooling that you're proud of that you've developed for yourself?Kevin [00:47:02]: Is it just a test script or is it, you know? I think it's a bit more, I guess the term that has come up is, uh, vibe coding, uh, vibe coding, some, no, sorry, that's actually something else in this case, but, uh, no, no, yes, um, vibe evals was a term that in one of the talks actually on, on, um, I think it might've been the first, the first or the first day at the conference, someone brought that up. Yeah. Uh, because yeah, a lot of the talks were about evals, right. Which is so important. And yeah, I think for us, it's a bit more vibe. Evals, you know, that's also part of, you know, being a startup, we can take risks, like we can take the cost of maybe sometimes it failing a little bit or being a little bit off and our users know that and they appreciate that in return, like we're moving fast and iterating and building, building amazing things, but you know, a Spotify or something like that, half of our features will probably be in a six month review through legal or I don't know what, uh, before they could sell them out.swyx [00:48:04]: Let's just say Spotify is not very good at podcasting. Um, I have a documented, uh, dislike for, for their podcast features, just overall, really, really well integrated any other like sort of LLM focused engineering challenges or problems that you, that you want to highlight.Kevin [00:48:20]: I think it's not unique to us, but it goes again in the direction of handling the uncertainty of LLMs. So for example, with last year, at the end of the year, we did sort of a snipped wrapped. And one of the things we thought it would be fun to, just to do something with, uh, with an LLM and something with the snips that, that a user has. And, uh, three, let's say unique LLM features were that we assigned a personality to you based on the, the snips that, that you have. It was, I mean, it was just all, I guess, a bit of a fun, playful way. I'm going to look up mine. I forgot mine already.swyx [00:48:57]: Um, yeah, I don't know whether it's actually still in the, in the, we all took screenshots of it.Kevin [00:49:01]: Ah, we posted it in the, in the discord. And the, the second one, it was, uh, we had a learning scorecard where we identified the topics that you snipped on the most, and you got like a little score for that. And the third one was a, a quote that stood out. And the quote is actually a very good example of where we would run that for user. And most of the time it was an interesting quote, but every now and then it was like a super boring quotes that you think like, like how, like, why did you select that? Like, come on for there. The solution was actually just to say, Hey, give me five. So it extracted five quotes as a candidate, and then we piped it into a different model as a judge, LLM as a judge, and there we use a, um, a much better model because with the, the initial model, again, as, as I mentioned also earlier, we do have to look at the, like the, the costs because it's like, we have so much text that goes into it. So we, there we use a bit more cheaper model, but then the judge can be like a really good model to then just choose one out of five. This is a practical example.swyx [00:50:03]: I can't find it. Bad search in discord. Yeah. Um, so, so you do recommend having a much smarter model as a judge, uh, and that works for you. Yeah. Yeah. Interesting. I think this year I'm very interested in LM as a judge being more developed as a concept, I think for things like, you know, snips, raps, like it's, it's fine. Like, you know, it's, it's, it's, it's entertaining. There's no right answer.Kevin [00:50:29]: I mean, we also have it. Um, we also use the same concept for our books feature where we identify the, the mention. Books. Yeah. Because there it's the same thing, like 90% of the time it, it works perfectly out of the box one shot and every now and then it just, uh, starts identifying books that were not really mentioned or that are not books or made, yeah, starting to make up books. And, uh, they are basically, we have the same thing of like another LLM challenging it. Um, yeah. And actually with the speakers, we do the same now that I think about it. Yeah. Um, so I'm, I think it's a, it's a great technique. Interesting.swyx [00:51:05]: You run a lot of calls.Kevin [00:51:07]: Yeah.swyx [00:51:08]: Okay. You know, you mentioned costs. You move from self hosting a lot of models to the, to the, you know, big lab models, open AI, uh, and Google, uh, non-topic.Kevin [00:51:18]: Um, no, we love Claude. Like in my opinion, Claude is the, the best one when it comes to the way it formulates things. The personality. Yeah. The personality. Okay. I actually really love it. But yeah, the cost is. It's still high.swyx [00:51:36]: So you cannot, you tried Haiku, but you're, you're like, you have to have Sonnet.Kevin [00:51:40]: Uh, like basically we like with Haiku, we haven't experimented too much. We obviously work a lot with 3.5 Sonnet. Uh, also, you know, coding. Yeah. For coding, like in cursor, just in general, also brainstorming. We use it a lot. Um, I think it's a great brainstorm partner, but yeah, with, uh, with, with a lot of things that we've done done, we opted for different models.swyx [00:52:00]: What I'm trying to drive at is how much cheaper can you get if you go from cloud to cloud? Closed models to open models. And maybe it's like 0% cheaper, maybe it's 5% cheaper, or maybe it's like 50% cheaper. Do you have a sense?Kevin [00:52:13]: It's very difficult to, to judge that. I don't really have a sense, but I can, I can give you a couple of thoughts that have gone through our minds over the time, because obviously we do realize like, given that we, we have a couple of tasks where there are just so many tokens going in, um, at some point it will make sense to, to offload some of that. Uh, to an open source model, but going back to like, we're, we're a startup, right? Like we're not an AI lab or whatever, like for us, actually the most important thing is to iterate fast because we need to learn from our users, improve that. And yeah, just this velocity of this, these iterations. And for that, the closed models hosted by open AI, Google is, uh, and swapping, they're just unbeatable because you just, it's just an API call. Yeah. Um, so you don't need to worry about. Yeah. So much complexity behind that. So this is, I would say the biggest reason why we're not doing more in this space, but there are other thoughts, uh, also for the future. Like I see two different, like we basically have two different usage patterns of LLMs where one is this, this pre-processing of a podcast episode, like this initial processing, like the transcription, speaker diarization, chapterization. We do that once. And this, this usage pattern it's, it's quite predictable. Because we know how many podcasts get released when, um, so we can sort of have a certain capacity and we can, we, we're running that 24 seven, it's one big queue running 24 seven.swyx [00:53:44]: What's the queue job runner? Uh, is it a Django, just like the Python one?Kevin [00:53:49]: No, that, that's just our own, like our database and the backend talking to the database, picking up jobs, finding it back. I'm just curious in orchestration and queues. I mean, we, we of course have like, uh, a lot of other orchestration where we're, we're, where we use, uh, the Google pub sub, uh, thing, but okay. So we have this, this, this usage pattern of like very predictable, uh, usage, and we can max out the, the usage. And then there's this other pattern where it's, for example, the snippet where it's like a user, it's a user action that triggers an LLM call and it has to be real time. And there can be moments where it's by usage and there can be moments when there's very little usage for that. There. So that's, that's basically where these LLM API calls are just perfect because you don't need to worry about scaling this up, scaling this down, um, handling, handling these issues. Serverless versus serverful.swyx [00:54:44]: Yeah, exactly. Okay.Kevin [00:54:45]: Like I see them a bit, like I see open AI and all of these other providers, I see them a bit as the, like as the Amazon, sorry, AWS of, of AI. So it's a bit similar how like back before AWS, you would have to have your, your servers and buy new servers or get rid of servers. And then with AWS, it just became so much easier to just ramp stuff up and down. Yeah. And this is like the taking it even, even, uh, to the next level for AI. Yeah.swyx [00:55:18]: I am a big believer in this. Basically it's, you know, intelligence on demand. Yeah. We're probably not using it enough in our daily lives to do things. I should, we should be able to spin up a hundred things at once and go through things and then, you know, stop. And I feel like we're still trying to figure out how to use LLMs in our lives effectively. Yeah. Yeah.Kevin [00:55:38]: 100%. I think that goes back to the whole, like that, that's for me where the big opportunity is for, if you want to do a startup, um, it's not about, but you can let the big labs handleswyx [00:55:48]: the challenge of more intelligence, but, um, it's the... Existing intelligence. How do you integrate? How do you actually incorporate it into your life? AI engineering. Okay, cool. Cool. Cool. Cool. Um, the one, one other thing I wanted to touch on was multimodality in frontier models. Dwarcash had a interesting application of Gemini recently where he just fed raw audio in and got diarized transcription out or timestamps out. And I think that will come. So basically what we're saying here is another wave of transformers eating things because right now models are pretty much single modality things. You know, you have whisper, you have a pipeline and everything. Yeah. You can't just say, Oh, no, no, no, we only fit like the raw, the raw files. Do you think that will be realistic for you? I 100% agree. Okay.Kevin [00:56:38]: Basically everything that we talked about earlier with like the speaker diarization and heuristics and everything, I completely agree. Like in the, in the future that would just be put everything into a big multimodal LLM. Okay. And it will output, uh, everything that you want. Yeah. So I've also experimented with that. Like just... With, with Gemini 2? With Gemini 2.0 Flash. Yeah. Just for fun. Yeah. Yeah. Because the big difference right now is still like the cost difference of doing speaker diarization this way or doing transcription this way is a huge difference to the pipeline that we've built up. Huh. Okay.swyx [00:57:15]: I need to figure out what, what that cost is because in my mind 2.0 Flash is so cheap. Yeah. But maybe not cheap enough for you.Kevin [00:57:23]: Uh, no, I mean, if you compare it to, yeah, whisper and speaker diarization and especially self-hosting it and... Yeah. Yeah. Yeah.swyx [00:57:30]: Yeah.Kevin [00:57:30]: Okay. But we will get there, right? Like this is just a question of time.swyx [00:57:33]: And, um, at some point, as soon as that happens, we'll be the first ones to switch. Yeah. Awesome. Anything else that you're like sort of eyeing on the horizon as like, we are thinking about this feature, we're thinking about incorporating this new functionality of AI into our, into our app? Yeah.Kevin [00:57:50]: I mean, we, there's so many areas that we're thinking about, like our challenge is a bit more... Choosing. Yeah. Choosing. Yeah. So, I mean, I think for me, like looking into like the next couple of years, like the big areas that interest us a lot, basically four areas, like one is content. Um, right now it's, it's podcasts. I mean, you did mention, I think you mentioned like you can also upload audio books and YouTube videos. YouTube. I actually use the YouTube one a fair amount. But in the future, we, we want to also have audio books natively in the app. And, uh, we want to enable AI generated content. Like just think of, take deep research and notebook analysis. Like put these together. That should be, that should be in our app. The second area is discovery. I think in general. Yeah.swyx [00:58:38]: I noticed that you don't have, so you

Shehzad Malik aka Theshehzadshow is a content creator and an indie filmmaker.Shehzad Malik comes on for a mad fun episode of The Pakistan Experience where we discuss Ducky Bhai vs Shaam Idrees, the Youtube community, Daily Vlogging, Gen Z, Story telling, Instagram vs Youtube, Shehzad Roy, Sindh's Got Latent, Ghazals and more.The Pakistan Experience is an independently produced podcast looking to tell stories about Pakistan through conversations. Please consider supporting us on Patreon:https://www.patreon.com/thepakistanexperienceTo support the channel:Jazzcash/Easypaisa - 0325 -2982912Patreon.com/thepakistanexperienceAnd Please stay in touch:https://twitter.com/ThePakistanExp1https://www.facebook.com/thepakistanexperiencehttps://instagram.com/thepakistanexpeperienceThe podcast is hosted by comedian and writer, Shehzad Ghias Shaikh. Shehzad is a Fulbright scholar with a Masters in Theatre from Brooklyn College. He is also one of the foremost Stand-up comedians in Pakistan and frequently writes for numerous publications. Instagram.com/shehzadghiasshaikhFacebook.com/Shehzadghias/Twitter.com/shehzad89Join this channel to get access to perks:https://www.youtube.com/channel/UC44l9XMwecN5nSgIF2Dvivg/joinChapters0:00 Introduction2:30 Ducky Bhai vs Shaam Idrees7:19 Youtube community14:17 Daily Vlogging and Structure of the Vlog17:00 Gen Z and Mental Health22:10 Personal Story Telling and Consistent24:14 Instagram vs Youtube35:00 Quality of Content39:00 Do what you want46:30 Gary Vee and Motivational Speakers51:59 Shehzad Roy55:00 Gatekeeping and Pretentiousness in Art1:03:00 What made Shehzad start Vlogging1:08:30 Kids on TV and the Internet is a dark place1:18:30 Being vulnerable online1:22:24 Seeing everything as content1:30:00 Daily Vlogging1:36:30 Branding and Marketing1:40:52 Society cannot say anything to me1:45:20 Favourite Ghazal1:48:44 Heartbreak Talk and Relationships1:57:36 Ranjish hee sahee and Bollywood songs2:07:30 Audience Questions

