Podcasts about Diogo

Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks:We are no strangers to learning in public and are no strangers to the stress of getting things wrong when you have a big platform. However, we were at Anthropic for the Computer Use launch, there for Claude Cowork with the first big podcast on it, organized the first Computer Use track at AIE presenting the state of the art, and were close to the OpenAI-Sky Software acquisition that now powers the complete domination of computer use that Codex enjoys today. This is why we're excited to bring you today's first guest, Ari Weinstein, cofounder of Sky and now leading all the amazing CUA progress that casuals might miss:Ari explains why Computer Use is now “180 degrees different” from where it was months ago, how agents are learning to debug and recover from failures, why combining screenshots with accessibility data, the DOM, Playwright, and generated code changes the speed equation, and why the next frontier is making agents literally superhuman at using software.OpenAI clones JevIn the second half, Nikunj Handa from OpenAI's API team breaks down the new developer stack: async tool calling, mid-turn steering, WebSockets, UltraFast inference, the Decisions API, prompt caching, pre-warming, compaction, and the Agents API. Given that we were the first Jev podcast, we particularly focus on the unusually fast sprint on the Decisions API:And why it is just a Luna wrapper for now but the team is motivated and egoless enough to clone what they consider to be good patterns.We discuss:* Why OpenAI thinks Computer Use has changed dramatically in just the last few months* Dots and what changes when every agent gets its own Linux computer* Why Computer Use can now complete some tasks faster than the average human* The path from human-level to “literally superhuman” computer use* Why modern agents are much better at debugging and recovering from failure* How screenshots, accessibility trees, the DOM, Playwright, and generated JavaScript work together* App Shots and why they give models much richer context than ordinary screenshots* Why Computer Use can close the loop between writing software and testing it* Trust, permissions, and safety when agents can make payments and operate websites* Async function calling and why models no longer need to stop reasoning while tools run* Mid-turn steering, WebSockets, and the architecture behind more responsive agents* UltraFast inference and how OpenAI is pushing frontier models toward much lower latency* The rapid internal story behind the Decisions API* Why Decisions API is more than structured outputs at low latency* GPT Live, fast tool calling, and real-time computer control* How OpenAI is already using Decisions API for support classification and internal workflows* Longer prompt caching, cache pre-warming, and cache-aware applications* Server-side compaction vs manual compaction for long-running agent threads* What should live inside an Agents API versus a developer's own harness* OpenAI as an “AI cloud” and the search for higher-level primitives beyond raw model APIsAri Weinstein* Product & Engineering, Computer Use at OpenAI* X: https://x.com/AriX* LinkedIn: https://www.linkedin.com/in/weinsteinari/Nikunj Handa* Product, API at OpenAI* X: https://x.com/nikunjhanda* LinkedIn: https://www.linkedin.com/in/nikunjhanda/Timestamps00:00:00 OpenAI DevDay: Dots, GPT-6.1, Agents API, and Decisions API00:02:52 Dots and Personal Cloud Computers00:04:59 Why Computer Use Is “180 Degrees Different”00:06:04 From Sky to Self-Debugging Computer Use Agents00:09:24 How Computer Use Sees and Operates Software00:12:09 From Faster Than Humans to Superhuman Computer Use00:16:03 Agents API: Trust, Permissions, and Safety00:17:31 Computer Use for Coding, Testing, and QA00:19:14 GPT-6 APIs, Async Tool Calling, and UltraFast Inference00:23:21 The Rapid Story Behind Decisions API00:25:32 What Decisions API Is and How It Works00:30:24 What OpenAI Is Building With the New APIs00:32:23 Prompt Caching, Pre-Warming, and API Performance00:35:20 Context Compaction for Long-Running Agents00:37:13 Memory, Higher-Level APIs, and the AI CloudTranscriptIntroduction: OpenAI DevDay and the New Agent StackVibhu [00:00:00]: Okay. We're very excited to be here. Today is OpenAI DevDay. Special podcastSwyx [00:00:08]: We're the first podcast after your livestream.Vibhu [00:00:10]: First podcast. We have Ari here, who leads the product and engineering team for Computer Use agents. Before we kick in and dive deep on Computer Use, you wanna give a quick recap? What was announced? What's the quick slew of announcements you guys had today?Ari Weinstein [00:00:24]: Yeah. yeah, it was a super exciting day. we just got out of the keynote. It was really sick. there were a bunch of Computer Use announcements that I think are worth thinking about. We have, Dots, which is the new, sort of personal assistant product, and, that has some really exciting Computer Use features. There's GPT-6.1 Sol, which is this amazing new model, that I think is particularly great for Computer Use ‘cause of, sort of the cost and speed, advantages. I think, I think we shared that it's, a fifth of the cost of Astra and a seventh of the cost if you're looking at Computer Use specifically, which is really amazing. sorry, there were so many things. I'm trying to sort through it.Swyx [00:01:02]: And the API.Ari Weinstein [00:01:03]: Agents API, which now has Computer Use in it, which is really cool, ‘cause now developers can build on the same Computer Use, that is part of Codex, and ChatGPT. and then there were some demos of our existing Computer Use features, like app shots, where you can take the context of something you're doing on your computer and bring it into Codex and ChatGPT really fast. And then, like, native Computer Use on your Mac, where Roman had it taking screenshots of his app, automatically, and he could do other things on his computer while Computer Use was using his applications. so yeah, really exciting keynote.Swyx [00:01:35]: And not to mention the Decisions API.Ari Weinstein [00:01:37]: Decisions API.Swyx [00:01:38]: Off the bat, are they all the same model? Like, this is. Or the same dataset distilled to different models?Swyx [00:01:44]: Like, basically, like, is Computer Use using Decisions API, or are they, like, kinda separate?Ari Weinstein [00:01:49]: So what's really cool about the Decisions API is it, you know, it has all these new capabilities. It does inference in parallel. it doesn't have reasoning. It's a smaller model, than the ones we use for Computer Use. and so those capabilities make it really fast.Dots and Delegating Work to a Cloud ComputerSwyx [00:02:07]: Yeah.Ari Weinstein [00:02:07]: They also make it a little bit less good at doing, like, long horizon, sort of sophisticated tasks. And so I think I would say it's still an open area of research for how we, like, bring those approaches together. But, yeah, I'm really excited to see what people build with the Decisions API.Vibhu [00:02:24]: One of the interesting things is Dots now have attached personal computers.Ari Weinstein [00:02:28]: Yeah.Vibhu [00:02:28]: So it seems like they're very much more persistent. You've been using them for a while. How should people push the bounds? Like, what should people aim for? What should they try? Personally, right now I use it for a lot of customer service. LikeAri Weinstein [00:02:41]: CoolVibhu [00:02:41]: “Oh, this was wrong. I don't wanna sign in. I don't wanna authenticate.” Find whatever and just get it fixed.Ari Weinstein [00:02:45]: Yeah.Vibhu [00:02:46]: How should we push further? What should people try?Ari Weinstein [00:02:50]: Dots Are a really cool product because each Dot has access to its own Linux virtual computer in the cloud, which is different from our other products. you know, traditionally, we've have access to a browser in the cloud, or it has access to your own computer, but now you get your own entire Linux computer in the cloud. And so it can run full desktop applications, and it can also use a web browser. And so, yeah, you know, I think the powerful thing about Computer Use and the reason why I think it's so, exciting is because it makes it so that the agent can do anything you as a, as a person can do, because all the software in the world was designed for humans, and now agents can use that same software, and you can delegate to the agent. So, yeah, like, anything that you would do on a computer, you can ask a Dot to do. Yeah, I think what particularly is useful is gonna really depend on who the end user is and what- what's valuable in their life. but yeah, I would just start by thinking about, like, one of the things that you spend time on and how could you delegate those to an agent.Swyx [00:03:47]: Yeah, a lot of flight booking and shopping and honestly even, like, playing a game or whatever, right?Ari Weinstein [00:03:52]: Totally.Swyx [00:03:52]: Yeah.Ari Weinstein [00:03:53]: Yeah, I don't know. For me, something I did recently, I've been working on. I've, subscribed to a meal prep service ‘cause I was trying to, like, eat healthy, you know? And I really like this meal prep service I found because it lets me customize the meals I order to, like, a high degree of granularity. So I can say, like, “I want this many grams of chicken and this many grams of rice.” but it was so complicated. It took me two hours to do an order, and I found that I could ask Computer Use to do it for me, and it did it in 15 minutes. so I actually saved two hours. it both did it eight times faster than I could, and it saved me two hours on GPT-6.1 Sol.Swyx [00:04:32]: Yeah.Ari Weinstein [00:04:32]: So those are the kinds of tasks that I feel like, are really powerful.Swyx [00:04:36]: As a creator, I can tell you automatically, immediately, my number one use case is automating YouTube.Ari Weinstein [00:04:40]: Nice.Swyx [00:04:40]: Because, YouTube doesn't expose a lot of things via API.Ari Weinstein [00:04:43]: Yeah.Swyx [00:04:43]: And you have to just put it in a VM and just, like, run it, for, like, let's say, let's say their AB testing feature or making community posts. None of this is available by API ‘cause they hate developers.Swyx [00:04:53]: Anyway, so,Ari Weinstein [00:04:55]: I've heard that from our developer experience team too. They use it with YouTube a lot. Yeah. It's really awesome.Swyx [00:04:59]: So I wanna draw for, you know. let's say, I wanna get a little bit spicy. One of our, the leading AI podcasts, our friends, is famous for saying that Computer Use hasn't advanced in the last two years.How Computer Use Has Changed in the Last YearAri Weinstein [00:05:12]: Yeah.Swyx [00:05:13]: Which is a very interesting statement, and I think you're one of the best people in the world to talk about this, like, how have things have progressed, right?Ari Weinstein [00:05:20]: Yeah. You know, they said that a few months ago, I think, and I hope they have a different perspective now because Computer Use is, like, 180 degrees different than it was.Swyx [00:05:26]: He's a, he's a tough guy to impress.Ari Weinstein [00:05:27]: Yeah, okay. well, we're working on it.Swyx [00:05:30]: But, you know, you worked on. You've, like, basically spent your whole career working on, like, some kind of computer automation, right?Ari Weinstein [00:05:34]: Yeah.Swyx [00:05:34]: Like shortcutsAri Weinstein [00:05:35]: YeahSwyx [00:05:35]: At Apple, and then Sky, and then, and then joining OpenAI. Can you draw, like, what your through line is for, like, what is driving you and what- you, what wasn't possible back then maybeAri Weinstein [00:05:47]: Yeah.Swyx [00:05:48]: And, like, what your sort of milestones were.Vibhu [00:05:49]: I guess to add on to that as a follow-up question, what's the major change from using Codex Computer Use from, like, last weekAri Weinstein [00:05:57]: YeahVibhu [00:05:57]: Through to today? Is it model? Is it dots? Is it harness? So all the history plus what really just changed in today's announcements?Ari Weinstein [00:06:04]: Yeah. On the through line, I guess I've always been excited about automation and helping people automate tasks because then you can, like, save time in your life and focus on things that are more important to you than, like, operating a computer very intricately. And so, yeah, that was why we worked on some of those products. I was at Apple before. we made a company called Sky. we ended up joining OpenAI, which is really exciting. and I think something that wasSwyx [00:06:27]: And almost like you have to hack around Apple until Apple was like, “Fine, like, we'll just hire you and you can just work on the inside,” right? Like.Ari Weinstein [00:06:35]: It was, it was a cool place to get to work. what was really interesting looking back at Sky is we were, we were working on Computer Use there as well, and the models were so much less capable. And now the models, just in the last one year, have become extraordinarily capable at Computer Use. I think the biggest delta that I see is before they could, like, reliably start tasks, but then they would run into problems, and now they're really good at debugging. They're really good at trying again, introspecting what is and isn't working. and I think we've also brought the Computer Use the Computer Use field itself has moved forward. I think we're using more techniques. now Computer Use, often writes code. So if you actually look at it in Codex and you expand the tool calls manually, you can see that it's not just doing one action at a time. It's actually writing JavaScript code that it executes, that the computer executes to perform sometimes many actions at once, which is a great, you know, speed up and great capability. We use more accessibility, sort of multimodal interfaces. So, the model may use screenshots, it may use accessibility, it may use Playwright. it can use a lot of different mechanisms, based on the task at hand. and then, yeah, the model acceleration has been, has been just amazing. So, yeah, what's different today? I