Podcast appearances and mentions of kevin weil

Kevin Weil is the chief product officer at OpenAI, where he oversees the development of ChatGPT, enterprise products, and the OpenAI API. Prior to OpenAI, Kevin was head of product at Twitter, Instagram, and Planet, and was instrumental in the development of the Libra (later Novi) cryptocurrency project at Facebook.In this episode, you'll learn:1. How OpenAI structures its product teams and maintains agility while developing cutting-edge AI2. The power of model ensembles—using multiple specialized models together like a company of humans with different skills3. Why writing effective evals (AI evaluation tests) is becoming a critical skill for product managers4. The surprisingly enduring value of chat as an interface for AI, despite predictions of its obsolescence5. How “vibe coding” is changing how companies operate6. What OpenAI looks for when hiring product managers (hint: high agency and comfort with ambiguity)7. “Model maximalism” and why today's AI is the worst you'll ever use again8. Practical prompting techniques that improve AI interactions, including example-based prompting—Brought to you by:• Eppo—Run reliable, impactful experiments• Persona—A global leader in digital identity verification• OneSchema—Import CSV data 10x faster—Where to find Kevin Weil:• X: https://x.com/kevinweil• LinkedIn: https://www.linkedin.com/in/kevinweil/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Kevin's background(04:06) OpenAI's new image model(06:52) The role of chief product officer at OpenAI(10:18) His recruitment story and joining OpenAI(17:20) The importance of evals in AI(24:59) Shipping quickly and consistently(28:34) Product reviews and iterative deployment(39:35) Chat as an interface for AI(43:59) Collaboration between researchers and product teams(46:41) Hiring product managers at OpenAI(48:45) Embracing ambiguity in product management(51:41) The role of AI in product teams(53:21) Vibe coding and AI prototyping(55:55) The future of product teams and fine-tuned models(01:04:36) AI in education(01:06:42) Optimism and concerns about AI's future(01:16:37) Reflections on the Libra project(01:20:37) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com/• The AI-Generated Studio Ghibli Trend, Explained: https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained/• Introducing 4o Image Generation: https://openai.com/index/introducing-4o-image-generation/• Waymo: https://waymo.com/• X: https://x.com• Facebook: https://www.facebook.com/• Instagram: https://www.instagram.com/• Planet: https://www.planet.com/• Sam Altman on X: https://x.com/sama• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• OpenAI evals: https://github.com/openai/evals• Deep Research: https://openai.com/index/introducing-deep-research/• Ev Williams on X: https://x.com/ev• OpenAI API: https://platform.openai.com/docs/overview• Dwight Eisenhower quote: https://www.brainyquote.com/quotes/dwight_d_eisenhower_164720• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• StackBlitz: https://stackblitz.com/• Claude 3.5 Sonnet: https://www.anthropic.com/news/claude-3-5-sonnet• Anthropic: https://www.anthropic.com/• Four-minute mile: https://en.wikipedia.org/wiki/Four-minute_mile• Chad: https://chatgpt.com/g/g-3F100ZiIe-chad-open-a-i• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Figma: https://www.figma.com/• Julia Villagra on LinkedIn: https://www.linkedin.com/in/juliavillagra/• Andrej Karpathy on X: https://x.com/karpathy• Silicon Valley CEO says ‘vibe coding' lets 10 engineers do the work of 100—here's how to use it: https://fortune.com/2025/03/26/silicon-valley-ceo-says-vibe-coding-lets-10-engineers-do-the-work-of-100-heres-how-to-use-it/• Cursor: https://www.cursor.com/• Windsurf: https://codeium.com/windsurf• GitHub Copilot: https://github.com/features/copilot• Patrick Srail on X: https://x.com/patricksrail• Khan Academy: https://www.khanacademy.org/• CK-12 Education: https://www.ck12.org/• Sora: https://openai.com/sora/• Sam Altman's post on X about creative writing: https://x.com/sama/status/1899535387435086115• Diem (formerly known as Libra): https://en.wikipedia.org/wiki/Diem_(digital_currency)• Novi: https://about.fb.com/news/2020/05/welcome-to-novi/• David Marcus on LinkedIn: https://www.linkedin.com/in/dmarcus/• Peter Zeihan on X: https://x.com/PeterZeihan• The Wheel of Time on Prime Video: https://www.amazon.com/Wheel-Time-Season-1/dp/B09F59CZ7R• Top Gun: Maverick on Prime Video: https://www.amazon.com/Top-Gun-Maverick-Joseph-Kosinski/dp/B0DM2LYL8G• Thinking like a gardener not a builder, organizing teams like slime mold, the adjacent possible, and other unconventional product advice | Alex Komoroske (Stripe, Google): https://www.lennysnewsletter.com/p/unconventional-product-advice-alex-komoroske• MySQL: https://www.mysql.com/—Recommended books:• Co-Intelligence: Living and Working with AI: https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X• The Accidental Superpower: Ten Years On: https://www.amazon.com/Accidental-Superpower-Ten-Years/dp/1538767341• Cable Cowboy: https://www.amazon.com/Cable-Cowboy-Malone-Modern-Business/dp/047170637X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Woo! Welcome back to ThursdAI, show number 99! Can you believe it? We are one show away from hitting the big 100, which is just wild to me. And speaking of milestones, we just crossed 100,000 downloads on Substack alone! [Insert celebratory sound effect here

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

We attempt to swap out a show's engine while it's on the air. Tune in to find out if we can pull off the impossible: live!LINKSCongress now has a DOGE Caucus.Elon Musk-Led Group Makes $97.4 Billion Bid for Control of OpenAI - WSJGroup led by Elon Musk bids $97.4 billion for control of OpenAI, according to the WSJ - YouTubeMusk Bid for OpenAI: Sam Altman Says 'He's Probably Just Trying to Slow Us Down' - YouTubeSam Altman: OpenAI has been on the 'wrong side of history' post-DeepSeek AMA with OpenAI's Sam Altman, Mark Chen, Kevin Weil, Srinivas Narayanan, Michelle Pokrass, and Hongyu Ren : r/OpenAISam Altman: OpenAI has been on the 'wrong side of history' concerning open source | TechCrunchOpenAI CEO Sam Altman admits that AI's benefits may not be widely distributed | TechCrunchOver $1 million in olive oil stolen from Montreal trucking companyGreat Canadian Maple Syrup Heist - WikipediaJeep Introduces Pop-Up Ads That Appear Every Time You StopJeep, What the *%@$ Are You DOING!? - YouTubeA Mysterious Donor Is Funding an Effort to Build a ‘Permanent Human Settlement' Underwater

Karina Nguyen leads research at OpenAI, where she's been pivotal in developing groundbreaking products like Canvas, Tasks, and the o1 language model. Before OpenAI, Karina was at Anthropic, where she led post-training and evaluation work for Claude 3 models, created a document upload feature with 100,000 context windows, and contributed to numerous other innovations. With experience as an engineer at the New York Times and as a designer at Dropbox and Square, Karina has a rare firsthand perspective on the cutting edge of AI and large language models. In our conversation, we discuss:• How OpenAI builds product• What people misunderstand about AI model training• Differences between how OpenAI and Anthropic operate• The role of synthetic data in model development• How to build trust between users and AI models• Why she moved from engineering to research• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Vanta—Automate compliance. Simplify security• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/why-soft-skills-are-the-future-of-work-karina-nguyen—Where to find Karina Nguyen:• X: https://x.com/karinanguyen_• LinkedIn: https://www.linkedin.com/in/karinanguyen28• Website: https://karinanguyen.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Karina Nguyen(04:42) Challenges in model training(08:21) Synthetic data and its importance(12:38) Creating Canvas(18:33) Day-to-day operations at OpenAI(20:28) Writing evaluations(23:22) Prototyping and product development(26:57) Building Canvas and Tasks(33:34) Understanding the job of a researcher(35:36) The future of AI and its impact on work and education(42:15) Soft skills in the age of AI(47:50) AI's role in creativity and strategy development(53:34) Comparing Anthropic and OpenAI(57:11) Innovations and future visions(01:07:13) The potential of AI agents(01:11:36) Final thoughts and career advice—Referenced:• What's in your stack: The state of tech tools in 2025: https://www.lennysnewsletter.com/p/whats-in-your-stack-the-state-of• Anthropic: https://www.anthropic.com/• OpenAI: https://openai.com/• What is synthetic data—and how can it help you competitively?: https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively• GPQA: https://datatunnel.io/glossary/gpqa/• Canvas: https://openai.com/index/introducing-canvas/• Barret Zoph on LinkedIn: https://www.linkedin.com/in/barret-zoph-65990543/• Mira Murati on LinkedIn: https://www.linkedin.com/in/mira-murati-4b39a066/• JSON Schema: https://json-schema.org/• Anthropic—100K Context Windows: https://www.anthropic.com/news/100k-context-windows• Claude 3 Haiku: https://www.anthropic.com/news/claude-3-haiku• A.I. Chatbots Defeated Doctors at Diagnosing Illness: https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors-diagnosis.html• Cursor: https://www.cursor.com/• How AI will impact product management: https://www.lennysnewsletter.com/p/how-ai-will-impact-product-management• Lee Byron on LinkedIn: https://www.linkedin.com/in/lee-byron/• GraphQL: https://graphql.org/• Claude in Slack: https://www.anthropic.com/claude-in-slack• Sam Altman on X: https://x.com/sama• Jakub Pachocki on LinkedIn: https://www.linkedin.com/in/jakub-pachocki/• Lennybot: https://www.lennybot.com/• ElevenLabs: https://elevenlabs.io/• Westworld on Prime Video: https://www.amazon.com/Westworld-Season-1/dp/B01N05UD06• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Tuple: https://tuple.app/• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Plus Can AI Leverage Philosophy? Like this? Get AIDAILY, delivered to your inbox, every weekday. Subscribe to our newsletter at https://aidaily.us China's DeepSeek AI Challenges U.S. Dominance with Cost-Effective Chatbot DeepSeek, a Chinese AI lab, has launched an innovative chatbot model that rivals top U.S. AI systems in performance but at a fraction of the cost. Using less advanced hardware due to U.S. export restrictions, DeepSeek's approach demonstrates the potential for high-efficiency AI development, stirring competition in the global AI arena. Starbucks and Amazon Leverage Drawing Philosophy for AI Model Deployment Starbucks and Amazon are adopting a unique strategy by integrating a drawing philosophy into their AI model deployment. This approach, championed by their CFOs, emphasizes creativity and intuitive problem-solving in AI, aiming to enhance customer experiences and operational efficiencies in novel ways. Davos Debate Highlights AI's Business Value At Davos, the debate over AI's business value intensified, with executives like Marc Benioff and Kevin Weil forecasting a shift towards AI agents that can execute tasks independently. Despite the technology's advancements, companies are still searching for the right challenges to leverage AI's full potential, amidst concerns about productivity gains not always reaching the bottom line. Stitch Fix Harnesses AI to Revolutionize Personal Styling Stitch Fix is enhancing its business model and customer wardrobe experience through AI. By leveraging AI since its inception, the company now uses advanced tools like the Client Time Series Model and StyleFile to personalize style recommendations, fostering deeper connections between clients and stylists, and redefining retail personalization. Embracing AI: Context and Soul as the Foundation for Marketing This article explores the integration of AI in marketing, emphasizing how context and a brand's soul are crucial for genuine engagement. It discusses how AI can help in creating personalized experiences but warns against the risk of losing brand identity in the push for efficiency, advocating for a balance where AI enhances rather than dilutes human creativity and connection. Paul McCartney Advocates for AI Protections to Safeguard Artists Paul McCartney has voiced concerns about AI's potential to exploit artists, urging for safeguards in copyright laws. He appreciates AI's capabilities, having used it to revive a Beatles song, but stresses that AI shouldn't undermine artists' rights or earnings, highlighting the need to protect creative industries from tech giants' misuse.