While everyone is now repeating that 2025 is the “Year of the Agent”, OpenAI is heads down building towards it. In the first 2 months of the year they released Operator and Deep Research (arguably the most successful agent archetype so far), and today they are bringing a lot of those capabilities to the API:* Responses API* Web Search Tool* Computer Use Tool* File Search Tool* A new open source Agents SDK with integrated Observability ToolsWe cover all this and more in today's lightning pod on YouTube!More details here:Responses APIIn our Michelle Pokrass episode we talked about the Assistants API needing a redesign. Today OpenAI is launching the Responses API, “a more flexible foundation for developers building agentic applications”. It's a superset of the chat completion API, and the suggested starting point for developers working with OpenAI models. One of the big upgrades is the new set of built-in tools for the responses API: Web Search, Computer Use, and Files. Web Search ToolWe previously had Exa AI on the podcast to talk about web search for AI. OpenAI is also now joining the race; the Web Search API is actually a new “model” that exposes two 4o fine-tunes: gpt-4o-search-preview and gpt-4o-mini-search-preview. These are the same models that power ChatGPT Search, and are priced at $30/1000 queries and $25/1000 queries respectively. The killer feature is inline citations: you do not only get a link to a page, but also a deep link to exactly where your query was answered in the result page. Computer Use ToolThe model that powers Operator, called Computer-Using-Agent (CUA), is also now available in the API. The computer-use-preview model is SOTA on most benchmarks, achieving 38.1% success on OSWorld for full computer use tasks, 58.1% on WebArena, and 87% on WebVoyager for web-based interactions.As you will notice in the docs, `computer-use-preview` is both a model and a tool through which you can specify the environment. Usage is priced at $3/1M input tokens and $12/1M output tokens, and it's currently only available to users in tiers 3-5.File Search ToolFile Search was also available in the Assistants API, and it's now coming to Responses too. OpenAI is bringing search + RAG all under one umbrella, and we'll definitely see more people trying to find new ways to build all-in-one apps on OpenAI. Usage is priced at $2.50 per thousand queries and file storage at $0.10/GB/day, with the first GB free.Agent SDK: Swarms++!https://github.com/openai/openai-agents-pythonTo bring it all together, after the viral reception to Swarm, OpenAI is releasing an officially supported agents framework (which was previewed at our AI Engineer Summit) with 4 core pieces:* Agents: Easily configurable LLMs with clear instructions and built-in tools.* Handoffs: Intelligently transfer control between agents.* Guardrails: Configurable safety checks for input and output validation.* Tracing & Observability: Visualize agent execution traces to debug and optimize performance.Multi-agent workflows are here to stay!OpenAI is now explicitly designs for a set of common agentic patterns: Workflows, Handoffs, Agents-as-Tools, LLM-as-a-Judge, Parallelization, and Guardrails. OpenAI previewed this in part 2 of their talk at NYC:Further coverage of the launch from Kevin Weil, WSJ, and OpenAIDevs, AMA here.Show Notes* Assistants API* Swarm (OpenAI)* Fine-Tuning in AI* 2024 OpenAI DevDay Recap with Romain* Michelle Pokrass episode (API lead)Timestamps* 00:00 Intros* 02:31 Responses API * 08:34 Web Search API * 17:14 Files Search API * 18:46 Files API vs RAG * 20:06 Computer Use / Operator API * 22:30 Agents SDKAnd of course you can catch up with the full livestream here:TranscriptAlessio [00:00:03]: Hey, everyone. Welcome back to another Latent Space Lightning episode. This is Alessio, partner and CTO at Decibel, and I'm joined by Swyx, founder of Small AI.swyx [00:00:11]: Hi, and today we have a super special episode because we're talking with our old friend Roman. Hi, welcome.Romain [00:00:19]: Thank you. Thank you for having me.swyx [00:00:20]: And Nikunj, who is most famously, if anyone has ever tried to get any access to anything on the API, Nikunj is the guy. So I know your emails because I look forward to them.Nikunj [00:00:30]: Yeah, nice to meet all of you.swyx [00:00:32]: I think that we're basically convening today to talk about the new API. So perhaps you guys want to just kick off. What is OpenAI launching today?Nikunj [00:00:40]: Yeah, so I can kick it off. We're launching a bunch of new things today. We're going to do three new built-in tools. So we're launching the web search tool. This is basically chat GPD for search, but available in the API. We're launching an improved file search tool. So this is you bringing your data to OpenAI. You upload it. We, you know, take care of parsing it, chunking it. We're embedding it, making it searchable, give you this like ready vector store that you can use. So that's the file search tool. And then we're also launching our computer use tool. So this is the tool behind the operator product in chat GPD. So that's coming to developers today. And to support all of these tools, we're going to have a new API. So, you know, we launched chat completions, like I think March 2023 or so. It's been a while. So we're looking for an update over here to support all the new things that the models can do. And so we're launching this new API. It is, you know, it works with tools. We think it'll be like a great option for all the future agentic products that we build. And so that is also launching today. Actually, the last thing we're launching is the agents SDK. We launched this thing called Swarm last year where, you know, it was an experimental SDK for people to do multi-agent orchestration and stuff like that. It was supposed to be like educational experimental, but like people, people really loved it. They like ate it up. And so we are like, all right, let's, let's upgrade this thing. Let's give it a new name. And so we're calling it the agents SDK. It's going to have built-in tracing in the OpenAI dashboard. So lots of cool stuff going out. So, yeah.Romain [00:02:14]: That's a lot, but we said 2025 was the year of agents. So there you have it, like a lot of new tools to build these agents for developers.swyx [00:02:20]: Okay. I guess, I guess we'll just kind of go one by one and we'll leave the agents SDK towards the end. So responses API, I think the sort of primary concern that people have and something I think I've voiced to you guys when, when, when I was talking with you in the, in the planning process was, is chat completions going away? So I just wanted to let it, let you guys respond to the concerns that people might have.Romain [00:02:41]: Chat completion is definitely like here to stay, you know, it's a bare metal API we've had for quite some time. Lots of tools built around it. So we want to make sure that it's maintained and people can confidently keep on building on it. At the same time, it was kind of optimized for a different world, right? It was optimized for a pre-multi-modality world. We also optimized for kind of single turn. It takes two problems. It takes prompt in, it takes response out. And now with these agentic workflows, we, we noticed that like developers and companies want to build longer horizon tasks, you know, like things that require multiple returns to get the task accomplished. And computer use is one of those, for instance. And so that's why the responses API came to life to kind of support these new agentic workflows. But chat completion is definitely here to stay.swyx [00:03:27]: And assistance API, we've, uh, has a target sunset date of first half of 2020. So this is kind of like, in my mind, there was a kind of very poetic mirroring of the API with the models. This, I kind of view this as like kind of the merging of assistance API and chat completions, right. Into one unified responses. So it's kind of like how GPT and the old series models are also unifying.Romain [00:03:48]: Yeah, that's exactly the right, uh, that's the right framing, right? Like, I think we took the best of what we learned from the assistance API, especially like being able to access tools very, uh, very like conveniently, but at the same time, like simplifying the way you have to integrate, like, you no longer have to think about six different objects to kind of get access to these tools with the responses API. You just get one API request and suddenly you can weave in those tools, right?Nikunj [00:04:12]: Yeah, absolutely. And I think we're going to make it really easy and straightforward for assistance API users to migrate over to responsive. Right. To the API without any loss of functionality or data. So our plan is absolutely to add, you know, assistant like objects and thread light objects to that, that work really well with the responses API. We'll also add like the code interpreter tool, which is not launching today, but it'll come soon. And, uh, we'll add async mode to responses API, because that's another difference with, with, uh, assistance. I will have web hooks and stuff like that, but I think it's going to be like a pretty smooth transition. Uh, once we have all of that in place. And we'll be. Like a full year to migrate and, and help them through any issues they, they, they face. So overall, I feel like assistance users are really going to benefit from this longer term, uh, with this more flexible, primitive.Alessio [00:05:01]: How should people think about when to use each type of API? So I know that in the past, the assistance was maybe more stateful, kind of like long running, many tool use kind of like file based things. And the chat completions is more stateless, you know, kind of like traditional completion API. Is that still the mental model that people should have? Or like, should you buy the.Nikunj [00:05:20]: So the responses API is going to support everything that it's at launch, going to support everything that chat completion supports, and then over time, it's going to support everything that assistance supports. So it's going to be a pretty good fit for anyone starting out with open AI. Uh, they should be able to like go to responses responses, by the way, also has a stateless mode, so you can pass in store false and they'll make the whole API stateless, just like chat completions. You're really trying to like get this unification. A story in so that people don't have to juggle multiple endpoints. That being said, like chat completions, just like the most widely adopted API, it's it's so popular. So we're still going to like support it for years with like new models and features. But if you're a new user, you want to or if you want to like existing, you want to tap into some of these like built in tools or something, you should feel feel totally fine migrating to responses and you'll have more capabilities and performance than the tech completions.swyx [00:06:16]: I think the messaging that I agree that I think resonated the most. When I talked to you was that it is a strict superset, right? Like you should be able to do everything that you could do in chat completions and with assistants. And the thing that I just assumed that because you're you're now, you know, by default is stateful, you're actually storing the chat logs or the chat state. I thought you'd be charging me for it. So, you know, to me, it was very surprising that you figured out how to make it free.Nikunj [00:06:43]: Yeah, it's free. We store your state for 30 days. You can turn it off. But yeah, it's it's free. And the interesting thing on state is that it just like makes particularly for me, it makes like debugging things and building things so much simpler, where I can like create a responses object that's like pretty complicated and part of this more complex application that I've built, I can just go into my dashboard and see exactly what happened that mess up my prompt that is like not called one of these tools that misconfigure one of the tools like the visual observability of everything that you're doing is so, so helpful. So I'm excited, like about people trying that out and getting benefits from it, too.swyx [00:07:19]: Yeah, it's a it's really, I think, a really nice to have. But all I'll say is that my friend Corey Quinn says that anything that can be used as a database will be used as a database. So be prepared for some abuse.Romain [00:07:34]: All right. Yeah, that's a good one. Some of that I've tried with the metadata. That's some people are very, very creative at stuffing data into an object. Yeah.Nikunj [00:07:44]: And we do have metadata with responses. Exactly. Yeah.Alessio [00:07:48]: Let's get through it. All of these. So web search. I think the when I first said web search, I thought you were going to just expose a API that then return kind of like a nice list of thing. But the way it's name is like GPD for all search preview. So I'm guessing you have you're using basically the same model that is in the chat GPD search, which is fine tune for search. I'm guessing it's a different model than the base one. And it's impressive the jump in performance. So just to give an example, in simple QA, GPD for all is 38% accuracy for all search is 90%. But we always talk about. How tools are like models is not everything you need, like tools around it are just as important. So, yeah, maybe give people a quick review on like the work that went into making this special.Nikunj [00:08:29]: Should I take that?Alessio [00:08:29]: Yeah, go for it.Nikunj [00:08:30]: So firstly, we're launching web search in two ways. One in responses API, which is our API for tools. It's going to be available as a web search tool itself. So you'll be able to go tools, turn on web search and you're ready to go. We still wanted to give chat completions people access to real time information. So in that. Chat completions API, which does not support built in tools. We're launching the direct access to the fine tuned model that chat GPD for search uses, and we call it GPD for search preview. And how is this model built? Basically, we have our search research team has been working on this for a while. Their main goal is to, like, get information, like get a bunch of information from all of our data sources that we use to gather information for search and then pick the right things and then cite them. As accurately as possible. And that's what the search team has really focused on. They've done some pretty cool stuff. They use like synthetic data techniques. They've done like all series model distillation to, like, make these four or fine tunes really good. But yeah, the main thing is, like, can it remain factual? Can it answer questions based on what it retrieves and get cited accurately? And that's what this like fine tune model really excels at. And so, yeah, so we're excited that, like, it's going to be directly available in chat completions along with being available as a tool. Yeah.Alessio [00:09:49]: Just to clarify, if I'm using the responses API, this is a tool. But if I'm using chat completions, I have to switch model. I cannot use 01 and call search as a tool. Yeah, that's right. Exactly.Romain [00:09:58]: I think what's really compelling, at least for me and my own uses of it so far, is that when you use, like, web search as a tool, it combines nicely with every other tool and every other feature of the platform. So think about this for a second. For instance, imagine you have, like, a responses API call with the web search tool, but suddenly you turn on function calling. You also turn on, let's say, structure. So you can have, like, the ability to structure any data from the web in real time in the JSON schema that you need for your application. So it's quite powerful when you start combining those features and tools together. It's kind of like an API for the Internet almost, you know, like you get, like, access to the precise schema you need for your app. Yeah.Alessio [00:10:39]: And then just to wrap up on the infrastructure side of it, I read on the post that people, publisher can choose to appear in the web search. So are people by default in it? Like, how can we get Latent Space in the web search API?Nikunj [00:10:53]: Yeah. Yeah. I think we have some documentation around how websites, publishers can control, like, what shows up in a web search tool. And I think you should be able to, like, read that. I think we should be able to get Latent Space in for sure. Yeah.swyx [00:11:10]: You know, I think so. I compare this to a broader trend that I started covering last year of online LLMs. Actually, Perplexity, I think, was the first. It was the first to say, to offer an API that is connected to search, and then Gemini had the sort of search grounding API. And I think you guys, I actually didn't, I missed this in the original reading of the docs, but you even give like citations with like the exact sub paragraph that is matching, which I think is the standard nowadays. I think my question is, how do we take what a knowledge cutoff is for something like this, right? Because like now, basically there's no knowledge cutoff is always live, but then there's a difference between what the model has sort of internalized in its back propagation and what is searching up its rag.Romain [00:11:53]: I think it kind of depends on the use case, right? And what you want to showcase as the source. Like, for instance, you take a company like Hebbia that has used this like web search tool. They can combine like for credit firms or law firms, they can find like, you know, public information from the internet with the live sources and citation that sometimes you do want to have access to, as opposed to like the internal knowledge. But if you're building something different, well, like, you just want to have the information. If you want to have an assistant that relies on the deep knowledge that the model has, you may not need to have these like direct citations. So I think it kind of depends on the use case a little bit, but there are many, uh, many companies like Hebbia that will need that access to these citations to precisely know where the information comes from.swyx [00:12:34]: Yeah, yeah, uh, for sure. And then one thing on the, on like the breadth, you know, I think a lot of the deep research, open deep research implementations have this sort of hyper parameter about, you know, how deep they're searching and how wide they're searching. I don't see that in the docs. But is that something that we can tune? Is that something you recommend thinking about?Nikunj [00:12:53]: Super interesting. It's definitely not a parameter today, but we should explore that. It's very interesting. I imagine like how you would do it with the web search tool and responsive API is you would have some form of like, you know, agent orchestration over here where you have a planning step and then each like web search call that you do like explicitly goes a layer deeper and deeper and deeper. But it's not a parameter that's available out of the box. But it's a cool. It's a cool thing to think about. Yeah.swyx [00:13:19]: The only guidance I'll offer there is a lot of these implementations offer top K, which is like, you know, top 10, top 20, but actually don't really want that. You want like sort of some kind of similarity cutoff, right? Like some matching score cuts cutoff, because if there's only five things, five documents that match fine, if there's 500 that match, maybe that's what I want. Right. Yeah. But also that might, that might make my costs very unpredictable because the costs are something like $30 per a thousand queries, right? So yeah. Yeah.Nikunj [00:13:49]: I guess you could, you could have some form of like a context budget and then you're like, go as deep as you can and pick the best stuff and put it into like X number of tokens. There could be some creative ways of, of managing cost, but yeah, that's a super interesting thing to explore.Alessio [00:14:05]: Do you see people using the files and the search API together where you can kind of search and then store everything in the file so the next time I'm not paying for the search again and like, yeah, how should people balance that?Nikunj [00:14:17]: That's actually a very interesting question. And let me first tell you about how I've seen a really cool way I've seen people use files and search together is they put their user preferences or memories in the vector store and so a query comes in, you use the file search tool to like get someone's like reading preferences or like fashion preferences and stuff like that, and then you search the web for information or products that they can buy related to those preferences and you then render something beautiful to show them, like, here are five things that you might be interested in. So that's how I've seen like file search, web search work together. And by the way, that's like a single responses API call, which is really cool. So you just like configure these things, go boom, and like everything just happens. But yeah, that's how I've seen like files and web work together.Romain [00:15:01]: But I think that what you're pointing out is like interesting, and I'm sure developers will surprise us as they always do in terms of how they combine these tools and how they might use file search as a way to have memory and preferences, like Nikum says. But I think like zooming out, what I find very compelling and powerful here is like when you have these like neural networks. That have like all of the knowledge that they have today, plus real time access to the Internet for like any kind of real time information that you might need for your app and file search, where you can have a lot of company, private documents, private details, you combine those three, and you have like very, very compelling and precise answers for any kind of use case that your company or your product might want to enable.swyx [00:15:41]: It's a difference between sort of internal documents versus the open web, right? Like you're going to need both. Exactly, exactly. I never thought about it doing memory as well. I guess, again, you know, anything that's a database, you can store it and you will use it as a database. That sounds awesome. But I think also you've been, you know, expanding the file search. You have more file types. You have query optimization, custom re-ranking. So it really seems like, you know, it's been fleshed out. Obviously, I haven't been paying a ton of attention to the file search capability, but it sounds like your team has added a lot of features.Nikunj [00:16:14]: Yeah, metadata filtering was like the main thing people were asking us for for a while. And I'm super excited about it. I mean, it's just so critical once your, like, web store size goes over, you know, more than like, you know, 5,000, 10,000 records, you kind of need that. So, yeah, metadata filtering is coming, too.Romain [00:16:31]: And for most companies, it's also not like a competency that you want to rebuild in-house necessarily, you know, like, you know, thinking about embeddings and chunking and, you know, how of that, like, it sounds like very complex for something very, like, obvious to ship for your users. Like companies like Navant, for instance. They were able to build with the file search, like, you know, take all of the FAQ and travel policies, for instance, that you have, you, you put that in file search tool, and then you don't have to think about anything. Now your assistant becomes naturally much more aware of all of these policies from the files.swyx [00:17:03]: The question is, like, there's a very, very vibrant RAG industry already, as you well know. So there's many other vector databases, many other frameworks. Probably if it's an open source stack, I would say like a lot of the AI engineers that I talk to want to own this part of the stack. And it feels like, you know, like, when should we DIY and when should we just use whatever OpenAI offers?Nikunj [00:17:24]: Yeah. I mean, like, if you're doing something completely from scratch, you're going to have more control, right? Like, so super supportive of, you know, people trying to, like, roll up their sleeves, build their, like, super custom chunking strategy and super custom retrieval strategy and all of that. And those are things that, like, will be harder to do with OpenAI tools. OpenAI tool has, like, we have an out-of-the-box solution. We give you the tools. We use some knobs to customize things, but it's more of, like, a managed RAG service. So my recommendation would be, like, start with the OpenAI thing, see if it, like, meets your needs. And over time, we're going to be adding more and more knobs to make it even more customizable. But, you know, if you want, like, the completely custom thing, you want control over every single thing, then you'd probably want to go and hand roll it using other solutions. So we're supportive of both, like, engineers should pick. Yeah.Alessio [00:18:16]: And then we got computer use. Which I think Operator was obviously one of the hot releases of the year. And we're only two months in. Let's talk about that. And that's also, it seems like a separate model that has been fine-tuned for Operator that has browser access.Nikunj [00:18:31]: Yeah, absolutely. I mean, the computer use models are exciting. The cool thing about computer use is that we're just so, so early. It's like the GPT-2 of computer use or maybe GPT-1 of computer use right now. But it is a separate model that has been, you know, the computer. The computer use team has been working on, you send it screenshots and it tells you what action to take. So the outputs of it are almost always tool calls and you're inputting screenshots based on whatever computer you're trying to operate.Romain [00:19:01]: Maybe zooming out for a second, because like, I'm sure your audience is like super, super like AI native, obviously. But like, what is computer use as a tool, right? And what's operator? So the idea for computer use is like, how do we let developers also build agents that can complete tasks for the users, but using a computer? Okay. Or a browser instead. And so how do you get that done? And so that's why we have this custom model, like optimized for computer use that we use like for operator ourselves. But the idea behind like putting it as an API is that imagine like now you want to, you want to automate some tasks for your product or your own customers. Then now you can, you can have like the ability to spin up one of these agents that will look at the screen and act on the screen. So that means able, the ability to click, the ability to scroll. The ability to type and to report back on the action. So that's what we mean by computer use and wrapping it as a tool also in the responses API. So now like that gives a hint also at the multi-turned thing that we were hinting at earlier, the idea that like, yeah, maybe one of these actions can take a couple of minutes to complete because there's maybe like 20 steps to complete that task. But now you can.swyx [00:20:08]: Do you think a computer use can play Pokemon?Romain [00:20:11]: Oh, interesting. I guess we tried it. I guess we should try it. You know?swyx [00:20:17]: Yeah. There's a lot of interest. I think Pokemon really is a good agent benchmark, to be honest. Like it seems like Claude is, Claude is running into a lot of trouble.Romain [00:20:25]: Sounds like we should make that a new eval, it looks like.swyx [00:20:28]: Yeah. Yeah. Oh, and then one more, one more thing before we move on to agents SDK. I know you have a hard stop. There's all these, you know, blah, blah, dash preview, right? Like search preview, computer use preview, right? And you see them all like fine tunes of 4.0. I think the question is, are we, are they all going to be merged into the main branch or are we basically always going to have subsets? Of these models?Nikunj [00:20:49]: Yeah, I think in the early days, research teams at OpenAI like operate with like fine tune models. And then once the thing gets like more stable, we sort of merge it into the main line. So that's definitely the vision, like going out of preview as we get more comfortable with and learn about all the developer use cases and we're doing a good job at them. We'll sort of like make them part of like the core models so that you don't have to like deal with the bifurcation.Romain [00:21:12]: You should think of it this way as exactly what happened last year when we introduced vision capabilities, you know. Yes. Vision capabilities were in like a vision preview model based off of GPT-4 and then vision capabilities now are like obviously built into GPT-4.0. You can think about it the same way for like the other modalities like audio and those kind of like models, like optimized for search and computer use.swyx [00:21:34]: Agents SDK, we have a few minutes left. So let's just assume that everyone has looked at Swarm. Sure. I think that Swarm has really popularized the handoff technique, which I thought was like, you know, really, really interesting for sort of a multi-agent. What is new with the SDK?Nikunj [00:21:50]: Yeah. Do you want to start? Yeah, for sure. So we've basically added support for types. We've made this like a lot. Yeah. Like we've added support for types. We've added support for guard railing, which is a very common pattern. So in the guardrail example, you basically have two things happen in parallel. The guardrail can sort of block the execution. It's a type of like optimistic generation that happens. And I think we've added support for tracing. So I think that's really cool. So you can basically look at the traces that the Agents SDK creates in the OpenAI dashboard. We also like made this pretty flexible. So you can pick any API from any provider that supports the ChatCompletions API format. So it supports responses by default, but you can like easily plug it in to anyone that uses the ChatCompletions API. And similarly, on the tracing side, you can support like multiple tracing providers. By default, it sort of points to the OpenAI dashboard. But, you know, there's like so many tracing providers. There's so many tracing companies out there. And we'll announce some partnerships on that front, too. So just like, you know, adding lots of core features and making it more usable, but still centered around like handoffs is like the main, main concept.Romain [00:22:59]: And by the way, it's interesting, right? Because Swarm just came to life out of like learning from customers directly that like orchestrating agents in production was pretty hard. You know, simple ideas could quickly turn very complex. Like what are those guardrails? What are those handoffs, et cetera? So that came out of like learning from customers. And it was initially shipped. It was not as a like low-key experiment, I'd say. But we were kind of like taken by surprise at how much momentum there was around this concept. And so we decided to learn from that and embrace it. To be like, okay, maybe we should just embrace that as a core primitive of the OpenAI platform. And that's kind of what led to the Agents SDK. And I think now, as Nikuj mentioned, it's like adding all of these new capabilities to it, like leveraging the handoffs that we had, but tracing also. And I think what's very compelling for developers is like instead of having one agent to rule them all and you stuff like a lot of tool calls in there that can be hard to monitor, now you have the tools you need to kind of like separate the logic, right? And you can have a triage agent that based on an intent goes to different kind of agents. And then on the OpenAI dashboard, we're releasing a lot of new user interface logs as well. So you can see all of the tracing UIs. Essentially, you'll be able to troubleshoot like what exactly happened. In that workflow, when the triage agent did a handoff to a secondary agent and the third and see the tool calls, et cetera. So we think that the Agents SDK combined with the tracing UIs will definitely help users and developers build better agentic workflows.Alessio [00:24:28]: And just before we wrap, are you thinking of connecting this with also the RFT API? Because I know you already have, you kind of store my text completions and then I can do fine tuning of that. Is that going to be similar for agents where you're storing kind of like my traces? And then help me improve the agents?Nikunj [00:24:43]: Yeah, absolutely. Like you got to tie the traces to the evals product so that you can generate good evals. Once you have good evals and graders and tasks, you can use that to do reinforcement fine tuning. And, you know, lots of details to be figured out over here. But that's the vision. And I think we're going to go after it like pretty hard and hope we can like make this whole workflow a lot easier for developers.Alessio [00:25:05]: Awesome. Thank you so much for the time. I'm sure you'll be busy on Twitter tomorrow with all the developer feedback. Yeah.Romain [00:25:12]: Thank you so much for having us. And as always, we can't wait to see what developers will build with these tools and how we can like learn as quickly as we can from them to make them even better over time.Nikunj [00:25:21]: Yeah.Romain [00:25:22]: Thank you, guys.Nikunj [00:25:23]: Thank you.Romain [00:25:23]: Thank you both. Awesome. Get full access to Latent.Space at www.latent.space/subscribe

La poesia és més que mai viva a Catalunya Nord! Les Edicions Paraules publiquen, en la col·lecció Guergal, el primer recull de poesia i relats breus de l'Íngrid Obiol.El seu títol? Donívola Latent. Una melopea entre dolor, combativitat i etern femení. Un llibre que “mostra l'agressivitat d'un recorregut silenciós i solitari que ens endinsa en la crueltat d'un dolor invisible que persisteix en moltes genètiques femenines al primer quart del segle XXI.De la necessitat sorgeix la sola i única arma disponible, l'escriptura feta a mà amb llapis i paper per trencar el silenci sòrdid que envolta els mals donívols. Sense poder escollir ni poder evitar el combat, una riuada de paraules surten de l'interior del ventre per combatre el patiment. Íngrid Obiol assumeix la veu pròpia enmig d'un context bèl·lic rimat al femení, per tractar d'alliberar el dolor paït durant anys per la sola raó d'haver nascut dona en un món creat per a homes.I en la catarsi, la seva veu literària i el seu jo poètic s'expressa enmig d'un món fosc que cerca petites llums d'esperança per on poder avançar.” (ed. Paraules)En parlem doncs amb l'escriptora Íngrid Obiol.

Special lightning pod with David Hershey from Anthropic, the person behind Claude Plays Pokémon. Sonnet 3.7 is currently trying to complete Pokémon Red live on Twitch thanks to a special harness that David built so that it can see the screen, navigate through it, remember facts about the game, and more. (Since recording, it has successfully escaped Mt Moon! You can follow along on Twitch: https://www.twitch.tv/claudeplayspokemon) Get full access to Latent.Space at www.latent.space/subscribe

In this first recorded episode of 2025, Eric and Glenn start with Eric being pedantic in his “A Truth, A Lie, and a Mandela Effect”. The guys catch up on New Year stuff and then launch into a review of a research paper from New South Wales, Australia, titled “How often do fingerprint examiners disagree in routine casework?” by O'Connor and Chapman (2024) from Forensic Science International. Eric first discusses some of the important differences in casework workflow and conclusions between Aussie examiners and U.S. examiners. Then they discuss the results of the paper and the significance of the findings. At the end, they discuss solutions and ideas for resolving conflict and ultimately find that conflicting results and examiner disagreements are a normal, expected, natural part of the examination process. Find the paper at: https://doi.org/10.1016/j.forsciint.2024.112139

Today on TPE we look at Comedy, India, Ranveer Allahbadia going to the Supreme Court and the India's Got Latent Controversy.The Pakistan Experience is an independently produced podcast looking to tell stories about Pakistan through conversations. Please consider supporting us on Patreon:https://www.patreon.com/thepakistanexperienceTo support the channel:Jazzcash/Easypaisa - 0325 -2982912Patreon.com/thepakistanexperienceAnd Please stay in touch:https://twitter.com/ThePakistanExp1https://www.facebook.com/thepakistanexperiencehttps://instagram.com/thepakistanexpeperienceThe podcast is hosted by comedian and writer, Shehzad Ghias Shaikh. Shehzad is a Fulbright scholar with a Masters in Theatre from Brooklyn College. He is also one of the foremost Stand-up comedians in Pakistan and frequently writes for numerous publications. Instagram.com/shehzadghiasshaikhFacebook.com/Shehzadghias/Twitter.com/shehzad89Join this channel to get access to perks:https://www.youtube.com/channel/UC44l9XMwecN5nSgIF2Dvivg/joinChapters0:00 Ranveer's bad joke2:04 India's Got Latent Format3:30 Comedy in America and Freedom of Speech5:30 Difference between subject and target of the joke9:20 Indian News Media12:43 The Attention Economy18:47 Outrage Culture and Indian Culture21:00 Comedians should push boundaries23:10 Gaaliyan, Nature of Content and Competition25:51 Women responding in kind to insults30:03 Abuses and Sexual Content in Comedy33:50 Insult Comedy is a Genre35:15 Supreme Court and Ranveer Allahbadia36:30 This will be used to curb freedom

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

Dr Matthew Burman is a TB clinician and researcher from East London, Queen Mary University and the Blizzard Institute. Today he presents the data from the CATAPULT trial which randomised patients to either have their latent TB infection managed by a TB clinic in secondary care, or be managed in primary care by their GP in concert with a community pharmacist. This amazing trial re-evaluates how we think about TB preventative therapy and looks at new ways of approaching the burden of latent TB infection.REFERENCESBurman, M., et al. "Protocol for a cluster randomised control trial evaluating the efficacy and safety of treatment for latent tuberculosis infection in recent migrants within primary care: the CATAPuLT trial." BMC Public Health 19 (2019): 1-7.Goswami, Neela D., et al. "Predictors of latent tuberculosis treatment initiation and completion at a US public health clinic: a prospective cohort study." BMC public health 12 (2012): 1-8.Loutet, Miranda G., et al. "National roll-out of latent tuberculosis testing and treatment for new migrants in England: a retrospective evaluation in a high-incidence area." European Respiratory Journal 51.1 (2018).Alsdurf, Hannah, et al. "The cascade of care in diagnosis and treatment of latent tuberculosis infection: a systematic review and meta-analysis." The Lancet Infectious Diseases 16.11 (2016): 1269-1278.

Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses. SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + RESEARCH OVERVIEW:https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0Clem and Matthew-https://www.linkedin.com/in/clement-bonnet16/https://github.com/clement-bonnethttps://mvmacfarlane.github.io/TOC1. LPN Fundamentals [00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview [00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis [00:06:55] 1.3 Induction vs Transduction in Machine Learning2. LPN Architecture and Latent Space [00:11:50] 2.1 LPN Architecture and Latent Space Implementation [00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture [00:20:25] 2.3 Gradient-Based Search Training Strategy [00:23:39] 2.4 LPN Model Architecture and Implementation Details3. Implementation and Scaling [00:27:34] 3.1 Training Data Generation and re-ARC Framework [00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search [00:34:43] 3.3 Program Composition and Computational Graph Architecture4. Advanced Concepts and Future Directions [00:45:09] 4.1 AI Creativity and Program Synthesis Approaches [00:49:47] 4.2 Scaling and Interpretability in Latent Space ModelsREFS[00:00:05] ARC benchmark, Chollethttps://arxiv.org/abs/2412.04604[00:02:10] Latent Program Spaces, Bonnet, Macfarlanehttps://arxiv.org/abs/2411.08706[00:07:45] Kevin Ellis work on program generationhttps://www.cs.cornell.edu/~ellisk/[00:08:45] Induction vs transduction in abstract reasoning, Li et al.https://arxiv.org/abs/2411.02272[00:17:40] VAEs, Kingma, Wellinghttps://arxiv.org/abs/1312.6114[00:27:50] re-ARC, Hodelhttps://github.com/michaelhodel/re-arc[00:29:40] Grid size in ARC tasks, Chollethttps://github.com/fchollet/ARC-AGI[00:33:00] Critique of deep learning, Marcushttps://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf

00:00 - Gian of Brown Mundeh01:53 - BeerBiceps - Ranveer Allahbadia controversy05:30 - Indian News Commentary 07:57 - Vir Das - Two Indias09:25 - Pulling the clip out of context12:14 - The comment section is unhinged13:38 - The apology video + police involvement16:27 - Akaash Singh's Support19:26 - Indian Government demonising Beer Biceps21:09 - Ed Sheeran in India23:40 - Youtubers in India + Pakistan + Langar28:18 - Brown Man on Thumbnail + Terrorism35:35 - Explaining your "Special Friend"39:35 - "The vibes are off" but in punjabi44:57 - Gaslighting, Toxicity + Real Men49:26 - Shall I put a sign on my head?51:49 - Indy's mortgage updateVir Das Two Indias Speech - https://www.youtube.com/watch?v=5A-F9qu6c_4 Follow Us On:Tik Tok - https://bit.ly/indy-and-dr-tik-tokInstagram - http://bit.ly/indy-and-dr-instaFacebook - http://bit.ly/indy-and-dr-facebookSpotify - http://bit.ly/indy-and-drAlso available at all podcasting outlets.#beerbiceps #ranveerallahbadia #indiasgotlatent

The free livestreams for AI Engineer Summit are now up! Please hit the bell to help us appease the algo gods. We're also announcing a special Online Track later today.Today's Deep Research episode is our last in our series of AIE Summit preview podcasts - thanks for following along with our OpenAI, Portkey, Pydantic, Bee, and Bret Taylor episodes, and we hope you enjoy the Summit! Catch you on livestream.Everybody's going deep now. Deep Work. Deep Learning. DeepMind. If 2025 is the Year of Agents, then the 2020s are the Decade of Deep.While “LLM-powered Search” is as old as Perplexity and SearchGPT, and open source projects like GPTResearcher and clones like OpenDeepResearch exist, the difference with “Deep Research” products is they are both “agentic” (loosely meaning that an LLM decides the next step in a workflow, usually involving tools) and bundling custom-tuned frontier models (custom tuned o3 and Gemini 1.5 Flash).The reception to OpenAI's Deep Research agent has been nothing short of breathless:"Deep Research is the best public-facing AI product Google has ever released. It's like having a college-educated researcher in your pocket." - Jason Calacanis“I have had [Deep Research] write a number of ten-page papers for me, each of them outstanding. I think of the quality as comparable to having a good PhD-level research assistant, and sending that person away with a task for a week or two, or maybe more. Except Deep Research does the work in five or six minutes.” - Tyler Cowen“Deep Research is one of the best bargains in technology.” - Ben Thompson“my very approximate vibe is that it can do a single-digit percentage of all economically valuable tasks in the world, which is a wild milestone.” - sama“Using Deep Research over the past few weeks has been my own personal AGI moment. It takes 10 mins to generate accurate and thorough competitive and market research (with sources) that previously used to take me at least 3 hours.” - OAI employee“It's like a bazooka for the curious mind” - Dan Shipper“Deep research can be seen as a new interface for the internet, in addition to being an incredible agent… This paradigm will be so powerful that in the future, navigating the internet manually via a browser will be "old-school", like performing arithmetic calculations by hand.” - Jason Wei“One notable characteristic of Deep Research is its extreme patience. I think this is rapidly approaching “superhuman patience”. One realization working on this project was that intelligence and patience go really well together.” - HyungWon“I asked it to write a reference Interaction Calculus evaluator in Haskell. A few exchanges later, it gave me a complete file, including a parser, an evaluator, O(1) interactions and everything. The file compiled, and worked on my test inputs. There are some minor issues, but it is mostly correct. So, in about 30 minutes, o3 performed a job that would take me a day or so.” - Victor Taelin“Can confirm OpenAI Deep Research is quite strong. In a few minutes it did what used to take a dozen hours. The implications to knowledge work is going to be quite profound when you just ask an AI Agent to perform full tasks for you and come back with a finished result.” - Aaron Levie“Deep Research is genuinely useful” - Gary MarcusWith the advent of “Deep Research” agents, we are now routinely asking models to go through 100+ websites and generate in-depth reports on any topic. The Deep Research revolution has hit the AI scene in the last 2 weeks: * Dec 11th: Gemini Deep Research (today's guest!) rolls out with Gemini Advanced* Feb 2nd: OpenAI releases Deep Research* Feb 3rd: a dozen “Open Deep Research” clones launch* Feb 5th: Gemini 2.0 Flash GA* Feb 15th: Perplexity launches Deep Research * Feb 17th: xAI launches Deep SearchIn today's episode, we welcome Aarush Selvan and Mukund Sridhar, the lead PM and tech lead for Gemini Deep Research, the originators of the entire category. We asked detailed questions from inspiration to implementation, why they had to finetune a special model for it instead of using the standard Gemini model, how to run evals for them, and how to think about the distribution of use cases. (We also have an upcoming Gemini 2 episode with our returning first guest Logan Kilpatrick so stay tuned

In this episode of Go Gaddis Real Estate Radio, Cleve Gaddis takes you on a deep dive into Chattahoochee Reserve in Duluth in our Neighborhood Spotlight, and we're also covering some hot real estate topics, including latent defects in home sales and the shocking story of a historic Cobb County home listed for just $1! Segment Highlights:

A new startup founded by a former Google DeepMind scientist is exiting stealth with $50 million in funding. Latent Labs is building AI foundation models to “make biology programmable,” and it plans to partner with biotech and pharmaceutical companies to generate and optimize proteins. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, hosts Kiran and Shunali of India's Got Latent discuss the Ranveer Allahbadia controversy, analyzing his vulgar remarks and the public reaction. They explore the blurred line between humor and vulgarity, the influence of cancel culture, and whether such responses are justified in today's society.