think we're making computers better all the time, so I think just, like, one day's difference, is probably a little bit less consequential than, like, even the past month or the past two months. but, yeah, I think the Computer Use in Dot is really exciting as well as, the new model that we came out with.Measuring Computer Use and Improving the HarnessVibhu [00:08:03]: On the keynote, Tejal was mentioning 7x improvements in Computer Use speed, a lot better on a few benchmarks. How do you guys think about measuring it? Computer Use is one of those things where, as you say, you know, it's improvements over time.Ari Weinstein [00:08:20]: Yeah.Vibhu [00:08:20]: Is it harness? Is it model? Is it post-training?Ari Weinstein [00:08:22]: Right.Vibhu [00:08:22]: How do you guys look at it internally about measuring how good it is, and what were the changes with the new model?Ari Weinstein [00:08:29]: We actually have a bunch of different ways of measuring it, some of which are on different permutations and configurations of the harness. It's a bit of a complicated story because, you know, our production products have, you know, some more safety checks, and, you know, those are configured differently based on the needs of the, of the task at hand. So there's a lot of ways to measure it, but I think regardless of how we measure it, we find pretty consistent gains. and those gains are, sometimes in the harness and sometimes in the model. and yeah, I was really excited by this result that GPT-6.1 is even more cost-effective for Computer Use than its baseline cost improvement as compared to Astra. It's, like, really cool to see.Swyx [00:09:10]: Yeah. I mean, one of the visuals I really liked from the livestream was that, you're sort of improving the Pareto frontier of, your, curve, and there was a lot of talking about how you're improving it together with the harness.Ari Weinstein [00:09:24]: Yeah.Swyx [00:09:24]: Can you give some examples of aha moments that you had, whether it's on, like, model driving the harness driving the model, whatever?Ari Weinstein [00:09:32]: I don't mean to repeat myself, but I think, like, introducing more modalities has been really powerful.Swyx [00:09:36]: Okay.Ari Weinstein [00:09:36]: One more specific example of that is, in the past, I think we saw a lot of Computer Use, products had to spend a lot of time, like, scrolling, you know? So it would, like, take a screenshot. It would try to do something. It would be like, “Oh, I gotta, like, scroll down to the next page of results,” and then it would take a screenshot, and then it would try to do something. It would scroll down again. And so I think, with accessibility and other. and, direct access to the DOM and other things like that, now the language model can actually see, like, an entire page or an entire application. It can write code that can do multiple steps at once. And so I think those have been probably the biggest single aha moments. There's, like, a lot of tiny ones that are less exciting in comparison, but actually we do find also that a lot of speed improvements are driven by, like, a lot of little paper cuts that we gotta go in and introspect.App Shots, Accessibility, and Better Computer ContextSwyx [00:10:21]: Yeah. A lot of really hard engineering.Ari Weinstein [00:10:23]: Yeah.Swyx [00:10:23]: I mean, app shots in general, right? Like, I think people don't quite get the difference if. because there's, like, a nice visual in Codex when itAri Weinstein [00:10:30]: YeahSwyx [00:10:30]: When you take an app shot, but they don't maybe they get the difference that, you are able to actually drive each button and you have the, you have each text, in a very optimal representation.Ari Weinstein [00:10:40]: Yeah. Exactly. Yeah. It's kind of fun actually. If you wanna be, like, really nerdy about it, you can go into Codex, take an app shot by hitting the two command keys. So you grab the content from whatever app you're working with, bring it into the, Codex or ChatGPT chat. And then the. if you click on the attachment and you click on this, like, little tiny button in the top right, you can see the raw text and you see the raw accessibility representation. And yeah, we've put a lot of work into, puttingSwyx [00:11:04]: Just dumping everything out. Yeah.Ari Weinstein [00:11:05]: Dumping it out, but also making it token-efficient, doing it efficiently. There's, like, a bit of an art to it. And, you know, it turns out that the same technology that was invented for humans, you know, who maybe have accessibility needs, who wanna use a screen reader technology, that technology is really helpful for them to be able to use computers. It's also really helpful for LLMs to be able to use computers. So that's been, like, really fun to get to work on.Vibhu [00:11:27]: For context, I feel like a lot of people don't understand app shots. They don't even know it's a feature.Ari Weinstein [00:11:30]: Yeah.Vibhu [00:11:31]: It's when you double hit command, it pulls in what looks like a screenshotAri Weinstein [00:11:34]: RightVibhu [00:11:34]: And you're like, “Oh, why have I opened up just a screenshot and thrown it in?” No, it's actually pulling all the metadata, all the code, everything.Ari Weinstein [00:11:40]: Yeah, exactly. Yeah. So it's like, you know, if you take a screenshot of a webpage that has a link- The screenshot doesn't include where the link goes. It doesn't include, you know, maybe you take a screenshot of your calendar, the ca- event ti- titles are truncated, you know? But when you take an app shot, it gives, like, the language model, like, full context about everything and, that lets it, just sort of, like, do much more.Swyx [00:12:02]: Yeah. For those who wanna see more, Jason Liu, I invited him to do a full workshop on this, at AI Engineer.Ari Weinstein [00:12:07]: Amazing.Swyx [00:12:08]: Did a great job.Vibhu [00:12:09]: I have a broader vision questionToward Superhuman Computer UseAri Weinstein [00:12:11]: YeahVibhu [00:12:11]: On Computer Use agents. So your example of take a screenshot, scroll page, take a screenshot is where we were.Ari Weinstein [00:12:17]: Right.Vibhu [00:12:17]: Today, they can automate a lot. what are the bottlenecks? Is it models? Is it harnesses? What. Where do you see it going in, like, two years? Do you see it just running for hours? How do we get there? Any predictions on where Computer Use goes?Ari Weinstein [00:12:32]: Yeah. I mean, I think what's really crazy that I think, You know, the team's accomplished over the past couple of months is that now Computer Use is, like, faster at accomplishing tasks than, like, the average human probably in most cases. and I think that the next frontier is to have Computer Use be, like, literally superhuman in its performance where it actually is as fast or faster at using software than, like, expert Computer Users like us. and I think that'll be really consequential and exciting when that happens because I think we'll be able to all of a sudden build products, that, provide just much more real-time experiences. And I think it'll also. lowering the barrier to entry of, or the activation energy, I suppose, of using Computer Use I think will make us start to default to doing certain things in agents that we've become accustomed to doing manually. And I think that's exciting also ‘cause it'll save us a ton of time. and I think there's a, you know, there are a lot of different little paper cuts and bottlenecks that are sort of standing in the way of that. I think that there's, yeah, there's things on the model side, there's things on the inference side, there's things on the harness side, there's things in the, in the representation. You know, we find that as Computer Use gets faster, we're increasingly bottlenecked by just, like, the speed of doing an operation. Like, for example, you know, a non-trivial amount of time in our benchmarks of Computer Use tasks is actually, like, let's say you're automating a task on doordash.com. Like, a lot of the time is actually waiting for doordash.com itself to load, you know?Swyx [00:14:04]: Yeah, then you just write a wait and then you execute the wait.Ari Weinstein [00:14:07]: Yeah, totally. And you wanna get. Yeah, actually, it's actually really important that you get that de- like, you want as little delay as possible between when it finally finishes loading and when you go andSwyx [00:14:16]: YeahAri Weinstein [00:14:16]: Trigger the LLM to do the next action, which is actually- itself a statistical science.Swyx [00:14:20]: Like an event-driven way maybe to do that.Ari Weinstein [00:14:22]: When possible, you want it to be event-driven.Swyx [00:14:24]: JavaScript has some load events.Ari Weinstein [00:14:25]: And JavaScript has load events for. or the web browser has load events for web navigation, but there's other types of events that actually really can't be event-driven. So there's a lot of complexityVibhu [00:14:34]: The one that comes to mind is, like, chatting with customer service.Ari Weinstein [00:14:37]: Yeah.Vibhu [00:14:37]: Replies could take 30 seconds, could take three minutes.Ari Weinstein [00:14:39]: Oh, right.Swyx [00:14:41]: I have dealt with so many bots with Codex. it's great, but I also wonder if the other side knows that they're talking to a bot ‘cause I'm, like, answering in complete sentences. Like, I'm capitalized correctly.Ari Weinstein [00:14:50]: That's hilarious.Swyx [00:14:51]: Like, I'm giving full num- full reference numbers and everything. Like, it's too. it's clearly too good. I don't care. Like Like, I'm just, like, trying to get my support case.Vibhu [00:14:58]: I've prompted it to, like, you know, “Don't pretend you're a bot. Be very annoyed human.”Vibhu [00:15:02]: Short one-liners, likeSwyx [00:15:04]: YeahVibhu [00:15:04]: Push it, do all this. I also tell it, “While you're waiting for responses, like, use subagents to research better ways to figure out what we need.”Ari Weinstein [00:15:12]: Nice.Vibhu [00:15:12]: It's just, like, human little intervention.Ari Weinstein [00:15:14]: That's awesome. I also feel like half the time it's a bot on the other end, so now youVibhu [00:15:17]: YeahAri Weinstein [00:15:17]: Got the bots talking to each other.Swyx [00:15:18]: Yeah. I will also say, you know, like, you know, one milestone of Computer Use that we are, we're at now is, you know, three, four years ago, we were scared of hooking up LLMs to the, to the web and toAri Weinstein [00:15:31]: YeahSwyx [00:15:31]: To our, to our devices. And now I'm having it configure DNS for me.Ari Weinstein [00:15:35]: Wow.Swyx [00:15:36]: I'm having it pay my bills, and, like, really, like, tens of thousands of dollars of, like, stuff I'm just sending it over and yoloing with Computer Use and, like, you know, what's the, what's the worst thing that can happen?Swyx [00:15:48]: So that- that's all, that's all really good.Building Safely With Computer Use in the Agents APIAri Weinstein [00:15:50]: Yeah.Swyx [00:15:50]: I think now that you've. you know, obviously, you also have to dogfood your own products and all these things. Now that you've sort of released this in API, what are some pitfalls or tips that you wanna tell developers, because they're about to, I guess, encounter all this, firsthand?Ari Weinstein [00:16:03]: First of all, I'm just really excited that we brought Computer Use into the Agents API. I think this is, really great because obviously a lot of developers are building applications that wanna be able to work with third-party websites and services. And so Computer Use has this universality to it. It can work with anything. So now all of a sudden, developers can build using the same Computer Use implementation that we're building on. I think there's great work to be done if you wanna build your own Computer Use harness, but it's hard. And also, we train our models on our Computer Use harness, so there is, an advantage to using the one that's in distribution for the model. There actually might be a speed and cost and accuracy advantage. So I think it's really great for people to get to build on top of that. And, yeah, you know, I think kind of to the point that you were making, like, I think we're all sort of still in the process and maybe, like, some of us are ahead of many people in the world of, like, getting comfortable with this technology and trusting it. And so I think it's incumbent on us to, sort of build that trust over time by making sure we're building things that are reliable, by building, the right kinds of safety checks, by asking for the user's consent before doing something consequential like making a payment, by, asking, you know, maybe depending on the application, making sure you're only letting it access the websites or applications that it actually needs for the task. So that's, I think, something important to think about. but yeah, I'd really encourage people to try the new Agents API, build all kinds of cool stuff on it. We'd love to hear your feed- feedback if, you know, depending on how it goes.Vibhu [00:17:31]: Have you seen any changes in the way it affects dev workflows? So one of the things with dots is, you know, you're seeing it in Slack.Ari Weinstein [00:17:38]: Yeah.Vibhu [00:17:38]: You're seeing people use voice and build. the example Roman showed of change this app and send me screenshots along the way and all this.Computer Use for Testing and Closing the Software LoopAri Weinstein [00:17:46]: Yeah.Vibhu [00:17:46]: Is anything that you're seeing there in adoption about how people are using Computer Use for coding workflows? Any tips people should take from that?Ari Weinstein [00:17:55]: One of my favorite use cases for Computer Use actually, and one that we see a lot in the wild, is Computer Use letting the agent- actually test the software that the agent has built, which is far more consequential than it sounds. Because traditionally, you know, you'd build something in Codex and then the-- and the Codex builds it for you, and then you have to test it, and you are now like QA for the agent, right? So with Computer