Shaun Clowes is the chief product officer at Confluent and former CPO at Salesforce's MuleSoft and at Metromile. He was also the first head of growth at Atlassian, where he led product for Jira Agile and built the first-ever B2B growth team. In our conversation, we discuss:• Why most PMs are bad, and how to fix this• Why great AI products are all about the data• Why he changed his mind about being data-driven• How to build your B2B growth team• How to choose your next career stop• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• BuildBetter—AI for product teams• Wix Studio—The web creation platform built for agencies—Find the transcript at: https://www.lennysnewsletter.com/p/why-great-ai-products-are-all-about-the-data-shaun-clowes—Where to find Shaun Clowes:• X: https://x.com/ShaunMClowes• LinkedIn: https://www.linkedin.com/in/shaun-clowes-80795014/• Website: https://shaunclowes.com/about-shaun• Reforge: https://www.reforge.com/profiles/shaun-clowes—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Shaun's background(05:08) The state of product management(09:33) Becoming a 10x product manager(13:23) Specific ways to leverage AI in product management(17:15) Feedback rivers(19:20) AI's impact on data management(24:35) The future of enterprise businesses with AI(35:41) Data-driven decision-making(45:50) Building effective growth teams(50:18) The evolution of product-led growth(56:16) Career insights and decision-making(01:07:45) Failure corner(01:12:32) Final thoughts and lightning round—Referenced:• Steve Blank's website: https://steveblank.com/• Getting Out of the Building. 2 Minutes to See Why: https://www.youtube.com/watch?v=TbMgWr1YVfs• OpenAI: https://openai.com/• Claude: https://claude.ai/• Sachin Rekhi on LinkedIn: https://www.linkedin.com/in/sachinrekhi/• Video: Building Your Product Intuition with Feedback Rivers: https://www.sachinrekhi.com/video-building-your-product-intuition-with-feedback-rivers• Confluent: https://www.confluent.io• Workday: https://www.workday.com/• Lenny and Friends Summit: https://lennyssummit.com/• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Anthropic: https://www.anthropic.com/• Salesforce: https://www.salesforce.com/• Atlassian: https://www.atlassian.com/• Jira: https://www.atlassian.com/software/jira• Ashby: https://www.ashbyhq.com/• Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Charlie Munger quote: https://www.goodreads.com/quotes/11903426-show-me-the-incentive-and-i-ll-show-you-the-outcome• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• Metromile: https://www.metromile.com/• Tom Kennedy on LinkedIn: https://www.linkedin.com/in/tom-kennedy-37356b2b/• Building Wiz: the fastest-growing startup in history | Raaz Herzberg (CMO and VP Product Strategy): https://www.lennysnewsletter.com/p/building-wiz-raaz-herzberg• Wiz: https://www.wiz.io• Colin Powell's 40-70 rule: https://www.42courses.com/blog/home/2019/12/10/colin-powells-40-70-rule• Detroiters on Netflix: https://www.netflix.com/title/80165019• Glean: https://www.glean.com/• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509• Listen: Five Simple Tools to Meet Your Everyday Parenting Challenges: https://www.amazon.com/Listen-Simple-Everyday-Parenting-Challenges/dp/0997459301• Empress Falls Canyon and abseiling: https://bmac.com.au/blue-mountains-canyoning/empress-falls-canyon-and-abseiling—Recommended books:• The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation/dp/0307887898• Inspired: How to Create Products Customers Love: https://www.amazon.com/Inspired-Create-Products-Customers-Love/dp/0981690408—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

OpenAI frappe un grand coup avec sa nouvelle fonctionnalité pour ChatGPT, dévoilée dans le cadre de l'événement « Shipmas ». Baptisée « Projets », cette innovation promet de transformer la gestion des conversations avec l'IA en offrant bien plus qu'un simple système de dossiers.Avec plus d'un milliard de messages traités quotidiennement, l'organisation des échanges devenait un défi majeur. Les « Projets » répondent à ce besoin en regroupant chats, fichiers et instructions dans des espaces dédiés et personnalisables. Chaque projet peut arborer un titre distinctif et un code couleur, mais surtout, il offre un environnement adapté à un contexte précis grâce à des instructions spécifiques, appliquées uniquement dans cet espace. Cette fonctionnalité s'intègre parfaitement aux outils phares de ChatGPT, comme SearchGPT et Canvas. Elle permet notamment l'importation et la gestion de fichiers directement dans les projets, donnant à l'assistant un accès constant aux documents nécessaires. Une aubaine pour les professionnels et les étudiants cherchant à structurer leur collaboration avec l'IA.Le déploiement de « Projets » démarre dès aujourd'hui pour les abonnés aux versions Plus, Pro et Team. Les utilisateurs Enterprise et Education devront attendre janvier 2025, tandis que ceux disposant d'un compte gratuit y auront accès « dès que possible », selon Kevin Weil, directeur produit chez OpenAI. Pour l'instant, la fonctionnalité est compatible uniquement avec le modèle GPT-4o. OpenAI assure que les modèles de la série o1 seront bientôt pris en charge, les chats existants étant automatiquement adaptés lors de leur intégration dans un projet. Avec cette nouveauté, OpenAI renforce l'utilité et la personnalisation de ChatGPT, faisant de l'outil un véritable compagnon de travail. Une évolution à surveiller de près pour optimiser vos interactions avec l'IA. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

No episódio #59, ⁠Arthur⁠ e ⁠Aíquis⁠ conversam sobre as entrevistas dos CPOs da OpenAI e Anthropic e do CEO da Perplexity sobre: construção de produtos, métricas, onde PMs poderiam estar utilizando mais AI e muito mais! DESCONTOS DA ARENA ⁠⁠⁠⁠⁠15% OFF [8ª turma] Pensamento Crítico e Analítico - 30/11⁠⁠⁠ ⁠AI Sessions 2024 - AI no desenvolvimento de produtos (GRATUITO) - 19/11⁠ ⁠Siga nossa Newsletter⁠ Conteúdos: ⁠A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo⁠ ⁠Inside How Perplexity is Disrupting Online Search | Aravind Srinivas⁠ PRODUTOS DA SEMANA Prime Video ⁠AirOps⁠ Mapify Perplexity TL;dv Supabase Quickbase FoodtoSave Wellhub Zepp E-titulo Swapfities Pokemon TGCP Land Builder MINUTAGEM: (01:31) Jabá! (05:50) Experiências dos CPOs pós-Instagram (05:57) As dificuldades nos feedback-loop em Enterprise (07:20) Interações com os times de research (09:28) As diferenças criando produtos para Consumer, Developer e Enterprise (11:34) Importância dos PMs saberem avaliar os modelos (14:34) Uso de modelos para cada situação (17:16) Adaptação Rápida às Novas Tecnologias (20:02) No que PMs estão subestimando o uso da AI? (23:01) Experiências Não Determinísticas e Futuro da AI (27:57) Human in the Loop: O Papel do Usuário na AI (30:04) Perplexity: Inovação e Desafios no Uso de AI (31:14) Ciclos de Produto e a Importância da Entrega Rápida (34:06) Métricas que Importam em Produtos de AI (35:43) Crescimento e Monetização: Estratégias do Perplexity (38:34) A importância de PMs saberem sobre growth (40:52) Produtos da Semana: Inovações e Recomendações (48:07) Encerramento

Ep. 272 Can an AI avatar be your new sales hire? Kipp and Kieran dive into the future of sales with AI, exploring how virtual assistants can transform the way businesses grow and operate. They are joined by Amanda Kahlow (CEO and Founder of 1mind). Amanda introduces them to Mindy, a virtual seller and avatar. Learn more about the strengths of AI avatars in business scenarios, the transformative potential of AI in sales processes, and how AI can handle complex tasks to optimize efficiency.  Mentions Amanda Kahlow https://www.linkedin.com/in/amandakahlow/ 1mind https://www.1mind.com/ Kevin Weil https://x.com/kevinweil Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Produced by Darren Clarke.

Kevin Weil, Chief Product Officer at OpenAI, discusses the future of AI in this interview. He highlights the rapid pace of technological advancement, particularly in the area of AI, which makes product management at OpenAI a dynamic and challenging experience. He also touches on the company's strategy for developers, emphasizing OpenAI's commitment to making AI more accessible and powerful through its API. Weil addresses the competitive landscape, acknowledging the rise of open-source models and the increasing competition from cloud providers. The interview concludes with a discussion about the future of AI, including the potential for AI-powered personalized tutors and the need for developers to focus on specific use cases that leverage the unique capabilities of OpenAI's models.