Bundle tickets for AIE Summit NYC have now sold out. You can now sign up for the livestream — where we will be making a big announcement soon. NYC-based readers and Summit attendees should check out the meetups happening around the Summit.2024 was a very challenging year for AI Hardware. After the buzz of CES last January, 2024 was marked by the meteoric rise and even harder fall of AI Wearables companies like Rabbit and Humane, with an assist from a pre-wallpaper-app MKBHD. Even Friend.com, the first to launch in the AI pendant category, and which spurred Rewind AI to rebrand to Limitless and follow in their footsteps, ended up delaying their wearable ship date and launching an experimental website chatbot version. We have been cautiously excited about this category, keeping tabs on most of the top entrants, including Omi and Compass. However, to date the biggest winner still standing from the AI Wearable wars is Bee AI, founded by today's guests Maria and Ethan. Bee is an always on hardware device with beamforming microphones, 7 day battery life and a mute button, that can be worn as a wristwatch or a clip-on pin, backed by an incredible transcription, diarization and very long context memory processing pipeline that helps you to remember your day, your todos, and even perform actions by operating a virtual cloud phone. This is one of the most advanced, production ready, personal AI agents we've ever seen, so we were excited to be their first podcast appearance. We met Bee when we ran the world's first Personal AI meetup in April last year.As a user of Bee (and not an investor! just a friend!) it's genuinely been a joy to use, and we were glad to take advantage of the opportunity to ask hard questions about the privacy and legal/ethical side of things as much as the AI and Hardware engineering side of Bee. We hope you enjoy the episode and tune in next Friday for Bee's first conference talk: Building Perfect Memory.Show Notes* Bee Website* Ethan Sutin, Maria de Lourdes Zollo* Bee @ Personal AI Meetup* Buy Bee with Listener Discount Code!Timestamps* 00:00:00 Introductions and overview of Bee Computer* 00:01:58 Personal context and use cases for Bee* 00:03:02 Origin story of Bee and the founders' background* 00:06:56 Evolution from app to hardware device* 00:09:54 Short-term value proposition for users* 00:12:17 Demo of Bee's functionality* 00:17:54 Hardware form factor considerations* 00:22:22 Privacy concerns and legal considerations* 00:30:57 User adoption and reactions to wearing Bee* 00:35:56 CES experience and hardware manufacturing challenges* 00:41:40 Software pipeline and inference costs* 00:53:38 Technical challenges in real-time processing* 00:57:46 Memory and personal context modeling* 01:02:45 Social aspects and agent-to-agent interactions* 01:04:34 Location sharing and personal data exchange* 01:05:11 Personality analysis capabilities* 01:06:29 Hiring and future of always-on AITranscriptAlessio [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 SmallAI.swyx [00:00:12]: Hey, and today we are very honored to have in the studio Maria and Ethan from Bee.Maria [00:00:16]: Hi, thank you for having us.swyx [00:00:20]: And you are, I think, the first hardware founders we've had on the podcast. I've been looking to have had a hardware founder, like a wearable hardware, like a wearable hardware founder for a while. I think we're going to have two or three of them this year. And you're the ones that I wear every day. So thank you for making Bee. Thank you for all the feedback and the usage. Yeah, you know, I've been a big fan. You are the speaker gift for the Engineering World's Fair. And let's start from the beginning. What is Bee Computer?Ethan [00:00:52]: Bee Computer is a personal AI system. So you can think of it as AI living alongside you in first person. So it can kind of capture your in real life. So with that understanding can help you in significant ways. You know, the obvious one is memory, but that's that's really just the base kind of use case. So recalling and reflective. I know, Swyx, that you you like the idea of journaling, but you don't but still have some some kind of reflective summary of what you experienced in real life. But it's also about just having like the whole context of a human being and understanding, you know, giving the machine the ability to understand, like, what's going on in your life. Your attitudes, your desires, specifics about your preferences, so that not only can it help you with recall, but then anything that you need it to do, it already knows, like, if you think about like somebody who you've worked with or lived with for a long time, they just know kind of without having to ask you what you would want, it's clear that like, that is the future that personal AI, like, it's just going to be very, you know, the AI is just so much more valuable with personal context.Maria [00:01:58]: I will say that one of the things that we are really passionate is really understanding this. Personal context, because we'll make the AI more useful. Think about like a best friend that know you so well. That's one of the things that we are seeing from the user. They're using from a companion standpoint or professional use cases. There are many ways to use B, but companionship and professional are the ones that we are seeing now more.swyx [00:02:22]: Yeah. It feels so dry to talk about use cases. Yeah. Yeah.Maria [00:02:26]: It's like really like investor question. Like, what kind of use case?Ethan [00:02:28]: We're just like, we've been so broken and trained. But I mean, on the base case, it's just like, don't you want your AI to know everything you've said and like everywhere you've been, like, wouldn't you want that?Maria [00:02:40]: Yeah. And don't stay there and repeat every time, like, oh, this is what I like. You already know that. And you do things for me based on that. That's I think is really cool.swyx [00:02:50]: Great. Do you want to jump into a demo? Do you have any other questions?Alessio [00:02:54]: I want to maybe just cover the origin story. Just how did you two meet? What was the was this the first idea you started working on? Was there something else before?Maria [00:03:02]: I can start. So Ethan and I, we know each other from six years now. He had a company called Squad. And before that was called Olabot and was a personal AI. Yeah, I should. So maybe you should start this one. But yeah, that's how I know Ethan. Like he was pivoting from personal AI to Squad. And there was a co-watching with friends product. I had experience working with TikTok and video content. So I had the pivoting and we launched Squad and was really successful. And at the end. The founders decided to sell that to Twitter, now X. So both of us, we joined X. We launched Twitter Spaces. We launched many other products. And yeah, till then, we basically continue to work together to the start of B.Ethan [00:03:46]: The interesting thing is like this isn't the first attempt at personal AI. In 2016, when I started my first company, it started out as a personal AI company. This is before Transformers, no BERT even like just RNNs. You couldn't really do any convincing dialogue at all. I met Esther, who was my previous co-founder. We both really interested in the idea of like having a machine kind of model or understand a dynamic human. We wanted to make personal AI. This was like more geared towards because we had obviously much limited tools, more geared towards like younger people. So I don't know if you remember in 2016, there was like a brief chatbot boom. It was way premature, but it was when Zuckerberg went up on F8 and yeah, M and like. Yeah. The messenger platform, people like, oh, bots are going to replace apps. It was like for about six months. And then everybody realized, man, these things are terrible and like they're not replacing apps. But it was at that time that we got excited and we're like, we tried to make this like, oh, teach the AI about you. So it was just an app that you kind of chatted with and it would ask you questions and then like give you some feedback.Maria [00:04:53]: But Hugging Face first version was launched at the same time. Yeah, we started it.Ethan [00:04:56]: We started out the same office as Hugging Face because Betaworks was our investor. So they had to think. They had a thing called Bot Camp. Betaworks is like a really cool VC because they invest in out there things. They're like way ahead of everybody else. And like back then it was they had something called Bot Camp. They took six companies and it was us and Hugging Face. And then I think the other four, I'm pretty sure, are dead. But and Hugging Face was the one that really got, you know, I mean, 30% success rate is pretty good. Yeah. But yeah, when we it was, it was like it was just the two founders. Yeah, they were kind of like an AI company in the beginning. It was a chat app for teenagers. A lot of people don't know that Hugging Face was like, hey, friend, how was school? Let's trade selfies. But then, you know, they built the Transformers library, I believe, to help them make their chat app better. And then they open sourced and it was like it blew up. And like they're like, oh, maybe this is the opportunity. And now they're Hugging Face. But anyway, like we were obsessed with it at that time. But then it was clear that there's some people who really love chatting and like answering questions. But it's like a lot of work, like just to kind of manually.Maria [00:06:00]: Yeah.Ethan [00:06:01]: Teach like all these things about you to an AI.Maria [00:06:04]: Yeah, there were some people that were super passionate, for example, teenagers. They really like, for example, to speak about themselves a lot. So they will reply to a lot of questions and speak about them. But most of the people, they don't really want to spend time.Ethan [00:06:18]: And, you know, it's hard to like really bring the value with it. We had like sentence similarity and stuff and could try and do, but it was like it was premature with the technology at the time. And so we pivoted. We went to YC and the long story, but like we pivoted to consumer video and that kind of went really viral and got a lot of usage quickly. And then we ended up selling it to Twitter, worked there and left before Elon, not related to Elon, but left Twitter.swyx [00:06:46]: And then I should mention this is the famous time when well, when when Elon was just came in, this was like Esther was the famous product manager who slept there.Ethan [00:06:56]: My co-founder, my former co-founder, she sleeping bag. She was the sleep where you were. Yeah, yeah, she stayed. We had left by that point.swyx [00:07:03]: She very stayed, she's famous for staying.Ethan [00:07:06]: Yeah, but later, later left or got, I think, laid off, laid off. Yeah, I think the whole product team got laid off. She was a product manager, director. But yeah, like we left before that. And then we're like, oh, my God, things are different now. You know, I think this is we really started working on again right before ChatGPT came out. But we had an app version and we kind of were trying different things around it. And then, you know, ultimately, it was clear that, like, there were some limitations we can go on, like a good question to ask any wearable company is like, why isn't this an app? Yes. Yeah. Because like.Maria [00:07:40]: Because we tried the app at the beginning.Ethan [00:07:43]: Yeah. Like the idea that it could be more of a and B comes from ambient. So like if it was more kind of just around you all the time and less about you having to go open the app and do the effort to, like, enter in data that led us down the path of hardware. Yeah. Because the sensors on this are microphones. So it's capturing and understanding audio. We started actually our first hardware with a vision component, too. And we can talk about why we're not doing that right now. But if you wanted to, like, have a continuous understanding of audio with your phone, it would monopolize your microphone. It would get interrupted by calls and you'd have to remember to turn it on. And like that little bit of friction is actually like a substantial barrier to, like, get your phone. It's like the experience of it just being with you all the time and like living alongside you. And so I think that that's like the key reason it's not an app. And in fact, we do have Apple Watch support. So anybody who has a watch, Apple Watch can use it right away without buying any hardware. Because we worked really hard to make a version for the watch that can run in the background, not super drain your battery. But even with the watch, there's still friction because you have to remember to turn it on and it still gets interrupted if somebody calls you. And you have to remember to. We send a notification, but you still have to go back and turn it on because it's just the way watchOS works.Maria [00:09:04]: One of the things that we are seeing from our Apple Watch users, like I love the Apple Watch integration. One of the things that we are seeing is that people, they start using it from Apple Watch and after a couple of days they buy the B because they just like to wear it.Ethan [00:09:17]: Yeah, we're seeing.Maria [00:09:18]: That's something that like they're learning and it's really cool. Yeah.Ethan [00:09:21]: I mean, I think like fundamentally we like to think that like a personal AI is like the mission. And it's more about like the understanding. Connecting the dots, making use of the data to provide some value. And the hardware is like the ears of the AI. It's not like integrating like the incoming sensor data. And that's really what we focus on. And like the hardware is, you know, if we can do it well and have a great experience on the Apple Watch like that, that's just great. I mean, but there's just some platform restrictions that like existing hardware makes it hard to provide that experience. Yeah.Alessio [00:09:54]: What do people do in like two or three days that then convinces them to buy it? They buy the product. This feels like a product where like after you use it for a while, you have enough data to start to get a lot of insights. But it sounds like maybe there's also like a short term.Maria [00:10:07]: From the Apple Watch users, I believe that because every time that you receive a call after, they need to go back to B and open it again. Or for example, every day they need to charge Apple Watch and reminds them to open the app every day. They feel like, okay, maybe this is too much work. I just want to wear the B and just keep it open and that's it. And I don't need to think about it.Ethan [00:10:27]: I think they see the kind of potential of it just from the watch. Because even if you wear it a day, like we send a summary notification at the end of the day about like just key things that happened to you in your day. And like I didn't even think like I'm not like a journaling type person or like because like, oh, I just live the day. Why do I need to like think about it? But like it's actually pretty sometimes I'm surprised how interesting it is to me just to kind of be like, oh, yeah, that and how it kind of fits together. And I think that's like just something people get immediately with the watch. But they're like, oh, I'd like an easier watch. I'd like a better way to do this.swyx [00:10:58]: It's surprising because I only know about the hardware. But I use the watch as like a backup for when I don't have the hardware. I feel like because now you're beamforming and all that, this is significantly better. Yeah, that's the other thing.Ethan [00:11:11]: We have way more control over like the Apple Watch. You're limited in like you can't set the gain. You can't change the sample rate. There's just very limited framework support for doing anything with audio. Whereas if you control it. Then you can kind of optimize it for your use case. The Apple Watch isn't meant to be kind of recording this. And we can talk when we get to the part about audio, why it's so hard. This is like audio on the hardest level because you don't know it has to work in all environments or you try and make it work as best as it can. Like this environment is very great. We're in a studio. But, you know, afterwards at dinner in a restaurant, it's totally different audio environment. And there's a lot of challenges with that. And having really good source audio helps. But then there's a lot more. But with the machine learning that still is, you know, has to be done to try and account because like you can tune something for one environment or another. But it'll make one good and one bad. And like making something that's flexible enough is really challenging.Alessio [00:12:10]: Do we want to do a demo just to set the stage? And then we kind of talk about.Maria [00:12:14]: Yeah, I think we can go like a walkthrough and the prod.Alessio [00:12:17]: Yeah, sure.swyx [00:12:17]: So I think we said I should. So for listeners, we'll be switching to video. That was superimposed on. And to this video, if you want to see it, go to our YouTube, like and subscribe as always. Yeah.Maria [00:12:31]: And by the bee. Yes.swyx [00:12:33]: And by the bee. While you wait. While you wait. Exactly. It doesn't take long.Maria [00:12:39]: Maybe you should have a discount code just for the listeners. Sure.swyx [00:12:43]: If you want to offer it, I'll take it. All right. Yeah. Well, discount code Swyx. Oh s**t. Okay. Yeah. There you go.Ethan [00:12:49]: An important thing to mention also is that the hardware is meant to work with the phone. And like, I think, you know, if you, if you look at rabbit or, or humane, they're trying to create like a new hardware platform. We think that the phone's just so dominant and it will be until we have the next generation, which is not going to be for five, you know, maybe some Orion type glasses that are cheap enough and like light enough. Like that's going to take a long time before with the phone rather than trying to just like replace it. So in the app, we have a summary of your days, but at the top, it's kind of what's going on now. And that's updating your phone. It's updating continuously. So right now it's saying, I'm discussing, you know, the development of, you know, personal AI, and that's just kind of the ongoing conversation. And then we give you a readable form. That's like little kind of segments of what's the important parts of the conversations. We do speaker identification, which is really important because you don't want your personal AI thinking you said something and attributing it to you when it was just somebody else in the conversation. So you can also teach it other people's voices. So like if some, you know, somebody close to you, so it can start to understand your relationships a little better. And then we do conversation end pointing, which is kind of like a task that didn't even exist before, like, cause nobody needed to do this. But like if you had somebody's whole day, how do you like break it into logical pieces? And so we use like not just voice activity, but other signals to try and split up because conversations are a little fuzzy. They can like lead into one, can start to the next. So also like the semantic content of it. When a conversation ends, we run it through larger models to try and get a better, you know, sense of the actual, what was said and then summarize it, provide key points. What was the general atmosphere and tone of the conversation and potential action items that might've come of that. But then at the end of the day, we give you like a summary of all your day and where you were and just kind of like a step-by-step walkthrough of what happened and what were the key points. That's kind of just like the base capture layer. So like if you just want to get a kind of glimpse or recall or reflect that's there. But really the key is like all of this is now like being influenced on to generate personal context about you. So we generate key items known to be true about you and that you can, you know, there's a human in the loop aspect is like you can, you have visibility. Right. Into that. And you can, you know, I have a lot of facts about technology because that's basically what I talk about all the time. Right. But I do have some hobbies that show up and then like, how do you put use to this context? So I kind of like measure my day now and just like, what is my token output of the day? You know, like, like as a human, how much information do I produce? And it's kind of measured in tokens and it turns out it's like around 200,000 or so a day. But so in the recall case, we have, um. A chat interface, but the key here is on the recall of it. Like, you know, how do you, you know, I probably have 50 million tokens of personal context and like how to make sense of that, make it useful. So I can ask simple, like, uh, recall questions, like details about the trip I was on to Taiwan, where recently we're with our manufacturer and, um, in real time, like it will, you know, it has various capabilities such as searching through your, your memories, but then also being able to search the web or look at my calendar, we have integrations with Gmail and calendars. So like connecting the dots between the in real life and the digital life. And, you know, I just asked it about my Taiwan trip and it kind of gives me the, the breakdown of the details, what happened, the issues we had around, you know, certain manufacturing problems and it, and it goes back and references the conversation so I can, I can go back to the source. Yeah.Maria [00:16:46]: Not just the conversation as well, the integrations. So we have as well Gmail and Google calendar. So if there is something there that was useful to have more context, we can see that.Ethan [00:16:56]: So like, and it can, I never use the word agentic cause it's, it's cringe, but like it can search through, you know, if I, if I'm brainstorming about something that spans across, like search through my conversation, search the email, look at the calendar and then depending on what's needed. Then synthesize, you know, something with all that context.Maria [00:17:18]: I love that you did the Spotify wrapped. That was pretty cool. Yeah.Ethan [00:17:22]: Like one thing I did was just like make a Spotify wrap for my 2024, like of my life. You can do that. Yeah, you can.Maria [00:17:28]: Wait. Yeah. I like those crazy.Ethan [00:17:31]: Make a Spotify wrapped for my life in 2024. Yeah. So it's like surprisingly good. Um, it like kind of like game metrics. So it was like you visited three countries, you shipped, you know, XMini, beta. Devices.Maria [00:17:46]: And that's kind of more personal insights and reflection points. Yeah.swyx [00:17:51]: That's fascinating. So that's the demo.Ethan [00:17:54]: Well, we have, we can show something that's in beta. I don't know if we want to do it. I don't know.Maria [00:17:58]: We want to show something. Do it.Ethan [00:18:00]: And then we can kind of fit. Yeah.Maria [00:18:01]: Yeah.Ethan [00:18:02]: So like the, the, the, the vision is also like, not just about like AI being with you in like just passively understanding you through living your experience, but also then like it proactively suggesting things to you. Yeah. Like at the appropriate time. So like not just pool, but, but kind of, it can step in and suggest things to you. So, you know, one integration we have that, uh, is in beta is with WhatsApp. Maria is asking for a recommendation for an Italian restaurant. Would you like me to look up some highly rated Italian restaurants nearby and send her a suggestion?Maria [00:18:34]: So what I did, I just sent to Ethan a message through WhatsApp in his own personal phone. Yeah.Ethan [00:18:41]: So, so basically. B is like watching all my incoming notifications. And if it meets two criteria, like, is it important enough for me to raise a suggestion to the user? And then is there something I could potentially help with? So this is where the actions come into place. So because Maria is my co-founder and because it was like a restaurant recommendation, something that it could probably help with, it proposed that to me. And then I can, through either the chat and we have another kind of push to talk walkie talkie style button. It's actually a multi-purpose button to like toggle it on or off, but also if you push to hold, you can talk. So I can say, yes, uh, find one and send it to her on WhatsApp is, uh, an Android cloud phone. So it's, uh, going to be able to, you know, that has access to all my accounts. So we're going to abstract this away and the execution environment is not really important, but like we can go into technically why Android is actually a pretty good one right now. But, you know, it's searching for Italian restaurants, you know, and we don't have to watch this. I could be, you know, have my ear AirPods in and in my pocket, you know, it's going to go to WhatsApp, going to find Maria's thread, send her the response and then, and then let us know. Oh my God.Alessio [00:19:56]: But what's the, I mean, an Italian restaurant. Yeah. What did it choose? What did it choose? It's easy to say. Real Italian is hard to play. Exactly.Ethan [00:20:04]: It's easy to say. So I doubt it. I don't know.swyx [00:20:06]: For the record, since you have the Italians, uh, best Italian restaurant in SF.Maria [00:20:09]: Oh my God. I still don't have one. What? No.Ethan [00:20:14]: I don't know. Successfully found and shared.Alessio [00:20:16]: Let's see. Let's see what the AI says. Bottega. Bottega? I think it's Bottega.Maria [00:20:21]: Have you been to Bottega? How is it?Alessio [00:20:24]: It's fine.Maria [00:20:25]: I've been to one called like Norcina, I think it was good.Alessio [00:20:29]: Bottega is on Valencia Street. It's fine. The pizza is not good.Maria [00:20:32]: It's not good.Alessio [00:20:33]: Some of the pastas are good.Maria [00:20:34]: You know, the people I'm sorry to interrupt. Sorry. But there is like this Delfina. Yeah. That here everybody's like, oh, Pizzeria Delfina is amazing. I'm overrated. This is not. I don't know. That's great. That's great.swyx [00:20:46]: The North Beach Cafe. That place you took us with Michele last time. Vega. Oh.Alessio [00:20:52]: The guy at Vega, Giuseppe, he's Italian. Which one is that? It's in Bernal Heights. Ugh. He's nice. He's not nice. I don't know that one. What's the name of the place? Vega. Vega. Vega. Cool. We got the name. Vega. But it's not Vega.Maria [00:21:02]: It's Italian. Whatswyx [00:21:10]: Vega. Vega.swyx [00:21:16]: Vega. Vega. Vega. Vega. Vega. Vega. Vega. Vega. Vega.Ethan [00:21:29]: Vega. Vega. Vega. Vega. Vega.Ethan [00:21:40]: We're going to see a lot of innovation around hardware and stuff, but I think the real core is being able to do something useful with the personal context. You always had the ability to capture everything, right? We've always had recorders, camcorders, body cameras, stuff like that. But what's different now is we can actually make sense and find the important parts in all of that context.swyx [00:22:04]: Yeah. So, and then one last thing, I'm just doing this for you, is you also have an API, which I think I'm the first developer against. Because I had to build my own. We need to hire a developer advocate. Or just hire AI engineers. The point is that you should be able to program your own assistant. And I tried OMI, the former friend, the knockoff friend, and then real friend doesn't have an API. And then Limitless also doesn't have an API. So I think it's very important to own your data. To be able to reprocess your audio, maybe. Although, by default, you do not store audio. And then also just to do any corrections. There's no way that my needs can be fully met by you. So I think the API is very important.Ethan [00:22:47]: Yeah. And I mean, I've always been a consumer of APIs in all my products.swyx [00:22:53]: We are API enjoyers in this house.Ethan [00:22:55]: Yeah. It's very frustrating when you have to go build a scraper. But yeah, it's for sure. Yeah.swyx [00:23:03]: So this whole combination of you have my location, my calendar, my inbox. It really is, for me, the sort of personal API.Alessio [00:23:10]: And is the API just to write into it or to have it take action on external systems?Ethan [00:23:16]: Yeah, we're expanding it. It's right now read-only. In the future, very soon, when the actions are more generally available, it'll be fully supported in the API.Alessio [00:23:27]: Nice. I'll buy one after the episode.Ethan [00:23:30]: The API thing, to me, is the most interesting. Yeah. We do have real-time APIs, so you can even connect a socket and connect it to whatever you want it to take actions with. Yeah. It's too smart for me.Alessio [00:23:43]: Yeah. I think when I look at these apps, and I mean, there's so many of these products, we launch, it's great that I can go on this app and do things. But most of my work and personal life is managed somewhere else. Yeah. So being able to plug into it. Integrate that. It's nice. I have a bunch of more, maybe, human questions. Sure. I think maybe people might have. One, is it good to have instant replay for any argument that you have? I can imagine arguing with my wife about something. And, you know, there's these commercials now where it's basically like two people arguing, and they're like, they can throw a flag, like in football, and have an instant replay of the conversation. I feel like this is similar, where it's almost like people cannot really argue anymore or, like, lie to each other. Because in a world in which everybody adopts this, I don't know if you thought about it. And also, like, how the lies. You know, all of us tell lies, right? How do you distinguish between when I'm, there's going to be sometimes things that contradict each other, because I might say something publicly, and I might think something, really, that I tell someone else. How do you handle that when you think about building a product like this?Maria [00:24:48]: I would say that I like the fact that B is an objective point of view. So I don't care too much about the lies, but I care more about the fact that can help me to understand what happened. Mm-hmm. And the emotions in a really objective way, like, really, like, critical and objective way. And if you think about humans, they have so many emotions. And sometimes something that happened to me, like, I don't know, I would feel, like, really upset about it or really angry or really emotional. But the AI doesn't have those emotions. It can read the conversation, understand what happened, and be objective. And I think the level of support is the one that I really like more. Instead of, like, oh, did this guy tell me a lie? I feel like that's not exactly, like, what I feel. I find it curious for me in terms of opportunity.Alessio [00:25:35]: Is the B going to interject in real time? Say I'm arguing with somebody. The B is like, hey, look, no, you're wrong. What? That person actually said.Ethan [00:25:43]: The proactivity is something we're very interested in. Maybe not for, like, specifically for, like, selling arguments, but more for, like, and I think that a lot of the challenge here is, you know, you need really good reasoning to kind of pull that off. Because you don't want it just constantly interjecting, because that would be super annoying. And you don't want it to miss things that it should be interjecting. So, like, it would be kind of a hard task even for a human to be, like, just come in at the right times when it's appropriate. Like, it would take the, you know, with the personal context, it's going to be a lot better. Because, like, if somebody knows about you, but even still, it requires really good reasoning to, like, not be too much or too little and just right.Maria [00:26:20]: And the second part about, well, like, some things, you know, you say something to somebody else, but after I change my mind, I send something. Like, it's every time I have, like, different type of conversation. And I'm like, oh, I want to know more about you. And I'm like, oh, I want to know more about you. I think that's something that I found really fascinating. One of the things that we are learning is that, indeed, humans, they evolve over time. So, for us, one of the challenges is actually understand, like, is this a real fact? Right. And so far, what we do is we give, you know, to the, we have the human in the loop that can