Use, you can complete the develop-- the software development life cycle, where, the agent can build software, it can test it. So I have a lot of fun, you know, building stuff, having the agent test it. By the time it comes to me, it's already working. I have, extra fun because sometimes I'm, like, developing Computer Use itself, and so now I have a Computer Use agent that's using my Computer Use agent that's using something else. so yeah, I really, I really think this is a super powerful class of use case.Swyx [00:18:44]: I have a visual play test skill that I've developed that, really catches a lot of design issues,Ari Weinstein [00:18:49]: NiceSwyx [00:18:50]: That, you know, normally when you just look at code, you wouldn't really pick it up. it's also really good for cloning apps, though. If you're using a shitty SaaS and you wanna kill the SaaS You just clone it screen by screen by screen. and Obviously, Computer Use can completely drive everything, take screenshots, note it down, and then clone everything with Codex.Ari Weinstein [00:19:06]: That's really cool.Swyx [00:19:06]: But yeah, thanks for all your progress. I think, that isAri Weinstein [00:19:08]: AbsolutelySwyx [00:19:09]: Our time.Nikunj Handa: What's New in the OpenAI APIAri Weinstein [00:19:10]: Yeah.Swyx [00:19:10]: This is not the last that we're gonna talk.Ari Weinstein [00:19:12]: Yeah, cool. This has been really fun. Thank you guys for having me.Swyx [00:19:14]: All right.Vibhu [00:19:14]: All right. Okay, we're a strict cutoff. We're just gonna dive right in.Nikunj Handa [00:19:17]: Let's do it, yeah.Vibhu [00:19:19]: Okay, so, Nikunj, we're very excited to have you. You shipped a lot on the API side, like we justNikunj Handa [00:19:25]: YeahVibhu [00:19:25]: Talked about with Ari. You can now build with Computer Use agents. Anything you wanna highlight, the API side of changes, and introduce yourself a little and what you do?Nikunj Handa [00:19:34]: Yeah, for sure. My name is Nikunj. I lead product for the API team. Been here for roughly three years. been working on launching models. I feel like that's just been, like, a thing, constant thing throughout my time, here at OpenAI. And, with every new model, we try to, like, basically work super closely with the post-training team, the research team, to figure out what's new in it. and then we, like, expose those capabilities in the API. so that's, like, the basic way of putting it. and if you just look at, everything that's new with GPT-6, the cool new capabilities that we launched were, firstly, async function calling. so what you see with, like a lot of the things that you're seeing in, like, Codex and Dots and everything is that tool calls take so long that you don't have to, like, pause the model's execution while, the tool is running. So you could just, like, kick off a tool call, keep running, keep reasoning, and then check back in. so we launched async tool calling. We launched, like, mid-turn steering, so now you can, like, inject messages while the model is reasoning, in the middle. so as your tool call finishes, you can put in that instructions.Async Tool Calls, Mid-Turn Steering, and WebSocketsSwyx [00:20:43]: And that's also partially a model alignment capability, right?Nikunj Handa [00:20:46]: Yeah.Swyx [00:20:46]: Like, they have to train in the ability to train.Nikunj Handa [00:20:48]: Exactly, yeah. AndVibhu [00:20:49]: I feel like we've had it in the app. You could always, as it's reasoning, you could steer.Nikunj Handa [00:20:54]: Yes.Vibhu [00:20:54]: It wasn't the best. It's gotten much better.Nikunj Handa [00:20:57]: Yeah.Vibhu [00:20:57]: Excited to see how it does this in versionNikunj Handa [00:20:58]: Yeah, and I like our mainVibhu [00:20:59]: And nowNikunj Handa [00:21:00]: Goal in, our main goal in the API is to, like, put things in the API once it's trained into the harness. And so we kinda wait for that moment until it's good enough. And a lot of that is, like, actually being powered by WebSockets, which we launched, a few, I wanna say months ago. And so WebSockets just opens this, like, whole bidirectional, like, communication thing with the model. This is not, the GPT Life thing. I'm just talking about GPT-6. and you can do all these, like, async tool calling, async reasoning, injecting messages. It's a really fun API to work on. I think, like, really enjoying.Swyx [00:21:33]: Yeah. This is why we are the engineering podcast, because we get to talk about WebSockets.UltraFast and the Inference StackNikunj Handa [00:21:36]: Yeah.Swyx [00:21:37]: This also pairs very well with UltraFast, right?Nikunj Handa [00:21:39]: Oh, yeah.Swyx [00:21:39]: Like, that is now, like, I think for the first time ever available in the API.Nikunj Handa [00:21:43]: Yes.Swyx [00:21:43]: Which is, which is basically the theoretical fastest speed you can ever get, Frontier of Intelligence.Nikunj Handa [00:21:49]: Yeah. It's been so exciting to work on that project. I think, before I go into the API, the most fun part of, UltraFast has been just watching the inference team cook with Astra. Like, they're just, like, constantly having these, like, Codex agents running, trying to, like, squeeze out more performance. And, I would say, like, at least for a couple of months, a lot of it was focused on efficiency and driving the cost down, which is how we, like, were able to cut the Luna price by, like, 80%. It was, like, a lot of that was driven by, like, all the inference improvements they landed. And then now they've, like, shifted gears towards, like, how can we make this run as fast as possible? And so UltraFast has just been, like, amazing to see on a mo- on a model like Astra. Like, to go that fast has been really cool. And yeah, WebSockets is like. actually it was like the first time we launched WebSockets, it was for GPT, 5.3 Codex Spark, which was. Can't believe we named a model that, but, you know, that's what we launched it for. And obviously, it helps so much because, like, you gotta have the tool calls. you had, like, really reduced the overhead, of going back and forth with tools. And so, WebSockets is awesome for that.Swyx [00:22:57]: Yeah. it's always cute to see, like, I have my reset usage limit, and then I have my Spark usage limit that I never use.Nikunj Handa [00:23:03]: Yeah.Swyx [00:23:04]: Like, it's there if I want it.Nikunj Handa [00:23:05]: I think it's gone finally.Swyx [00:23:06]: It's gone. It's gone, yeah.Nikunj Handa [00:23:07]: I know it's gone, so.Swyx [00:23:08]: Yeah. you're slowly killing off all the, you know, theNikunj Handa [00:23:11]: The old ones, yeah.Swyx [00:23:11]: Oldies.Vibhu [00:23:11]: This is a great week. I mean, it was the first time we had Frontier Intelligence at extreme speeds.Nikunj Handa [00:23:17]: Yeah.Vibhu [00:23:18]: People really liked it.Nikunj Handa [00:23:19]: Yeah.Vibhu [00:23:19]: SoSwyx [00:23:20]: YeahVibhu [00:23:20]: First time it comes back.Swyx [00:23:21]: Yeah. for, 5.3 Spark is explicitly attributed to Cerebras. You guys are not confirming or denying that, UltraFast is related to Ce- Cerebras, but people are. I'll just say that people do care and, are wondering about it. And you have your own silicon as well. elephant in the room, decision models.Decisions API: OpenAI's Fast Decision ModelNikunj Handa [00:23:38]: Oh, yeah.Swyx [00:23:38]: Decisions API. We were the first podcast to do a big Jev, deep dive with, Diogo, and I also, you know, featured him at AI Engineer. How quickly did you see Jev and go likeNikunj Handa [00:23:49]: Oh my gosh. Yeah.Nikunj Handa [00:23:50]: Yeah. Firstly, like, huge props to Diogo and, like, the Jev team for, like, really inspiring theSwyx [00:23:55]: YesNikunj Handa [00:23:55]: Like, whole segment in the market. Like, obviously Jev comes out, everyone's, like, losing their minds over it. Our users are, like, hitting us up. But also, like, our internal teams are like, “We need, like, a much faster classification system.” We can. I don't wanna, like, get ahead of some of the dots features that are gonna comeSwyx [00:24:16]: WhooNikunj Handa [00:24:16]: But you're gonna see, like, some cool, like, really snappy, fast things built on top of the decisions API. but, you know, like, yeah. Props to Jev for, like, inspiring this whole thing. obviously a bunch of people at OpenAI get nerd sniped by that, and they're like, “How can we, like, make this work? We're not gonna, like-”Swyx [00:24:33]: Okay.Nikunj Handa [00:24:33]: “. train a new model.” ButSwyx [00:24:34]: Like, four weeks ago, this was not on the dev radar, right?Nikunj Handa [00:24:37]: No, not at all. No.Swyx [00:24:37]: Okay.Nikunj Handa [00:24:37]: This is likeSwyx [00:24:38]: WowNikunj Handa [00:24:38]: Jev-inspired and, likeSwyx [00:24:40]: I think you are officially the first one to your lab to, like, clone and, adopt this.Nikunj Handa [00:24:44]: Yeah. Yeah. I feel like, OpenAI has such a strong, like, hacker culture and, like, people are just, like, they get excited about things. And so, guy from inference, this one awesome guy from, the infra team are like, “ this is amazing. We're gonna, like, hack on it.” They build a prototype, it, like, works, and now we- we are just, like, hill climbing on latency and trying to make this as fast as possible, and we wanna, like, launch it in the coming days. so as soon as we hit our, like, latency target, we'll try to get this out.Vibhu [00:25:13]: It's interesting. At the same time of hacker culture, you also, as Sam said, like 99%, one of the most reliable APIs withNikunj Handa [00:25:20]: Mm-hmmVibhu [00:25:20]: I think probably the most usage, which is your team directly. how should people see decisions API? I feel like a lot of people saw Jev, heard the buzz, haven't built with it. You're making it very mainstream.What Decision Models Are Good ForNikunj Handa [00:25:32]: Mm-hmm.Vibhu [00:25:33]: What should people see it as? How should they use it?Nikunj Handa [00:25:36]: Yeah. I think the main use cases we've seen is, like, really fast classification. all the Computer Use demos have been amazing and really cool. I think there will be limitations, of course, in terms of, you know, having Astra, like, write, like, a JavaScript-like script to control your computer, versus having Luna pick, like, one action at a time. I think, it's not gonna be at the same intelligence level, but, like, maybe there's some Computer Use tasks that this is good enough for. So excited to see that come through. the other cool prototype I've seen internally is people hooking it up with GPT Live. So GPT Live is like, you know, our bidirectional, like, real-time,Swyx [00:26:14]: VoicingNikunj Handa [00:26:14]: A- API. And, it's built on this, like, model of front-end models and back-end models. So GPT Live is this, likeSwyx [00:26:20]: Think or talkerNikunj Handa [00:26:21]: Super fast. Yeah, think or, talker thing. So GPT Live is the talker, super fast, really good at delegation, and you have something like Astra sitting at the ba- at the back. But tool calling has always felt, like, really slow in GPT Live. and so people have been, like, putting together these, like, tool calling demos of GPT Live controlling a computer, and it just feels like so much more snappy and natural. So I'm, like, kinda excited to see, like, what people do with Live and with Luna on decisions API. so that'll be pretty exciting. Yeah.Swyx [00:26:55]: So I wanna iron this out for people, especially from the product side, because a lot of people have been putting out Jev clones. There's been about 100 in the last two weeks.What Makes a Decision Model DifferentNikunj Handa [00:27:01]: Oh, really? That's amazing.Vibhu [00:27:03]: The first couple days.Swyx [00:27:04]: But like, it. Like, they can clone a Jev API, which is honestly structured outputsNikunj Handa [00:27:09]: YeahSwyx [00:27:09]: Which OpenAI was first to.Nikunj Handa [00:27:10]: Yeah.Swyx [00:27:11]: Right? So, like, I think let's iron out for people what is a decision model, as far asNikunj Handa [00:27:16]: YeahSwyx [00:27:17]: As far as, like, what is important? It is not just latency. It's not just structured output, right? Because I could just have Luna as it'- The decision model is priced the same as Luna, right?Nikunj Handa [00:27:26]: Mm-hmm.Swyx [00:27:27]: Have turned off reasoning and then have structured output. Do I have a Jev? you know, no, right? And that's theNikunj Handa [00:27:33]: YeahSwyx [00:27:33]: That's the realVibhu [00:27:34]: There's a confidence there.Swyx [00:27:35]: Yeah.Nikunj Handa [00:27:36]: Yeah, totally. I think, the way that. So we haven't trained, like, a new model for this.Swyx [00:27:40]: Yeah.Nikunj Handa [00:27:40]: We're, like, building this purely on top of the same Luna weights that we have.Swyx [00:27:44]: Oh.Nikunj Handa [00:27:44]: So yeah. This is, like, really just Luna. And, on top of that, what you're doing is you're constraining. So, like, structured output's a big part of it. you're really optimizing the inference stack to, like, get very fast on TTFD. And because you can have multiple questions, what you do is, like, you basically run those in parallel,Swyx [00:28:05]: As a batch.Nikunj Handa [00:28:06]: Yeah. You run those in the-- as a batch. you-- All sorts of, like, inference techniques people are working on to try to make it as fast as possible. But I'd say, like, at least our implementation of it at the start and this first version is, like, zero-shotting this on top of Luna, to see how it goes. And obviously, you wanna, like, put it out there. Like, this is OpenAI's, like, classic iterative deployment thing. Put it out there, see what people think, and then, like, we'll make more model improvements, as needed. so yeah. That's, the decisions API.Swyx [00:28:38]: Yeah. And, obviously as a benefit, you have vision. They don't have vision, right?Nikunj Handa [00:28:42]: That's true.Swyx [00:28:42]: Obviously, Jev's comes withNikunj Handa [00:28:43]: Yeah. Like, we get it for free with Luna. Yeah.Swyx [00:28:45]: Yeah. I do think that, like, you know, some of the innovations, it