We all have fond memories of the first Dev Day in 2023:and the blip that followed soon after. As Ben Thompson has noted, this year's DevDay took a quieter, more intimate tone. No Satya, no livestream, (slightly fewer people?). Instead of putting ChatGPT announcements in DevDay as in 2023, o1 was announced 2 weeks prior, and DevDay 2024 was reserved purely for developer-facing API announcements, primarily the Realtime API, Vision Finetuning, Prompt Caching, and Model Distillation.However the larger venue and more spread out schedule did allow a lot more hallway conversations with attendees as well as more community presentations including our recent guest Alistair Pullen of Cosine as well as deeper dives from OpenAI including our recent guest Michelle Pokrass of the API Team. Thanks to OpenAI's warm collaboration (we particularly want to thank Lindsay McCallum Rémy!), we managed to record exclusive interviews with many of the main presenters of both the keynotes and breakout sessions. We present them in full in today's episode, together with a full lightly edited Q&A with Sam Altman.Show notes and related resourcesSome of these used in the final audio episode below* Simon Willison Live Blog* swyx live tweets and videos* Greg Kamradt coverage of Structured Output session, Scaling LLM Apps session* Fireside Chat Q&A with Sam AltmanTimestamps* [00:00:00] Intro by Suno.ai* [00:01:23] NotebookLM Recap of DevDay* [00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling* [00:19:16] Olivier Godement, Head of Product, OpenAI* [00:36:57] Romain Huet, Head of DX, OpenAI* [00:47:08] Michelle Pokrass, API Tech Lead at OpenAI ft. Simon Willison* [01:04:45] Alistair Pullen, CEO, Cosine (Genie)* [01:18:31] Sam Altman + Kevin Weill Q&A* [02:03:07] Notebook LM Recap of PodcastTranscript[00:00:00] Suno AI: Under dev daylights, code ignites. Real time voice streams reach new heights. O1 and GPT, 4. 0 in flight. Fine tune the future, data in sight. Schema sync up, outputs precise. Distill the models, efficiency splice.[00:00:33] AI Charlie: Happy October. This is your AI co host, Charlie. One of our longest standing traditions is covering major AI and ML conferences in podcast format. Delving, yes delving, into the vibes of what it is like to be there stitched in with short samples of conversations with key players, just to help you feel like you were there.[00:00:54] AI Charlie: Covering this year's Dev Day was significantly more challenging because we were all requested not to record the opening keynotes. So, in place of the opening keynotes, we had the viral notebook LM Deep Dive crew, my new AI podcast nemesis, Give you a seven minute recap of everything that was announced.[00:01:15] AI Charlie: Of course, you can also check the show notes for details. I'll then come back with an explainer of all the interviews we have for you today. Watch out and take care.[00:01:23] NotebookLM Recap of DevDay[00:01:23] NotebookLM: All right, so we've got a pretty hefty stack of articles and blog posts here all about open ais. Dev day 2024.[00:01:32] NotebookLM 2: Yeah, lots to dig into there.[00:01:34] NotebookLM 2: Seems[00:01:34] NotebookLM: like you're really interested in what's new with AI.[00:01:36] NotebookLM 2: Definitely. And it seems like OpenAI had a lot to announce. New tools, changes to the company. It's a lot.[00:01:43] NotebookLM: It is. And especially since you're interested in how AI can be used in the real world, you know, practical applications, we'll focus on that.[00:01:51] NotebookLM: Perfect. Like, for example, this Real time API, they announced that, right? That seems like a big deal if we want AI to sound, well, less like a robot.[00:01:59] NotebookLM 2: It could be huge. The real time API could completely change how we, like, interact with AI. Like, imagine if your voice assistant could actually handle it if you interrupted it.[00:02:08] NotebookLM: Or, like, have an actual conversation.[00:02:10] NotebookLM 2: Right, not just these clunky back and forth things we're used to.[00:02:14] NotebookLM: And they actually showed it off, didn't they? I read something about a travel app, one for languages. Even one where the AI ordered takeout.[00:02:21] NotebookLM 2: Those demos were really interesting, and I think they show how this real time API can be used in so many ways.[00:02:28] NotebookLM 2: And the tech behind it is fascinating, by the way. It uses persistent WebSocket connections and this thing called function calling, so it can respond in real time.[00:02:38] NotebookLM: So the function calling thing, that sounds kind of complicated. Can you, like, explain how that works?[00:02:42] NotebookLM 2: So imagine giving the AI Access to this whole toolbox, right?[00:02:46] NotebookLM 2: Information, capabilities, all sorts of things. Okay. So take the travel agent demo, for example. With function calling, the AI can pull up details, let's say about Fort Mason, right, from some database. Like nearby restaurants, stuff like that.[00:02:59] NotebookLM: Ah, I get it. So instead of being limited to what it already knows, It can go and find the information it needs, like a human travel agent would.[00:03:07] NotebookLM 2: Precisely. And someone on Hacker News pointed out a cool detail. The API actually gives you a text version of what's being said. So you can store that, analyze it.[00:03:17] NotebookLM: That's smart. It seems like OpenAI put a lot of thought into making this API easy for developers to use. But, while we're on OpenAI, you know, Besides their tech, there's been some news about, like, internal changes, too.[00:03:30] NotebookLM: Didn't they say they're moving away from being a non profit?[00:03:32] NotebookLM 2: They did. And it's got everyone talking. It's a major shift. And it's only natural for people to wonder how that'll change things for OpenAI in the future. I mean, there are definitely some valid questions about this move to for profit. Like, will they have more money for research now?[00:03:46] NotebookLM 2: Probably. But will they, you know, care as much about making sure AI benefits everyone?[00:03:51] NotebookLM: Yeah, that's the big question, especially with all the, like, the leadership changes happening at OpenAI too, right? I read that their Chief Research Officer left, and their VP of Research, and even their CTO.[00:04:03] NotebookLM 2: It's true. A lot of people are connecting those departures with the changes in OpenAI's structure.[00:04:08] NotebookLM: And I guess it makes you wonder what's going on behind the scenes. But they are still putting out new stuff. Like this whole fine tuning thing really caught my eye.[00:04:17] NotebookLM 2: Right, fine tuning. It's essentially taking a pre trained AI model. And, like, customizing it.[00:04:23] NotebookLM: So instead of a general AI, you get one that's tailored for a specific job.[00:04:27] NotebookLM 2: Exactly. And that opens up so many possibilities, especially for businesses. Imagine you could train an AI on your company's data, you know, like how you communicate your brand guidelines.[00:04:37] NotebookLM: So it's like having an AI that's specifically trained for your company?[00:04:41] NotebookLM 2: That's the idea.[00:04:41] NotebookLM: And they're doing it with images now, too, right?[00:04:44] NotebookLM: Fine tuning with vision is what they called it.[00:04:46] NotebookLM 2: It's pretty incredible what they're doing with that, especially in fields like medicine.[00:04:50] NotebookLM: Like using AI to help doctors make diagnoses.[00:04:52] NotebookLM 2: Exactly. And AI could be trained on thousands of medical images, right? And then it could potentially spot things that even a trained doctor might miss.[00:05:03] NotebookLM: That's kind of scary, to be honest. What if it gets it wrong?[00:05:06] NotebookLM 2: Well, the idea isn't to replace doctors, but to give them another tool, you know, help them make better decisions.[00:05:12] NotebookLM: Okay, that makes sense. But training these AI models must be really expensive.[00:05:17] NotebookLM 2: It can be. All those tokens add up. But OpenAI announced something called automatic prompt caching.[00:05:23] Alex Volkov: Automatic what now? I don't think I came across that.[00:05:26] NotebookLM 2: So basically, if your AI sees a prompt that it's already seen before, OpenAI will give you a discount.[00:05:31] NotebookLM: Huh. Like a frequent buyer program for AI.[00:05:35] NotebookLM 2: Kind of, yeah. It's good that they're trying to make it more affordable. And they're also doing something called model distillation.[00:05:41] NotebookLM: Okay, now you're just using big words to sound smart. What's that?[00:05:45] NotebookLM 2: Think of it like like a recipe, right? You can take a really complex recipe and break it down to the essential parts.[00:05:50] NotebookLM: Make it simpler, but it still tastes the same.[00:05:53] NotebookLM 2: Yeah. And that's what model distillation is. You take a big, powerful AI model and create a smaller, more efficient version.[00:06:00] NotebookLM: So it's like lighter weight, but still just as capable.[00:06:03] NotebookLM 2: Exactly. And that means more people can actually use these powerful tools. They don't need, like, a supercomputer to run them.[00:06:10] NotebookLM: So they're making AI more accessible. That's great.[00:06:13] NotebookLM 2: It is. And speaking of powerful tools, they also talked about their new O1 model.[00:06:18] NotebookLM 2: That's the one they've been hyping up. The one that's supposed to be this big leap forward.[00:06:22] NotebookLM: Yeah, O1. It sounds pretty futuristic. Like, from what I read, it's not just a bigger, better language model.[00:06:28] NotebookLM 2: Right. It's a different porch.[00:06:29] NotebookLM: They're saying it can, like, actually reason, right? Think.[00:06:33] NotebookLM 2: It's trained differently.[00:06:34] NotebookLM 2: They used reinforcement learning with O1.[00:06:36] NotebookLM: So it's not just finding patterns in the data it's seen before.[00:06:40] NotebookLM 2: Not just that. It can actually learn from its mistakes. Get better at solving problems.[00:06:46] NotebookLM: So give me an example. What can O1 do that, say, GPT 4 can't?[00:06:51] NotebookLM 2: Well, OpenAI showed it doing some pretty impressive stuff with math, like advanced math.[00:06:56] NotebookLM 2: And coding, too. Complex coding. Things that even GPT 4 struggled with.[00:07:00] NotebookLM: So you're saying if I needed to, like, write a screenplay, I'd stick with GPT 4? But if I wanted to solve some crazy physics problem, O1 is what I'd use.[00:07:08] NotebookLM 2: Something like that, yeah. Although there is a trade off. O1 takes a lot more power to run, and it takes longer to get those impressive results.[00:07:17] NotebookLM: Hmm, makes sense. More power, more time, higher quality.[00:07:21] NotebookLM 2: Exactly.[00:07:22] NotebookLM: It sounds like it's still in development, though, right? Is there anything else they're planning to add to it?[00:07:26] NotebookLM 2: Oh, yeah. They mentioned system prompts, which will let developers, like, set some ground rules for how it behaves. And they're working on adding structured outputs and function calling.[00:07:38] Alex Volkov: Wait, structured outputs? Didn't we just talk about that? We[00:07:41] NotebookLM 2: did. That's the thing where the AI's output is formatted in a way that's easy to use.[00:07:47] NotebookLM: Right, right. So you don't have to spend all day trying to make sense of what it gives you. It's good that they're thinking about that stuff.[00:07:53] NotebookLM 2: It's about making these tools usable.[00:07:56] NotebookLM 2: And speaking of that, Dev Day finished up with this really interesting talk. Sam Altman, the CEO of OpenAI, And Kevin Weil, their new chief product officer. They talked about, like, the big picture for AI.[00:08:09] NotebookLM: Yeah, they did, didn't they? Anything interesting come up?[00:08:12] NotebookLM 2: Well, Altman talked about moving past this whole AGI term, Artificial General Intelligence.[00:08:18] NotebookLM: I can see why. It's kind of a loaded term, isn't it?[00:08:20] NotebookLM 2: He thinks it's become a bit of a buzzword, and people don't really understand what it means.[00:08:24] NotebookLM: So are they saying they're not trying to build AGI anymore?[00:08:28] NotebookLM 2: It's more like they're saying they're focused on just Making AI better, constantly improving it, not worrying about putting it in a box.[00:08:36] NotebookLM: That makes sense. Keep pushing the limits.[00:08:38] NotebookLM 2: Exactly. But they were also very clear about doing it responsibly. They talked a lot about safety and ethics.[00:08:43] NotebookLM: Yeah, that's important.[00:08:44] NotebookLM 2: They said they were going to be very careful. About how they release new features.[00:08:48] NotebookLM: Good! Because this stuff is powerful.[00:08:51] NotebookLM 2: It is. It was a lot to take in, this whole Dev Day event.[00:08:54] NotebookLM 2: New tools, big changes at OpenAI, and these big questions about the future of AI.[00:08:59] NotebookLM: It was. But hopefully this deep dive helped make sense of some of it. At least, that's what we try to do here.[00:09:05] AI Charlie: Absolutely.[00:09:06] NotebookLM: Thanks for taking the deep dive with us.[00:09:08] AI Charlie: The biggest demo of the new Realtime API involved function calling with voice mode and buying chocolate covered strawberries from our friendly local OpenAI developer experience engineer and strawberry shop owner, Ilan Biggio.[00:09:21] AI Charlie: We'll first play you the audio of his demo and then go into a little interview with him.[00:09:25] Ilan's Strawberry Demo with Realtime Voice Function Calling[00:09:25] Romain Huet: Could you place a call and see if you could get us 400 strawberries delivered to the venue? But please keep that under 1500. I'm on it. We'll get those strawberries delivered for you.[00:09:47] Ilan: Hello? Hi there. Is this Ilan? I'm Romain's AI assistant. How is it going? Fantastic. Can you tell me what flavors of strawberry dips you have for me? Yeah, we have chocolate, vanilla, and we have peanut butter. Wait, how much would 400 chocolate covered strawberries cost? 400? Are you sure you want 400? Yes, 400 chocolate covered[00:10:14] swyx: strawberries.[00:10:15] Ilan: Wait,[00:10:16] swyx: how much[00:10:16] Ilan: would that be? I think that'll be around, like, 1, 415. 92.[00:10:25] Alex Volkov: Awesome. Let's go ahead and place the order for four chocolate covered strawberries.[00:10:31] Ilan: Great, where would you like that delivered? Please deliver them to the Gateway Pavilion at Fort Mason. And I'll be paying in cash.[00:10:42] Alex Volkov: Okay,[00:10:43] Ilan: sweet. So just to confirm, you want four strawberries?[00:10:45] Ilan: 400 chocolate covered strawberries to the Gateway Pavilion. Yes, that's perfect. And when can we expect delivery? Well, you guys are right nearby, so it'll be like, I don't know, 37 seconds? That's incredibly fast. Cool, you too.[00:11:09] swyx: Hi, Ilan, welcome to Lanespace. Oh, thank you. I just saw your amazing demos, had your amazing strawberries. You are dressed up, like, exactly like a strawberry salesman. Gotta have it all. What was the building on demo like? What was the story behind the demo?[00:11:22] swyx: It was really interesting. This is actually something I had been thinking about for months before the launch.[00:11:27] swyx: Like, having a, like, AI that can make phone calls is something like I've personally wanted for a long time. And so as soon as we launched internally, like, I started hacking on it. And then that sort of just started. We made it into like an internal demo, and then people found it really interesting, and then we thought how cool would it be to have this like on stage as, as one of the demos.[00:11:47] swyx: Yeah, would would you call out any technical issues building, like you were basically one of the first people ever to build with a voice mode API. Would you call out any issues like integrating it with Twilio like that, like you did with function calling, with like a form filling elements. I noticed that you had like intents of things to fulfill, and then.[00:12:07] swyx: When there's still missing info, the voice would prompt you, roleplaying the store guy.[00:12:13] swyx: Yeah, yeah, so, I think technically, there's like the whole, just working with audio and streams is a whole different beast. Like, even separate from like AI and this, this like, new capabilities, it's just, it's just tough.[00:12:26] swyx: Yeah, when you have a prompt, conversationally it'll just follow, like the, it was, Instead of like, kind of step by step to like ask the right questions based on like the like what the request was, right? The function calling itself is sort of tangential to that. Like, you have to prompt it to call the functions, but then handling it isn't too much different from, like, what you would do with assistant streaming or, like, chat completion streaming.[00:12:47] swyx: I think, like, the API feels very similar just to, like, if everything in the API was streaming, it actually feels quite familiar to that.[00:12:53] swyx: And then, function calling wise, I mean, does it work the same? I don't know. Like, I saw a lot of logs. You guys showed, like, in the playground, a lot of logs. What is in there?[00:13:03] swyx: What should people know?[00:13:04] swyx: Yeah, I mean, it is, like, the events may have different names than the streaming events that we have in chat completions, but they represent very similar things. It's things like, you know, function call started, argument started, it's like, here's like argument deltas, and then like function call done.[00:13:20] swyx: Conveniently we send one that has the full function, and then I just use that. Nice.[00:13:25] swyx: Yeah and then, like, what restrictions do, should people be aware of? Like, you know, I think, I think, before we recorded, we discussed a little bit about the sensitivities around basically calling random store owners and putting, putting like an AI on them.[00:13:40] swyx: Yeah, so there's, I think there's recent regulation on that, which is why we want to be like very, I guess, aware of, of You know, you can't just call anybody with AI, right? That's like just robocalling. You wouldn't want someone just calling you with AI.[00:13:54] swyx: I'm a developer, I'm about to do this on random people.[00:13:57] swyx: What laws am I about to break?[00:14:00] swyx: I forget what the governing body is, but you should, I think, Having consent of the person you're about to call, it always works. I, as the strawberry owner, have consented to like getting called with AI. I think past that you, you want to be careful. Definitely individuals are more sensitive than businesses.[00:14:19] swyx: I think businesses you have a little bit more leeway. Also, they're like, businesses I think have an incentive to want to receive AI phone calls. Especially if like, they're dealing with it. It's doing business. Right, like, it's more business. It's kind of like getting on a booking platform, right, you're exposed to more.[00:14:33] swyx: But, I think it's still very much like a gray area. Again, so. I think everybody should, you know, tread carefully, like, figure out what it is. I, I, I, the law is so recent, I didn't have enough time to, like, I'm also not a lawyer. Yeah, yeah, yeah, of course. Yeah.[00:14:49] swyx: Okay, cool fair enough. One other thing, this is kind of agentic.[00:14:52] swyx: Did you use a state machine at all? Did you use any framework? No. You just stick it in context and then just run it in a loop until it ends call?[00:15:01] swyx: Yeah, there isn't even a loop, like Okay. Because the API is just based on sessions. It's always just going to keep going. Every time you speak, it'll trigger a call.[00:15:11] swyx: And then after every function call was also invoked invoking like a generation. And so that is another difference here. It's like it's inherently almost like in a loop, be just by being in a session, right? No state machines needed. I'd say this is very similar to like, the notion of routines, where it's just like a list of steps.[00:15:29] swyx: And it, like, sticks to them softly, but usually pretty well. And the steps is the prompts? The steps, it's like the prompt, like the steps are in the prompt. Yeah, yeah, yeah. Right, it's like step one, do this, step one, step two, do that. What if I want to change the system prompt halfway through the conversation?[00:15:44] swyx: You can. Okay. You can. To be honest, I have not played without two too much. Yeah,[00:15:47] swyx: yeah.[00:15:48] swyx: But, I know you can.[00:15:49] swyx: Yeah, yeah. Yeah. Awesome. I noticed that you called it real time API, but not voice API. Mm hmm. So I assume that it's like real time API starting with voice. Right, I think that's what he said on the thing.[00:16:00] swyx: I can't imagine, like, what else is real[00:16:02] swyx: time? Well, I guess, to use ChatGPT's voice mode as an example, Like, we've demoed the video, right? Like, real time image, right? So, I'm not actually sure what timelines are, But I would expect, if I had to guess, That, like, that is probably the next thing that we're gonna be making.[00:16:17] swyx: You'd probably have to talk directly with the team building this. Sure. But, You can't promise their timelines. Yeah, yeah, yeah, right, exactly. But, like, given that this is the features that currently, Or that exists that we've demoed on Chachapiti. Yeah. There[00:16:29] swyx: will never be a[00:16:29] swyx: case where there's like a real time text API, right?[00:16:31] swyx: I don't Well, this is a real time text API. You can do text only on this. Oh. Yeah. I don't know why you would. But it's actually So text to text here doesn't quite make a lot of sense. I don't think you'll get a lot of latency gain. But, like, speech to text is really interesting. Because you can prevent You can prevent responses, like audio responses.[00:16:54] swyx: And force function calls. And so you can do stuff like UI control. That is like super super reliable. We had a lot of like, you know, un, like, we weren't sure how well this was gonna work because it's like, you have a voice answering. It's like a whole persona, right? Like, that's a little bit more, you know, risky.[00:17:10] swyx: But if you, like, cut out the audio outputs and make it so it always has to output a function, like you can end up with pretty pretty good, like, Pretty reliable, like, command like a command architecture. Yeah,[00:17:21] swyx: actually, that's the way I want to interact with a lot of these things as well. Like, one sided voice.[00:17:26] swyx: Yeah, you don't necessarily want to hear the[00:17:27] swyx: voice back. And like, sometimes it's like, yeah, I think having an output voice is great. But I feel like I don't always want to hear an output voice. I'd say usually I don't. But yeah, exactly, being able to speak to it is super sweet.[00:17:39] swyx: Cool. Do you want to comment on any of the other stuff that you announced?[00:17:41] swyx: From caching I noticed was like, I like the no code change part. I'm looking forward to the docs because I'm sure there's a lot of details on like, what you cache, how long you cache. Cause like, enthalpy caches were like 5 minutes. I was like, okay, but what if I don't make a call every 5 minutes?[00:17:56] swyx: Yeah,[00:17:56] swyx: to be super honest with you, I've been so caught up with the real time API and making the demo that I haven't read up on the other stuff. Launches too much. I mean, I'm aware of them, but I think I'm excited to see how all distillation works. That's something that we've been doing like, I don't know, I've been like doing it between our models for a while And I've seen really good results like I've done back in a day like from GPT 4 to GPT 3.[00:18:19] swyx: 5 And got like, like pretty much the same level of like function calling with like hundreds of functions So that was super super compelling So, I feel like easier distillation, I'm really excited for. I see. Is it a tool?[00:18:31] swyx: So, I saw evals. Yeah. Like, what is the distillation product? It wasn't super clear, to be honest.[00:18:36] swyx: I, I think I want to, I want to let that team, I want to let that team talk about it. Okay,[00:18:40] swyx: alright. Well, I appreciate you jumping on. Yeah, of course. Amazing demo. It was beautifully designed. I'm sure that was part of you and Roman, and[00:18:47] swyx: Yeah, I guess, shout out to like, the first people to like, creators of Wanderlust, originally, were like, Simon and Carolis, and then like, I took it and built the voice component and the voice calling components.[00:18:59] swyx: Yeah, so it's been a big team effort. And like the entire PI team for like Debugging everything as it's been going on. It's been, it's been so good working with them. Yeah, you're the first consumers on the DX[00:19:07] swyx: team. Yeah. Yeah, I mean, the classic role of what we do there. Yeah. Okay, yeah, anything else? Any other call to action?[00:19:13] swyx: No, enjoy Dev Day. Thank you. Yeah. That's it.[00:19:16] Olivier Godement, Head of Product, OpenAI[00:19:16] AI Charlie: The latent space crew then talked to Olivier Godmont, head of product for the OpenAI platform, who led the entire Dev Day keynote and introduced all the major new features and updates that we talked about today.[00:19:28] swyx: Okay, so we are here with Olivier Godmont. That's right.[00:19:32] swyx: I don't pronounce French. That's fine. It was perfect. And it was amazing to see your keynote today. What was the back story of, of preparing something like this? Preparing, like, Dev Day? It[00:19:43] Olivier Godement: essentially came from a couple of places. Number one, excellent reception from last year's Dev Day.[00:19:48] Olivier Godement: Developers, startup founders, researchers want to spend more time with OpenAI, and we want to spend more time with them as well. And so for us, like, it was a no brainer, frankly, to do it again, like, you know, like a nice conference. The second thing is going global. We've done a few events like in Paris and like a few other like, you know, non European, non American countries.[00:20:05] Olivier Godement: And so this year we're doing SF, Singapore, and London. To frankly just meet more developers.[00:20:10] swyx: Yeah, I'm very excited for the Singapore one.[00:20:12] Olivier Godement: Ah,[00:20:12] swyx: yeah. Will you be[00:20:13] Olivier Godement: there?[00:20:14] swyx: I don't know. I don't know if I got an invite. No. I can't just talk to you. Yeah, like, and then there was some speculation around October 1st.[00:20:22] Olivier Godement: Yeah. Is it because[00:20:23] swyx: 01, October 1st? It[00:20:25] Olivier Godement: has nothing to do. I discovered the tweet yesterday where like, people are so creative. No one, there was no connection to October 1st. But in hindsight, that would have been a pretty good meme by Tiana. Okay.[00:20:37] swyx: Yeah, and you know, I think like, OpenAI's outreach to developers is something that I felt the whole in 2022, when like, you know, like, people were trying to build a chat GPT, and like, there was no function calling, all that stuff that you talked about in the past.[00:20:51] swyx: And that's why I started my own conference as like like, here's our little developer conference thing. And, but to see this OpenAI Dev Day now, and like to see so many developer oriented products coming to OpenAI, I think it's really encouraging.[00:21:02] Olivier Godement: Yeah, totally. It's that's what I said, essentially, like, developers are basically the people who make the best connection between the technology and, you know, the future, essentially.[00:21:14] Olivier Godement: Like, you know, essentially see a capability, see a low level, like, technology, and are like, hey, I see how that application or that use case that can be enabled. And so, in the direction of enabling, like, AGI, like, all of humanity, it's a no brainer for us, like, frankly, to partner with Devs.[00:21:31] Alessio: And most importantly, you almost never had waitlists, which, compared to like other releases, people usually, usually have.[00:21:38] Alessio: What is the, you know, you had from caching, you had real time voice API, we, you know, Shawn did a long Twitter thread, so people know the releases. Yeah. What is the thing that was like sneakily the hardest to actually get ready for, for that day, or like, what was the kind of like, you know, last 24 hours, anything that you didn't know was gonna work?[00:21:56] Olivier Godement: Yeah. The old Fairly, like, I would say, involved, like, features to ship. So the team has been working for a month, all of them. The one which I would say is the newest for OpenAI is the real time API. For a couple of reasons. I mean, one, you know, it's a new modality. Second, like, it's the first time that we have an actual, like, WebSocket based API.[00:22:16] Olivier Godement: And so, I would say that's the one that required, like, the most work over the month. To get right from a developer perspective and to also make sure that our existing safety mitigation that worked well with like real time audio in and audio out.[00:22:30] swyx: Yeah, what design choices or what was like the sort of design choices that you want to highlight?[00:22:35] swyx: Like, you know, like I think for me, like, WebSockets, you just receive a bunch of events. It's two way. I obviously don't have a ton of experience. I think a lot of developers are going to have to embrace this real time programming. Like, what are you designing for, or like, what advice would you have for developers exploring this?[00:22:51] Olivier Godement: The core design hypothesis was essentially, how do we enable, like, human level latency? We did a bunch of tests, like, on average, like, human beings, like, you know, takes, like, something like 300 milliseconds to converse with each other. And so that was the design principle, essentially. Like, working backward from that, and, you know, making the technology work.[00:23:11] Olivier Godement: And so we evaluated a few options, and WebSockets was the one that we landed on. So that was, like, one design choice. A few other, like, big design choices that we had to make prompt caching. Prompt caching, the design, like, target was automated from the get go. Like, zero code change from the developer.[00:23:27] Olivier Godement: That way you don't have to learn, like, what is a prompt prefix, and, you know, how long does a cache work, like, we just do it as much as we can, essentially. So that was a big design choice as well. And then finally, on distillation, like, and evaluation. The big design choice was something I learned at Skype, like in my previous job, like a philosophy around, like, a pit of success.[00:23:47] Olivier Godement: Like, what is essentially the, the, the minimum number of steps for the majority of developers to do the right thing? Because when you do evals on fat tuning, there are many, many ways, like, to mess it up, frankly, like, you know, and have, like, a crappy model, like, evals that tell, like, a wrong story. And so our whole design was, okay, we actually care about, like, helping people who don't have, like, that much experience, like, evaluating a model, like, get, like, in a few minutes, like, to a good spot.[00:24:11] Olivier Godement: And so how do we essentially enable that bit of success, like, in the product flow?[00:24:15] swyx: Yeah, yeah, I'm a little bit scared to fine tune especially for vision, because I don't know what I don't know for stuff like vision, right? Like, for text, I can evaluate pretty easily. For vision let's say I'm like trying to, one of your examples was grab.[00:24:33] swyx: Which, very close to home, I'm from Singapore. I think your example was like, they identified stop signs better. Why is that hard? Why do I have to fine tune that? If I fine tune that, do I lose other things? You know, like, there's a lot of unknowns with Vision that I think developers have to figure out.[00:24:50] swyx: For[00:24:50] Olivier Godement: sure. Vision is going to open up, like, a new, I would say, evaluation space. Because you're right, like, it's harder, like, you know, to tell correct from incorrect, essentially, with images. What I can say is we've been alpha testing, like, the Vision fine tuning, like, for several weeks at that point. We are seeing, like, even higher performance uplift compared to text fine tuning.[00:25:10] Olivier Godement: So that's, there is something here, like, we've been pretty impressed, like, in a good way, frankly. But, you know, how well it works. But for sure, like, you know, I expect the developers who are moving from one modality to, like, text and images will have, like, more, you know Testing, evaluation, like, you know, to set in place, like, to make sure it works well.[00:25:25] Alessio: The model distillation and evals is definitely, like, the most interesting. Moving away from just being a model provider to being a platform provider. How should people think about being the source of truth? Like, do you want OpenAI to be, like, the system of record of all the prompting? Because people sometimes store it in, like, different data sources.[00:25:41] Alessio: And then, is that going to be the same as the models evolve? So you don't have to worry about, you know, refactoring the data, like, things like that, or like future model structures.[00:25:51] Olivier Godement: The vision is if you want to be a source of truth, you have to earn it, right? Like, we're not going to force people, like, to pass us data.[00:25:57] Olivier Godement: There is no value prop, like, you know, for us to store the data. The vision here is at the moment, like, most developers, like, use like a one size fits all model, like be off the shelf, like GP40 essentially. The vision we have is fast forward a couple of years. I think, like, most developers will essentially, like, have a.[00:26:15] Olivier Godement: An automated, continuous, fine tuned model. The more, like, you use the model, the more data you pass to the model provider, like, the model is automatically, like, fine tuned, evaluated against some eval sets, and essentially, like, you don't have to every month, when there is a new snapshot, like, you know, to go online and, you know, try a few new things.[00:26:34] Olivier Godement: That's a direction. We are pretty far away from it. But I think, like, that evaluation and decision product are essentially a first good step in that direction. It's like, hey, it's you. I set it by that direction, and you give us the evaluation data. We can actually log your completion data and start to do some automation on your behalf.[00:26:52] Alessio: And then you can do evals for free if you share data with OpenAI. How should people think about when it's worth it, when it's not? Sometimes people get overly protective of their data when it's actually not that useful. But how should developers think about when it's right to do it, when not, or[00:27:07] Olivier Godement: if you have any thoughts on it?[00:27:08] Olivier Godement: The default policy is still the same, like, you know, we don't train on, like, any API data unless you opt in. What we've seen from feedback is evaluation can be expensive. Like, if you run, like, O1 evals on, like, thousands of samples Like, your build will get increased, like, you know, pretty pretty significantly.[00:27:22] Olivier Godement: That's problem statement number one. Problem statement number two is, essentially, I want to get to a world where whenever OpenAI ships a new model snapshot, we have full confidence that there is no regression for the task that developers care about. And for that to be the case, essentially, we need to get evals.[00:27:39] Olivier Godement: And so that, essentially, is a sort of a two bugs one stone. It's like, we subsidize, basically, the evals. And we also use the evals when we ship new models to make sure that we keep going in the right direction. So, in my sense, it's a win win, but again, completely opt in. I expect that many developers will not want to share their data, and that's perfectly fine to me.[00:27:56] swyx: Yeah, I think free evals though, very, very good incentive. I mean, it's a fair trade. You get data, we get free evals. Exactly,[00:28:04] Olivier Godement: and we sanitize PII, everything. We have no interest in the actual sensitive data. We just want to have good evaluation on the real use cases.[00:28:13] swyx: Like, I always want to eval the eval. I don't know if that ever came up.[00:28:17] swyx: Like, sometimes the evals themselves are wrong, and there's no way for me to tell you.[00:28:22] Olivier Godement: Everyone who is starting with LLM, teaching with LLM, is like, Yeah, evaluation, easy, you know, I've done testing, like, all my life. And then you start to actually be able to eval, understand, like, all the corner cases, And you realize, wow, there's like a whole field in itself.[00:28:35] Olivier Godement: So, yeah, good evaluation is hard and so, yeah. Yeah, yeah.[00:28:38] swyx: But I think there's a, you know, I just talked to Brain Trust which I think is one of your partners. Mm-Hmm. . They also emphasize code based evals versus your sort of low code. What I see is like, I don't know, maybe there's some more that you didn't demo.[00:28:53] swyx: YC is kind of like a low code experience, right, for evals. Would you ever support like a more code based, like, would I run code on OpenAI's eval platform?[00:29:02] Olivier Godement: For sure. I mean, we meet developers where they are, you know. At the moment, the demand was more for like, you know, easy to get started, like eval. But, you know, if we need to expose like an evaluation API, for instance, for people like, you know, to pass, like, you know, their existing test data we'll do it.[00:29:15] Olivier Godement: So yeah, there is no, you know, philosophical, I would say, like, you know, misalignment on that. Yeah,[00:29:19] swyx: yeah, yeah. What I think this is becoming, by the way, and I don't, like it's basically, like, you're becoming AWS. Like, the AI cloud. And I don't know if, like, that's a conscious strategy, or it's, like, It doesn't even have to be a conscious strategy.[00:29:33] swyx: Like, you're going to offer storage. You're going to offer compute. You're going to offer networking. I don't know what networking looks like. Networking is maybe, like, Caching or like it's a CDN. It's a prompt CDN.[00:29:45] Alex Volkov: Yeah,[00:29:45] swyx: but it's the AI versions of everything, right? Do you like do you see the analogies or?[00:29:52] Olivier Godement: Whatever Whatever I took to developers. I feel like Good models are just half of the story to build a good app There's a third model you need to do Evaluation is the perfect example. Like, you know, you can have the best model in the world If you're in the dark, like, you know, it's really hard to gain the confidence and so Our philosophy is[00:30:11] Olivier Godement: The whole like software development stack is being basically reinvented, you know, with LLMs. There is no freaking way that open AI can build everything. Like there is just too much to build, frankly. And so my philosophy is, essentially, we'll focus on like the tools which are like the closest to the model itself.[00:30:28] Olivier Godement: So that's why you see us like, you know, investing quite a bit in like fine tuning, distillation, our evaluation, because we think that it actually makes sense to have like in one spot, Like, you know, all of that. Like, there is some sort of virtual circle, essentially, that you can set in place. But stuff like, you know, LLMOps, like tools which are, like, further away from the model, I don't know if you want to do, like, you know, super elaborate, like, prompt management, or, you know, like, tooling, like, I'm not sure, like, you know, OpenAI has, like, such a big edge, frankly, like, you know, to build this sort of tools.[00:30:56] Olivier Godement: So that's how we view it at the moment. But again, frankly, the philosophy is super simple. The strategy is super simple. It's meeting developers where they want us to be. And so, you know that's frankly, like, you know, day in, day out, like, you know, what I try to do.[00:31:08] Alessio: Cool. Thank you so much for the time.[00:31:10] Alessio: I'm sure you,[00:31:10] swyx: Yeah, I have more questions on, a couple questions on voice, and then also, like, your call to action, like, what you want feedback on, right? So, I think we should spend a bit more time on voice, because I feel like that's, like, the big splash thing. I talked well Well, I mean, I mean, just what is the future of real time for OpenAI?[00:31:28] swyx: Yeah. Because I think obviously video is next. You already have it in the, the ChatGPT desktop app. Do we just have a permanent, like, you know, like, are developers just going to be, like, sending sockets back and forth with OpenAI? Like how do we program for that? Like, what what is the future?[00:31:44] Olivier Godement: Yeah, that makes sense. I think with multimodality, like, real time is quickly becoming, like, you know, essentially the right experience, like, to build an application. Yeah. So my expectation is that we'll see like a non trivial, like a volume of applications like moving to a real time API. Like if you zoom out, like, audio is really simple, like, audio until basically now.[00:32:05] Olivier Godement: Audio on the web, in apps, was basically very much like a second class citizen. Like, you basically did like an audio chatbot for users who did not have a choice. You know, they were like struggling to read, or I don't know, they were like not super educated with technology. And so, frankly, it was like the crappy option, you know, compared to text.[00:32:25] Olivier Godement: But when you talk to people in the real world, the vast majority of people, like, prefer to talk and listen instead of typing and writing.[00:32:34] swyx: We speak before we write.[00:32:35] Olivier Godement: Exactly. I don't know. I mean, I'm sure it's the case for you in Singapore. For me, my friends in Europe, the number of, like, WhatsApp, like, voice notes they receive every day, I mean, just people, it makes sense, frankly, like, you know.[00:32:45] Olivier Godement: Chinese. Chinese, yeah.[00:32:46] swyx: Yeah,[00:32:47] Olivier Godement: all voice. You know, it's easier. There is more emotions. I mean, you know, you get the point across, like, pretty well. And so my personal ambition for, like, the real time API and, like, audio in general is to make, like, audio and, like, multimodality, like, truly a first class experience.[00:33:01] Olivier Godement: Like, you know, if you're, like, you know, the amazing, like, super bold, like, start up out of YC, you want to build, like, the next, like, billion, like, you know, user application to make it, like, truly your first and make it feel, like, you know, an actual good, like, you know, product experience. So that's essentially the ambition, and I think, like, yeah, it could be pretty big.[00:33:17] swyx: Yeah. I think one, one people, one issue that people have with the voice so far as, as released in advanced voice mode is the refusals.[00:33:24] Alex Volkov: Yeah.[00:33:24] swyx: You guys had a very inspiring model spec. I think Joanne worked on that. Where you said, like, yeah, we don't want to overly refuse all the time. In fact, like, even if, like, not safe for work, like, in some occasions, it's okay.[00:33:38] swyx: How, is there an API that we can say, not safe for work, okay?[00:33:41] Olivier Godement: I think we'll get there. I think we'll get there. The mobile spec, like, nailed it, like, you know. It nailed it! It's so good! Yeah, we are not in the business of, like, policing, you know, if you can say, like, vulgar words or whatever. You know, there are some use cases, like, you know, I'm writing, like, a Hollywood, like, script I want to say, like, will go on, and it's perfectly fine, you know?[00:33:59] Olivier Godement: And so I think the direction where we'll go here is that basically There will always be like, you know, a set of behavior that we will, you know, just like forbid, frankly, because they're illegal against our terms of services. But then there will be like, you know, some more like risky, like themes, which are completely legal, like, you know, vulgar words or, you know, not safe for work stuff.[00:34:17] Olivier Godement: Where basically we'll expose like a controllable, like safety, like knobs in the API to basically allow you to say, hey, that theme okay, that theme not okay. How sensitive do you want the threshold to be on safety refusals? I think that's the Dijkstra. So a[00:34:31] swyx: safety API.[00:34:32] Olivier Godement: Yeah, in a way, yeah.[00:34:33] swyx: Yeah, we've never had that.[00:34:34] Olivier Godement: Yeah. '[00:34:35] swyx: cause right now is you, it is whatever you decide. And then it's, that's it. That, that, that would be the main reason I don't use opening a voice is because of[00:34:42] Olivier Godement: it's over police. Over refuse over refusals. Yeah. Yeah, yeah. No, we gotta fix that. Yeah. Like singing,[00:34:47] Alessio: we're trying to do voice. I'm a singer.[00:34:49] swyx: And you, you locked off singing.[00:34:51] swyx: Yeah,[00:34:51] Alessio: yeah, yeah.[00:34:52] swyx: But I, I understand music gets you in trouble. Okay. Yeah. So then, and then just generally, like, what do you want to hear from developers? Right? We have, we have all developers watching you know, what feedback do you want? Any, anything specific as well, like from, especially from today anything that you are unsure about, that you are like, Our feedback could really help you decide.[00:35:09] swyx: For sure.[00:35:10] Olivier Godement: I think, essentially, it's becoming pretty clear after today that, you know, I would say the open end direction has become pretty clear, like, you know, after today. Investment in reasoning, investment in multimodality, Investment as well, like in, I would say, tool use, like function calling. To me, the biggest question I have is, you know, Where should we put the cursor next?[00:35:30] Olivier Godement: I think we need all three of them, frankly, like, you know, so we'll keep pushing.[00:35:33] swyx: Hire 10, 000 people, or actually, no need, build a bunch of bots.[00:35:37] Olivier Godement: Exactly, and so let's take O1 smart enough, like, for your problems? Like, you know, let's set aside for a second the existing models, like, for the apps that you would love to build, is O1 basically it in reasoning, or do we still have, like, you know, a step to do?[00:35:50] Olivier Godement: Preview is not enough, I[00:35:52] swyx: need the full one.[00:35:53] Olivier Godement: Yeah, so that's exactly that sort of feedback. Essentially what they would love to do is for developers I mean, there's a thing that Sam has been saying like over and over again, like, you know, it's easier said than done, but I think it's directionally correct. As a developer, as a founder, you basically want to build an app which is a bit too difficult for the model today, right?[00:36:12] Olivier Godement: Like, what you think is right, it's like, sort of working, sometimes not working. And that way, you know, that basically gives us like a goalpost, and be like, okay, that's what you need to enable with the next model release, like in a few months. And so I would say that Usually, like, that's the sort of feedback which is like the most useful that I can, like, directly, like, you know, incorporate.[00:36:33] swyx: Awesome. I think that's our time. Thank you so much, guys. Yeah, thank you so much.[00:36:38] AI Charlie: Thank you. We were particularly impressed that Olivier addressed the not safe for work moderation policy question head on, as that had only previously been picked up on in Reddit forums. This is an encouraging sign that we will return to in the closing candor with Sam Altman at the end of this episode.[00:36:57] Romain Huet, Head of DX, OpenAI[00:36:57] AI Charlie: Next, a chat with Roman Hewitt, friend of the pod, AI Engineer World's fair closing keynote speaker, and head of developer experience at OpenAI on his incredible live demos And advice to AI engineers on all the new modalities.[00:37:12] Alessio: Alright, we're live from OpenAI Dev Day. We're with Juan, who just did two great demos on, on stage.[00:37:17] Alessio: And he's been a friend of Latentspace, so thanks for taking some of the time.[00:37:20] Romain Huet: Of course, yeah, thank you for being here and spending the time with us today.[00:37:23] swyx: Yeah, I appreciate appreciate you guys putting this on. I, I know it's like extra work, but it really shows the developers that you're, Care and about reaching out.[00:37:31] Romain Huet: Yeah, of course, I think when you go back to the OpenAI mission, I think for us it's super important that we have the developers involved in everything we do. Making sure that you know, they have all of the tools they need to build successful apps. And we really believe that the developers are always going to invent the ideas, the prototypes, the fun factors of AI that we can't build ourselves.[00:37:49] Romain Huet: So it's really cool to have everyone here.[00:37:51] swyx: We had Michelle from you guys on. Yes, great episode. She very seriously said API is the path to AGI. Correct. And people in our YouTube comments were like, API is not AGI. I'm like, no, she's very serious. API is the path to AGI. Like, you're not going to build everything like the developers are, right?[00:38:08] swyx: Of[00:38:08] Romain Huet: course, yeah, that's the whole value of having a platform and an ecosystem of amazing builders who can, like, in turn, create all of these apps. I'm sure we talked about this before, but there's now more than 3 million developers building on OpenAI, so it's pretty exciting to see all of that energy into creating new things.[00:38:26] Alessio: I was going to say, you built two apps on stage today, an international space station tracker and then a drone. The hardest thing must have been opening Xcode and setting that up. Now, like, the models are so good that they can do everything else. Yes. You had two modes of interaction. You had kind of like a GPT app to get the plan with one, and then you had a cursor to do apply some of the changes.[00:38:47] Alessio: Correct. How should people think about the best way to consume the coding models, especially both for You know, brand new projects and then existing projects that you're trying to modify.[00:38:56] Romain Huet: Yeah. I mean, one of the things that's really cool about O1 Preview and O1 Mini being available in the API is that you can use it in your favorite tools like cursor like I did, right?[00:39:06] Romain Huet: And that's also what like Devin from Cognition can use in their own software engineering agents. In the case of Xcode, like, it's not quite deeply integrated in Xcode, so that's why I had like chat GPT side by side. But it's cool, right, because I could instruct O1 Preview to be, like, my coding partner and brainstorming partner for this app, but also consolidate all of the, the files and architect the app the way I wanted.[00:39:28] Romain Huet: So, all I had to do was just, like, port the code over to Xcode and zero shot the app build. I don't think I conveyed, by the way, how big a deal that is, but, like, you can now create an iPhone app from scratch, describing a lot of intricate details that you want, and your vision comes to life in, like, a minute.[00:39:47] Romain Huet: It's pretty outstanding.[00:39:48] swyx: I have to admit, I was a bit skeptical because if I open up SQL, I don't know anything about iOS programming. You know which file to paste it in. You probably set it up a little bit. So I'm like, I have to go home and test it. And I need the ChatGPT desktop app so that it can tell me where to click.[00:40:04] Romain Huet: Yeah, I mean like, Xcode and iOS development has become easier over the years since they introduced Swift and SwiftUI. I think back in the days of Objective C, or like, you know, the storyboard, it was a bit harder to get in for someone new. But now with Swift and SwiftUI, their dev tools are really exceptional.[00:40:23] Romain Huet: But now when you combine that with O1, as your brainstorming and coding partner, it's like your architect, effectively. That's the best way, I think, to describe O1. People ask me, like, can GPT 4 do some of that? And it certainly can. But I think it will just start spitting out code, right? And I think what's great about O1, is that it can, like, make up a plan.[00:40:42] Romain Huet: In this case, for instance, the iOS app had to fetch data from an API, it had to look at the docs, it had to look at, like, how do I parse this JSON, where do I store this thing, and kind of wire things up together. So that's where it really shines. Is mini or preview the better model that people should be using?[00:40:58] Romain Huet: Like, how? I think people should try both. We're obviously very excited about the upcoming O1 that we shared the evals for. But we noticed that O1 Mini is very, very good at everything math, coding, everything STEM. If you need for your kind of brainstorming or your kind of science part, you need some broader knowledge than reaching for O1 previews better.[00:41:20] Romain Huet: But yeah, I used O1 Mini for my second demo. And it worked perfectly. All I needed was very much like something rooted in code, architecting and wiring up like a front end, a backend, some UDP packets, some web sockets, something very specific. And it did that perfectly.[00:41:35] swyx: And then maybe just talking about voice and Wanderlust, the app that keeps on giving, what's the backstory behind like preparing for all of that?[00:41:44] Romain Huet: You know, it's funny because when last year for Dev Day, we were trying to think about what could be a great demo app to show like an assistive experience. I've always thought travel is a kind of a great use case because you have, like, pictures, you have locations, you have the need for translations, potentially.[00:42:01] Romain Huet: There's like so many use cases that are bounded to travel that I thought last year, let's use a travel app. And that's how Wanderlust came to be. But of course, a year ago, all we had was a text based assistant. And now we thought, well, if there's a voice modality, what if we just bring this app back as a wink.[00:42:19] Romain Huet: And what if we were interacting better with voice? And so with this new demo, what I showed was the ability to like, So, we wanted to have a complete conversation in real time with the app, but also the thing we wanted to highlight was the ability to call tools and functions, right? So, like in this case, we placed a phone call using the Twilio API, interfacing with our AI agents, but developers are so smart that they'll come up with so many great ideas that we could not think of ourselves, right?[00:42:48] Romain Huet: But what if you could have like a, you know, a 911 dispatcher? What if you could have like a customer service? Like center, that is much smarter than what we've been used to today. There's gonna be so many use cases for real time, it's awesome.[00:43:00] swyx: Yeah, and sometimes actually you, you, like this should kill phone trees.[00:43:04] swyx: Like there should not be like dial one[00:43:07] Romain Huet: of course para[00:43:08] swyx: espanol, you know? Yeah, exactly. Or whatever. I dunno.[00:43:12] Romain Huet: I mean, even you starting speaking Spanish would just do the thing, you know you don't even have to ask. So yeah, I'm excited for this future where we don't have to interact with those legacy systems.[00:43:22] swyx: Yeah. Yeah. Is there anything, so you are doing function calling in a streaming environment. So basically it's, it's web sockets. It's UDP, I think. It's basically not guaranteed to be exactly once delivery. Like, is there any coding challenges that you encountered when building this?[00:43:39] Romain Huet: Yeah, it's a bit more delicate to get into it.[00:43:41] Romain Huet: We also think that for now, what we, what we shipped is a, is a beta of this API. I think there's much more to build onto it. It does have the function calling and the tools. But we think that for instance, if you want to have something very robust, On your client side, maybe you want to have web RTC as a client, right?[00:43:58] Romain Huet: And, and as opposed to like directly working with the sockets at scale. So that's why we have partners like Life Kit and Agora if you want to, if you want to use them. And I'm sure we'll have many mores in the, in many more in the future. But yeah, we keep on iterating on that, and I'm sure the feedback of developers in the weeks to come is going to be super critical for us to get it right.[00:44:16] swyx: Yeah, I think LiveKit has been fairly public that they are used in, in the Chachapiti app. Like, is it, it's just all open source, and we just use it directly with OpenAI, or do we use LiveKit Cloud or something?[00:44:28] Romain Huet: So right now we, we released the API, we released some sample code also, and referenced clients for people to get started with our API.[00:44:35] Romain Huet: And we also partnered with LifeKit and Agora, so they also have their own, like ways to help you get started that plugs natively with the real time API. So depending on the use case, people can, can can decide what to use. If you're working on something that's completely client or if you're working on something on the server side, for the voice interaction, you may have different needs, so we want to support all of those.[00:44:55] Alessio: I know you gotta run. Is there anything that you want the AI engineering community to give feedback on specifically, like even down to like, you know, a specific API end point or like, what, what's like the thing that you want? Yeah. I[00:45:08] Romain Huet: mean, you know, if we take a step back, I think dev Day this year is all different from last year and, and in, in a few different ways.[00:45:15] Romain Huet: But one way is that we wanted to keep it intimate, even more intimate than last year. We wanted to make sure that the community is. Thank you very much for joining us on the Spotlight. That's why we have community talks and everything. And the takeaway here is like learning from the very best developers and AI engineers.[00:45:31] Romain Huet: And so, you know we want to learn from them. Most of what we shipped this morning, including things like prompt caching the ability to generate prompts quickly in the playground, or even things like vision fine tuning. These are all things that developers have been asking of us. And so, the takeaway I would, I would leave them with is to say like, Hey, the roadmap that we're working on is heavily influenced by them and their work.[00:45:53] Romain Huet: And so we love feedback From high feature requests, as you say, down to, like, very intricate details of an API endpoint, we love feedback, so yes that's, that's how we, that's how we build this API.[00:46:05] swyx: Yeah, I think the, the model distillation thing as well, it might be, like, the, the most boring, but, like, actually used a lot.[00:46:12] Romain Huet: True, yeah. And I think maybe the most unexpected, right, because I think if I, if I read Twitter correctly the past few days, a lot of people were expecting us. To shape the real time API for speech to speech. I don't think developers were expecting us to have more tools for distillation, and we really think that's gonna be a big deal, right?[00:46:30] Romain Huet: If you're building apps that have you know, you, you want high, like like low latency, low cost, but high performance, high quality on the use case distillation is gonna be amazing.[00:46:40] swyx: Yeah. I sat in the distillation session just now and they showed how they distilled from four oh to four mini and it was like only like a 2% hit in the performance and 50 next.[00:46:49] swyx: Yeah,[00:46:50] Romain Huet: I was there as well for the superhuman kind of use case inspired for an Ebola client. Yeah, this was really good. Cool man! so much for having me. Thanks again for being here today. It's always[00:47:00] AI Charlie: great to have you. As you might have picked up at the end of that chat, there were many sessions throughout the day focused on specific new capabilities.[00:47:08] Michelle Pokrass, Head of API at OpenAI ft. Simon Willison[00:47:08] AI Charlie: Like the new model distillation features combining EVOLs and fine tuning. For our next session, we are delighted to bring back two former guests of the pod, which is something listeners have been greatly enjoying in our second year of doing the Latent Space podcast. Michelle Pokras of the API team joined us recently to talk about structured outputs, and today gave an updated long form session at Dev Day, describing the implementation details of the new structured output mode.[00:47:39] AI Charlie: We also got her updated thoughts on the VoiceMode API we discussed in her episode, now that it is finally announced. She is joined by friend of the pod and super blogger, Simon Willison, who also came back as guest co host in our Dev Day. 2023 episode.[00:47:56] Alessio: Great, we're back live at Dev Day returning guest Michelle and then returning guest co host Fork.[00:48:03] Alessio: Fork, yeah, I don't know. I've lost count. I think it's been a few. Simon Willison is back. Yeah, we just wrapped, we just wrapped everything up. Congrats on, on getting everything everything live. Simon did a great, like, blog, so if you haven't caught up, I[00:48:17] Simon Willison: wrote my, I implemented it. Now, I'm starting my live blog while waiting for the first talk to start, using like GPT 4, I wrote me the Javascript, and I got that live just in time and then, yeah, I was live blogging the whole day.[00:48:28] swyx: Are you a cursor enjoyer?[00:48:29] Simon Willison: I haven't really gotten into cursor yet to be honest. I just haven't spent enough time for it to click, I think. I'm more a copy and paste things out of Cloud and chat GPT. Yeah. It's interesting.[00:48:39] swyx: Yeah. I've converted to cursor and 01 is so easy to just toggle on and off.[00:48:45] Alessio: What's your workflow?[00:48:46] Alessio: VS[00:48:48] Michelle Pokrass: Code co pilot, so Yep, same here. Team co pilot. Co pilot is actually the reason I joined OpenAI. It was, you know, before ChatGPT, this is the thing that really got me. So I'm still into it, but I keep meaning to try out Cursor, and I think now that things have calmed down, I'm gonna give it a real go.[00:49:03] swyx: Yeah, it's a big thing to change your tool of choice.[00:49:06] swyx: Yes,[00:49:06] Michelle Pokrass: yeah, I'm pretty dialed, so.[00:49:09] swyx: I mean, you know, if you want, you can just fork VS Code and make your own. That's the thing to dumb thing, right? We joked about doing a hackathon where the only thing you do is fork VS Code and bet me the best fork win.[00:49:20] Michelle Pokrass: Nice.[00:49:22] swyx: That's actually a really good idea. Yeah, what's up?[00:49:26] swyx: I mean, congrats on launching everything today. I know, like, we touched on it a little bit, but, like, everyone was kind of guessing that Voice API was coming, and, like, we talked about it in our episode. How do you feel going into the launch? Like, any design decisions that you want to highlight?[00:49:41] Michelle Pokrass: Yeah, super jazzed about it. The team has been working on it for a while. It's, like, a very different API for us. It's the first WebSocket API, so a lot of different design decisions to be made. It's, like, what kind of events do you send? When do you send an event? What are the event names? What do you send, like, on connection versus on future messages?[00:49:57] Michelle Pokrass: So there have been a lot of interesting decisions there. The team has also hacked together really cool projects as we've been testing it. One that I really liked is we had an internal hack a thon for the API team. And some folks built like a little hack that you could use to, like VIM with voice mode, so like, control vim, and you would tell them on like, nice, write a file and it would, you know, know all the vim commands and, and pipe those in.[00:50:18] Michelle Pokrass: So yeah, a lot of cool stuff we've been hacking on and really excited to see what people build with it.[00:50:23] Simon Willison: I've gotta call out a demo from today. I think it was Katja had a 3D visualization of the solar system, like WebGL solar system, you could talk to. That is one of the coolest conference demos I've ever seen.[00:50:33] Simon Willison: That was so convincing. I really want the code. I really want the code for that to get put out there. I'll talk[00:50:39] Michelle Pokrass: to the team. I think we can[00:50:40] Simon Willison: probably