say, like, yes, this is true, this is not. Or they can edit their own fact. For sure, in the future, we want to have all of that automatized inside of the product.Ethan [00:26:57]: But, I mean, I think your question kind of hits on, and I know that we'll talk about privacy, but also just, like, if you have some memory and you want to confirm it with somebody else, that's one thing. But it's for sure going to be true that in the future, like, not even that far into the future, that it's just going to be kind of normalized. And we're kind of in a transitional period now. And I think it's, like, one of the key things that is for us to kind of navigate that and make sure we're, like, thinking of all the consequences. And how to, you know, make the right choices in the way that everything's designed. And so, like, it's more beneficial than it could be harmful. But it's just too valuable for your AI to understand you. And so if it's, like, MetaRay bands or the Google Astra, I think it's just people are going to be more used to it. So people's behaviors and expectations will change. Whether that's, like, you know, something that is going to happen now or in five years, it's probably in that range. And so, like, I think we... We kind of adapt to new technologies all the time. Like, when the Ring cameras came out, that was kind of quite controversial. It's like... But now it's kind of... People just understand that a lot of people have cameras on their doors. And so I think that...Maria [00:28:09]: Yeah, we're in a transitional period for sure.swyx [00:28:12]: I will press on the privacy thing because that is the number one thing that everyone talks about. Obviously, I think in Silicon Valley, people are a little bit more tech-forward, experimental, whatever. But you want to go mainstream. You want to sell to consumers. And we have to worry about this stuff. Baseline question. The hardest version of this is law. There are one-party consent states where this is perfectly legal. Then there are two-party consent states where they're not. What have you come around to this on?Ethan [00:28:38]: Yeah, so the EU is a totally different regulatory environment. But in the U.S., it's basically on a state-by-state level. Like, in Nevada, it's single-party. In California, it's two-party. But it's kind of untested. You know, it's different laws, whether it's a phone call, whether it's in person. In a state like California, it's two-party. Like, anytime you're in public, there's no consent comes into play because the expectation of privacy is that you're in public. But we process the audio and nothing is persisted. And then it's summarized with the speaker identification focusing on the user. Now, it's kind of untested on a legal, and I'm not a lawyer, but does that constitute the same as, like, a recording? So, you know, it's kind of a gray area and untested in law right now. I think that the bigger question is, you know, because, like, if you had your Ray-Ban on and were recording, then you have a video of something that happened. And that's different than kind of having, like, an AI give you a summary that's focused on you that's not really capturing anybody's voice. You know, I think the bigger question is, regardless of the legal status, like, what is the ethical kind of situation with that? Because even in Nevada that we're—or many other U.S. states where you can record. Everything. And you don't have to have consent. Is it still, like, the right thing to do? The way we think about it is, is that, you know, we take a lot of precautions to kind of not capture personal information of people around. Both through the speaker identification, through the pipeline, and then the prompts, and the way we store the information to be kind of really focused on the user. Now, we know that's not going to, like, satisfy a lot of people. But I think if you do try it and wear it again. It's very hard for me to see anything, like, if somebody was wearing a bee around me that I would ever object that it captured about me as, like, a third party to it. And like I said, like, we're in this transitional period where the expectation will just be more normalized. That it's, like, an AI. It's not capturing, you know, a full audio recording of what you said. And it's—everything is fully geared towards helping the person kind of understand their state and providing valuable information to them. Not about, like, logging details about people they encounter.Alessio [00:30:57]: You know, I've had the same question also with the Zoom meeting transcribers thing. I think there's kind of, like, the personal impact that there's a Firefly's AI recorder. Yeah. I just know that it's being recorded. It's not like a—I don't know if I'm going to say anything different. But, like, intrinsically, you kind of feel—because it's not pervasive. And I'm curious, especially, like, in your investor meetings. Do people feel differently? Like, have you had people ask you to, like, turn it off? Like, in a business meeting, to not record? I'm curious if you've run into any of these behaviors.Maria [00:31:29]: You know what's funny? On my end, I wear it all the time. I take my coffee, a blue bottle with it. Or I work with it. Like, obviously, I work on it. So, I wear it all the time. And so far, I don't think anybody asked me to turn it off. I'm not sure if because they were really friendly with me that they know that I'm working on it. But nobody really cared.swyx [00:31:48]: It's because you live in SF.Maria [00:31:49]: Actually, I've been in Italy as well. Uh-huh. And in Italy, it's a super privacy concern. Like, Europe is a super privacy concern. And again, they're nothing. Like, it's—I don't know. Yeah. That, for me, was interesting.Ethan [00:32:01]: I think—yeah, nobody's ever asked me to turn it off, even after giving them full demos and disclosing. I think that some people have said, well, my—you know, in a personal relationship, my partner initially was, like, kind of uncomfortable about it. We heard that from a few users. And that was, like, more in just, like— It's not like a personal relationship situation. And the other big one is people are like, I do like it, but I cannot wear this at work. I guess. Yeah. Yeah. Because, like, I think I will get in trouble based on policies or, like, you know, if you're wearing it inside a research lab or something where you're working on things that are kind of sensitive that, like—you know, so we're adding certain features like geofencing, just, like, at this location. It's just never active.swyx [00:32:50]: I mean, I've often actually explained to it the other way, where maybe you only want it at work, so you never take it from work. And it's just a work device, just like your Zoom meeting recorder is a work device.Ethan [00:33:09]: Yeah, professionals have been a big early adopter segment. And you say in San Francisco, but we have out there our daily shipment of over 100. If you go look at the addresses, Texas, I think, is our biggest state, and Florida, just the biggest states. A lot of professionals who talk for, and we didn't go out to build it for that use case, but I think there is a lot of demand for white-collar people who talk for a living. And I think we're just starting to talk with them. I think they just want to be able to improve their performance around, understand what they were doing.Alessio [00:33:47]: How do you think about Gong.io? Some of these, for example, sales training thing, where you put on a sales call and then it coaches you. They're more verticalized versus having more horizontal platform.Ethan [00:33:58]: I am not super familiar with those things, because like I said, it was kind of a surprise to us. But I think that those are interesting. I've seen there's a bunch of them now, right? Yeah. It kind of makes sense. I'm terrible at sales, so I could probably use one. But it's not my job, fundamentally. But yeah, I think maybe it's, you know, we heard also people with restaurants, if they're able to understand, if they're doing well.Maria [00:34:26]: Yeah, but in general, I think a lot of people, they like to have the double check of, did I do this well? Or can you suggest me how I can do better? We had a user that was saying to us that he used for interviews. Yeah, he used job interviews. So he used B and after asked to the B, oh, actually, how do you think my interview went? What I should do better? And I like that. And like, oh, that's actually like a personal coach in a way.Alessio [00:34:50]: Yeah. But I guess the question is like, do you want to build all of those use cases? Or do you see B as more like a platform where somebody is going to build like, you know, the sales coach that connects to B so that you're kind of the data feed into it?Ethan [00:35:02]: I don't think this is like a data feed, more like an understanding kind of engine and like definitely. In the future, having third parties to the API and building out for all the different use cases is something that we want to do. But the like initial case we're trying to do is like build that layer for all that to work. And, you know, we're not trying to build all those verticals because no startup could do that well. But I think that it's really been quite fascinating to see, like, you know, I've done consumer for a long time. Consumer is very hard to predict, like, what's going to be. It's going to be like the thing that's the killer feature. And so, I mean, we really believe that it's the future, but we don't know like what exactly like process it will take to really gain mass adoption.swyx [00:35:50]: The killer consumer feature is whatever Nikita Beer does. Yeah. Social app for teens.Ethan [00:35:56]: Yeah, well, I like Nikita, but, you know, he's good at building bootstrap companies and getting them very viral. And then selling them and then they shut down.swyx [00:36:05]: Okay, so you just came back from CES.Maria [00:36:07]: Yeah, crazy. Yeah, tell us. It was my first time in Vegas and first time CES, both of them were overwhelming.swyx [00:36:15]: First of all, did you feel like you had to do it because you're in consumer hardware?Maria [00:36:19]: Then we decided to be there and to have a lot of partners and media meetings, but we didn't have our own booth. So we decided to just keep that. But we decided to be there and have a presence there, even just us and speak with people. It's very hard to stand out. Yeah, I think, you know, it depends what type of booth you have. I think if you can prepare like a really cool booth.Ethan [00:36:41]: Have you been to CES?Maria [00:36:42]: I think it can be pretty cool.Ethan [00:36:43]: It's massive. It's huge. It's like 80,000, 90,000 people across the Venetian and the convention center. And it's, to me, I always wanted to go just like...Maria [00:36:53]: Yeah, you were the one who was like...swyx [00:36:55]: I thought it was your idea.Ethan [00:36:57]: I always wanted to go just as a, like, just as a fan of...Maria [00:37:01]: Yeah, you wanted to go anyways.Ethan [00:37:02]: Because like, growing up, I think CES like kind of peaked for a while and it was like, oh, I want to go. That's where all the cool, like... gadgets, everything. Yeah, now it's like SmartBitch and like, you know, vacuuming the picks up socks. Exactly.Maria [00:37:13]: There are a lot of cool vacuums. Oh, they love it.swyx [00:37:15]: They love the Roombas, the pick up socks.Maria [00:37:16]: And pet tech. Yeah, yeah. And dog stuff.swyx [00:37:20]: Yeah, there's a lot of like robot stuff. New TVs, new cars that never ship. Yeah. Yeah. I'm thinking like last year, this time last year was when Rabbit and Humane launched at CES and Rabbit kind of won CES. And now this year, no wearables except for you guys.Ethan [00:37:32]: It's funny because it's obviously it's AI everything. Yeah. Like every single product. Yeah.Maria [00:37:37]: Toothbrush with AI, vacuums with AI. Yeah. Yeah.Ethan [00:37:41]: We like hair blow, literally a hairdryer with AI. We saw.Maria [00:37:45]: Yeah, that was cool.Ethan [00:37:46]: But I think that like, yeah, we didn't, another kind of difference like around our, like we didn't want to do like a big overhypey promised kind of Rabbit launch. Because I mean, they did, hats off to them, like on the presentation and everything, obviously. But like, you know, we want to let the product kind of speak for itself and like get it out there. And I think we were really happy. We got some very good interest from media and some of the partners there. So like it was, I think it was definitely worth going. I would say like if you're in hardware, it's just kind of how you make use of it. Like I think to do it like a big Rabbit style or to have a huge show on there, like you need to plan that six months in advance. And it's very expensive. But like if you, you know, go there, there's everybody's there. All the media is there. There's a lot of some pre-show events that it's just great to talk to people. And the industry also, all the manufacturers, suppliers are there. So we learned about some really cool stuff that we might like. We met with somebody. They have like thermal energy capture. And it's like, oh, could you maybe not need to charge it? Because they have like a thermal that can capture your body heat. And what? Yeah, they're here. They're actually here. And in Palo Alto, they have like a Fitbit thing that you don't have to charge.swyx [00:39:01]: Like on paper, that's the power you can get from that. What's the power draw for this thing?Ethan [00:39:05]: It's more than you could get from the body heat, it turns out. But it's quite small. I don't want to disclose technically. But I think that solar is still, they also have one where it's like this thing could be like the face of it. It's just a solar cell. And like that is more realistic. Or kinetic. Kinetic, apparently, I'm not an expert in this, but they seem to think it wouldn't be enough. Kinetic is quite small, I guess, on the capture.swyx [00:39:33]: Well, I mean, watch. Watchmakers have been powering with kinetic for a long time. Yeah. We don't have to talk about that. I just want to get a sense of CES. Would you do it again? I definitely would not. Okay. You're just a fan of CES. Business point of view doesn't make sense. I happen to be in the conference business, right? So I'm kind of just curious. Yeah.Maria [00:39:49]: So I would say as we did, so without the booth and really like straightforward conversations that were already planned. Three days. That's okay. I think it was okay. Okay. But if you need to invest for a booth that is not. Okay. A good one. Which is how much? I think.Ethan [00:40:06]: 10 by 10 is 5,000. But on top of that, you need to. And then they go like 10 by 10 is like super small. Yeah. And like some companies have, I think would probably be more in like the six figure range to get. And I mean, I think that, yeah, it's very noisy. We heard this, that it's very, very noisy. Like obviously if you're, everything is being launched there and like everything from cars to cell phones are being launched. Yeah. So it's hard to stand out. But like, I think going in with a plan of who you want to talk to, I feel like.Maria [00:40:36]: That was worth it.Ethan [00:40:37]: Worth it. We had a lot of really positive media coverage from it and we got the word out and like, so I think we accomplished what we wanted to do.swyx [00:40:46]: I mean, there's some world in which my conference is kind of the CES of whatever AI becomes. Yeah. I think that.Maria [00:40:52]: Don't do it in Vegas. Don't do it in Vegas. Yeah. Don't do it in Vegas. That's the only thing. I didn't really like Vegas. That's great. Amazing. Those are my favorite ones.Alessio [00:41:02]: You can not fit 90,000 people in SF. That's really duh.Ethan [00:41:05]: You need to do like multiple locations so you can do Moscone and then have one in.swyx [00:41:09]: I mean, that's what Salesforce conferences. Well, GDC is how many? That might be 50,000, right? Okay. Form factor, right? Like my way to introduce this idea was that I was at the launch in Solaris. What was the old name of it? Newton. Newton. Of Tab when Avi first launched it. He was like, I thought through everything. Every form factor, pendant is the thing. And then we got the pendants for this original. The first one was just pendants and I took it off and I forgot to put it back on. So you went through pendants, pin, bracelet now, and maybe there's sort of earphones in the future, but what was your iterations?Maria [00:41:49]: So we had, I believe now three or four iterations. And one of the things that we learned is indeed that people don't like the pendant. In particular, woman, you don't want to have like anything here on the chest because it's maybe you have like other necklace or any other stuff.Ethan [00:42:03]: You just ship a premium one that's gold. Yeah. We're talking some fashion reached out to us.Maria [00:42:11]: Some big fashion. There is something there.swyx [00:42:13]: This is where it helps to have an Italian on the team.Maria [00:42:15]: There is like some big Italian luxury. I can't say anything. So yeah, bracelet actually came from the community because they were like, oh, I don't want to wear anything like as necklace or as a pendant. Like it's. And also like the one that we had, I don't know if you remember, like it was like circle, like it was like this and was like really bulky. Like people didn't like it. And also, I mean, I actually, I don't dislike, like we were running fast when we did that. Like our, our thing was like, we wanted to ship them as soon as possible. So we're not overthinking the form factor or the material. We were just want to be out. But after the community organically, basically all of them were like, well, why you don't just don't do the bracelet? Like he's way better. I will just wear it. And that's it. So that's how we ended up with the bracelet, but it's still modular. So I still want to play around the father is modular and you can, you know, take it off and wear it as a clip or in the future, maybe we will bring back the pendant. But I like the fact that there is some personalization and right now we have two colors, yellow and black. Soon we will have other ones. So yeah, we can play a lot around that.Ethan [00:43:25]: I think the form factor. Like the goal is for it to be not super invasive. Right. And something that's easy. So I think in the future, smaller, thinner, not like apple type obsession with thinness, but it does matter like the, the size and weight. And we would love to have more context because that will help, but to make it work, I think it really needs to have good power consumption, good battery life. And, you know, like with the humane swapping the batteries, I have one, I mean, I'm, I'm, I think we've made, and there's like pretty incredible, some of the engineering they did, but like, it wasn't kind of geared towards solving the problem. It was just, it's too heavy. The swappable batteries is too much to man, like the heat, the thermals is like too much to light interface thing. Yeah. Like that. That's cool. It's cool. It's cool. But it's like, if, if you have your handout here, you want to use your phone, like it's not really solving a problem. Cause you know how to use your phone. It's got a brilliant display. You have to kind of learn how to gesture this low range. Yeah. It's like a resolution laser, but the laser is cool that the fact they got it working in that thing, even though if it did overheat, but like too heavy, too cumbersome, too complicated with the multiple batteries. So something that's power efficient, kind of thin, both in the physical sense and also in the edge compute kind of way so that it can be as unobtrusive as possible. Yeah.Maria [00:44:47]: Users really like, like, I like when they say yes, I like to wear it and forget about it because I don't need to charge it every single day. On the other version, I believe we had like 35 hours or something, which was okay. But people, they just prefer the seven days battery life and-swyx [00:45:03]: Oh, this is seven days? Yeah. Oh, I've been charging every three days.Maria [00:45:07]: Oh, no, you can like keep it like, yeah, it's like almost seven days.swyx [00:45:11]: The other thing that occurs to me, maybe there's an Apple watch strap so that I don't have to double watch. Yeah.Maria [00:45:17]: That's the other one that, yeah, I thought about it. I saw as well the ones that like, you can like put it like back on the phone. Like, you know- Plog. There is a lot.swyx [00:45:27]: So yeah, there's a competitor called Plog. Yeah. It's not really a competitor. They only transcribe, right? Yeah, they only transcribe. But they're very good at it. Yeah.Ethan [00:45:33]: No, they're great. Their hardware is really good too.swyx [00:45:36]: And they just launched the pin too. Yeah.Ethan [00:45:38]: I think that the MagSafe kind of form factor has a lot of advantages, but some disadvantages. You can definitely put a very huge battery on that, you know? And so like the battery life's not, the power consumption's not so much of a concern, but you know, downside the phone's like in your pocket. And so I think that, you know, form factors will continue to evolve, but, and you know, more sensors, less obtrusive and-Maria [00:46:02]: Yeah. We have a new version.Ethan [00:46:04]: Easier to use.Maria [00:46:05]: Okay.swyx [00:46:05]: Looking forward to that. Yeah. I mean, we'll, whenever we launch this, we'll try to show whatever, but I'm sure you're going to keep iterating. Last thing on hardware, and then we'll go on to the software side, because I think that's where you guys are also really, really strong. Vision. You wanted to talk about why no vision? Yeah.Ethan [00:46:20]: I think it comes down to like when you're, when you're a startup, especially in hardware, you're just, you work within the constraints, right? And so like vision is super useful and super interesting. And what we actually started with, there's two issues with vision that make it like not the place we decided to start. One is power consumption. So you know, you kind of have to trade off your power budget, like capturing even at a low frame rate and transmitting the radio is actually the thing that takes up the majority of the power. So. Yeah. So you would really have to have quite a, like unacceptably, like large and heavy battery to do it continuously all day. We have, I think, novel kind of alternative ways that might allow us to do that. And we have some prototypes. The other issue is form factor. So like even with like a wide field of view, if you're wearing something on your chest, it's going, you know, obviously the wrist is not really that much of an option. And if you're wearing it on your chest, it's, it's often gone. You're going to probably be not capturing like the field of view of what's interesting to you. So that leaves you kind of with your head and face. And then anything that goes on, on the face has to look cool. Like I don't know if you remember the spectacles, it was kind of like the first, yeah, but they kind of, they didn't, they were not very successful. And I think one of the reasons is they were, they're so weird looking. Yeah. The camera was so big on the side. And if you look at them at array bands where they're way more successful, they, they look almost indistinguishable from array bands. And they invested a lot into that and they, they have a partnership with Qualcomm to develop custom Silicon. They have a stake in Luxottica now. So like they coming from all the angles, like to make glasses, I think like, you know, I don't know if you know, Brilliant Labs, they're cool company, they make frames, which is kind of like a cool hackable glasses and, and, and like, they're really good, like on hardware, they're really good. But even if you look at the frames, which I would say is like the most advanced kind of startup. Yeah. Yeah. Yeah. There was one that launched at CES, but it's not shipping yet. Like one that you can buy now, it's still not something you'd wear every day and the battery life is super short. So I think just the challenge of doing vision right, like off the bat, like would require quite a bit more resources. And so like audio is such a good entry point and it's also the privacy around audio. If you, if you had images, that's like another huge challenge to overcome. So I think that. Ideally the personal AI would have, you know, all the senses and you know, we'll, we'll get there. Yeah. Okay.swyx [00:48:57]: One last hardware thing. I have to ask this because then we'll move to the software. Were either of you electrical engineering?Ethan [00:49:04]: No, I'm CES. And so I have a, I've taken some EE courses, but I, I had done prior to working on, on the hardware here, like I had done a little bit of like embedded systems, like very little firmware, but we have luckily on the team, somebody with deep experience. Yeah.swyx [00:49:21]: I'm just like, you know, like you have to become hardware people. Yeah.Ethan [00:49:25]: Yeah. I mean, I learned to worry about supply chain power. I think this is like radio.Maria [00:49:30]: There's so many things to learn.Ethan [00:49:32]: I would tell this about hardware, like, and I know it's been said before, but building a prototype and like learning how the electronics work and learning about firmware and developing, this is like, I think fun for a lot of engineers and it's, it's all totally like achievable, especially now, like with, with the tools we have, like stuff you might've been intimidated about. Like, how do I like write this firmware now? With Sonnet, like you can, you can get going and actually see results quickly. But I think going from prototype to actually making something manufactured is a enormous jump. And it's not all about technology, the supply chain, the procurement, the regulations, the cost, the tooling. The thing about software that I'm used to is it's funny that you can make changes all along the way and ship it. But like when you have to buy tooling for an enclosure that's expensive.swyx [00:50:24]: Do you buy your own tooling? You have to.Ethan [00:50:25]: Don't you just subcontract out to someone in China? Oh, no. Do we make the tooling? No, no. You have to have CNC and like a bunch of machines.Maria [00:50:31]: Like nobody makes their own tooling, but like you have to design this design and you submitEthan [00:50:36]: it and then they go four to six weeks later. Yeah. And then if there's a problem with it, well, then you're not, you're not making any, any of your enclosures. And so you have to really plan ahead. And like.swyx [00:50:48]: I just want to leave tips for other hardware founders. Like what resources or websites are most helpful in your sort of manufacturing journey?Ethan [00:50:55]: You know, I think it's different depending on like it's hardware so specialized in different ways.Maria [00:51:00]: I will say that, for example, I should choose a manufacturer company. I speak with other founders and like we can give you like some, you know, some tips of who is good and who is not, or like who's specialized in something versus somebody else. Yeah.Ethan [00:51:15]: Like some people are good in plastics. Some people are good.Maria [00:51:18]: I think like for us, it really helped at the beginning to speak with others and understand. Okay. Like who is around. I work in Shenzhen. I lived almost two years in China. I have an idea about like different hardware manufacturer and all of that. Soon I will go back to Shenzhen to check out. So I think it's good also to go in place and check.Ethan [00:51:40]: Yeah, you have to like once you, if you, so we did some stuff domestically and like if you have that ability. The reason I say ability is very expensive, but like to build out some proof of concepts and do field testing before you take it to a manufacturer, despite what people say, there's really good domestic manufacturing for small quantities at extremely high prices. So we got our first PCB and the assembly done in LA. So there's a lot of good because of the defense industry that can do quick churn. So it's like, we need this board. We need to find out if it's working. We have this deadline we want to start, but you need to go through this. And like if you want to have it done and fabricated in a week, they can do it for a price. But I think, you know, everybody's kind of trending even for prototyping now moving that offshore because in China you can do prototyping and get it within almost the same timeline. But the thing is with manufacturing, like it really helps to go there and kind of establish the relationship. Yeah.Alessio [00:52:38]: My first company was a hardware company and we did our PCBs in China and took a long time. Now things are better. But this was, yeah, I don't know, 10 years ago, something like that. Yeah.Ethan [00:52:47]: I think that like the, and I've heard this too, we didn't run into this problem, but like, you know, if it's something where you don't have the relationship, they don't see you, they don't know you, you know, you might get subcontracted out or like they're not paying attention. But like if you're, you know, you have the relationship and a priority, like, yeah, it's really good. We ended up doing the fabrication assembly in Taiwan for various reasons.Maria [00:53:11]: And I think it really helped the fact that you went there at some point. Yeah.Ethan [00:53:15]: We're really happy with the process and, but I mean the whole process of just Choosing the right people. Choosing the right people, but also just sourcing the bill materials and all of that stuff. Like, I guess like if you have time, it's not that bad, but if you're trying to like really push the speed at that, it's incredibly stressful. Okay. We got to move to the software. Yeah.Alessio [00:53:38]: Yeah. So the hardware, maybe it's hard for people to understand, but what software people can understand is that running. Transcription and summarization, all of these things in real time every day for 24 hours a day. It's not easy. So you mentioned 200,000 tokens for a day. Yeah. How do you make it basically free to run all of this for the consumer?Ethan [00:53:59]: Well, I think that the pipeline and the inference, like people think about all of these tokens, but as you know, the price of tokens is like dramatically dropping. You guys probably have some charts somewhere that you've posted. We do. And like, if you see that trend in like 250,000 input tokens, it's not really that much, right? Like the output.swyx [00:54:21]: You do several layers. You do live. Yeah.Ethan [00:54:23]: Yeah. So the speech to text is like the most challenging part actually, because you know, it requires like real time processing and then like later processing with a larger model. And one thing that is fairly obvious is that like, you don't need to transcribe things that don't have any voice in it. Right? So good voice activity is key, right? Because like the majority of most people's day is not spent with voice activity. Right? So that is the first step to cutting down the amount of compute you have to do. And voice activity is a fairly cheap thing to do. Very, very cheap thing to do. The models that need to summarize, you don't need a Sonnet level kind of model to summarize. You do need a Sonnet level model to like execute things like the agent. And we will be having a subscription for like features like that because it's, you know, although now with the R1, like we'll see, we haven't evaluated it. A deep seek? Yeah. I mean, not that one in particular, but like, you know, they're already there that can kind of perform at that level. I was like, it's going to stay in six months, but like, yeah. So self-hosted models help in the things where you can. So you are self-hosting models. Yes. You are fine tuning your own ASR. Yes. I will say that I see in the future that everything's trending down. Although like, I think there might be an intermediary step with things to become expensive, which is like, we're really interested because like the pipeline is very tedious and like a lot of tuning. Right. Which is brutal because it's just a lot of trial and error. Whereas like, well, wouldn't it be nice if an end to end model could just do all of this and learn it? If we could do transcription with like an LLM, there's so many advantages to that, but it's going to be a larger model and hence like more compute, you know, we're optim