sounds like, it's still to come if it's still the same Luna weights, which is, like, the confidence stuff, like, the in calibration is something that we've talked about on the podcast with, benchmarking calibration. ‘Cause basically, the whole point is that RLHF kind of collapses you towards what you want to hear.Calibration, Architecture, and the Open Research QuestionsNikunj Handa [00:29:03]: Yeah.Swyx [00:29:03]: But, like, not actually, like, what the amount of confidence is.Nikunj Handa [00:29:06]: Yeah. Yeah, totally. I'm eager to see how it pans out. Maybe there's, like, gonna be. These are gonna be, like, the key areas where we may have to, like, hill climbSwyx [00:29:15]: YeahNikunj Handa [00:29:15]: With the, with the future model release. But, yeah.Swyx [00:29:18]: And then architecture-wise, the other thing that's in the debate, obviously, you-- Nobody knows because Jev doesn't talk about it, but the two speculations are, one, maybe diffusion model instead of autoregressive.Nikunj Handa [00:29:28]: Mm-hmm.Swyx [00:29:29]: But you are able to achieve the parallel, generation in your way. And then the other one is some mech interp type thingNikunj Handa [00:29:37]: Mm-hmmSwyx [00:29:37]: That you're, like, analyzing the activations and then just outputtingNikunj Handa [00:29:40]: That would be coolSwyx [00:29:41]: The weights.Nikunj Handa [00:29:42]: Yeah.Swyx [00:29:42]: Which, like, you guys have all done the research on this. People have speculated.Vibhu [00:29:45]: There have been demos onSwyx [00:29:46]: YeahVibhu [00:29:46]: Both of these as well. I think Gemini shared a Gemini diffusion, Gemma diffusion on a Jev-style output.Nikunj Handa [00:29:53]: Oh, sick.Vibhu [00:29:53]: And, interp people have also, you know, pulled out interp from a middle layer, but this is all speculation.Swyx [00:29:59]: It's just like, what are you trying to aim for, right? Because you can achieve the API. Everyone can achieve the API. It's actually pretty trivial. But, like, then there's the speed, then there's the accuracy, then there's the other calibration features.Nikunj Handa [00:30:11]: Mm-hmm.Swyx [00:30:11]: I don't know what else.Nikunj Handa [00:30:13]: Yeah. Yeah. No, totally. It's so cool that this, like, whole space has been kicked off now and people are gonna do so much cool stuff and everyone's gonna learn from each other. And, yeah, I'm excited about it.What Developers Should Build NextVibhu [00:30:24]: I feel like being on the platform team, a lot of your job is to empower builders.Nikunj Handa [00:30:27]: Mm-hmm.Vibhu [00:30:28]: What do you think people should build with decisions API and also Computer Use agents? Any stuff that you've- been building with internally that you think really opens up after the new change?Nikunj Handa [00:30:39]: Yeah. okay, let's think. decisions API, use cases internally have been pretty obvious. Like, the user ops team was, like, jumping on it. We were like, “We gotta classify all of our support tickets.” what else came up? obviously, there were, like, the really cool GPT Live demos. I'm sure, like, the Codex app team might, like, pick this up and try to do something cool with it. So, you know, like, this whole thing started, like, a week ago, so it's, like, very early andSwyx [00:31:06]: Oh, one week.Nikunj Handa [00:31:07]: We're excited. Yeah. Yeah, pretty much.Vibhu [00:31:08]: There was a big push in, evals, LLM as a judge having really low latency there.Nikunj Handa [00:31:13]: Right. Yeah. That'll be interesting to see. and then, with the Agents API, we have-- we're basically, like, having a bunch of first-party products, like, at OpenAI built fully on top of it. we've had the Codex security stuff that just went out that's fully built on top of, the Agents API. We have, sort of the-- we- we are having, like, a meetings type of thing launching today.Agents API and OpenAI's First-Party ProductsSwyx [00:31:40]: Mm-hmm.Nikunj Handa [00:31:40]: I think there was, like, a demo. do you remember, like, the plugin extensions when Sam was showing it? There was, like, a demo for, like, you're in a calendar, you can sort of, like, have your meeting notesSwyx [00:31:51]: Like, drop into a singleNikunj Handa [00:31:52]: Flow into like your spaceSwyx [00:31:52]: Like, Google Docs type thing.Nikunj Handa [00:31:53]: Yeah.Swyx [00:31:54]: Right?Nikunj Handa [00:31:54]: And so the-- all of that stuff is, like, fully built on top of, the Agents API. and yeah, I'm, like, just excited to see. Like, we're just getting this out, and let's see what people build on top of it.Vibhu [00:32:04]: I think you showed it off very well. The whole edit spaces, pages, collaborate, add in your dot. Like, that's a lot, soNikunj Handa [00:32:12]: YeahVibhu [00:32:12]: There's a lot of inspiration people can go to.Nikunj Handa [00:32:14]: Yeah. All possible with Astra, you know. Like, thing- things just move so fast now. LikeSwyx [00:32:19]: YeahNikunj Handa [00:32:19]: People go from idea to execution so quickly, it's amazing.Swyx [00:32:23]: Is there something that you want, people to focus on to give you feedback? Like, what-- like, you know, maybe you're just putting this out there and you want-- and there's, like, a fork in the road and you want developers to help you decide.Responses API Performance and Long-Lived CachingNikunj Handa [00:32:35]: So I think Agents API and decisions API, they are like, these are our newest products. Would love, like, any and all feedback on that to figure out where to take them. I think, over here, we're, like, very open on Responses API, which is sort of like our workhorse over here. like, really focused on performance right now, and the performance comes in, like, two main ways. first is just, like, latency. We've been, like, rewriting the whole Responses API stack to, like, make it as fast as possible from a TTFT perspective, DVD perspective. So there's like-- that, like, continues to be, like, a main area of focus for us. The second thing we've been trying to do is, like, really go deep on caching, particularly with these, like, personal agents that are, you know, like, basically, like, a single thread that just goes on and on forever. We've been, trying to, like, really up our game on caching. We provide now guarantees of, like, cache hits within, like, 30 minutes. We're actually, like, we-- for one of our users, we just launched, like, a much longer cache window. So we have, like, a 12-hour caching guarantee, that we offer so that you have, like, guaranteed cache hits forSwyx [00:33:40]: Is that a public API?Nikunj Handa [00:33:42]: Not yet. That's in preview.Nikunj Handa [00:33:43]: We're gonna, like, try to get that out to everyone as soon as possible. But, like, just pay a little bit more for the cache write, and we, like, guarantee, like, cache reads for, like, a much longer period. So even if, like, your instinct thread, for example, like, you just, like, do something on it and then come back to it, like, three to four hours later, you- you're still getting the caching performance out of it. And launchedVibhu [00:34:04]: And you cut the cost there quite a bit too, right, with the new model?Nikunj Handa [00:34:07]: Oh, yeah. That's right.Vibhu [00:34:08]: Like, 25% cheaper, soNikunj Handa [00:34:08]: Yeah, with, like, driving down cache reads, yeah.Cache Pre-Warming and Cost-Efficient Agent ThreadsVibhu [00:34:10]: For builders, they should implementNikunj Handa [00:34:13]: YeahVibhu [00:34:13]: Because it's significantly cheaper.Nikunj Handa [00:34:14]: Yeah. Yeah. Just, like, building your apps with, like, to be very cache aware and sort of, like, use our prompt diagnostics or cache diagnostics tool to figure out, like, where things are dropping off. And, so the caching part is, like, really important. yeah, I also wanted to talk about pre-warming. We have that in the API now. So, like, if you know that, “Hey, I'm gonna get a cache,” like-- sorry, “I'm gonna get this prompt. I just wanna, like, pre-warm the cache, pay, like, the cache write fee right now, and then, like, have it sort of ready to go for the next 30 minutes for whenever.”Swyx [00:34:49]: And it can spawn many instances of that thread.Nikunj Handa [00:34:51]: Exactly, yeah.Swyx [00:34:52]: Yeah.Nikunj Handa [00:34:52]: You can just keep going and haveSwyx [00:34:54]: Yeah, just keep messing with the prompt thereNikunj Handa [00:34:55]: Tons and tons of that. and so, yeah, like, I'm very excited about getting feedback on, like, the low-level performance things that we can keep making Responses API the most performant and reliable way to, like, build on top of an LLM. And then you basically have our, like, new products where I'm just looking for, like, any and all feedback.Swyx [00:35:15]: Yeah, just use it, right?Nikunj Handa [00:35:16]: So yeah, just useSwyx [00:35:16]: Tell us what toNikunj Handa [00:35:17]: Yeah. Define our roadmap for us, please. So yeah.Swyx [00:35:20]: I think for me, the caching thing, great, right? Like, obviously very needed. But at the end of the day, you're still bumping up against a million-token contextCompaction and Managing Million-Token ContextsNikunj Handa [00:35:28]: Mm-hmmSwyx [00:35:28]: And that's probably not gonna change for the foreseeable future.Nikunj Handa [00:35:31]: Mm-hmm.Swyx [00:35:31]: Like, you still need good compression.Nikunj Handa [00:35:33]: Yeah.Swyx [00:35:33]: What is the best practice there?Nikunj Handa [00:35:34]: Yeah. Yeah, totally. so firstly, OpenAI has its own, like, proprietary compression, compSwyx [00:35:40]: Which is inNikunj Handa [00:35:41]: Compaction.Vibhu [00:35:42]: Compaction.Swyx [00:35:42]: It's in the agents.Vibhu [00:35:43]: It's in the API.Nikunj Handa [00:35:43]: Yes.Vibhu [00:35:43]: Agents API.Nikunj Handa [00:35:44]: Yeah.Swyx [00:35:44]: You decide for us, right?Nikunj Handa [00:35:45]: Yeah, exactly. So in the Agents API, it comes built into the harness. and if you're in Responses API, there's, like, two ways of doing it. One is what we call server-side compaction, which is you basically tell Responses API that if you ever hit this threshold of tokens, just auto-compact it and, like, go back, or sorry, like, reduce the context, being used. And the second way is, like, /compact, which is, like, if you want full control. So you can, like, /compact at any timeSwyx [00:36:15]: I hear youNikunj Handa [00:36:15]: Have your own logic on when to, likeSwyx [00:36:17]: It's not AGI.Nikunj Handa [00:36:18]: It.Swyx [00:36:18]: It's not AGI.Nikunj Handa [00:36:19]: Yeah. Yeah.Swyx [00:36:20]: Yeah. But it, I meanNikunj Handa [00:36:20]: YeahSwyx [00:36:20]: It is the manual override.Nikunj Handa [00:36:21]: Yeah, it is the manual way. And like, I don't know, but a lot of the big coding agents like to do it manually. I mean, like, if you look at the Codex implementation of it in the Code- open source Codex harness, you can see that they use /compact and do it. and, there's also, like, new, by the way, new compaction techniques that we are working on. Some of them you will be able to see in the Codex harness. Like, it's already implemented in the Codex harness. And so, they're like some file-based, systems that we are, like, experimenting with. So yeah, lots of cool stuff going on around in compaction as well.Swyx [00:36:57]: Cool. we are running out of time.Nikunj Handa [00:36:59]: Okay.Swyx [00:36:59]: I think you've talked about, a lot about performance and talked a lot about, the new APIs that you're launching. Can you give us any other hints as to things that you're interested in as far as the future of the platform is concerned?Higher-Level Platform Primitives and the AI CloudNikunj Handa [00:37:13]: We're obviously like very low level. Like, I used to work at Stripe before this, and, at Stripe a lot of the game was like building these higher level primitives and products on top of like the core payments primitives. and, I'm always like curious about what the best way of doing that is in AI. And I think we've had a couple of attempts at that. We like had launched assistance API like way back in the day, and like wasn't really the right fit. We were sort of like going off with this like Agents API, and, it gives you the codex harness, but like where's like the, what's the right amount of flexibility to give in that? That's like an open question. Like how should we like have memory walls and like all of these like higher level like API objects to take away, also like to abstract away more, like storage concepts. Like this is like a whole, like, there's a whole space that I'm like very curious about figuring out how we design. I think a lot of things in AI are just have a low-level API primitive and see an example harness and go and have your coding agent implement that. But how much of that do we build into the API is like a constant question that I'm thinking about.Swyx [00:38:24]: Yeah.Nikunj Handa [00:38:24]: So I don't know if folks have thoughts on that. If anyone has ideas, it would be super interesting to hear.Swyx [00:38:30]: Yeah. The analogy I always bring back to, and we'll end there, is, you're building an AI cloud, right?Nikunj Handa [00:38:35]: Mm-hmm.Swyx [00:38:35]: Like, which is, something that, Sam said a year agoNikunj Handa [00:38:38]: Mm-hmmSwyx [00:38:38]: Where, and you're, it's almost like you're kind of doing the AWS invention and you have to do, okay, this is EC2Nikunj Handa [00:38:45]: YeahSwyx [00:38:45]: And this is S3, and this is like. But you're doing the AI-native versions of each of these.Vibhu [00:38:48]: There are a lot of analogies, so you're pre-warming caches for stuff that you know will beNikunj Handa [00:38:53]: Yeah.Vibhu [00:38:53]: And it's nice that it's all exposed to buildersClosingNikunj Handa [00:38:56]: Mm-hmmVibhu [00:38:56]: ‘cause it just opens up ways that you can build new things.Nikunj Handa [00:38:59]: Yeah, absolutely.Swyx [00:39:00]: Okay.Vibhu [00:39:00]: Awesome. WellSwyx [00:39:01]: That's everything.Nikunj Handa [00:39:01]: Thank you, guys.Vibhu [00:39:02]: Thank you.Nikunj Handa [00:39:02]: Yeah. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