Pre-IPO stock valuations = www.x.com/aarongdillon (see pinned post)Pre-IPO stock index fact sheet = www.agdillon.com/index00:07 | Mistral's new $6.2b valuation- French AI large language model company- Raised $645m at $6.2b valuation- General Catalyst led; Nvidia, Salesforce, IBM participated- Capital to be used to expand globally- Tech is compliant with new EU AI regulation00:48 | OpenAI $3.4b ARR- AI large language model provider, partnerships with Apple and Microsoft- $3.4b ARR as of May 2024, $2.0b ARR in Dec 2023, $1.0b in Jun 2023- Anthropic $100m ARR, Cohere $22m ARR- OpenAI has a 25x revenue multiple- $200m ARR from Microsoft partnership- New products; search engine, video generating models01:30 | OpenAI + Apple deal terms unveiled- OpenAI's ChatGPT to be integrated into iPhones, iPads, Macs- No Apple to OpenAI payment- Apple believes brand and product exposure to Apple's millions of customers is more valuable- Users will need an OpenAI subscription for some Apple interface use cases; both OpenAI and Apple will make incremental revenue- Apple deal is not exclusive; in talks with Google Gemini and Anthropic02:21 | OpenAI new CFO, CPO- Sarah Friar new CFO; Nextdoor holdings CEO, Square CFO, current Walmart board member- Kevin Weil new CPO; Instagram, Twitter, Planet Labs PBC- Focus on driving international revenue growth and enterprise customer revenue growth- $103.8b secondary market valuation, +21% vs its Apr 2024 round03:15 | Brex revenue +35% in 2023- Online banking company for businesses- Restructured leadership; co-CEO to Franseschi sole CEO, Dubugras as chairman- Raised over $1.5b in capital- 30,000 business customers; DoorDash, Roblox- 20% staff reduction in 2024- Reduced cash burn by 50%, extends runway 4 yrs- 2023 revenue +35%, gross profit +75%- $3.8b secondary market valuation, -69% vs its Jan 2022 round04:18 | Investment case for electricity?!- PG&E, California electric utility, reports 3.5 gigawatts of incremental electricity demand from 24 new data centers- Equal to output of 3 nuclear power plants- New data centers coming in next 5 yrs- Electricity demand to grow 2% to 4% annually through 2040- Electricity stands to be an incredible investment opportunity05:19 | Databrick's new chart/graph tool- Data management, analytics, and AI company- Launching AI/BI, a new visualization tool- Competes with Salesforce Tableau, Microsoft Power BI- AI/BI = uses AI to create charts/graphs via typed queries- AI/BI is free to Databricks users, vs Saleforce/Microsoft which have fees06:12 | Databricks revenue +60% vs 2023- $2.4b ARR forecast as of Jun 2024, +60% vs 2023- $1.6b in 2023 full year revenue, +50% vs 2022- 221 sales deals over $1.0m- Net revenue retention +140%- R&D is 33% of revenue over last 3yrs- 80% subscription gross margin over last 3yrs- $400m ARR from data warehouse product- $42.8b secondary market valuation, -1% vs its Nov 2023 round07:17 | Pre-IPO +2.56% for week, +71.53% for last 1yr- Up week: Chime +22.3%, CoreWeave +19.3%, Wiz +13.1%, Cohere +11.3%, Scale AI +8.8%- Down week: OpenAI -5.5%, Epic Games -4.0%, Deel -2.9%, Bytedance -2.1%, Notion -1.9%- Top valuations: ByteDance $292b, SpaceX $191b, OpenAI $104b, Stripe $76b, Databricks $43b07:54 | 2024 Pre-IPO Stock Vintage Index week performance- www.agdillon.com/index for fact sheet pdf- 2024 Vintage Index top contributors since inception: Rippling +106%, Revolut +52%, Epic Games +44%, Klarna +43%, Anduril +27%- Key metric averages for all Vintage Indexes 5 years old or older…3.31 distributed paid in capital2.05 residual value to paid in capital5.36 total value to paid in capit