If you're in SF, join us tomorrow for a fun meetup at CodeGen Night!If you're in NYC, join us for AI Engineer Summit! The Agent Engineering track is now sold out, but 25 tickets remain for AI Leadership and 5 tickets for the workshops. You can see the full schedule of speakers and workshops at https://ai.engineer!It's exceedingly hard to introduce someone like Bret Taylor. We could recite his Wikipedia page, or his extensive work history through Silicon Valley's greatest companies, but everyone else already does that.As a podcast by AI engineers for AI engineers, we had the opportunity to do something a little different. We wanted to dig into what Bret sees from his vantage point at the top of our industry for the last 2 decades, and how that explains the rise of the AI Architect at Sierra, the leading conversational AI/CX platform.“Across our customer base, we are seeing a new role emerge - the role of the AI architect. These leaders are responsible for helping define, manage and evolve their company's AI agent over time. They come from a variety of both technical and business backgrounds, and we think that every company will have one or many AI architects managing their AI agent and related experience.”In our conversation, Bret Taylor confirms the Paul Buchheit legend that he rewrote Google Maps in a weekend, armed with only the help of a then-nascent Google Closure Compiler and no other modern tooling. But what we find remarkable is that he was the PM of Maps, not an engineer, though of course he still identifies as one. We find this theme recurring throughout Bret's career and worldview. We think it is plain as day that AI leadership will have to be hands-on and technical, especially when the ground is shifting as quickly as it is today:“There's a lot of power in combining product and engineering into as few people as possible… few great things have been created by committee.”“If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a maniacal focus on outcomes.”“And I think the reason why is if you look at like software as a service five years ago, maybe you can have a separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of technological breakthroughs required for most business applications. And if you're making expense reporting software or whatever, it's useful… You kind of know how databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem. "When you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it and the capabilities of the technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself.”This is the first time the difference between technical leadership for “normal” software and for “AI” software was articulated this clearly for us, and we'll be thinking a lot about this going forward. We left a lot of nuggets in the conversation, so we hope you'll just dive in with us (and thank Bret for joining the pod!)Timestamps* 00:00:02 Introductions and Bret Taylor's background* 00:01:23 Bret's experience at Stanford and the dot-com era* 00:04:04 The story of rewriting Google Maps backend* 00:11:06 Early days of interactive web applications at Google* 00:15:26 Discussion on product management and engineering roles* 00:21:00 AI and the future of software development* 00:26:42 Bret's approach to identifying customer needs and building AI companies* 00:32:09 The evolution of business models in the AI era* 00:41:00 The future of programming languages and software development* 00:49:38 Challenges in precisely communicating human intent to machines* 00:56:44 Discussion on Artificial General Intelligence (AGI) and its impact* 01:08:51 The future of agent-to-agent communication* 01:14:03 Bret's involvement in the OpenAI leadership crisis* 01:22:11 OpenAI's relationship with Microsoft* 01:23:23 OpenAI's mission and priorities* 01:27:40 Bret's guiding principles for career choices* 01:29:12 Brief discussion on pasta-making* 01:30:47 How Bret keeps up with AI developments* 01:32:15 Exciting research directions in AI* 01:35:19 Closing remarks and hiring at Sierra Transcript[00:02:05] Introduction and Guest Welcome[00:02:05] Alessio: 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.[00:02:17] swyx: Hey, and today we're super excited to have Bret Taylor join us. Welcome. Thanks for having me. It's a little unreal to have you in the studio.[00:02:25] swyx: I've read about you so much over the years, like even before. Open AI effectively. I mean, I use Google Maps to get here. So like, thank you for everything that you've done. Like, like your story history, like, you know, I think people can find out what your greatest hits have been.[00:02:40] Bret Taylor's Early Career and Education[00:02:40] swyx: How do you usually like to introduce yourself when, you know, you talk about, you summarize your career, like, how do you look at yourself?[00:02:47] Bret: Yeah, it's a great question. You know, we, before we went on the mics here, we're talking about the audience for this podcast being more engineering. And I do think depending on the audience, I'll introduce myself differently because I've had a lot of [00:03:00] corporate and board roles. I probably self identify as an engineer more than anything else though.[00:03:04] Bret: So even when I was. Salesforce, I was coding on the weekends. So I think of myself as an engineer and then all the roles that I do in my career sort of start with that just because I do feel like engineering is sort of a mindset and how I approach most of my life. So I'm an engineer first and that's how I describe myself.[00:03:24] Bret: You majored in computer[00:03:25] swyx: science, like 1998. And, and I was high[00:03:28] Bret: school, actually my, my college degree was Oh, two undergrad. Oh, three masters. Right. That old.[00:03:33] swyx: Yeah. I mean, no, I was going, I was going like 1998 to 2003, but like engineering wasn't as, wasn't a thing back then. Like we didn't have the title of senior engineer, you know, kind of like, it was just.[00:03:44] swyx: You were a programmer, you were a developer, maybe. What was it like in Stanford? Like, what was that feeling like? You know, was it, were you feeling like on the cusp of a great computer revolution? Or was it just like a niche, you know, interest at the time?[00:03:57] Stanford and the Dot-Com Bubble[00:03:57] Bret: Well, I was at Stanford, as you said, from 1998 to [00:04:00] 2002.[00:04:02] Bret: 1998 was near the peak of the dot com bubble. So. This is back in the day where most people that they're coding in the computer lab, just because there was these sun microsystems, Unix boxes there that most of us had to do our assignments on. And every single day there was a. com like buying pizza for everybody.[00:04:20] Bret: I didn't have to like, I got. Free food, like my first two years of university and then the dot com bubble burst in the middle of my college career. And so by the end there was like tumbleweed going to the job fair, you know, it was like, cause it was hard to describe unless you were there at the time, the like level of hype and being a computer science major at Stanford was like, A thousand opportunities.[00:04:45] Bret: And then, and then when I left, it was like Microsoft, IBM.[00:04:49] Joining Google and Early Projects[00:04:49] Bret: And then the two startups that I applied to were VMware and Google. And I ended up going to Google in large part because a woman named Marissa Meyer, who had been a teaching [00:05:00] assistant when I was, what was called a section leader, which was like a junior teaching assistant kind of for one of the big interest.[00:05:05] Bret: Yes. Classes. She had gone there. And she was recruiting me and I knew her and it was sort of felt safe, you know, like, I don't know. I thought about it much, but it turned out to be a real blessing. I realized like, you know, you always want to think you'd pick Google if given the option, but no one knew at the time.[00:05:20] Bret: And I wonder if I'd graduated in like 1999 where I've been like, mom, I just got a job at pets. com. It's good. But you know, at the end I just didn't have any options. So I was like, do I want to go like make kernel software at VMware? Do I want to go build search at Google? And I chose Google. 50, 50 ball.[00:05:36] Bret: I'm not really a 50, 50 ball. So I feel very fortunate in retrospect that the economy collapsed because in some ways it forced me into like one of the greatest companies of all time, but I kind of lucked into it, I think.[00:05:47] The Google Maps Rewrite Story[00:05:47] Alessio: So the famous story about Google is that you rewrote the Google maps back in, in one week after the map quest quest maps acquisition, what was the story there?[00:05:57] Alessio: Is it. Actually true. Is it [00:06:00] being glorified? Like how, how did that come to be? And is there any detail that maybe Paul hasn't shared before?[00:06:06] Bret: It's largely true, but I'll give the color commentary. So it was actually the front end, not the back end, but it turns out for Google maps, the front end was sort of the hard part just because Google maps was.[00:06:17] Bret: Largely the first ish kind of really interactive web application, say first ish. I think Gmail certainly was though Gmail, probably a lot of people then who weren't engineers probably didn't appreciate its level of interactivity. It was just fast, but. Google maps, because you could drag the map and it was sort of graphical.[00:06:38] Bret: My, it really in the mainstream, I think, was it a map[00:06:41] swyx: quest back then that was, you had the arrows up and down, it[00:06:44] Bret: was up and down arrows. Each map was a single image and you just click left and then wait for a few seconds to the new map to let it was really small too, because generating a big image was kind of expensive on computers that day.[00:06:57] Bret: So Google maps was truly innovative in that [00:07:00] regard. The story on it. There was a small company called where two technologies started by two Danish brothers, Lars and Jens Rasmussen, who are two of my closest friends now. They had made a windows app called expedition, which had beautiful maps. Even in 2000.[00:07:18] Bret: For whenever we acquired or sort of acquired their company, Windows software was not particularly fashionable, but they were really passionate about mapping and we had made a local search product that was kind of middling in terms of popularity, sort of like a yellow page of search product. So we wanted to really go into mapping.[00:07:36] Bret: We'd started working on it. Their small team seemed passionate about it. So we're like, come join us. We can build this together.[00:07:42] Technical Challenges and Innovations[00:07:42] Bret: It turned out to be a great blessing that they had built a windows app because you're less technically constrained when you're doing native code than you are building a web browser, particularly back then when there weren't really interactive web apps and it ended up.[00:07:56] Bret: Changing the level of quality that we [00:08:00] wanted to hit with the app because we were shooting for something that felt like a native windows application. So it was a really good fortune that we sort of, you know, their unusual technical choices turned out to be the greatest blessing. So we spent a lot of time basically saying, how can you make a interactive draggable map in a web browser?[00:08:18] Bret: How do you progressively load, you know, new map tiles, you know, as you're dragging even things like down in the weeds of the browser at the time, most browsers like Internet Explorer, which was dominant at the time would only load two images at a time from the same domain. So we ended up making our map tile servers have like.[00:08:37] Bret: Forty different subdomains so we could load maps and parallels like lots of hacks. I'm happy to go into as much as like[00:08:44] swyx: HTTP connections and stuff.[00:08:46] Bret: They just like, there was just maximum parallelism of two. And so if you had a map, set of map tiles, like eight of them, so So we just, we were down in the weeds of the browser anyway.[00:08:56] Bret: So it was lots of plumbing. I can, I know a lot more about browsers than [00:09:00] most people, but then by the end of it, it was fairly, it was a lot of duct tape on that code. If you've ever done an engineering project where you're not really sure the path from point A to point B, it's almost like. Building a house by building one room at a time.[00:09:14] Bret: The, there's not a lot of architectural cohesion at the end. And then we acquired a company called Keyhole, which became Google earth, which was like that three, it was a native windows app as well, separate app, great app, but with that, we got licenses to all this satellite imagery. And so in August of 2005, we added.[00:09:33] Bret: Satellite imagery to Google Maps, which added even more complexity in the code base. And then we decided we wanted to support Safari. There was no mobile phones yet. So Safari was this like nascent browser on, on the Mac. And it turns out there's like a lot of decisions behind the scenes, sort of inspired by this windows app, like heavy use of XML and XSLT and all these like.[00:09:54] Bret: Technologies that were like briefly fashionable in the early two thousands and everyone hates now for good [00:10:00] reason. And it turns out that all of the XML functionality and Internet Explorer wasn't supporting Safari. So people are like re implementing like XML parsers. And it was just like this like pile of s**t.[00:10:11] Bret: And I had to say a s**t on your part. Yeah, of[00:10:12] Alessio: course.[00:10:13] Bret: So. It went from this like beautifully elegant application that everyone was proud of to something that probably had hundreds of K of JavaScript, which sounds like nothing. Now we're talking like people have modems, you know, not all modems, but it was a big deal.[00:10:29] Bret: So it was like slow. It took a while to load and just, it wasn't like a great code base. Like everything was fragile. So I just got. Super frustrated by it. And then one weekend I did rewrite all of it. And at the time the word JSON hadn't been coined yet too, just to give you a sense. So it's all XML.[00:10:47] swyx: Yeah.[00:10:47] Bret: So we used what is now you would call JSON, but I just said like, let's use eval so that we can parse the data fast. And, and again, that's, it would literally as JSON, but at the time there was no name for it. So we [00:11:00] just said, let's. Pass on JavaScript from the server and eval it. And then somebody just refactored the whole thing.[00:11:05] Bret: And, and it wasn't like I was some genius. It was just like, you know, if you knew everything you wished you had known at the beginning and I knew all the functionality, cause I was the primary, one of the primary authors of the JavaScript. And I just like, I just drank a lot of coffee and just stayed up all weekend.[00:11:22] Bret: And then I, I guess I developed a bit of reputation and no one knew about this for a long time. And then Paul who created Gmail and I ended up starting a company with him too, after all of this told this on a podcast and now it's large, but it's largely true. I did rewrite it and it, my proudest thing.[00:11:38] Bret: And I think JavaScript people appreciate this. Like the un G zipped bundle size for all of Google maps. When I rewrote, it was 20 K G zipped. It was like much smaller for the entire application. It went down by like 10 X. So. What happened on Google? Google is a pretty mainstream company. And so like our usage is shot up because it turns out like it's faster.[00:11:57] Bret: Just being faster is worth a lot of [00:12:00] percentage points of growth at a scale of Google. So how[00:12:03] swyx: much modern tooling did you have? Like test suites no compilers.[00:12:07] Bret: Actually, that's not true. We did it one thing. So I actually think Google, I, you can. Download it. There's a, Google has a closure compiler, a closure compiler.[00:12:15] Bret: I don't know if anyone still uses it. It's gone. Yeah. Yeah. It's sort of gone out of favor. Yeah. Well, even until recently it was better than most JavaScript minifiers because it was more like it did a lot more renaming of variables and things. Most people use ES build now just cause it's fast and closure compilers built on Java and super slow and stuff like that.[00:12:37] Bret: But, so we did have that, that was it. Okay.[00:12:39] The Evolution of Web Applications[00:12:39] Bret: So and that was treated internally, you know, it was a really interesting time at Google at the time because there's a lot of teams working on fairly advanced JavaScript when no one was. So Google suggest, which Kevin Gibbs was the tech lead for, was the first kind of type ahead, autocomplete, I believe in a web browser, and now it's just pervasive in search boxes that you sort of [00:13:00] see a type ahead there.[00:13:01] Bret: I mean, chat, dbt[00:13:01] swyx: just added it. It's kind of like a round trip.[00:13:03] Bret: Totally. No, it's now pervasive as a UI affordance, but that was like Kevin's 20 percent project. And then Gmail, Paul you know, he tells the story better than anyone, but he's like, you know, basically was scratching his own itch, but what was really neat about it is email, because it's such a productivity tool, just needed to be faster.[00:13:21] Bret: So, you know, he was scratching his own itch of just making more stuff work on the client side. And then we, because of Lars and Yen sort of like setting the bar of this windows app or like we need our maps to be draggable. So we ended up. Not only innovate in terms of having a big sync, what would be called a single page application today, but also all the graphical stuff you know, we were crashing Firefox, like it was going out of style because, you know, when you make a document object model with the idea that it's a document and then you layer on some JavaScript and then we're essentially abusing all of this, it just was running into code paths that were not.[00:13:56] Bret: Well, it's rotten, you know, at this time. And so it was [00:14:00] super fun. And, and, you know, in the building you had, so you had compilers, people helping minify JavaScript just practically, but there is a great engineering team. So they were like, that's why Closure Compiler is so good. It was like a. Person who actually knew about programming languages doing it, not just, you know, writing regular expressions.[00:14:17] Bret: And then the team that is now the Chrome team believe, and I, I don't know this for a fact, but I'm pretty sure Google is the main contributor to Firefox for a long time in terms of code. And a lot of browser people were there. So every time we would crash Firefox, we'd like walk up two floors and say like, what the hell is going on here?[00:14:35] Bret: And they would load their browser, like in a debugger. And we could like figure out exactly what was breaking. And you can't change the code, right? Cause it's the browser. It's like slow, right? I mean, slow to update. So, but we could figure out exactly where the bug was and then work around it in our JavaScript.[00:14:52] Bret: So it was just like new territory. Like so super, super fun time, just like a lot of, a lot of great engineers figuring out [00:15:00] new things. And And now, you know, the word, this term is no longer in fashion, but the word Ajax, which was asynchronous JavaScript and XML cause I'm telling you XML, but see the word XML there, to be fair, the way you made HTTP requests from a client to server was this.[00:15:18] Bret: Object called XML HTTP request because Microsoft and making Outlook web access back in the day made this and it turns out to have nothing to do with XML. It's just a way of making HTTP requests because XML was like the fashionable thing. It was like that was the way you, you know, you did it. But the JSON came out of that, you know, and then a lot of the best practices around building JavaScript applications is pre React.[00:15:44] Bret: I think React was probably the big conceptual step forward that we needed. Even my first social network after Google, we used a lot of like HTML injection and. Making real time updates was still very hand coded and it's really neat when you [00:16:00] see conceptual breakthroughs like react because it's, I just love those things where it's like obvious once you see it, but it's so not obvious until you do.[00:16:07] Bret: And actually, well, I'm sure we'll get into AI, but I, I sort of feel like we'll go through that evolution with AI agents as well that I feel like we're missing a lot of the core abstractions that I think in 10 years we'll be like, gosh, how'd you make agents? Before that, you know, but it was kind of that early days of web applications.[00:16:22] swyx: There's a lot of contenders for the reactive jobs of of AI, but no clear winner yet. I would say one thing I was there for, I mean, there's so much we can go into there. You just covered so much.[00:16:32] Product Management and Engineering Synergy[00:16:32] swyx: One thing I just, I just observe is that I think the early Google days had this interesting mix of PM and engineer, which I think you are, you didn't, you didn't wait for PM to tell you these are my, this is my PRD.[00:16:42] swyx: This is my requirements.[00:16:44] mix: Oh,[00:16:44] Bret: okay.[00:16:45] swyx: I wasn't technically a software engineer. I mean,[00:16:48] Bret: by title, obviously. Right, right, right.[00:16:51] swyx: It's like a blend. And I feel like these days, product is its own discipline and its own lore and own industry and engineering is its own thing. And there's this process [00:17:00] that happens and they're kind of separated, but you don't produce as good of a product as if they were the same person.[00:17:06] swyx: And I'm curious, you know, if, if that, if that sort of resonates in, in, in terms of like comparing early Google versus modern startups that you see out there,[00:17:16] Bret: I certainly like wear a lot of hats. So, you know, sort of biased in this, but I really agree that there's a lot of power and combining product design engineering into as few people as possible because, you know few great things have been created by committee, you know, and so.[00:17:33] Bret: If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a. Maniacal focus on outcomes.[00:17:53] Bret: And I think the reason why it's, I think for some areas, if you look at like software as a service five years ago, maybe you can have a [00:18:00] separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of like. Technological breakthroughs required for most, you know, business applications.[00:18:11] Bret: And if you're making expense reporting software or whatever, it's useful. I don't mean to be dismissive of expense reporting software, but you probably just want to understand like, what are the requirements of the finance department? What are the requirements of an individual file expense report? Okay.[00:18:25] Bret: Go implement that. And you kind of know how web applications are implemented. You kind of know how to. How databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem when you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it.[00:18:58] Bret: And the capabilities of the [00:19:00] technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself. And that's why I use the word conversation. It's not literal. That's sort of funny to use that word in the age of conversational AI.[00:19:15] Bret: You're constantly sort of saying, like, ideally, you could sprinkle some magic AI pixie dust and solve all the world's problems, but it's not the way it works. And it turns out that actually, I'll just give an interesting example.[00:19:26] AI Agents and Modern Tooling[00:19:26] Bret: I think most people listening probably use co pilots to code like Cursor or Devon or Microsoft Copilot or whatever.[00:19:34] Bret: Most of those tools are, they're remarkable. I'm, I couldn't, you know, imagine development without them now, but they're not autonomous yet. Like I wouldn't let it just write most code without my interactively inspecting it. We just are somewhere between it's an amazing co pilot and it's an autonomous software engineer.[00:19:53] Bret: As a product manager, like your aspirations for what the product is are like kind of meaningful. But [00:20:00] if you're a product person, yeah, of course you'd say it should be autonomous. You should click a button and program should come out the other side. The requirements meaningless. Like what matters is like, what is based on the like very nuanced limitations of the technology.[00:20:14] Bret: What is it capable of? And then how do you maximize the leverage? It gives a software engineering team, given those very nuanced trade offs. Coupled with the fact that those nuanced trade offs are changing more rapidly than any technology in my memory, meaning every few months you'll have new models with new capabilities.[00:20:34] Bret: So how do you construct a product that can absorb those new capabilities as rapidly as possible as well? That requires such a combination of technical depth and understanding the customer that you really need more integration. Of product design and engineering. And so I think it's why with these big technology waves, I think startups have a bit of a leg up relative to incumbents because they [00:21:00] tend to be sort of more self actualized in terms of just like bringing those disciplines closer together.[00:21:06] Bret: And in particular, I think entrepreneurs, the proverbial full stack engineers, you know, have a leg up as well because. I think most breakthroughs happen when you have someone who can understand those extremely nuanced technical trade offs, have a vision for a product. And then in the process of building it, have that, as I said, like metaphorical conversation with the technology, right?[00:21:30] Bret: Gosh, I ran into a technical limit that I didn't expect. It's not just like changing that feature. You might need to refactor the whole product based on that. And I think that's, that it's particularly important right now. So I don't, you know, if you, if you're building a big ERP system, probably there's a great reason to have product and engineering.[00:21:51] Bret: I think in general, the disciplines are there for a reason. I think when you're dealing with something as nuanced as the like technologies, like large language models today, there's a ton of [00:22:00] advantage of having. Individuals or organizations that integrate the disciplines more formally.[00:22:05] Alessio: That makes a lot of sense.[00:22:06] Alessio: I've run a lot of engineering teams in the past, and I think the product versus engineering tension has always been more about effort than like whether or not the feature is buildable. But I think, yeah, today you see a lot more of like. Models actually cannot do that. And I think the most interesting thing is on the startup side, people don't yet know where a lot of the AI value is going to accrue.[00:22:26] Alessio: So you have this rush of people building frameworks, building infrastructure, layered things, but we don't really know the shape of the compute. I'm curious that Sierra, like how you thought about building an house, a lot of the tooling for evals or like just, you know, building the agents and all of that.[00:22:41] Alessio: Versus how you see some of the startup opportunities that is maybe still out there.[00:22:46] Bret: We build most of our tooling in house at Sierra, not all. It's, we don't, it's not like not invented here syndrome necessarily, though, maybe slightly guilty of that in some ways, but because we're trying to build a platform [00:23:00] that's in Dorian, you know, we really want to have control over our own destiny.[00:23:03] Bret: And you had made a comment earlier that like. We're still trying to figure out who like the reactive agents are and the jury is still out. I would argue it hasn't been created yet. I don't think the jury is still out to go use that metaphor. We're sort of in the jQuery era of agents, not the react era.[00:23:19] Bret: And, and that's like a throwback for people listening,[00:23:22] swyx: we shouldn't rush it. You know?[00:23:23] Bret: No, yeah, that's my point is. And so. Because we're trying to create an enduring company at Sierra that outlives us, you know, I'm not sure we want to like attach our cart to some like to a horse where it's not clear that like we've figured out and I actually want as a company, we're trying to enable just at a high level and I'll, I'll quickly go back to tech at Sierra, we help consumer brands build customer facing AI agents.[00:23:48] Bret: So. Everyone from Sonos to ADT home security to Sirius XM, you know, if you call them on the phone and AI will pick up with you, you know, chat with them on the Sirius XM homepage. It's an AI agent called Harmony [00:24:00] that they've built on our platform. We're what are the contours of what it means for someone to build an end to end complete customer experience with AI with conversational AI.[00:24:09] Bret: You know, we really want to dive into the deep end of, of all the trade offs to do it. You know, where do you use fine tuning? Where do you string models together? You know, where do you use reasoning? Where do you use generation? How do you use reasoning? How do you express the guardrails of an agentic process?[00:24:25] Bret: How do you impose determinism on a fundamentally non deterministic technology? There's just a lot of really like as an important design space. And I could sit here and tell you, we have the best approach. Every entrepreneur will, you know. But I hope that in two years, we look back at our platform and laugh at how naive we were, because that's the pace of change broadly.[00:24:45] Bret: If you talk about like the startup opportunities, I'm not wholly skeptical of tools companies, but I'm fairly skeptical. There's always an exception for every role, but I believe that certainly there's a big market for [00:25:00] frontier models, but largely for companies with huge CapEx budgets. So. Open AI and Microsoft's Anthropic and Amazon Web Services, Google Cloud XAI, which is very well capitalized now, but I think the, the idea that a company can make money sort of pre training a foundation model is probably not true.[00:25:20] Bret: It's hard to, you're competing with just, you know, unreasonably large CapEx budgets. And I just like the cloud infrastructure market, I think will be largely there. I also really believe in the applications of AI. And I define that not as like building agents or things like that. I define it much more as like, you're actually solving a problem for a business.[00:25:40] Bret: So it's what Harvey is doing in legal profession or what cursor is doing for software engineering or what we're doing for customer experience and customer service. The reason I believe in that is I do think that in the age of AI, what's really interesting about software is it can actually complete a task.[00:25:56] Bret: It can actually do a job, which is very different than the value proposition of [00:26:00] software was to ancient history two years ago. And as a consequence, I think the way you build a solution and For a domain is very different than you would have before, which means that it's not obvious, like the incumbent incumbents have like a leg up, you know, necessarily, they certainly have some advantages, but there's just such a different form factor, you know, for providing a solution and it's just really valuable.[00:26:23] Bret: You know, it's. Like just think of how much money cursor is saving software engineering teams or the alternative, how much revenue it can produce tool making is really challenging. If you look at the cloud market, just as a analog, there are a lot of like interesting tools, companies, you know, Confluent, Monetized Kafka, Snowflake, Hortonworks, you know, there's a, there's a bunch of them.[00:26:48] Bret: A lot of them, you know, have that mix of sort of like like confluence or have the open source or open core or whatever you call it. I, I, I'm not an expert in this area. You know, I do think [00:27:00] that developers are fickle. I think that in the tool space, I probably like. Default towards open source being like the area that will win.[00:27:09] Bret: It's hard to build a company around this and then you end up with companies sort of built around open source to that can work. Don't get me wrong, but I just think that it's nowadays the tools are changing so rapidly that I'm like, not totally skeptical of tool makers, but I just think that open source will broadly win, but I think that the CapEx required for building frontier models is such that it will go to a handful of big companies.[00:27:33] Bret: And then I really believe in agents for specific domains which I think will, it's sort of the analog to software as a service in this new era. You know, it's like, if you just think of the cloud. You can lease a server. It's just a low level primitive, or you can buy an app like you know, Shopify or whatever.[00:27:51] Bret: And most people building a storefront would prefer Shopify over hand rolling their e commerce storefront. I think the same thing will be true of AI. So [00:28:00] I've. I tend to like, if I have a, like an entrepreneur asked me for advice, I'm like, you know, move up the stack as far as you can towards a customer need.[00:28:09] Bret: Broadly, but I, but it doesn't reduce my excitement about what is the reactive building agents kind of thing, just because it is, it is the right question to ask, but I think we'll probably play out probably an open source space more than anything else.[00:28:21] swyx: Yeah, and it's not a priority for you. There's a lot in there.[00:28:24] swyx: I'm kind of curious about your idea maze towards, there are many customer needs. You happen to identify customer experience as yours, but it could equally have been coding assistance or whatever. I think for some, I'm just kind of curious at the top down, how do you look at the world in terms of the potential problem space?[00:28:44] swyx: Because there are many people out there who are very smart and pick the wrong problem.[00:28:47] Bret: Yeah, that's a great question.[00:28:48] Future of Software Development[00:28:48] Bret: By the way, I would love to talk about the future of software, too, because despite the fact it didn't pick coding, I have a lot of that, but I can talk to I can answer your question, though, you know I think when a technology is as [00:29:00] cool as large language models.[00:29:02] Bret: You just see a lot of people starting from the technology and searching for a problem to solve. And I think it's why you see a lot of tools companies, because as a software engineer, you start building an app or a demo and you, you encounter some pain points. You're like,[00:29:17] swyx: a lot of[00:29:17] Bret: people are experiencing the same pain point.[00:29:19] Bret: What if I make it? That it's just very incremental. And you know, I always like to use the metaphor, like you can sell coffee beans, roasted coffee beans. You can add some value. You took coffee beans and you roasted them and roasted coffee beans largely, you know, are priced relative to the cost of the beans.[00:29:39] Bret: Or you can sell a latte and a latte. Is rarely priced directly like as a percentage of coffee bean prices. In fact, if you buy a latte at the airport, it's a captive audience. So it's a really expensive latte. And there's just a lot that goes into like. How much does a latte cost? And I bring it up because there's a supply chain from growing [00:30:00] coffee beans to roasting coffee beans to like, you know, you could make one at home or you could be in the airport and buy one and the margins of the company selling lattes in the airport is a lot higher than the, you know, people roasting the coffee beans and it's because you've actually solved a much more acute human problem in the airport.[00:30:19] Bret: And, and it's just worth a lot more to that person in that moment. It's kind of the way I think about technology too. It sounds funny to liken it to coffee beans, but you're selling tools on top of a large language model yet in some ways your market is big, but you're probably going to like be price compressed just because you're sort of a piece of infrastructure and then you have open source and all these other things competing with you naturally.[00:30:43] Bret: If you go and solve a really big business problem for somebody, that's actually like a meaningful business problem that AI facilitates, they will value it according to the value of that business problem. And so I actually feel like people should just stop. You're like, no, that's, that's [00:31:00] unfair. If you're searching for an idea of people, I, I love people trying things, even if, I mean, most of the, a lot of the greatest ideas have been things no one believed in.[00:31:07] Bret: So I like, if you're passionate about something, go do it. Like who am I to say, yeah, a hundred percent. Or Gmail, like Paul as far, I mean I, some of it's Laura at this point, but like Gmail is Paul's own email for a long time. , and then I amusingly and Paul can't correct me, I'm pretty sure he sent her in a link and like the first comment was like, this is really neat.[00:31:26] Bret: It would be great. It was not your email, but my own . I don't know if it's a true story. I'm pretty sure it's, yeah, I've read that before. So scratch your own niche. Fine. Like it depends on what your goal is. If you wanna do like a venture backed company, if its a. Passion project, f*****g passion, do it like don't listen to anybody.[00:31:41] Bret: In fact, but if you're trying to start, you know an enduring company, solve an important business problem. And I, and I do think that in the world of agents, the software industries has shifted where you're not just helping people more. People be more productive, but you're actually accomplishing tasks autonomously.[00:31:58] Bret: And as a consequence, I think the [00:32:00] addressable market has just greatly expanded just because software can actually do things now and actually accomplish tasks and how much is coding autocomplete worth. A fair amount. How much is the eventual, I'm certain we'll have it, the software agent that actually writes the code and delivers it to you, that's worth a lot.[00:32:20] Bret: And so, you know, I would just maybe look up from the large language models and start thinking about the economy and, you know, think from first principles. I don't wanna get too far afield, but just think about which parts of the economy. We'll benefit most from this intelligence and which parts can absorb it most easily.[00:32:38] Bret: And what would an agent in this space look like? Who's the customer of it is the technology feasible. And I would just start with these business problems more. And I think, you know, the best companies tend to have great engineers who happen to have great insight into a market. And it's that last part that I think some people.[00:32:56] Bret: Whether or not they have, it's like people start so much in the technology, they [00:33:00] lose the forest for the trees a little bit.[00:33:02] Alessio: How do you think about the model of still selling some sort of software versus selling more package labor? I feel like when people are selling the package labor, it's almost more stateless, you know, like it's easier to swap out if you're just putting an input and getting an output.[00:33:16] Alessio: If you think about coding, if there's no ID, you're just putting a prompt and getting back an app. It doesn't really matter. Who generates the app, you know, you have less of a buy in versus the platform you're building, I'm sure on the backend customers have to like put on their documentation and they have, you know, different workflows that they can tie in what's kind of like the line to draw there versus like going full where you're managed customer support team as a service outsource versus.[00:33:40] Alessio: This is the Sierra platform that you can build on. What was that decision? I'll sort of[00:33:44] Bret: like decouple the question in some ways, which is when you have something that's an agent, who is the person using it and what do they want to do with it? So let's just take your coding agent for a second. I will talk about Sierra as well.[00:33:59] Bret: Who's the [00:34:00] customer of a, an agent that actually produces software? Is it a software engineering manager? Is it a software engineer? And it's there, you know, intern so to speak. I don't know. I mean, we'll figure this out over the next few years. Like what is that? And is it generating code that you then review?[00:34:16] Bret: Is it generating code with a set of unit tests that pass, what is the actual. For lack of a better word contract, like, how do you know that it did what you wanted it to do? And then I would say like the product and the pricing, the packaging model sort of emerged from that. And I don't think the world's figured out.[00:34:33] Bret: I think it'll be different for every agent. You know, in our customer base, we do what's called outcome based pricing. So essentially every time the AI agent. Solves the problem or saves a customer or whatever it might be. There's a pre negotiated rate for that. We do that. Cause it's, we think that that's sort of the correct way agents, you know, should be packaged.[00:34:53] Bret: I look back at the history of like cloud software and notably the introduction of the browser, which led to [00:35:00] software being delivered in a browser, like Salesforce to. Famously invented sort of software as a service, which is both a technical delivery model through the browser, but also a business model, which is you subscribe to it rather than pay for a perpetual license.[00:35:13] Bret: Those two things are somewhat orthogonal, but not really. If you think about the idea of software running in a browser, that's hosted. Data center that you don't own, you sort of needed to change the business model because you don't, you can't really buy a perpetual license or something otherwise like, how do you afford making changes to it?[00:35:31] Bret: So it only worked when you were buying like a new version every year or whatever. So to some degree, but then the business model shift actually changed business as we know it, because now like. Things like Adobe Photoshop. Now you subscribe to rather than purchase. So it ended up where you had a technical shift and a business model shift that were very logically intertwined that actually the business model shift was turned out to be as significant as the technical as the shift.[00:35:59] Bret: And I think with [00:36:00] agents, because they actually accomplish a job, I do think that it doesn't make sense to me that you'd pay for the privilege of like. Using the software like that coding agent, like if it writes really bad code, like fire it, you know, I don't know what the right metaphor is like you should pay for a job.[00:36:17] Bret: Well done in my opinion. I mean, that's how you pay your software engineers, right? And[00:36:20] swyx: and well, not really. We paid to put them on salary and give them options and they vest over time. That's fair.[00:36:26] Bret: But my point is that you don't pay them for how many characters they write, which is sort of the token based, you know, whatever, like, There's a, that famous Apple story where we're like asking for a report of how many lines of code you wrote.[00:36:40] Bret: And one of the engineers showed up with like a negative number cause he had just like done a big refactoring. There was like a big F you to management who didn't understand how software is written. You know, my sense is like the traditional usage based or seat based thing. It's just going to look really antiquated.[00:36:55] Bret: Cause it's like asking your software engineer, how many lines of code did you write today? Like who cares? Like, cause [00:37:00] absolutely no correlation. So my old view is I don't think it's be different in every category, but I do think that that is the, if an agent is doing a job, you should, I think it properly incentivizes the maker of that agent and the customer of, of your pain for the job well done.[00:37:16] Bret: It's not always perfect to measure. It's hard to measure engineering productivity, but you can, you should do something other than how many keys you typed, you know Talk about perverse incentives for AI, right? Like I can write really long functions to do the same thing, right? So broadly speaking, you know, I do think that we're going to see a change in business models of software towards outcomes.[00:37:36] Bret: And I think you'll see a change in delivery models too. And, and, you know, in our customer base you know, we empower our customers to really have their hands on the steering wheel of what the agent does they, they want and need that. But the role is different. You know, at a lot of our customers, the customer experience operations folks have renamed themselves the AI architects, which I think is really cool.[00:37:55] Bret: And, you know, it's like in the early days of the Internet, there's the role of the webmaster. [00:38:00] And I don't know whether your webmaster is not a fashionable, you know, Term, nor is it a job anymore? I just, I don't know. Will they, our tech stand the test of time? Maybe, maybe not. But I do think that again, I like, you know, because everyone listening right now is a software engineer.[00:38:14] Bret: Like what is the form factor of a coding agent? And actually I'll, I'll take a breath. Cause actually I have a bunch of pins on them. Like I wrote a blog post right before Christmas, just on the future of software development. And one of the things that's interesting is like, if you look at the way I use cursor today, as an example, it's inside of.[00:38:31] Bret: A repackaged visual studio code environment. I sometimes use the sort of agentic parts of it, but it's largely, you know, I've sort of gotten a good routine of making it auto complete code in the way I want through tuning it properly when it actually can write. I do wonder what like the future of development environments will look like.[00:38:55] Bret: And to your point on what is a software product, I think it's going to change a lot in [00:39:00] ways that will surprise us. But I always use, I use the metaphor in my blog post of, have you all driven around in a way, Mo around here? Yeah, everyone has. And there are these Jaguars, the really nice cars, but it's funny because it still has a steering wheel, even though there's no one sitting there and the steering wheels like turning and stuff clearly in the future.[00:39:16] Bret: If once we get to that, be more ubiquitous, like why have the steering wheel and also why have all the seats facing forward? Maybe just for car sickness. I don't know, but you could totally rearrange the car. I mean, so much of the car is oriented around the driver, so. It stands to reason to me that like, well, autonomous agents for software engineering run through visual studio code.[00:39:37] Bret: That seems a little bit silly because having a single source code file open one at a time is kind of a goofy form factor for when like the code isn't being written primarily by you, but it begs the question of what's your relationship with that agent. And I think the same is true in our industry of customer experience, which is like.[00:39:55] Bret: Who are the people managing this agent? What are the tools do they need? And they definitely need [00:40:00] tools, but it's probably pretty different than the tools we had before. It's certainly different than training a contact center team. And as software engineers, I think that I would like to see particularly like on the passion project side or research side.[00:40:14] Bret: More innovation in programming languages. I think that we're bringing the cost of writing code down to zero. So the fact that we're still writing Python with AI cracks me up just cause it's like literally was designed to be ergonomic to write, not safe to run or fast to run. I would love to see more innovation and how we verify program correctness.[00:40:37] Bret: I studied for formal verification in college a little bit and. It's not very fashionable because it's really like tedious and slow and doesn't work very well. If a lot of code is being written by a machine, you know, one of the primary values we can provide is verifying that it actually does what we intend that it does.[00:40:56] Bret: I think there should be lots of interesting things in the software development life cycle, like how [00:41:00] we think of testing and everything else, because. If you think about if we have to manually read every line of code that's coming out as machines, it will just rate limit how much the machines can do. The alternative is totally unsafe.[00:41:13] Bret: So I wouldn't want to put code in production that didn't go through proper code review and inspection. So my whole view is like, I actually think there's like an AI native I don't think the coding agents don't work well enough to do this yet, but once they do, what is sort of an AI native software development life cycle and how do you actually.[00:41:31] Bret: Enable the creators of software to produce the highest quality, most robust, fastest software and know that it's correct. And I think that's an incredible opportunity. I mean, how much C code can we rewrite and rust and make it safe so that there's fewer security vulnerabilities. Can we like have more efficient, safer code than ever before?[00:41:53] Bret: And can you have someone who's like that guy in the matrix, you know, like staring at the little green things, like where could you have an operator [00:42:00] of a code generating machine be like superhuman? I think that's a cool vision. And I think too many people are focused on like. Autocomplete, you know, right now, I'm not, I'm not even, I'm guilty as charged.[00:42:10] Bret: I guess in some ways, but I just like, I'd like to see some bolder ideas. And that's why when you were joking, you know, talking about what's the react of whatever, I think we're clearly in a local maximum, you know, metaphor, like sort of conceptual local maximum, obviously it's moving really fast. I think we're moving out of it.[00:42:26] Alessio: Yeah. At the end of 23, I've read this blog post from syntax to semantics. Like if you think about Python. It's taking C and making it more semantic and LLMs are like the ultimate semantic program, right? You can just talk to them and they can generate any type of syntax from your language. But again, the languages that they have to use were made for us, not for them.[00:42:46] Alessio: But the problem is like, as long as you will ever need a human to intervene, you cannot change the language under it. You know what I mean? So I'm curious at what point of automation we'll need to get, we're going to be okay making changes. To the underlying languages, [00:43:00] like the programming languages versus just saying, Hey, you just got to write Python because I understand Python and I'm more important at the end of the day than the model.[00:43:08] Alessio: But I think that will change, but I don't know if it's like two years or five years. I think it's more nuanced actually.[00:43:13] Bret: So I think there's a, some of the more interesting programming languages bring semantics into syntax. So let me, that's a little reductive, but like Rust as an example, Rust is memory safe.[00:43:25] Bret: Statically, and that was a really interesting conceptual, but it's why it's hard to write rust. It's why most people write python instead of rust. I think rust programs are safer and faster than python, probably slower to compile. But like broadly speaking, like given the option, if you didn't have to care about the labor that went into it.[00:43:45] Bret: You should prefer a program written in Rust over a program written in Python, just because it will run more efficiently. It's almost certainly safer, et cetera, et cetera, depending on how you define safe, but most people don't write Rust because it's kind of a pain in the ass. And [00:44:00] the audience of people who can is smaller, but it's sort of better in most, most ways.[00:44:05] Bret: And again, let's say you're making a web service and you didn't have to care about how hard it was to write. If you just got the output of the web service, the rest one would be cheaper to operate. It's certainly cheaper and probably more correct just because there's so much in the static analysis implied by the rest programming language that it probably will have fewer runtime errors and things like that as well.[00:44:25] Bret: So I just give that as an example, because so rust, at least my understanding that came out of the Mozilla team, because. There's lots of security vulnerabilities in the browser and it needs to be really fast. They said, okay, we want to put more of a burden at the authorship time to have fewer issues at runtime.[00:44:43] Bret: And we need the constraint that it has to be done statically because browsers need to be really fast. My sense is if you just think about like the, the needs of a programming language today, where the role of a software engineer is [00:45:00] to use an AI to generate functionality and audit that it does in fact work as intended, maybe functionally, maybe from like a correctness standpoint, some combination thereof, how would you create a programming system that facilitated that?[00:45:15] Bret: And, you know, I bring up Rust is because I think it's a good example of like, I think given a choice of writing in C or Rust, you should choose Rust today. I think most people would say that, even C aficionados, just because. C is largely less safe for very similar, you know, trade offs, you know, for the, the system and now with AI, it's like, okay, well, that just changes the game on writing these things.[00:45:36] Bret: And so like, I just wonder if a combination of programming languages that are more structurally oriented towards the values that we need from an AI generated program, verifiable correctness and all of that. If it's tedious to produce for a person, that maybe doesn't matter. But one thing, like if I asked you, is this rest program memory safe?[00:45:58] Bret: You wouldn't have to read it, you just have [00:46:00] to compile it. So that's interesting. I mean, that's like an, that's one example of a very modest form of formal verification. So I bring that up because I do think you have AI inspect AI, you can have AI reviewed. Do AI code reviews. It would disappoint me if the best we could get was AI reviewing Python and having scaled a few very large.[00:46:21] Bret: Websites that were written on Python. It's just like, you know, expensive and it's like every, trust me, every team who's written a big web service in Python has experimented with like Pi Pi and all these things just to make it slightly more efficient than it naturally is. You don't really have true multi threading anyway.[00:46:36] Bret: It's just like clearly that you do it just because it's convenient to write. And I just feel like we're, I don't want to say it's insane. I just mean. I do think we're at a local maximum. And I would hope that we create a programming system, a combination of programming languages, formal verification, testing, automated code reviews, where you can use AI to generate software in a high scale way and trust it.[00:46:59] Bret: And you're [00:47:00] not limited by your ability to read it necessarily. I don't know exactly what form that would take, but I feel like that would be a pretty cool world to live in.[00:47:08] Alessio: Yeah. We had Chris Lanner on the podcast. He's doing great work with modular. I mean, I love. LVM. Yeah. Basically merging rust in and Python.[00:47:15] Alessio: That's kind of the idea. Should be, but I'm curious is like, for them a big use case was like making it compatible with Python, same APIs so that Python developers could use it. Yeah. And so I, I wonder at what point, well, yeah.[00:47:26] Bret: At least my understanding is they're targeting the data science Yeah. Machine learning crowd, which is all written in Python, so still feels like a local maximum.[00:47:34] Bret: Yeah.[00:47:34] swyx: Yeah, exactly. I'll force you to make a prediction. You know, Python's roughly 30 years old. In 30 years from now, is Rust going to be bigger than Python?[00:47:42] Bret: I don't know this, but just, I don't even know this is a prediction. I just am sort of like saying stuff I hope is true. I would like to see an AI native programming language and programming system, and I use language because I'm not sure language is even the right thing, but I hope in 30 years, there's an AI native way we make [00:48:00] software that is wholly uncorrelated with the current set of programming languages.[00:48:04] Bret: or not uncorrelated, but I think most programming languages today were designed to be efficiently authored by people and some have different trade offs.[00:48:15] Evolution of Programming Languages[00:48:15] Bret: You know, you have Haskell and others that were designed for abstractions for parallelism and things like that. You have programming languages like Python, which are designed to be very easily written, sort of like Perl and Python lineage, which is why data scientists use it.[00:48:31] Bret: It's it can, it has a. Interactive mode, things like that. And I love, I'm a huge Python fan. So despite all my Python trash talk, a huge Python fan wrote at least two of my three companies were exclusively written in Python and then C came out of the birth of Unix and it wasn't the first, but certainly the most prominent first step after assembly language, right?[00:48:54] Bret: Where you had higher level abstractions rather than and going beyond go to, to like abstractions, [00:49:00] like the for loop and the while loop.[00:49:01] The Future of Software Engineering[00:49:01] Bret: So I just think that if the act of writing code is no longer a meaningful human exercise, maybe it will be, I don't know. I'm just saying it sort of feels like maybe it's one of those parts of history that just will sort of like go away, but there's still the role of this offer engineer, like the person actually building the system.[00:49:20] Bret: Right. And. What does a programming system for that form factor look like?[00:49:25] React and Front-End Development[00:49:25] Bret: And I, I just have a, I hope to be just like I mentioned, I remember I was at Facebook in the very early days when, when, what is now react was being created. And I remember when the, it was like released open source I had left by that time and I was just like, this is so f*****g cool.[00:49:42] Bret: Like, you know, to basically model your app independent of the data flowing through it, just made everything easier. And then now. You know, I can create, like there's a lot of the front end software gym play is like a little chaotic for me, to be honest with you. It is like, it's sort of like [00:50:00] abstraction soup right now for me, but like some of those core ideas felt really ergonomic.[00:50:04] Bret: I just wanna, I'm just looking forward to the day when someone comes up with a programming system that feels both really like an aha moment, but completely foreign to me at the same time. Because they created it with sort of like from first principles recognizing that like. Authoring code in an editor is maybe not like the primary like reason why a programming system exists anymore.[00:50:26] Bret: And I think that's like, that would be a very exciting day for me.[00:50:28] The Role of AI in Programming[00:50:28] swyx: Yeah, I would say like the various versions of this discussion have happened at the end of the day, you still need to precisely communicate what you want. As a manager of people, as someone who has done many, many legal contracts, you know how hard that is.[00:50:42] swyx: And then now we have to talk to machines doing that and AIs interpreting what we mean and reading our minds effectively. I don't know how to get across that barrier of translating human intent to instructions. And yes, it can be more declarative, but I don't know if it'll ever Crossover from being [00:51:00] a programming language to something more than that.[00:51:02] Bret: I agree with you. And I actually do think if you look at like a legal contract, you know, the imprecision of the English language, it's like a flaw in the system. How many[00:51:12] swyx: holes there are.[00:51:13] Bret: And I do think that when you're making a mission critical software system, I don't think it should be English language prompts.[00:51:19] Bret: I think that is silly because you want the precision of a a programming language. My point was less about that and more about if the actual act of authoring it, like if you.[00:51:32] Formal Verification in Software[00:51:32] Bret: I'll think of some embedded systems do use formal verification. I know it's very common in like security protocols now so that you can, because the importance of correctness is so great.[00:51:41] Bret: My intellectual exercise is like, why not do that for all software? I mean, probably that's silly just literally to do what we literally do for. These low level security protocols, but the only reason we don't is because it's hard and tedious and hard and tedious are no longer factors. So, like, if I could, I mean, [00:52:00] just think of, like, the silliest app on your phone right now, the idea that that app should be, like, formally verified for its correctness feels laughable right now because, like, God, why would you spend the time on it?[00:52:10] Bret: But if it's zero costs, like, yeah, I guess so. I mean, it never crashed. That's probably good. You know, why not? I just want to, like, set our bars really high. Like. We should make, software has been amazing. Like there's a Mark Andreessen blog post, software is eating the world. And you know, our whole life is, is mediated digitally.[00:52:26] Bret: And that's just increasing with AI. And now we'll have our personal agents talking to the agents on the CRO platform and it's agents all the way down, you know, our core infrastructure is running on these digital systems. We now have like, and we've had a shortage of software developers for my entire life.[00:52:45] Bret: And as a consequence, you know if you look, remember like health care, got healthcare. gov that fiasco security vulnerabilities leading to state actors getting access to critical infrastructure. I'm like. We now have like created this like amazing system that can [00:53:00] like, we can fix this, you know, and I, I just want to, I'm both excited about the productivity gains in the economy, but I just think as software engineers, we should be bolder.[00:53:08] Bret: Like we should have aspirations to fix these systems so that like in general, as you said, as precise as we want to be in the specification of the system. We can make it work correctly now, and I'm being a little bit hand wavy, and I think we need some systems. I think that's where we should set the bar, especially when so much of our life depends on this critical digital infrastructure.[00:53:28] Bret: So I'm I'm just like super optimistic about it. But actually, let's go to w