I'm squeezing in one more episode for Solo September and it's one of the games I've wanted to play (and solo!) the longest - Primal Quest by Diogo Nogueira!   There are also a bunch of supplements for Primal Quest, including the solo supplement Primal Quest: Feral from Exalted Funeral, which has lots of great oracles and tools for you to play your Weird Stone & Sorcery game!   This game rules. Stone Age old school vibes but with cool dice pool tag mechanics, an amazing setting called Thaia, which you can read more about on Diogo's blog, and I played one short little starter event and it's made me want to play more of this sooo bad... Hope you enjoy!   Should I? Still not sure what I'm going to do through the end of the year or next year, so if you're reading the podcast descriptions, definitely join the DMs Discord or reach out to me to let me know what you think I should play next!   ----more---- Join the DMs After Dark Discord channel!   I made a Ko-Fi if you feel absurdly generous and want to help cover podcast hosting costs & all the upkeep. I'm still working on whether I want to offer anything special over there or just give my extreme gratitude (maybe some stickers or something in the mail) to those who donate, but no pressure whatsoever :)   Where to Follow Rene Plays Games: LinkTree |  BlueSky | Threads | Instagram | Facebook | DMs After Dark Rene's Games: MECH | MECH Cities 2 | One Last Quest | I Know I Know You, But I Don't Know How... email: RenePlaysGamesPod@gmail.com   Music in the Episode (in order of appearance): Lost Continent by Monument Studios Shuttle Crash by Tabletop Audio Dinotopia by Tabletop Audio Skull Island by Tabletop Audio Fire Dance by Tabletop Audio Rene Plays Games Theme written & produced by Dan Pomfret | @danfrombothbands

AI is changing application security at breakneck speed — and it's not just about generating code faster.In this episode, Keelin Conant's guest is Diogo Rispoli, an application security leader, architect, and longtime industry expert. They explore what happens when AI agents start writing, testing and fixing code, and even talking to each other. The episode also digs into rogue agents, exploitability, AI-powered testing, the limits of auto-remediation, and why security teams need to look beyond the code itself.Plus, Diogo looks ahead to 2030 and asks a big question: will finding vulnerabilities even be the job of AppSec anymore?As featured on Million Podcasts' Best 100 Cybersecurity Podcasts  Top 50 Chief Information Security Officer CISO Podcasts Top 70 Security Hacking PodcastsThis list is the most comprehensive ranking of Cyber Security Podcasts online and we are honoured to feature amongst the best!Follow or subscribe to the show on your preferred podcast platform.Share the show with others in the cybersecurity world.Get in touch via reimaginingcyber@gmail.com

a16z's Ben Horowitz and Martin Casado sit down with TypeSafe AI founder Diogo Almeida to ask a simple question: AI has become remarkably capable, so where is all the automation?Diogo argues that coding agents may help us write software faster, but the software they produce still largely works the way software always has. TypeSafe is taking a different approach with Jev: putting intelligence inside software itself, so developers can build programs that reason about intent and make probabilistic decisions rather than simply generate text for a human to interpret.They discuss why reliability is the key to making AI genuinely programmable, how this could open a new era of probabilistic software, and why established SaaS companies may be particularly well positioned to benefit. Ultimately, Diogo's goal is straightforward: technology that can reliably “do what I mean.”Resources:Follow Diogo Almeida: https://x.com/CompleteSkepticLearn more about TypeSafe AI: https://typesafe.ai/Follow TypeSafe AI: https://x.com/typesafeaiFollow Ben Horowitz on X: https://x.com/bhorowitzFollow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

O MLSTP, principal partido de oposição, chegou à frente nas legislativas deste domingo 27 de Setembro de 2026 em São Tomé e Príncipe, com uma diferença de algumas centenas de votos face à ADI, no governo. Enquanto o MLSTP reivindica pelo menos 26 mandatos, a ADI no poder, veio afirmar esta tarde que existe a possibilidade de “um empate técnico” a 24 mandatos com o MLSTP. Outro dado destas eleições, de acordo com a Comissão Eleitoral Nacional, a taxa de abstenção situou-se quase nos 59%, ou seja no mesmo nível constatado nas presidenciais de 19 de Julho. Dados preliminares que analisamos com o sociólogo são-tomense Olívio Diogo. RFI: Como analisa na globalidade os primeiros resultados divulgados hoje? Olívio Diogo: Primeira coisa que tenho que falar sobre as eleições é dizer que elas correram de forma ordeira. Mais uma vez, o povo são-tomense mostrou que está muito evoluído positivamente para aquilo que diz respeito às eleições, naquilo que diz respeito à votação e que a partir do momento que as pessoas votam, elas transformam o dia da eleição numa festa. Não há nenhum tipo de conflito. Não há incidente e se houver algum outro caso, são casos naturais que acontecem em qualquer eleição, como em qualquer parte do mundo. Mas eles mostraram que estão bastante maduros para a questão das eleições. A outra questão tem a ver com aquilo que foram as abstenções que aconteceram. Esta abstenção acontece sobretudo porque as pessoas ficam cada vez mais cansadas daquilo que é a votação. Eu estive em alguns círculos eleitorais. Eu vi que as pessoas estavam ali, estavam perto do local da votação, mas não iriam votar porque estavam a participar da festa. É que os governantes, qualquer um deles, já não estão a trazer para si aquilo que acham que vai melhorar as suas condições de vida. RFI: Relativamente ainda á abstenção, esta abstenção, a taxa é mais ou menos semelhante àquela que foi constatada durante as presidenciais de 19 de Julho. Outro dado é que, entretanto, há algumas semanas, houve um apelo ao boicote a estas eleições legislativas. Como é que deve ser interpretada esta taxa de abstenção? Olívio Diogo: É verdade que mesmo antes do período eleitoral, houve um apelo ao boicote pelo ex-primeiro ministro Patrice Trovoada para que as pessoas não fossem às urnas. Quando nós falámos com as pessoas aqui, nós percebemos que este boicote pode não ter sido a causa fundamental das pessoas não irem votar, mas este boicote aproveita a oportunidade que as pessoas estão tão desencontradas com aquilo que os políticos fazem e as pessoas vêem que as suas vidas não estão a ser melhoradas. Mas é preciso também dizer que esta abstenção tem dois ou três aspectos ainda que são importantes falar sem falar da questão dos boicotes, sem falar que as pessoas estão desinteressadas em irem votar. é que esta forma de votação que nós estamos a ter agora, é uma votação diferenciada, todos os indivíduos que têm 18 anos, eles não precisam ir fazer a inscrição. Portanto, há muitos jovens com 18 anos que nem sabem que estão inscritos no caderno eleitoral. Outro aspecto tem a ver com o facto de estes dados terem sido extraídos e muitas pessoas não sabem em que local estão inscritas. O terceiro aspecto muito importante também, que se deve frisar, tem a ver com a emigração. Esta eleição a que estamos a assistir agora é uma eleição que apanhou esta vaga de emigração. É uma emigração de pessoas que muitas delas estão sem as suas condições de vida regularizadas. Elas não sabem sequer que poderiam votar no exterior. RFI: Este voto maioritário no MLSTP é um voto de adesão ao projecto do MLSTP ou de rejeição ao partido da situação? Olívio Diogo: O MLSTP, para quem acompanha as eleições em São Tomé e Príncipe, de algum tempo a esta parte, sabe que o MLSTP tem um número mais ou menos de votos que não se altera muito e portanto o MLSTP fez o seu papel de garantir que este número de votos seja aplicado. E por outro lado, trabalhou para que aumentasse mais um ou dois, o número deputados. Portanto, aqui há duas coisas. A primeira coisa é dizer que isto foi claramente um boicote ao Américo Ramos e as pessoas não se encontraram com aquilo que o Américo Ramos fez durante o seu mandato de dois anos. A população não se entendeu com Américo Ramos e certamente a forma como se tornou líder do Partido da Acção Democrática Independente e ele não conseguiu criar uma empatia com a população, que era uma empatia com aquilo que são as necessidades básicas da população. Se reparar, o MLSTP não cresceu muito. Foi a ADI que baixou significativamente. A ADI tinha nas últimas eleições 30 deputados e saiu de 30 deputados para ter agora 22 ou 23 que ainda não sabemos. Mostra claramente que foi a ADI que perdeu e que o grande perdedor destas eleições é inevitavelmente, o Américo Ramos, o primeiro-ministro em funções. RFI: Patrice Trovoada, pode retirar algum dividendo político deste cenário? Olívio Diogo: Naturalmente que Patrice Trovoada vai tirar dividendos políticos deste cenário, por ter apelado ao boicote às eleições, apelado ao aumento da abstenção e a abstenção vir equilibrada, como foi já nas eleições presidenciais, demonstra claramente que a sua palavra, a sua imagem e a informação que ele quis passar foi encaixada sobretudo naquelas pessoas que são fidedignas àquilo que é a política de Patrice Trovoada. Essas claramente não foram votar e essas claramente disseram que continuam com ele independentemente daquilo que venha a contar. Portanto ele pode perfeitamente bem tirar dividendos disto. RFI: Ainda não se sabe exactamente como é que se repartem os mandatos, uma vez que é o Tribunal Constitucional que deve dar esta informação. O MLSTP reivindica pelo menos 26 mandatos, o que é muito próximo da maioria absoluta. Mas não tendo a maioria absoluta, para que partido é que se vai virar naturalmente para eventualmente formar uma coligação? Olivio Diogo: No que diz respeito ao facto de ser o Tribunal Constitucional a fazer o mosaico natural de número de mandatos que cada partido deve ter, isto leva a entender que o MLSTP, tendo em conta que isto é uma realidade, está a fazer pressão para dizer ao Tribunal Constitucional que este resultado são resultados que dificilmente se alteram. Mesmo que a ADI queira governar, teria que fazer uma coligação, mesmo que o MLSTP queira governar teria que fazer uma coligação. Perguntava-me anteriormente com quem é que se deve fazer esta coligação. Todos os partidos estão abertos e disponíveis porque eles sempre disseram o mesmo. O Basta e qualquer um destes partidos tiveram um ou dois deputados, estão disponíveis para o fazer. Essa coligação passará inevitavelmente pelo MCI-PS/PUN porque está em melhores condições. Nenhum partido poderá governar sem fazer uma coligação com esta formação. Isto é impossível, a não ser que haja uma coligação entre o MLSTP e a ADI para governar, o que eu acho que não acontece. A coabitação que deve haver entre o Presidente da República e uma cor política contrária é uma coabitação que nós, os observadores daquilo que se passa dentro da nossa política, acreditamos que sim, porque não podemos esquecer que estas duas forças, quer o MLSTP quer a ADI, apoiaram o Carlos Vila Nova a ser reeleito Presidente da República e agora não vejo razão para que o Carlos Vila Nova venha se opor à possível ascensão de uma dessas forças políticas.

Judy Carmichael interviews Diogo Brown

No episódio #27 do Podcast P&A – Lei do 1%, estivemos à conversa com Diogo Maia, da RE/MAX.O Diogo falou-nos do seu percurso, da Gestão de Marketing ao imobiliário, e da influência que o futebol teve na sua forma de olhar para as equipas e para o negócio.Conversámos sobre os desafios de construir equipas, a diferença entre gerir pessoas e ser empresário, e as responsabilidades que surgem quando um negócio cresce.Houve ainda tempo para olhar para o mercado imobiliário: as oportunidades que oferece, os desafios que coloca e o que exige de quem trabalha no setor.

On this episode, we talk with game designer Diogo Nogueira (Old Skull Publishing) about the Old School Renaissance (or is the Old School Revival?), science fantasy, and just making cool shit. ☉ Check out Diogo's work and blog: https://oldskull-publishing.com/ ☉ Pick up Just Make Shit on Diogo's Itch: https://diogo-old-skull.itch.io/just-make-shit ☉ Like the show? Support Stillfleet Studio & WWR on Patreon: https://www.patreon.com/cw/stillfleet ☉ Join the conversation on the Stillfleet Discord: https://discord.stillfleet.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Diogo Britto talks about how job loss affects criminal behavior in Brazil. “The Effect of Job Loss and Unemployment Insurance on Crime in Brazil” by Diogo Britto, Paolo Pinotti, and Breno Sampaio. OTHER RESEARCH WE DISCUSS IN THIS EPISODE: “Crime and the Labour Market" by Richard B. Freeman. “Job Displacement, Unemployment, and Crime: Evidence From Danish Microdata and Reforms" by Patrick Bennett and Amine Ouazad. “Job Displacement and Crime: Evidence From Norwegian Register Data” by Mari Rege, Torbjørn Skardhamar, Kjetil Telle, and Mark Votruba. “The Effects of Job Loss on Crime: Evidence From Administrative Data" by Evan Rose. “Job Loss, Credit, and Crime in Colombia" by Gaurav Khanna, Carlos Medina, Anant Nyshadham, Christian Posso, and Jorge Tamayo. "Snapping Back: Food Stamp Bans and Criminal Recidivism" by Cody Tuttle. Probable Causation Episode 48: Cody Tuttle. "Does Public Assistance Reduce Recidivism?" by Crystal S. Yang. "Does Welfare Prevent Crime? The Criminal Justice Outcomes of Youth Removed from SSI" by Manasi Deshpande and Michael Mueller-Smith. Probable Causation Episode 72: Manasi Deshpande. "Cash Transfers and Violent Crime in Indonesia" by Elías Cisneros, Krisztina Kis-Katos, Jan Priebe, and Lennart Reiners. "Job Displacement, Unemployment Benefits and Domestic Violence" by Sonia Bhalotra, Diogo Britto, Paolo Pinotti, and Breno Sampaio.   Want more? Check out my new book! The Science of Second Chances: A Revolution in Criminal Justice is available now. I have a Substack! Sign up for Probable Causation: The Newsletter.