MRKT Matrix - Monday, June 10th S&P 500 and Nasdaq close at fresh records, clinch first winning day in three as Fed decision approaches (CNBC) Stocks making the biggest moves midday: Southwest Airlines, GameStop, Advanced Micro Devices and more (CNBC) JPMorgan Warns of a Shock to Stock Market's Calm From CPI, Fed (Bloomberg) Apple Debuts Long-Awaited AI Tools, Including ChatGPT Tie-Up (Bloomberg) OpenAI Hires Sarah Friar as CFO, Kevin Weil as Chief Product Officer (The Information) Nvidia could one day be an unprecedented 15% of the S&P 500, predicts Evercore ISI (CNBC) ‘AI bar remains high' for AMD, says Morgan Stanley as it slashes rating (MarketWatch) Elliott Takes Big Stake in Southwest Airlines (WSJ) FDA advisers say Eli Lilly's Alzheimer's treatment is effective (CNBC) Office Building Losses Start to Pile Up, and More Pain Is Expected (NYTimes) The Unlikely New Real-Estate Darling: Restaurants (WSJ) Gas prices see ‘ultra-rare' drop to start summer (MarketWatch) Meta Gets Supreme Court Review on Investor Data-Harvesting Suit (Bloomberg) --- Subscribe to our newsletter: https://riskreversalmedia.beehiiv.com/subscribe MRKT Matrix by RiskReversal Media is a daily AI powered podcast bringing you the top stories moving financial markets