Jason Mehmel, Talk Gnosis Co-Host and long time member of the Parish of St. Joseph of Arimathea, explores Gnosis outside of Gnosticism and Religion

Dr. Punya Nachappa is an Associate professor at Colorado State University in the Department of Agricultural Biology. Her research program focuses on understanding the interactions between plants and insect vectors to manage plant pests and diseases in hemp. At CannMed 25, Punya will present "Biology and Management of Hop Latent Viroid", which will describe the impact of HLVd on hemp yield and cannabinoids, explain how thrips and aphids transmit the viroid, and evaluate the efficacy of using chemical elicitors to control HLVd infections. During our conversation, we discussed: How Hop Latent Viroid has been reported in every hemp-growing state in the US and Canada How HLVd spreads through infected clones, mechanical contact, seeds, and insect vectors like aphids and thrips. Possible reasons why hemp varieties appear more resistant than cannabis varieties (i.e., genetic diversity and environmental factors) The tradeoffs associated with using chemical elicitors on infected plants: increased yield, but reduced cannabinoids. Strategies for preventing HLVD spread, including strict sanitation, insect monitoring, frequent testing, and breeding for genetic resistance. Thanks to This Episode's Sponsor: Advanced Nutrients. Founded in 1999, Advanced Nutrients was the first to develop a complete nutrient system that unlocks the true genetic potential of the cannabis plant. Since its inception, the brand has introduced more than 50 innovations to the cultivation community and continues to revolutionize the space through proprietary scientific discoveries.  Learn more at AdvancedNutrients.com Additional Resources - Nachappa Lab Website (https://www.nachappalab.com/) - Register for CannMed 25 (https://cvent.me/emBPno) - Meet the CannMed 25 Speakers (https://cannmedevents.com/speakers/) - Review the Podcast (https://podcasts.apple.com/us/podcast/cannmed-coffee-talk/id1504218804) - CannMed Archive (https://cannmedevents.com/cannmed-video-archives/)

Thank you for watching! #emptyshelfgang Check out Bhavya! https://www.instagram.com/mynameisnot... Follow us on Instagram! https://www.instagram.com/thehavingsa... Check out Adi's channel! ​⁠​⁠ Follow him on IG: https://www.instagram.com/adisaidthat... Check out his new song: https://linktr.ee/adi.avg Follow Jeh on IG: https://www.instagram.com/coach.jeh?i... Sign up to our newsletter: https://linktr.ee/hstshow Join our FPL league: https://fantasy.premierleague.com/lea... Chapters 00:00 Intro 01:15 Taylor Swift Better Than Coldplay 04:40 Debate Competitions 11:40 Being A Comedian Today 28:00 India's Got Latent 35:30 Bhavya's Bollywood Debut 40:55 AI 44:10 Judge Bhavya

In this episode of Construction Blueprints, our experts dive into the world of latent defect insurance, exploring its benefits in the wake of high-profile contractor demise and how it can protect developers and owners. They also discuss the best time to secure a latent defect insurance (LDI) policy and the extra safeguards it provides against contractor insolvency.