Você publica vagas, recebe poucos candidatos e continua com posições em aberto? Será que realmente está faltando mão de obra ou o problema está na forma como sua empresa está contratando?No episódio #50 do podcast Um Passo à Frente, Maurício Cardoso recebe Diogo Kavazuru, fundador da startup Guby, para uma conversa sobre contratação, processos de RH e os desafios de encontrar profissionais qualificados.Um episódio para entender como empresas podem repensar seus processos e usar tecnologia para encontrar as pessoas certas.

Tickets for AIE NYC now open, and apply for the invite-only AIE CODE. Join us!We have an unusual relationship with today's guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT.In a launch video now viewed ~40M times (by comparison, GPT4o was 22M, Fable 5 was 15M, Navier Stokes was 74M, and 6 Astra was 137M), Diogo introduced Jev and it immediately took over the AI timeline — we'll skip full Jev explainers because your favorite AI influencer/educator has probably already done one. We also collected:* the official patterns and cookbooks you should see first, from Allie* Jev usecases* speed based - games and computer use* the voice + computer use example we discuss at 1h34 mins* voice + browser control* The must not miss Doom demo* Driving cars in games* Excalidraw* virtual try-ons* “Smart Games”/smart NPCs* guided responses in text messages* Jev for coding agents has an official guide * jev for linting* compacting tool calls* reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything* Programming Languages built atop Jev (Diogo's fave)* Jev for analytics replay and user journey review* “dark data”* entity resolution* natural language search* “smart software”* a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex* Jev as a judge* Jev memes* Jev vs LLM capabiltiies* blending transformers and classifiers* about the confidence api* Jev vs GLiNER (note difference/pushback, agreed, agreed, agreed)* Jev on trolley problem* Jev BushInstead we'll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created, and what you should expect next in terms of future models from TypeSafe (ReasoningJev?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev's API or doing a generic JevBench benchmark - something Diogo has rejected publicly.Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north starDiogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that accurately addressed what he saw as the core problem with making LLMs the heart of software: reliability.Jev's core innovation is "Reinforcement Learning for Calibrated Decisions”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn't integrate well with other software.We've talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo's AIE talk, which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation.At the end he also teases his contrarian opinion on scaling laws - which teases how to build a modern neolab without the billions of dollars the major labs have…The Bitterest Lesson: Tasks and Data beats ComputeWe spend a good amount of time discussing Diogo's essay on the Bitterest Lesson:His point is that “You get what you optimize for and the bitterest lesson in ML is that the most important part of it isn't ML at all.” - and picking the right north star, eg upvoting for user preference vs being integrated into tool calls - makes everything else fall in line.We're excited to catch up with a freshly dyed Diogo to discuss:* Why AI can solve extraordinarily hard problems but still fail to automate basic work* What System One Models are and why Jev is built for software rather than chat* RLHF, mode collapse, calibration, and the hidden costs of optimizing for human preferences* Why refusals become a problem when AI is buried inside software dependencies* Why TypeSafe rejects public benchmarks and optimizes for intelligence per dollar* The “bitterest lesson”: why the right task and the right data can matter more than compute* Why TypeSafe thinks of itself as a data lab rather than a model lab* RLCD vs. RLHF and RLVR as fundamentally different North Stars for AI* Why reliability and robustness matter more than simple determinism* Jev's programming primitives and how intelligence maps into software control flow* Why developers should decompose AI workflows into small, measurable decisions* How structured state replaces giant prompts and system messages* Why Diogo thinks AI should eventually disappear into the background of software* The “inverse SaaS-pocalypse” and how AI could supercharge existing software* System One vs. System Two intelligence and the limits of reasoning models* Dark data, computer use, real-time intelligence, and Jev's biggest early use cases* Why Jev could reshape coding agents built around a single-model architecture* Why Diogo says he wouldn't pre-train with $1 billion* The OpenAI journey that led to TypeSafe and why he thinks many neo-labs are approaching AI incorrectly* Coding agents beyond the KV cache, shared state, sub-agents, and the multi-agent futureDiogo Almeida* LinkedIn: https://www.linkedin.com/in/diogomda* X: https://x.com/CompleteSkeptic* TypeSafe AI: https://typesafe.ai/Timestamps00:00:00 Jev Launch Week and the AI Economic Revolution00:02:50 What Is Jev? System One Models and Programmable AI00:05:54 RLHF, Mode Collapse, Calibration, and Yann LeCun00:10:29 Programmatic AI, Refusals, and Safety Alignment00:17:21 Why TypeSafe Rejects Public Benchmarks00:20:43 The Bitterest Lesson: Data, Compute, and the Right Task00:24:59 RLCD vs. RLHF and RLVR00:28:42 Why Powerful AI Still Hasn't Automated the Economy00:39:55 Reliability, Robustness, and Determinism00:48:11 Model Versioning, LTS, Speed, and Intelligence per Dollar00:54:04 Inside Jev's API and Programming Primitives00:58:28 How to Build with Jev: Structure, Decomposition, and Small Decisions01:18:28 The Inverse SaaS-pocalypse and AI Disappearing into Software01:33:21 Computer Use, Dark Data, and Jev's Biggest Use Cases01:38:48 How Jev Could Reshape Coding Agents01:41:00 AI Safety, Frontier Pacing, and the Limits of RLVR01:48:03 Why Diogo Wouldn't Pre-Train with $1 Billion01:55:19 The OpenAI Story Behind TypeSafe02:01:41 Why Diogo Thinks Most Neo-Labs Are Getting AI Wrong02:08:00 Coding Agents Beyond the KV Cache and the Multi-Agent FutureTranscriptIntroduction: Jev Launch Week and Developer MomentumSwyx [00:00:00]: Okay, we're in the studio. A special occasion because this week, Diogo, my good buddy, launched Jev, and it's been taking over the complete timeline. How do you feel? What's it like to be you right now?Diogo Almeida [00:00:16]: Emotionally?Swyx [00:00:17]: Yeah.Diogo Almeida [00:00:17]: Never been worse. Like, I'm a ragged corpse of a person right now because there's so much going on, and I'm like a technical CEO, so I have, like, a lot of fires to fight.Swyx [00:00:29]: Yeah.Diogo Almeida [00:00:29]: But mentally, I feel—I say this all the time, and I've been saying this kind of for years in my over-under events. Like, I feel like the entire AI field is like one of those, like, carnival house of mirrors, and everyone is just insane and saying the weirdest stuff that doesn't make sense. And it feels like for just this week, like, I'm on a better in sync with reality and like, oh, people see it now. AI can be so much more than what was once thought.Diogo Almeida [00:01:06]: And like, yes, we are going to make. Like, an AI-based economic revolution is back on the table, and this is f*****g awesome.Diogo Almeida [00:01:17]: I'm so jazzed the developers get it. It's, it's, Yeah, and I want to show my eternal gratitude to the developers andSwyx [00:01:25]: Yeah.Diogo Almeida [00:01:26]: I'm so jazzed about the community and everything. It's so great.Swyx [00:01:28]: Yeah, you were saying yesterday that you decided to prioritize the town hall and not a bunch of, like, VIP, investor-type people because you wanted to make sure that they are the people that you get your most, attention, right? The engineers, the developers.Diogo Almeida [00:01:43]: Yeah, it felt a little like, oh man, I'm talking to, like, really important people right now.Swyx [00:01:47]: Yeah.Diogo Almeida [00:01:47]: I probably shouldn't reveal who.Swyx [00:01:48]: Yeah.Diogo Almeida [00:01:48]: But it feels a little bit dirty for me to, I'm, like, perhaps overly genuine in things. Like, it feels, like, dirty if, like, in my gigantic calendar event of people to talk to, the community isn't one of those.Swyx [00:02:04]: Yeah.Diogo Almeida [00:02:04]: And actually, in my ideal world, it would be, like, community all the time. I was thinking, “Should I host a town hall while walking to your studio?” And I'm like, “No, that's too crazy.”Swyx [00:02:12]: Sure. Yeah. Well, you guys have been hosting town halls on Discord. Discord is now 100,000 people. Your Twitter'sDiogo Almeida [00:02:19]: I don't follow these stats.Swyx [00:02:20]: Yeah.Diogo Almeida [00:02:20]: So holy s**t.Swyx [00:02:21]: Your Twitter's blown up. It was, it was really funny ‘cause, like, at AIE, you were like, “Yeah, follow me please,” and then you didn't, like, provide even your handle.Diogo Almeida [00:02:29]: I'm a noob. I'm a noob.Swyx [00:02:29]: You're such a noob.Diogo Almeida [00:02:30]: I'm a noob.Swyx [00:02:31]: But no, but that, like, that's, like, positive aura that, likeDiogo Almeida [00:02:33]: CoolSwyx [00:02:33]: You don't know how to promote yourself.Diogo Almeida [00:02:35]: Yeah. Someone, like, called me out when I posted, like, “Holy s**t, we're all three twending-- trending topics.” And then they're like, “That's a personal feed.”Swyx [00:02:42]: That's a personal, yeah.Diogo Almeida [00:02:43]: And I'm like, “Oh, no.”Swyx [00:02:44]: Of course, of course it'll trend to you.Diogo Almeida [00:02:45]: Cringe. Yeah.Swyx [00:02:45]: Yes, ‘cause it's what you clicked on.Diogo Almeida [00:02:47]: Yeah.Swyx [00:02:47]: So okay. Let's, Yeah, so congrats on everything.What Is Jev? System 1 Models and Intelligence per DollarDiogo Almeida [00:02:50]: Thank you.Swyx [00:02:50]: We'll talk about more, details as you have them. But let's, for people who are, like, living under a rock or just want, like, the definitive thing, what is Jev?Diogo Almeida [00:03:02]: Whew. Let me think about. That's a hard one.Swyx [00:03:07]: Okay. And I'm happy to, like, re-ask if you wanna kind ofDiogo Almeida [00:03:09]: No. I'm happy toSwyx [00:03:10]: OkayDiogo Almeida [00:03:10]: I'm happy to, like, just jam on it.Swyx [00:03:12]: Yeah.Diogo Almeida [00:03:13]: I will say, like, the first thing that I'm relieved about with this question is now I don't have to answer that question to my parents anymore ‘cause ChatGPT can just explain it.Swyx [00:03:20]: Nice.Diogo Almeida [00:03:21]: So the way I see it is we new-- need a new class of models. We're not attached to naming that class of models. Our-- the most accurate name we've come up with is System 1 models.Swyx [00:03:33]: Yeah.Diogo Almeida [00:03:33]: There will be reasons, but it's-- there's a reason why we don't call them decision models, because, like, they will be. Like, System 1 is beyond that. That's all I can say. We didn't expect this to be our big launch, so we have stuff in the tank.Swyx [00:03:48]: You should have said low-key research preview.Diogo Almeida [00:03:52]: It kind of was, right? It kind of was. But we. So there's a class of models that we describe them as, like, machine-native, System 1, large programmable. I think these are-- is the class of models where the goal is for code to be the consumer. So as opposed to, lar-- pre-trained large language models, which are meant for, like, autocomplete of the internet, or RLHF models, like chatbot instruction-following models, which are meant to, like, reply to text, or RLVR. It's in a weird gray area with RLHF. Like, these are meant to have things that directly are consumed by code, hence the name type safe. So the thing we really want is to have, like, AI, like, be as powerful as possible, and we think the way to do that is to integrate it with software. And we are designing everything, beyond just the outside, the deep internals of the model to be optimized for software. So number one, Jev is our first large programmable model, or a System 1 model, whatever you want to call it. Jev is meant to be optimized for intelligence per dollar, hence the name Jev.Swyx [00:05:03]: Jevons Paradox.Diogo Almeida [00:05:03]: Jevons Paradox, yeah. And it's optimized for intelligence per dollar. I love this debate with people about what is the most important between reliability, cost, calibration, and speed. And Jev is meant to be. Jev will be the name of models that will be on the frontier of intelligence per dollar. There's other ways to optimize it, like, ML, or at least if you're good at ML, it's all about trade-offs. And we are just going all out on that.Calibration, Mode Collapse, and the Limits of RLHFSwyx [00:05:31]: Yeah. And to me, like, calibration is one of the new things that people weren't talking about as much. We've done an episode In the past, with Clementine Foreia of Hugging Face, where they were like, “Yeah, actually, y- they're just.” Or, and this is your whole argument about RLHF, is they're more collapsing towards what you want to hear the mostDiogo Almeida [00:05:50]: OohSwyx [00:05:50]: Or what is most likely, instead of, like, their own internal confidence about a thing.Diogo Almeida [00:05:54]: Can I soapbox on that for a second?Swyx [00:05:56]: Go ahead. Yeah.Diogo Almeida [00:05:57]: Cool. Like, I've been heard that your audience is the most technical, so I actually want to get into that.Swyx [00:06:02]: Yeah.Diogo Almeida [00:06:03]: And if- I went through extreme precision to make sure everything in our launch video is accurate and real. Apparently, that's very unusual. One of the things that no one paid attention to was the downsides