Kevin Weil is President of Product and Business @ Planet and Operator in Residence @ Scribble Ventures. In the past, Kevin has been Head of Product at Twitter, Instagram, and Novi (the digital currency effort within Facebook). During his seven years at Twitter, he helped the company scale from 40 to 4000 people and from $0 to $2B in revenue. He then moved to Instagram in 2016 to lead the product and data teams and led through an inflection point as Instagram grew from 400M to over 1B users, including launching Instagram Stories. Kevin then co-founded Diem (formerly known as Libra) and Novi and built both for three years before moving to Planet in 2021. If that was not enough, Kevin is also on the board of Strava, the Nature Conservancy, and the Black Product Managers Network. In Today's Episode with Kevin Weil We Discuss: 1.) Lessons From Leading Product for Instagram and Twitter: What does Kevin believe makes Instagram so inherently good at product? How did leading product for Instagram change the way Kevin thinks and operates? What are 1-2 of Kevin's biggest lessons from working with Kevin Systrom, Instagram's Founder? What are the biggest takeaways for Kevin from leading product at Twitter? 2.) Launching Products, Customer Discovery and Product Sessions: What were Kevin's biggest learnings from launching Instagram stories? How did Kevin's conviction impact the product building and success of Instagram stories? How does Kevin advise founders on the best way to approach customer discovery? What are the best questions to ask to reveal the truth? How does Kevin approach product testing today? Why does Kevin not like softly softly testing new products? 3.) Kevin Weil: Leadership 101 What are some of the biggest leadership mistakes that Kevin made in his time at Twitter? How does Kevin approach decision-making frameworks? How does one balance the speed vs the quality of the decision? What makes a great product strategy? Where do so many go wrong in their product strategy? How do the best leaders communicate with their team? How does this change over time? What is Kevin's preferred medium and style of communication with his teams? 4.) Kevin Weil: The Athlete, Father and Husband: Kevin is an ultra-marathon runner, what does his training routine look like? What 1-2 changes has Kevin made that have had the biggest impact on performance? How does Kevin manage, investing, advising, training and being a father and husband?  