Forensically speaking, a latent footprint refers to an imprint left by a person's foot on a surface that is not immediately visible to the naked eye. These footprints are typically created when an individual transfers natural oils, dirt, or other substances from their feet onto a surface as they walk. These impressions are often faint and can only be revealed through specialized techniques like dusting, chemical treatments, or photography. Forensic experts use these methods to make latent footprints visible and then compare them to known footwear patterns to help identify or exclude potential suspects in criminal investigations.In the affidavit, investigators stated that they found a latent footprint at the scene of the crime that was consistent with the type of print you would find on a pair of Vans shoes. The problem with that? Just about everyone has Vans.In this episode, we take a look at the latent footprint as evidence and how it might be used by the prosecutors during the trial for Bryan Kohberger.to contact me:bobbycapucci@protonmail.com(commercial at 7:22)source:Clue in Idaho Murder Case Leaves Question About Bryan Kohberger Evidence (newsweek.com)

这一期很欢乐地和人在美国的 Stella & Amy 聊聊自由撰稿的工作，也交流了一下播客创作的小tips。录制当时我在台湾，所以台湾腔蛮浓的

In this episode of the Bioactive Podcast, host Dr. Riley Kirk dives deep into groundbreaking cannabis science with world-renowned molecular biologist Dr. Ali Bektaş. Dr. Bektaş has an impressive background in plant biology, microbes, and agriculture, specializing in affordable and distributed systems for detecting nucleic acids in agricultural environments. He has worked extensively with cannabis nurseries and biotech companies as a consultant, staff scientist, and Chief Science Officer. Notably, Dr. Bektaş was one of the first scientists to publicly identify Hop Latent Viroid (HLVd) in cannabis populations, a discovery that reshaped the industry's approach to disease management. What is Hop Latent Viroid (HLVd)? HLVd is a serious plant disease that infects cannabis, hop plants, and other medicinal plants like stinging nettle. Infected cannabis plants can suffer a significant reduction in cannabinoids and terpenes, drastically lowering both their monetary and medicinal value. Dr. Kirk and Dr. Bektaş explore the origins of HLVd, how it spreads, what to look for in affected plants, prevention strategies, and how innovative genetics are helping cannabis plants overcome this disease.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian statistics offers a robust framework for econometric modeling.State space models provide a comprehensive way to understand time series data.Gaussian random walks serve as a foundational model in time series analysis.Innovations represent external shocks that can significantly impact forecasts.Understanding the assumptions behind models is key to effective forecasting.Complex models are not always better; simplicity can be powerful.Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.Latent abilities can be modeled as Gaussian random walks.State space models can be highly flexible and diverse.Composability allows for the integration of different model components.Trends in time series should reflect real-world dynamics.Seasonality can be captured through Fourier bases.AR components help model residuals in time series data.Exogenous regression components can enhance state space models.Causal analysis in time series often involves interventions and counterfactuals.Time-varying regression allows for dynamic relationships between variables.Kalman filters were originally developed for tracking rockets in space.The Kalman filter iteratively updates beliefs based on new data.Missing data can be treated as hidden states in the Kalman filter framework.The Kalman filter is a practical application of Bayes' theorem in a sequential context.Understanding the dynamics of systems is crucial for effective modeling.The state space module in PyMC simplifies complex time series modeling tasks.Chapters:00:00 Introduction to Jesse Krabowski and Time Series Analysis04:33 Jesse's Journey into Bayesian Statistics10:51 Exploring State Space Models18:28 Understanding State Space Models and Their Components

Glenn and Eric interview Sarah Chu, director of policy and reform with the Perlmutter Center for Legal Justice's (PCLJ) forensic science policy initiatives. Sarah breaks down her doctoral thesis on quality management and oversight in forensic science laboratories.

Send us a textRegister your free place for the live online meditation and Q&A with Babaji: https://www.shivarudrabalayogi.org/en/online-satsang Shaping a better future | In Quest of Truth - Q&A with Babaji, No.205Recorded on 31st December 2023 with worldwide participants0:00 Intro0:08 How do we shape a better future for ourself?1:27 How can we remove or change our latent tendencies?5:19 Should we focus on our Self rather than the body and mind?7:33 So how we perceive ourself is important?8:40 Do all bodies and beings exist in one consciousness?11:58 The Truth of our Existence revealed when we silence the mind.15:30 Mind when it is silent it is pure consciousness and when it moves it is mind?18:12 Knowledge requires a mind but awareness does not?19:34 The wheel/axle analogy - what is beyond the almost stationary point at the middle of the axle?24:46 Are awareness and existence the same?26:26 Is it ok to consider consciousness as a field of all possibilities?26:45 At every moment we have a chance to be aware of the Self as opposed to the mind and body?28:33 What is everywhere, we don't notice.33:27 Latent tendencies, acquired habits and previous resolutions.34:54 Do we hold our current situation by holding on to our thoughts?38:55 Having a silent mind versus changing our thinking when in the world.43:41 How do we know when we are Self Realised?45:31 Do we have to keep our mind quiet at the time of death?47:59 What is the true meaning of dharma?  Is it a part of Brahman or is it an imagination?51:29 During the five year Tapas, was it the higher consciousness that prompted Babaji to be brought out of samadhi?53:22 Is the state of enlightenment permanent or does it have to be worked on all the time?58:20 Are there samskaras that continue to come up fro the past that we need to deal with consciously?1:02:33 Our final experiences are not happy ones, with the body failing, in pain.1:05:00 What causes the realisation that the world is not going to give what we are really seeking?1:08:17 How to work with Swamiji's image?___Website: http://www.srby.orgFacebook: https://www.facebook.com/shivarudrabalayogiTwitter: https://twitter.com/SRBYmissionInstagram: https://www.instagram.com/shivarudrabalayogi/Register your free place for the live online meditation and Q&A with Babaji: https://www.shivarudrabalayogi.org/en/online-satsang Website: http://www.srby.orgFacebook: https://www.facebook.com/shivarudrabalayogiTwitter: https://twitter.com/SRBYmissionInstagram: https://www.instagram.com/shivarudrabalayogi/

In episode 193 of PG Radio, host Prakhar Gupta sits down with Naman Arora, an actor and character comedy stand-up comedian. Naman shares his journey into the world of acting and comedy, discussing how he developed his unique comedic characters and his aspirations for the future. Additionally, Naman talks about his experiences with Samay Raina and "India's Got Latent" Naman Arora is an Indian actor and comedian who gained prominence after his performance on "India's Got Latent" and opening show for Samay Raina. Known for his character comedy, Naman has also worked as a writer on "The Kapil Sharma Show". This is what we talked about:

Forensically speaking, a latent footprint refers to an imprint left by a person's foot on a surface that is not immediately visible to the naked eye. These footprints are typically created when an individual transfers natural oils, dirt, or other substances from their feet onto a surface as they walk. These impressions are often faint and can only be revealed through specialized techniques like dusting, chemical treatments, or photography. Forensic experts use these methods to make latent footprints visible and then compare them to known footwear patterns to help identify or exclude potential suspects in criminal investigations.In the affidavit, investigators stated that they found a latent footprint at the scene of the crime that was consistent with the type of print you would find on a pair of Vans shoes. The problem with that? Just about everyone has Vans.In this episode, we take a look at the latent footprint as evidence and how it might be used by the prosecutors during the trial for Bryan Kohberger.to contact me:bobbycapucci@protonmail.com(commercial at 7:22)source:Clue in Idaho Murder Case Leaves Question About Bryan Kohberger Evidence (newsweek.com)

In this episode the guys discuss historical figures in the field of fingerprints. At the top of the episode Eric gives Glenn an Australian themed “A Truth, a Lie, and a Mandela Effect”. Glenn tells a couple of stories from his trip to London, which also inspired the topic for the episode. Because of Glenn's time in Switzerland this fall reading old texts, he learned a lot about the early days of fingerprints. The guys discuss contributions, stories and cases from Juan Vucetich, Sir Francis Galton, Sir Henry Faulds, Sir Edward Henry, Alphonse Bertillon, Dr. Edmond Locard, and more!

In this no-holds-barred episode of Cyrus Says, Cyrus and Punit Pania dive straight into the madness of today's headlines and beyond. From AR Rahman's divorce drama to Delhi's dystopian smog, they keep it raw, real, and ridiculously funny. The duo doesn't shy away from the mess of Maharashtra's election antics or the absurdity of paying 4 lakhs for a first grader's school fees. It's unfiltered chaos, the way only Cyrus and Punit can deliver. They riff on whether Messi's trip to India can outshine Coldplay's hype and crack up over Samay Raina's risky dark comedy moves. Toss in some banter about Nadal's jaw-dropping records and the wild ‘Trump Effect' on global politics, and you've got an episode that's as unpredictable as it is hilarious. Tune in for the laughs, stay for the madness, and don't forget to hit that subscribe button!See omnystudio.com/listener for privacy information.

The guys start out with a quick explanation of why Glenn's sound is so bad (he's in Switzerland recording). They also start the new “season”, post-IAI with a new game: "Truth, Lie, or Mandela Effect?" Eric talks about his fall conference junket and then the guys finally catch up on the IAI. They discuss their favorite lectures, workshops, and activities at the 2024 IAI Conference in Reno, NV. They summarize some of their standout lectures that they attended. Glenn then discusses how the Double Loop Podcast vendor booth went with Rebecca Coutant running it during the conference. Rebecca also was “Our Girl Friday”, doing her impromptu interviews with conference attendees.

Interested in joining the REI Masters Mentorship Program? Head to www.reimasters.ca Or email us at info@reimasters.ca   Got a question you'd like answered on the show? Email us at info@reimorningshow.com   Hosts: Wayne and Gabby Hillier Edmonton Alberta Real Estate Investors Coaches at the Real Estate Investing Masters Mentorship Program

In this special crossover episode of The Cognitive Revolution, Nathan shares an insightful conversation from the Latent.Space podcast. Swyx and Alessio interview Alistair Pullen of Cosine, creators of Genie, showcasing the cutting edge of AI automation in software engineering. Learn how Cosine achieves state-of-the-art results on the SWE-bench benchmark by implementing advanced AI techniques. This episode complements Nathan's recent discussion on AI Automation, demonstrating how far these practices can be pushed in real-world applications. Don't miss this opportunity to explore the future of AI-driven software development and its implications for businesses across industries. Check out the Latent.Space podcast here: https://www.latent.space Apply to join over 400 Founders and Execs in the Turpentine Network: https://www.turpentinenetwork.co/ SPONSORS: WorkOS: Building an enterprise-ready SaaS app? WorkOS has got you covered with easy-to-integrate APIs for SAML, SCIM, and more. Join top startups like Vercel, Perplexity, Jasper & Webflow in powering your app with WorkOS. Enjoy a free tier for up to 1M users! Start now at https://bit.ly/WorkOS-Turpentine-Network Weights & Biases Weave: Weights & Biases Weave is a lightweight AI developer toolkit designed to simplify your LLM app development. With Weave, you can trace and debug input, metadata and output with just 2 lines of code. Make real progress on your LLM development and visit the following link to get started with Weave today: https://wandb.me/cr 80,000 Hours: 80,000 Hours offers free one-on-one career advising for Cognitive Revolution listeners aiming to tackle global challenges, especially in AI. They connect high-potential individuals with experts, opportunities, and personalized career plans to maximize positive impact. Apply for a free call at https://80000hours.org/cognitiverevolution to accelerate your career and contribute to solving pressing AI-related issues. Omneky: Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ CHAPTERS: (00:00:00) About the Show (00:00:22) Sponsors: WorkOS (00:01:22) About the Episode (00:04:29) Alistair and Cosine intro (00:13:50) Building the Code Retrieval Tool (00:17:36) Sponsors: Weights & Biases Weave | 80,000 Hours (00:20:15) Developing Genie and Fine-tuning Process (00:27:41) Working with Customer Data (00:30:53) Code Retrieval Challenges and Solutions (00:36:39) Sponsors: Omneky (00:37:02) Planning and Reasoning in AI Models (00:45:55) Language Support and Generalization (00:49:46) Fine-tuning Experience with OpenAI (00:52:56) Synthetic Data and Self-improvement Loop (00:55:57) Benchmarking and SWE-bench Results (01:01:47) Future Plans for Genie (01:03:02) Industry Trends and Cursor's Success (01:05:23) Calls to Action and Ideal Customers (01:08:43) Outro

Krista Olson, PharmD reviews pharmacotherapeutic approaches to latent autoimmune diabetes in adults.   For more pharmacy content, follow Mayo Clinic Pharmacy Residency Programs @MayoPharmRes.  You can also connect with the Mayo Clinic's School of Continuous Professional Development online at https://ce.mayo.edu/ or on X @MayoMedE 

John Simmerman — Founder of Active Towns — is in good traffic to talk the push for more active towns across the U.S. John travels the world's cities via active mobility, and creates stellar content to summarize the trips. By trade, he's an exercise scientist that believes in the power of the built environment as an avenue for solving some of our most pertinent health challenges (obesity, isolation, etc...). We discuss: 00:00 The active towns concept. 02:03 John's background and career. 05:37 Transportation challenges in Honolulu. 17:41 Empathy and behavior change. 21:59 Mobility choice and infrastructure. 36:54 Evaluating active communities. 39:27 Categorizing active towns. 41:35 Emerging active towns. 42:47 Latent active towns and their transformation. 44:19 Success stories: Oklahoma City and Austin. 56:10 Wide and inclusive infrastructure. 01:00:26 Impact of active commutes. 01:02:39 Reframing mobility and the commuting. 01:05:30 Wrapping up. For context: activetowns.org The YouTube. Connect with John: On YouTube. On Instagram. On LinkedIn. On Patreon.

Jill and I try to define how we train, and we got it, eventually. It is not forced free or balanced training, but latent learning, and we explain what it is, and how it will change the response from your dog. This might be a slower podcast but stick with it.

Eric and Glenn are back from a little summer break, prepping for the 2024 IAI Conference in Reno. They do a final “Where in the Whorld?” game. Then they jump into a recent paper on “Inconclusive” decisions (Swofford, et al. (2024) “Inconclusive Decisions and Error Rates in Forensic Science”, Forensic Science International: Synergy (vol 8; 100472)) authored by several members of NIST. The paper proposes a method for computing and communicating error rates when “inconclusive” decisions are made. The paper also focuses on making clear distinctions between “method performance” versus “method conformance”. The guys discuss their views on the method and the implications the paper may have for fingerprint examiners and their agencies. Swofford, H. , Lund, S. , Iyer, H. , Butler, J. , Soons, J. , Thompson, R. , Desiderio, V. , Jones, J. and Ramotowski, R. (2024), Inconclusive Decisions and Error Rates in Forensic Science, Forensic Science International: Synergy, [online], https://doi.org/10.1016/j.fsisyn.2024.100472, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957335 (Accessed August 31, 2024) Link to open source paper here: https://doi.org/10.1016/j.fsisyn.2024.100472

Welcome back to our weekend Cabral HouseCall shows! This is where we answer our community's wellness, weight loss, and anti-aging questions to help people get back on track! Check out today's questions:    Anonymous: Hi Dr Cabral! I recently rediscovered feta cheese, and I love it , I guess I have some Mediterranean lineage. ;-) What's your view point on Feta / Greek cheese from grass fed and grass finished sheep's and goats? Ideally unpasteurized but I would like to hear your opinion on pasteurized as well. Something we can eat on a regular basis? ;-) I hope it's a yes, lol. Or does it fit better into a flex meal? Thanks for the work you do. Wishing you a fantastic day! :-D     Heather: Hi Dr. Cabral, I purchased the OS-01 Face & OS-1 Eye products based on your recommendation. Upon receipt, I scanned them into the Yuka app, which deciphers product labels & analyzes the health impact of food products & cosmetics. After scanning both items, the Yuka app rates them both as "poor," which surprised me. The app shows that they both include phenoxyethanol, which is used in cosmetics as a preservative to prevent growth of microorganisms. It states that phenoxyethanol has a moderate risk for us since it is a potential endocrine disrupter, is a potential allergen, & can also be an irritant. Can you share your thoughts on this? Also, I read research that states the GHK-Cu peptide is also great for anti-aging. Can you share how the OS-01 peptide & GHK-Cu peptides compare? Thanks!     Angie: Hi Dr Cabral! Thank you for all the work you do, I'm forever grateful. My sex hormone binding globulin has been high for years. No doctor can tell me what I can do to lower it. Because of this, my free testosterone has been super low. I've had no sex drive for years and overall feel no zest for life. For the last few years- My testosterone has been around 20-25 My SHBG has been around 185-215 My free testosterone has consistently been .1 A functional medicine doc had me take stinging nettle twice a day for 3 months and it didn't help. My primary doc told me to use zinc and strength train more and the number came down a little bit, but not much. Maybe I just have to continue with the zinc longer? I do quarterly detox's, did a limited yeast protocol, am active and eat healthy.     Anonymous: I want to start by saying thank you so much for all you do, you really are a blessing to us for all the help you provide! The other day, I heard on your weekend podcast you answer the question of if herpes can be cured, and you said yes. Hearing your response made me very worried and upset that some listeners may not understand the nuance and may misinterpret your answer. While you may get to a point of remission (no symptomatic outbreaks, it is very important to be aware that you still have the herpes virus and are still capable of passing it to others (it is a latent virus so it lives in your body forever). You can still asymptomatically shed viral load, and you are legally (and ethically) required to still disclose this to future intimate partners prior to physical contact.  This is very important to note because there is no reliable way of testing to see if you are shedding viral load asymptomatically or not, and people are entitled to know the risk they are facing, especially since this can be a life changing diagnosis. For example, I caught this from someone who was shedding asymptomatically and didn't know they had it (using a condom), and it really almost ruined my life and severely impacted my mental health. So, in conclusion, whether or not you stop having outbreaks, you still MUST disclose this to any intimate partners     Rana: Hi doctor Cabral, What factors commonly trigger awakenings during the latter half of your sleep cycle. I typically go to bed around 9:30 pm and experience good sleep until 3 or 4 am. However, after that time, I seem to wake up every half hour for the rest of the night. This disrupts my sleep quality, as reflected by the consistently red "restfulness" reading on my Oura ring. It's important to note that I maintain a regular sleep schedule even on weekends, avoid eating close to bedtime (4-5 hours before sleep), and ensure my sleeping environment is dark and cool. I would appreciate any insights you might have on potential causes. Thanks so much, Rana     Thank you for tuning into today's Cabral HouseCall and be sure to check back tomorrow where we answer more of our community's questions!    - - - Show Notes and Resources: StephenCabral.com/3109 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!  

Tuberculosis (TB) was historically called "consumption" due to the dramatic weight loss and wasting away experienced by patients. The modern name "tuberculosis" was first published by J. L. Schönlein in 1832. Today, between 3% and 5% of the U.S. population are estimated to be living with latent TB infection. Contrast that with the worldwide statistics which state that nearly one fourth of the world population has TB infection. In some countries in sub-Saharan Africa and Asia, the annual incidence is several hundred per 100,000 population. In the US, the annual incidence is