of RLHF, in particular mode dropping.Swyx [00:06:17]: Mode dropping or mode collapse?Diogo Almeida [00:06:19]: It's the same thing.Swyx [00:06:19]: Is that what you call it?Diogo Almeida [00:06:20]: It's the same thing.Swyx [00:06:20]: All right.Diogo Almeida [00:06:21]: And I wanna have a blog on this eventually, but I, like, want to tell as many people this as possible ‘cause I think it's a very interesting thing. So the spicy take, I believe in Yann LeCun a lot. I think Yann LeCun's takes are actually among the closest toSwyx [00:06:36]: What about this?Diogo Almeida [00:06:37]: Well, should I address this now or should I wait and go into mode collapse?Swyx [00:06:40]: No, later. Go mode, go mode collapse. I don't know.Diogo Almeida [00:06:42]: So I actually think that among takes, Yann LeCun's is among the most accurate. But he has this very famous/infamous slide about,Swyx [00:06:52]: The cake?Diogo Almeida [00:06:53]: LLMs are doomed.Swyx [00:06:54]: Okay.Diogo Almeida [00:06:54]: Like that one where he, like, has, like, a pie chart with, like, a tiny par-- tiny little thing- and says that as you increase sequence length, the probability of it making an error goes in. Yes, this one. This one. I love this one, because it's one of these things that seems mathematically obvious, but is obviously wrong, right? Like, it's mathematically obvious, but it doesn't empirically hold. And this is my favorite thing to teach people about, like, where youSwyx [00:07:21]: What's the disconnect, right?Diogo Almeida [00:07:22]: Exactly. And may I or you want to tell me?Swyx [00:07:27]: About mode collapse?Diogo Almeida [00:07:28]: Oh, no. Oh, so mode clop-- collapse is related to this.Swyx [00:07:31]: Yeah.Diogo Almeida [00:07:31]: The disconnect happens because if you are in a mode covering or a calibrated distribution, you are, like, not. You are not overly punished about having outliers. You'd expect, like, something. Some amount of the time you'd be out of distribution, some amount of time you'd be in distribution. That's what happens when you cover the distribution. This was like models before GANs. They made blurry images, right?Diogo Almeida [00:07:54]: Instead, GANs mode drop. They, like, drop the minority classes and just do the really common ones. And this is why this effect doesn't happen, right? Like, instead of be-- in order to generate really long strings, without making errors, they need to, like, be extremely conservative because it's e- really easy to see when an error happens. It's very hard to see when, like, a subtle thing that looks correct happens. And that calibration is, like, total poison into, like, the probability distributions of strings.Swyx [00:08:22]: Yeah.Diogo Almeida [00:08:23]: And it's, it's a nuanced take and like, I think that This is why this doesn't happen, and this is why strings are so bad at, decision-making or, overloading the string models are for decision-making is, like, a bad time.Yann LeCun, JEPA, Scaling Laws, and Practical ResearchSwyx [00:08:38]: And while we're on the topic of Yann, do you agree that his fix i- with-- which is like a world model, like a JEPA-type, embedding thing is the right solve? So basically, like, the. One of the reasons that it could fail is because you're trying to reason over token outputs and then, and then just looping back again and going. Keep, continuing going until you reach, like, a end of sentence. Like, is that, And his solve is JEPA, right?Diogo Almeida [00:09:02]: Yes.Swyx [00:09:02]: Which is, like, joint ambition,Diogo Almeida [00:09:04]: YeahSwyx [00:09:04]: Joint embedding prediction. So like, is that the solve or, like, do you have a. Do you have a take on that?Diogo Almeida [00:09:10]: Oh, man. I probably shouldn't talk too much about the insides of ML, but I will say that my brand, other than unhinged, is practical.Diogo Almeida [00:09:20]: Like, even my take here is practical. And like, I'm. Am I a scaling law fan? Depends. It dep-- it's, it's, it's, like, it's. Scaling laws tell you how much better you get at a thing for amount in.Diogo Almeida [00:09:33]: A scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those, like, linear gains are, like, really valuable. But it's all. To me, it's all about, like, what can we do with what we have to make the biggest possible f*****g difference? I can curse.Swyx [00:09:51]: Yeah.Diogo Almeida [00:09:51]: Yeah.Swyx [00:09:52]: Yeah.Diogo Almeida [00:09:52]: Yeah.Swyx [00:09:53]: We're, we're, we're approved for adults.Diogo Almeida [00:09:54]: Hell yeah.Swyx [00:09:55]: And also we have a scaling law thing if you wanna go into that later.Diogo Almeida [00:09:58]: Oh, I could if we. See, that part is not super relevant right now.Swyx [00:10:02]: Yeah.Diogo Almeida [00:10:03]: I actually. If you wanna go into my bitterest lesson, I think that's more relevant.Swyx [00:10:06]: Okay.Diogo Almeida [00:10:06]: But like, to me, I'm all about, like, pragmatics. And I think that the JEPA stuff is really cool early research. I really love awesome research. Is it practical yet?Diogo Almeida [00:10:21]: Probably shouldn't say. But like, there's just a lot of.Diogo Almeida [00:10:29]: I just think there's just, like, so many diamonds in the rough let all over the research world right now that haven't been polished because people don't know how to, like, do the right task. And I think that what our launch did, it. Does it kickstart us as a company? Like, yes. Will it be great for us as a company? Yes. I think it's gonna be, like, even greater for this direction of, like, programmatic AI. There was going to be, like, a gold rush on top of us for. ‘cause, like, software is super f*****g charged. But I think there's gonna be a gold rush parallel to us as well on, like, all the different ways we can expose things to make software more powerful so people can make even cooler stuff. And then we are back to, like, early internet energy?Swyx [00:11:12]: Yeah.Diogo Almeida [00:11:12]: And I think that's why, like, the Twitter is just like, “Jev.”? It's, it's like. It is a partySwyx [00:11:18]: It's inspiring because it's, it's, like, so different than what we're used to, which is, “I'm sorry you can't do this, but we do scaling laws and only the big labs can do it,” right?Diogo Almeida [00:11:28]: That. Actually, if I. I'll, I'll make a tangent if that's okay.Swyx [00:11:32]: Yeah.Diogo Almeida [00:11:32]: I think you might enjoy this.Swyx [00:11:33]: Really? Our five tangents in. It's good. It's fun. Yeah.Diogo Almeida [00:11:35]: Oh, yeah. I get lost at all my tangents.Swyx [00:11:37]: This is gonna be horrible for the listeners to figure it out, but they're gonna figure it out. It's fine.Safety Alignment, Refusals, and API PhilosophyDiogo Almeida [00:11:40]: Yeah, we can edit it in post.Swyx [00:11:40]: This is my response. Yeah.Diogo Almeida [00:11:41]: So popular thing on Discord, that people keep asking me, I haven't had the time to explain it yet, is why am I opposed to safety alignment and why do we not refuse? I'm not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. And refusal is just, like, obviously a type error. Like, if you're a human being and you're chatting with, like, a bot or whatever, you're cloud coding, and a refusal happens, like, “I'm sorry, I can't read DNA.py.” that's an annoying time. It's anno- it's, it's annoyingDiogo Almeida [00:12:18]: Right? But you can work with it, right? And you're forced to work with it ‘cause of Stockholm syndrome.Diogo Almeida [00:12:23]: I have stories about that too. I need another tangent deep in here. But like, if you ever want this in a dependency running in the background, what happens if that refuses? What if someone else is using that dependency? They don't know what that system is. Like, you want the software to just stochastically break because a user sent, like, a weird message in there?Diogo Almeida [00:12:42]: Like, that is, like, straight-up insanity. It's coming from a place of, like, people who do not understand software, do not understand programming, and like, they are obsessed with, like, I believe this, horseless carriage of, like, AI coworker instead of unearthing, like, the full power of AI.Swyx [00:13:01]: Fair enough.Diogo Almeida [00:13:01]: Yeah.Swyx [00:13:01]: You want something that is the core kernel that is usable everywhere.Diogo Almeida [00:13:05]: Yes. Exactly. Like, the cognitive core, right?Swyx [00:13:07]: Yeah.Diogo Almeida [00:13:08]: And you need this thing to be s- like, so general, so optimized for its use cases. You want it to be, like, you want it to work on all the future use cases, all the weird s**t that people are doing.Swyx [00:13:19]: Yeah.Diogo Almeida [00:13:19]: We obviously didn't train on any of that stuff. Is it surprising that it works? No, ‘cause we trained on weirder stuff, my friend.Diogo Almeida [00:13:28]: So. But one tangent up about, like, safety alignment.Swyx [00:13:32]: Okay.Diogo Almeida [00:13:32]: Safety alignment makes sense for a product, in my opinion, for, like, ChatGPT and Claude. Like, it, What safety, what makes safety and capability alignment different is capability alignment is, like, about doing what the user wants. That is sick for software engineers. They want their thing to do the thing, and the more predictable it is, the less they have to test it and play around with it. Jeb is not anywhere close to that yet. It could be, but like, there's so many more nines of reliability that we want in order to make it so good, like a database query, that you don't even have to think about it. It is just there when you need intelligence. But safety alignment is, like, the opposite of instruction following. It's when you want to follow someone else's instructions, like OpenAI and AnthropicSwyx [00:14:13]: The RAGs value stack.Diogo Almeida [00:14:14]: Exactly. And this makes a lot of sense for a product. Again, like, ChatGPT should do. Y- you sh- like, if they don't want to, like, do, like, some, not-safe-for-work role play with ChatGPT, that's on them because, like, maybe that's, what their users who have, like, parents and kids want. Like, n- that's fine. But in an API, that's nuts, right? Like, that's completely unacceptable because, like, people need to, like, program around this, and that is, that's so anti-user that it's. It. I'm. Huh. I can be an angry person, so I should try to calm down.Swyx [00:14:52]: It's, People get your passion, and I think that's really good. The one pushback I'll give you is, like, what if we use it to kill people, right? Like, that is the actual. Like, n- the not-safe-for-work thing, it's private, personal, whatever. But like, yes, like, we will use it in war. And like, that is, something that companies can reasonably prefer their APIs not be used for.Diogo Almeida [00:15:14]: I get that. I think that there's, like, pragmatic places where that opinion can be held. I don't think the foundation of, like, a general-purpose technology is that place, personally.Diogo Almeida [00:15:27]: Like, would I prefer that our stuff is not used to kill people? Obviously. Would I prefer it's used for, like, all sorts of, like, great stuff in the world? Obviously. Will I put my thumb in the scale for that? Yes. Will I do it at the technological layer? Absolutely not, because that will fracture the intelligence. Every single time you mean it to overfit to some weird stuff, you're fracturing its intelligence more and more. And like, these things are fractured to the, like. They're so darn fractured right now.Swyx [00:15:54]: Yeah.Diogo Almeida [00:15:54]: So and as a furthermore thing, to me, it's like I think intelligence will be more like a database than a coworker. Like, I don't think it's up to databases to add checks on whether or not they're used for, like, what's something that's not great? Like, CIA. Actually, I don't know what the CIA does, really. You can imagine. You can imagine, killing people who are not even bad or whatever.Diogo Almeida [00:16:21]: And like, I don't think it's the database's responsibility for that. And furthermore, like, a thing that has been weird to me is when people, like, sign up for our thing on Slack and they're like, “Hey, we're gonna deploy this. Can we deploy this thing?” I am just like, “My brother, we are an API. You are a developer. It's none of my business,” right? Like, you shouldn't know what the whole task even isSwyx [00:16:46]: YeahDiogo Almeida [00:16:46]: Because it should be decomposed into small things. We shouldn't be able to know what the downstream users are doing, and that is, like, a good boundary to give software engineers maximum power. Ideally, they use it for the good stuff, and ideally, we can, like, help them and like, we've talked about, like, doing open source and charity and all of that. We have absolutely no time for anything else right now. But like, they will get any of that bias out of the technological layer as long as I'm in charge.Privacy, Benchmarking, and Trusting IntelligenceSwyx [00:17:11]: Yeah, that's great. While we're on the topic, let's also briefly talk about your privacy stuff, terms of ser- terms of use, which, got a little bit ofDiogo Almeida [00:17:18]: OohSwyx [00:17:18]: Misunderstanding. I just wanna clarify that upfront.Diogo Almeida [00:17:21]: Hell yeah.Swyx [00:17:21]: I think this probably takes two sentences from you about, like, you will not. You're not being that restrictive about your API. Like, clearlyDiogo Almeida [00:17:27]: Oh, yeah. Oh, yeah, so yeahSwyx [00:17:27]: Ideologically, you articulate your role as a platform very seriously.Diogo Almeida [00:17:30]: Yes. Yes. I don't know what you're referring to, but like, this was. I've seen a couple of things about, like, benchmarking.Swyx [00:17:38]: Yes.Diogo Almeida [00:17:38]: Like, obviously we're not stopping people from do. Oh, man, I should