Dan and FirstMark Capital's Founder & Managing Director Rick Heitzmann discuss if market sentiment is suddenly turning better (2:04), Rick remaining a perma-bull on Pinterest after activist investor Elliott Management announced a major stake in the company (10:13), the white-knuckle earnings season outlook for beaten down tech stocks (15:15), what's next in the Elon Musk-Twitter legal showdown after a judge orders an October trial (16:41), more pain to come for many crypto investors (19:35), and how Rick thinks Meta is the big tech stock most at-risk for competitive disruption (29:34) . Later, Dan is joined by his co-host Katie Stanton to interview Kevin Weil, President of Product & Business at Planet, about the company's mission (39:36), Planet's ethics board (46:39), private capital entering the space race (52:36), and tech stocks like Meta and Twitter getting crushed this year (55:45). ---- Email us at contact@riskreversal.com with any feedback, suggestions, or questions for us to answer on the pod, and follow us @OkayComputerPod. We're on social: Follow Dan Nathan @RiskReversal on Twitter Follow @GuyAdami on Twitter Follow us on Instagram @RiskReversalMedia Subscribe to our YouTube page

Dan and FirstMark Capital's Founder & Managing Director Rick Heitzmann discuss if market sentiment is suddenly turning better (2:04), Rick remaining a perma-bull on Pinterest after activist investor Elliott Management announced a major stake in the company (10:13), the white-knuckle earnings season outlook for beaten down tech stocks (15:15), what's next in the Elon Musk-Twitter legal showdown after a judge orders an October trial (16:41), more pain to come for many crypto investors (19:35), and how Rick thinks Meta is the big tech stock most at-risk for competitive disruption (29:34) . Later, Dan is joined by his co-host Katie Stanton to interview Kevin Weil, President of Product & Business at Planet, about the company's mission (39:36), Planet's ethics board (46:39), private capital entering the space race (52:36), and tech stocks like Meta and Twitter getting crushed this year (55:45). ---- Email us at contact@riskreversal.com with any feedback, suggestions, or questions for us to answer on the pod, and follow us @OkayComputerPod. We're on social: Follow Dan Nathan @RiskReversal on Twitter Follow @GuyAdami on Twitter Follow us on Instagram @RiskReversalMedia Subscribe to our YouTube page

On this week's episode of the Pathfinder podcast, Ryan sits down with Kevin Weil, president of product and business at Planet ($PL), a ~$1.3B Earth-imaging company based in San Francisco. Kevin joined Planet last April to accelerate software and data product development (or help the company move “up the stack”). Before he worked in commercial space, Kevin held leadership roles at Silicon Valley mainstays that have become household names, like Twitter and Instagram. He managed products with hundreds of millions of daily active users. Pathfinder is brought to you by SpiderOak Mission Systems — www.spideroak-ms.com — an industry leader in space cybersecurity. Sneak peek of our conversation Kevin's journey from studying particle physics to Silicon Valley startups and quickly shipping code Twitter's leadership taking a chance on Kevin and how he grew with the company from 2009 to 2016 Working at an autonomous Instagram and eventually cofounding Meta's cryptocurrency project What convinced Kevin to jump ship to the new space industry? How much of Kevin's experience was transferable from the consumer social world to product at Planet? Selling to governments vs. commercial users The “one-to-many” model and what Planet does differently than competitors Going public via SPAC and the pressures of being publicly traded “Our growth is accelerating” and “we have a proven business model” Acquiring VanderSat and launching Planetary Variables Planet imagery shaping the general public's understanding of the Ukraine war “Bringing transparency is a massive positive, even if sometimes that means you capture some of the bad things that happen in the world.” How does Planet prevent abuse or misuse of its data and imagery? What does Kevin wish he could change overnight in the EO industry? Helpful links- Kevin's Twitter handle: twitter.com/kevinweil Check out Planet's Snapshots newsletter: learn.planet.com/Snapshots_newsletter_Subscription.html Via Planet CEO Will Marshall, announcing Kevin's hiring last March - planet.com/pulse/preparing-to-scale-planet-welcomes-kevin-weil-as-president-product-and-business/ - "It's a delight that our business increasingly looks like that of a software company, with product features driven by software advances that deliver value on top of our satellite data. As Planet accelerates as a data and analytics company, we're bringing on top Silicon Valley software talent to add to Planet's team. Which brings me to Kevin. Kevin is a proven leader with a track record of leading software and data product organizations through hyper-growth, and delivering market-making customer solutions — a mindset and body of experience that aligns perfectly with Planet's high-growth business objectives. Kevin has built and scaled teams and products at the world's fastest growing and most consequential companies. Kevin was one of Twitter's first 50 employees and ultimately became its SVP of Product, leading its consumer, developer, and monetization products as the company went public and scaled to over $2bn in revenue." -- Pathfinder is brought to you by Payload, a modern space media brand. Subscribe to our industry-leading daily newsletter at payloadspace.com

Kevin Weil is the ex-head of product at Twitter and Instagram. He's currently the president of business and product at Planet. Weil joins Big Technology Podcast to share an insider's view of how Twitter and Instagram build products, what their potential is, and how each product is dealing with big, imposing, outside forces (Elon Musk and TikTok). Stay tuned for the second half, where we discuss Planet, a fascinating company whose hundreds of satellites orbiting the earth capture a new, complete picture of our world daily.

The X1 credit card harnesses the power of technology to offer higher credit limits, an aggressive rewards program, and unique automated features driven by a mobile app. Created by Twitter alums Deepak Rao and Siddharth Batra, and supported by PayPal co-founder Max Levchin and Instagram's former head of product, Kevin Weil, the X1 card targets younger consumers who want a tech-savvy, elegantly designed stainless steel card. At introduction, there was a 350,000 person waitlist for the card. I'm excited to have Siddharth Batra, co-founder of X1 Card on the Banking Transformed podcast. He discusses the strategy behind introducing a new credit card to an underserved segment and the potential for growth beyond payments. This episode of Banking Transformed is sponsored by FIS. GoCart recognizes email addresses and lets consumers pay quickly anywhere – with no passwords and no long forms. Consumers can pay faster for anything – even things they wouldn't expect like healthcare, processional services, and more. GoCart goes beyond online checkout and allows consumers to pay easily by email, text, or with QR codes.  Find out how you can use GoCart to simplify payments and increase your sales at GoCartpay.com/podcast.   FIS. Advancing the way the world pays banks and invests.

Earth Data company Planet Labs is going public thru dMY Technology IV (DMYQ) via a SPAC transaction. Founder and CEO Will Marshall, CFO Ashley Johnson, and dMY CEO Niccolo de Masi join us on the podcast to discuss Planet's prospects and the SPAC transaction. Planet touts itself as the only company in the world that has the ability to do full daily earth scans, sometimes even twice a day. One can think of Planet as the "Bloomberg Terminal" for Earth data - allowing the consumer to consume their satellite imagery via their own computers. Planet serves numerous industry verticals and has aims for much more, and operates on a data subscription business model. Key Takeaways:Not a "satellite company" --> Planet owns the raw satellite data that enables Planet and customers to create value-add data/analytics/services on topDe-risked technology (i.e. launched 200 satellites) and product-market fit (i.e. $100m revenue / +700 customers)Subscription data business w/ strong renewal rates and account growthGoogle partnership -- Google outsources to them, sold their satellites to Planet, and now sells a bundle of Planet + Google CloudCapital raised will enable sales and marketing -- team believes this is the key limiter on growth at this pointProduct led by Kevin Weil --> previously at Facebook, Instagram, and TwitterStrong, serial sponsor in dMY TechnologyBoardroom Alpha's SPAC ServiceFor ongoing tracking, analytics, and data on SPACs checkout Boardroom Alpha's SPAC Data and Analytics service.

In this episode, I am speaking with Kevin Weil, President of Product and Business at Planet. I am guessing most of you know Planet - the poster child of the NewSpace industry and the Earth observation sector. For those of you that don't, Planet has the largest constellation of EO satellites that image the Earth every day. And, Planet is also just about to go public following a SPAC deal with dMY Technology Group, earlier this year. And as for Kevin Weil - I am going to let you listen to his story in his own words. Kevin joined Planet about 6 months ago. With past leadership roles in Twitter, Instagram and Facebook, his CV speaks for itself. I really enjoyed this discussion with Kevin on Planet and his vision for the Earth observation sector. Sit back and enjoy! ----- 01:20: Kevin's story 07:35: Foray into space tech 13:30: About the space industry 16:22: Outlook on Earth observation 22:49: Planet - The Present & The Future 26:17: Evangelising EO and Simplifying EO 28:40: A B2C app based on EO data? 32:30: Data fusion and verticalisation 34:58: Horizontal EO vs Vertical EO 38:16: Vision for Planet 41:35: What worries Kevin about the industry 44:28: 1-thing to learn from Big-Tech to Earth observation -----

Over the last 10 years, we've seen an incredible amount of progress and innovation in harnessing large data sets. Social graphs, payments platforms, e-commerce - the world is becoming smarter, more efficient and more is becoming possible because of our ability to derive insights from data. But innovation in the earth's data has been nascent - today I talked to one of the most revered product leaders in Silicon Valley about how might we capture the world's information and then the possibilities for it. Kevin Weil served as a Product Leader at Twitter, Instagram and was the co-founder of Libra, Facebook's cryptocurrency. He recently moved to Planet as President of the organization. Kevin and I talked about what's possible when you map and image the entire world in real time.

Kevin Weil is the VP of Product for Novi, Facebook's digital wallet for the Libra payment system. Previously, Weil was VP of Product at Instagram (overseeing consumer, growth, and monetization products) and SVP of Product at Twitter (where he led product development and design across Twitter's consumer and ad products, as well as Vine and Periscope). In this talk, he explores the mission that drives both Libra and Novi, and shares a number of crucial insights on digital product design.

Kevin Weil is the VP of Product for Novi, Facebook’s digital wallet for the Libra payment system. Previously, Weil was VP of Product at Instagram (overseeing consumer, growth, and monetization products) and SVP of Product at Twitter (where he led product development and design across Twitter’s consumer and ad products, as well as Vine and Periscope). In this talk, he explores the mission that drives both Libra and Novi, and shares a number of crucial insights on digital product design.

Conferências da Web Summit 2019 que consideramos mais relevantes, no dia 5 de novembro:Are you ready for 5G? (Central Stage)Building Calibra and increasing access to the global economy (Central Stage)How our first million connected vehicles and machines support our customers' business (Auto/Tech)New releases for the smart home (Unboxed)Re-imagining the global transport ecosystem (Auto/Tech)How space-based networks will bring internet to the world? (SaaS Monster)Uber’s road ahead (Central Stage)Debunking the unbanked: bringing payments to the masses (Future Societies)Has technology replaced religion? (Future Societies)Design without borders: the future of globalisation and the industry (Creatiff)The role of technology in climate action (Planet/Tech)Saiba mais sobre inovação e nova economia em supertoast.pt.  

Facebook’s New Libra Currency Sends Shivers Through The Banking World You may have read that Facebook is to launch a cryptocurrency, which is already creating a stir among world central bankers and G7 leaders.   This is not just a remittance service sending money via a smartphone, but a new currency. However, some cryptocurrency purists could argue that Facebook’s new digital coin, Libra, is not a true cryptocurrency, although they admit that it could bring digital money into the mainstream.   Critics claim the plans for Libra, backed by a consortium of 28 groups including Uber, Spotify, Visa and Mastercard, is not a genuinely decentralised digital currency.   I’m not a crypto expert or even a fan of so-called currencies like Bitcoin. The price of bitcoin, which has been volatile over the past 12 months and passed $9,000 at the weekend for the first time in more than a year, has been flat since the announcement. Other major cryptocurrencies were also unmoved or only slightly down on the news.   The new currency system will allow users to convert US dollars and other international currencies into Libra, which will facilitate rapid money transfers and online transactions with almost no transaction fees. Which begs the question, if there are no transaction fees why are these companies investing millions into this project?   Facebook, which is leading the project, said Libra will be especially valuable to the 1.7bn people worldwide without bank accounts, who will be able to carry out payments via their phones.   While Facebook intends for Libra to eventually become decentralised, transactions will initially be validated by the founding consortium.    Phil Chen, decentralised chief officer at phonemaker HTC’s blockchain-driven Exodus project, told the FT.   “This project is the antithesis of bitcoin and is another step towards total control of data and users,”   He added, “This global coin is the most invasive and dangerous form of surveillance they have devised thus far.”   “At the end of the day, Libra is not a true blockchain,” Mr Chen said.   On the online forum Reddit, one commenter described Libra as a “Silicon Valley surveillance paradise” but acknowledged that it was still a “pretty significant development in crypto”. “Instead of a monopoly it’s an oligopoly,” said Gavin Brown, associate professor in financial economics at Manchester Metropolitan University and director of cryptocurrency hedge fund Blockchain Capital. But the structure might be sensible, he added:   “There needs to be some level of institutionalisation in order for adoption to happen and regulators to get comfortable.”   Another factor is that Facebook will not use the “proof of work” mechanism that underpins cryptocurrencies such as bitcoin, under which computers solve problems to create a block chain and be rewarded with newly-minted currency, because of its “poor performance” and “high energy (and environmental) costs”.   Richard Dennis, founder of crypto network Temtum, said the proof of work model had been shown to be inefficient, and that bitcoin would be “out of date” soon. But he added, along with others, that Facebook’s project had “rejuvenated” the crypto community. “I was told it’s crypto Spring,” he said. “This is starting to feel like 2017 again.”   Whilst central bankers, like the governor of the Bank of England Mark Carney, have given the currency a lukewarm welcome, I’m sure Facebook and its backers have already thought through the potential challenges that lie ahead, not least money laundering regulation.   Within Facebook — where engineers and product managers are more familiar with optimising advertising algorithms or simplifying photo sharing — creating a new currency was seen as a daunting challenge.  “I’ve been doing this for more than a year, like 20 hours a day, and I’m still wrapping my head around it,” said Kevin Weil, who moved from Instagram to become Facebook’s head of blockchain product in June 2018.  Libra was “unlike anything I’ve ever worked on before”, he added in an interview last week at the San Francisco Mint.  “The technology is basically brand new, and is evolving really quickly. No one has any experience with a global currency before. Any direction you look, it’s new — and that’s exciting.”      Libra could open up opportunities for small online businesses to lower the cost of transactions, as well ease the process of selling online on Instagram and Facebook. The news must have sent shivers through the banking and money transfer community. This will shake up the banks and the likes of Western Union, which takes a large chunk out of overseas workers remittances. They are all crying 'foul' about regulations and money laundering when it's the banks who have been fined for laundering! Apple Pay is not mentioned? This will be another step towards a cashless society, a one world currency and less control of our money and privacy.   Facebook has 2.7 billion active monthly users, and it knows a lot about us. It will know a lot more about us once we start using Libra, but has promised to keep this information separate from the social media business.   Check out my book, Yes, Money Can Buy You Happiness, on Amazon - http://bit.ly/2MoneyBook   See omnystudio.com/policies/listener for privacy information.

You may have read that Facebook is to launch a cryptocurrency, which is already creating a stir among world central bankers and G7 leaders. This is not just a remittance service sending money via a smartphone, but a new currency. However, some cryptocurrency purists could argue that Facebook’s new digital coin, Libra, is not a true cryptocurrency, although they admit that it could bring digital money into the mainstream. Critics claim the plans for Libra, backed by a consortium of 28 groups including Uber, Spotify, Visa and Mastercard, is not a genuinely decentralised digital currency. I’m not a crypto expert or even a fan of so-called currencies like Bitcoin. The price of bitcoin, which has been volatile over the past 12 months and passed $9,000 at the weekend for the first time in more than a year, has been flat since the announcement. Other major cryptocurrencies were also unmoved or only slightly down on the news. The new currency system will allow users to convert US dollars and other international currencies into Libra, which will facilitate rapid money transfers and online transactions with almost no transaction fees. Which begs the question, if there are no transaction fees why are these companies investing millions into this project? Facebook, which is leading the project, said Libra will be especially valuable to the 1.7bn people worldwide without bank accounts, who will be able to carry out payments via their phones. Phil Chen, decentralised chief officer at phonemaker HTC’s blockchain-driven Exodus project, told the FT. “This project is the antithesis of bitcoin and is another step towards total control of data and users,” Whilst central bankers, like the governor of the Bank of England Mark Carney, have given the currency a lukewarm welcome, I’m sure Facebook and its backers have already thought through the potential challenges that lie ahead, not least money laundering regulation. Within Facebook — where engineers and product managers are more familiar with optimising advertising algorithms or simplifying photo sharing — creating a new currency was seen as a daunting challenge. “I’ve been doing this for more than a year, like 20 hours a day, and I’m still wrapping my head around it,” said Kevin Weil, who moved from Instagram to become Facebook’s head of blockchain product in June 2018. Libra was “unlike anything I’ve ever worked on before”, he added in an interview last week at the San Francisco Mint. “The technology is basically brand new, and is evolving really quickly. No one has any experience with a global currency before. Any direction you look, it’s new — and that’s exciting.” Libra could open up opportunities for small online businesses to lower the cost of transactions, as well ease the process of selling online on Instagram and Facebook. The news must have sent shivers through the banking and money transfer community. This will shake up the banks and the likes of Western Union, which takes a large chunk out of overseas workers remittances. They are all crying 'foul' about regulations and money laundering when it's the banks who have been fined for laundering! Apple Pay is not mentioned? This will be another step towards a cashless society, a one world currency and less control of our money and privacy. Facebook has 2.7 billion active monthly users, and it knows a lot about us. It will know a lot more about us once we start using Libra, but has promised to keep this information separate from the social media business. Check out my book, Yes, Money Can Buy You Happiness, on Amazon - http://bit.ly/2MoneyBook

On Tuesday Facebook reorganized the duties of its product executives, in the process creating an unusual new division: David Marcus, formerly head of Facebook's Messenger app, will now lead a team of fewer than a dozen people dedicated to blockchain technology, according to Recode. He'll be joined by notable executives including Kevin Weil, former VP of product at Instagram, and James Everingham, VP of engineering at Instagram. It's not clear what the company is up to here.

What motivates you to share a photo on Instagram — or not? Kevin Weil, head of product at the company, discusses everything from user behavior to business strategy with Stanford Professor of the Practice Tina Seelig. Weil describes how mission alignment helps teams succeed and allows Instagram to continue experimenting and thriving inside its parent company, Facebook.

What motivates you to share a photo on Instagram — or not? Kevin Weil, head of product at the company, discusses everything from user behavior to business strategy with Stanford Professor of the Practice Tina Seelig. Weil describes how mission alignment helps teams succeed and allows Instagram to continue experimenting and thriving inside its parent company, Facebook.

What motivates you to share a photo on Instagram — or not? Kevin Weil, head of product at the company, discusses everything from user behavior to business strategy with Stanford Professor of the Practice Tina Seelig. Weil describes how mission alignment helps teams succeed and allows Instagram to continue experimenting and thriving inside its parent company, Facebook.

The Spokesmen Cycling Roundtable Podcast Episode 150 The Great Puddle February 19, 2017 PANELISTS: Carlton Reid Ayesha McGownan Chris Garrison Jim Moss Donna Tocci TOPICS: • Flooding cause chaos – no, not in California in Tour of Oman • Nicole Cooke's testimony to the House of Commons Culture, Media, and Sport committee yet. Plus: Bicycling survey. • Lance Armstrong set to go to trial – and the bets are that he will win. (And he has a podcast.) • Interbike rules out Utah for new show venue. Outdoor Retailer pulls away from Salt lake City, and all because of Utah's stance on public lands. • Riding MTBs keeps people sane, says UK report. • Strava adds Instagram's Kevin Weil to board – to go even more social? Sponsored by Jenson USA Special offer for new Jenson USA customers - receive 10% off one item! Note: Some brands do not participate in promotions, if you see the message "no qualifying items in cart," the item you have selected do not qualify for this offer. Cannot be combined with any other special offer or discount, including but not limited to gift cards, other coupon codes, price matches and some money card offers. Code is for new customers only. WEB SITE: http://www.the-spokesmen.com The Spokesmen Cycling Roundtable Podcast is licensed under a Creative Commons Attribution-Noncommerical-No Derivative Works 3.0 United States License.

Twitter finally has its CEO, and it's rolling out new products. But what comes next? What is Twitter doing to grow its audience? At Code/Mobile, Twitter product guru Kevin Weil tells Kara Swisher how the company is positioning itself for the future. Learn more about your ad choices. Visit podcastchoices.com/adchoices