be careful about what I say. I'm realizingSwyx [00:17:43]: No, you said, you said it publicly thatDiogo Almeida [00:17:44]: YeahSwyx [00:17:44]: That was in the preview period. You didn't take it out for the launch.Diogo Almeida [00:17:47]: Yeah. Okay.Swyx [00:17:47]: And now you're gonna take it out.Diogo Almeida [00:17:48]: So the team is doing stuff thatSwyx [00:17:49]: YesDiogo Almeida [00:17:49]: I'm not even aware of, so it's great to know the team communicated that. I asked them to check in with the lawyers about that.Swyx [00:17:54]: Yeah.Diogo Almeida [00:17:54]: Like, we are obviously not stopping people from doing that type of thing. I'm extremely in favor. So I'm extremely anti-public benchmarks. I'm extremely in fa- I'm medium about private benchmarks that are proxies. ISwyx [00:18:09]: So are you worried about, saturation or, like, training on public benchmarks? So it's, like, easy to cheat.Diogo Almeida [00:18:15]: Not only is it easy to cheat, there's a lot of ins. So I think that we are. Or anyone who's, like, competition with us that, vaguely there is. Like, you could say, likeSwyx [00:18:28]: There's like 50 Jev clones, yeah.Diogo Almeida [00:18:30]: Well, sure.Swyx [00:18:31]: Yeah.Diogo Almeida [00:18:32]: Well, the, these. Let's say that there is competition.Swyx [00:18:34]: And we'll talk about those. Yeah.Diogo Almeida [00:18:34]: Or let's just say that there's. Let's just assume that there's an industry two years from now of people who are doing similar things to us. The thing that we are selling is intelligence per something, per, like, dollar or per second. The. No one. Like, people obsess about the cost and the speed. I believe that is. It's cool, but like, the thing that matters is the intelligence. Like, the cost and the speed are, like, are bad things. You're paying them for something, and you need the thing back, and the intelligence is what truly matters. The problem with intelligence is that there's a je ne sais quoi to it, right? Like, the good model smell. Like, the thing that happened after we launched of, like, two hours later that actually went way bigger than the video, which was like, “Holy s**t.”Swyx [00:19:16]: This is actually usable.Diogo Almeida [00:19:17]: It. WellSwyx [00:19:17]: Yeah.Diogo Almeida [00:19:17]: It's, like, beyond that.Swyx [00:19:20]: Yeah.Diogo Almeida [00:19:20]: Like, the. Whew, the launch was crazy, and people could really sense how hard we care about that, and that's truly what I think the long term of this is. And I think public benchmarks are antithetical to this. Like, they are a way to get people trust in intelligence because intelligence has a je ne sais quoi, but the public benchmarks are extremely gameable. Even if they try not to, they still will. Like, back in the old days, every lab had a team to collect data that looks like MMLU to make it look better, which is just benchmarking with extra steps.Diogo Almeida [00:19:58]: So I believe that in the long run, it needs to be vibes and trust until you put it into a workflow and evaluate it for that workflow and measure it and have your own sense of, like, how it does on the exact workflow that matters. And our job is to keep moving the nines of reliability. This is like an ever-present part of o- of what we need to be doing as a company, and we need to do everything to have people know that this is something we care so much about. Like, if we wanted to, we could have released Jev, like, a year and a half ago if we wanted it to be dumb.The Bitterest Lesson: Tasks, Data, and North StarsSwyx [00:20:34]: Oh.Diogo Almeida [00:20:34]: It. Like, the. My bitterest lesson, right? Like, architecture and Yeah.Swyx [00:20:40]: I'll bring it upDiogo Almeida [00:20:40]: Hell yeahSwyx [00:20:41]: Since you, since you talked about it, here.Diogo Almeida [00:20:43]: Hell yeah. T- like, Sutton says that algorithms beats compute very roughly. Data matters way more than compute, obviously. And doing the right task, having the North Star is the hardest, most important thing. This has happened, in LLM land twice so far, right? Maybe 2.2 times. There's RLHF, which, like, shifted the task to instruction following. No one realized that was possible. RLVR did, like, a tiny little, like, edit to the, to the direction, and now us, right? RLCD. We have a new task, and the goal is, programs in the loop. And yeah, data matters soSwyx [00:21:28]: RightDiogo Almeida [00:21:28]: Unbelievably much.Swyx [00:21:29]: SoDiogo Almeida [00:21:29]: Like, I can't, I can't emphasize it less.Swyx [00:21:31]: Yeah, you consider yourself a data lab rather than, like, a model lab. Is thatDiogo Almeida [00:21:35]: AbsolutelySwyx [00:21:35]: Something. That's the wording you guys use?Diogo Almeida [00:21:37]: Yeah. We are. We will always, like, care so much about data. To me, model capabilities means data. Data is so unbelievably complicated, and that is what gets nines. Like, you have no idea how much data can shift everything. Data is so important.TypeSafe as a Data Lab and Synthetic Data StrategySwyx [00:21:57]: Yeah.Diogo Almeida [00:21:57]: Holy crap. So if people are looking for a job, we are hiring infinite data people, actually infinite.Swyx [00:22:04]: What is a good data person? Like, clearly somebody who cares about reading through the transcripts of, whatever. You've said, for example, that y- all your data is synthetic.Diogo Almeida [00:22:15]: Yep.Swyx [00:22:15]: But that's only, like, the scratching the surface, right?Diogo Almeida [00:22:18]: Yeah.Swyx [00:22:19]: Like, it's not. Like, synthetic, so what, right? Synthetic, but we have people with a lot of taste and a lot of care looking at, looking at these, articulating what's wrong, going back, regenerating. Is that what a good data person is these days?Diogo Almeida [00:22:31]: Let me try to figure out how to. Like, it's, it's super complicated, and like, I literally onboard the data people with a Talk that I assume is longer than this podcast will end up being. So I will try to say, like, the high level of it. So number one, we don't do the kind of synthetic data that people ki. Well, I'll do. Actually, number is zero. Data and synthetic data depends on your task. Like, the shape of your data. The shape of your task changes the data. Like, RLVR's data is kind of environments, right?Swyx [00:23:03]: Yes.Diogo Almeida [00:23:04]: RLHF's is the human feedback? Each task has its own unique kind of data, and we, of course, have our own unique kind of data, right? So number one, we have that. Number two, the thing I. The reason why we don't want to train on our users' data, even if we could, right? Like, we could probably ask for any terms right now, and it will. We. I don't know if it would make a difference. We truly don't want that, because no matter what, the real-world data has so much bias. There's, like, a power law of, like, people, like, asking the same things where you'll end up, like, overfitting to it and like, fracturing to it and all of that. And number two, we are, like, aiming for, like, a complete sci-fi future years from now where, like, these models are going to be, like, the general infrastructure, layers and layers and layers and deep down the stack to, like, things people can't even imagine. Like, I would like to think of our model, like, kind of like, UDP as LLMs and TCP as our models. All sorts of stuff can be built on top of that, and we need to be able to nail those futuristic use cases such that software developers can actually build that futuristic stuff. And the way to do that is even if we had all of the data of the present, we would just overfit to the present, and then it wouldn't work. What we need is to, like.Diogo Almeida [00:24:21]: It almost feels like a. Like, they're the artists? They study this cognitive core. Our cognitive core is, like, way less jagged than anyone else's. And then they find the jaggednesses, and then they address them surgically in a way that. And you can never perfectly do this, right? But they do it in such a way that it addresses it in every single possible, like, dimension, past, present, future.Swyx [00:24:45]: The general case rather than the specific case.Diogo Almeida [00:24:47]: Exactly. And like, that requires a lot of intelligence every time.RLCD vs. RLHF: Defining a New TaskSwyx [00:24:50]: Okay, so we mentioned a little bit. You sort of criticized my thinking as r-- like, very RLVR influence, which is, like, very fair. Let us actually mention RLCDDiogo Almeida [00:24:59]: OohSwyx [00:24:59]: Which obviously you have some secret sauces to our knowledge. You've never actually published a paper or anything like that on it. No, right?Diogo Almeida [00:25:05]: No, not yet.Swyx [00:25:06]: But like, what should people get from this? Like, what. Can you give people some confidence that you're just not just making up jargon for the sake of sounding cool, right? Like, one thing for me is, like, calibration I do think is a. To me, like, well understood because we've covered it in. On the podcast.Diogo Almeida [00:25:22]: Yeah.Swyx [00:25:22]: But I don't know what you mean when you say RLCD versus what people are familiar with.Diogo Almeida [00:25:26]: It's a great question.Swyx [00:25:27]: Yes.Diogo Almeida [00:25:27]: And actually, I will give a related question.Swyx [00:25:29]: Okay.Diogo Almeida [00:25:29]: What is RLHF?Swyx [00:25:31]: Okay.Diogo Almeida [00:25:31]: Right? And actually, RLHF means multiple different things, right?Swyx [00:25:34]: Okay.Diogo Almeida [00:25:34]: Like, there's the RLHF of the original. I think it was, like, Paul Christiano teaching a robot to backflip or something like that. Wasn't there somethingSwyx [00:25:42]: Was that it?Diogo Almeida [00:25:43]: That was the originalSwyx [00:25:44]: I referenced the PPO paper, but I don't know.Diogo Almeida [00:25:46]: And so PPO was not necessarily from human feedback, if I recall.Swyx [00:25:51]: Okay. That's trueDiogo Almeida [00:25:52]: But I b- I believe it was, like, an OpenAI alignment work that could teach hard to specify outputs, like a backflip. I'm not 100% sure. And then there was actually learning to summarize. This was work, by a bunch of the team that helped with, instruct-- and co-authored, the instruction following paper, which was teaching, doing PPO on language models.Swyx [00:26:15]: This is the, sorry. I'm trying to, tryingDiogo Almeida [00:26:19]: YeahSwyx [00:26:19]: Trying to manipulate this thing. This is 2017.Diogo Almeida [00:26:23]: Yeah.Swyx [00:26:23]: Right.Diogo Almeida [00:26:23]: I'm not 100% sure, but like, that looks quite right.Swyx [00:26:26]: Yeah.Diogo Almeida [00:26:26]: If it has, like, a robot doing backflips or something like that might be it. Yes. Okay, cool. I guess I got it right. Hell yeah.Swyx [00:26:35]: There you go.Diogo Almeida [00:26:36]: Yeah.Swyx [00:26:36]: That's the one.Diogo Almeida [00:26:36]: So the idea was can, like, can you do, like, ill-specified things with it? So that's, like, version one. Version two was, the learning to summarize work, that, like, OpenAI did, which is actually, like, PPO on language models to do something somewhat ill-specified. This is, like, another thing that people refer to as RLHF Which I did not co-author.Diogo Almeida [00:26:57]: Oh, Dario's there. Cool. Hell yeah.Swyx [00:27:01]: And Radford.Diogo Almeida [00:27:02]: Yeah. Shout-outs to Alec and Ryan. Love them.Swyx [00:27:04]: Yeah.Diogo Almeida [00:27:05]: But the thing that I refer to RLHF is the, Oh, man.Diogo Almeida [00:27:13]: I'll get toSwyx [00:27:14]: You have comments on that, yeah.Diogo Almeida [00:27:15]: I have comments on that paper, but like, we're so many, tangents deep.Swyx [00:27:18]: Yeah.Diogo Almeida [00:27:18]: So the thing that really got. To me, the thing that I'm calling to RLHF is the task of instruction following. It's not about the PPO. That part doesn't matter. It's about, like, setting a North Star of this is a valuable direction. It's kind of like the Bitris lesson North Star.Diogo Almeida [00:27:34]: And for us, RLCD is this new task. And it is not. I don't see it as jargon. Like, I try to communicate with precision. It's just that, “Hey, here's another North Star.” Just like DPO and all of its, like, descendants also do RLHF, despite not using the algorithm in that paper.Swyx [00:27:55]: And so clear- clearly stating the North Star is, being program- programmable AI is one, word that I really catch onto, removing the human in the loop,Diogo Almeida [00:28:06]: YesSwyx [00:28:06]: From. Because RLHF is tuningDiogo Almeida [00:28:09]: YesSwyx [00:28:09]: For this so that you can automate everything.Diogo Almeida [00:28:11]: Yes. Everything that makesSwyx [00:28:13]: Did I miss anything else in the, in the thesis of, like, what the North Star is?Diogo Almeida [00:28:17]: There is. That is. That is right. I'm overly nuanced in my communication. The one nuance is that we need to be practical. We need to be aware of what language models can do really well. Like what AI can do.Diogo Almeida [00:28:30]: Right? Like, there could be programmatic types that are, like, sick AF, but if you. If the technology is not ready for it to. It's not a tragedy if that's not out in the world.Why Programmable AI MattersSwyx [00:28:41]: Yeah.Diogo Almeida [00:28:42]: But to me, like, the pre-Jev world was a tragedy becau-- it sounds arrogant. Hear me out.Swyx [00:28:49]: No. I strongly believe you.Diogo Almeida [00:28:50]: Cool. It sounds arrogant, but like, I felt this way since long before I even had a company.Swyx [00:28:54]: Yeah. I can, I can vouch that,Diogo Almeida [00:28:56]: Yes, I've been talking about this for so longSwyx [00:28:57]: You said this at All Around Her for, like, three years.Diogo Almeida [00:28:58]: Yeah, I've been talking about this for so long. And I've been saying it because I thought it would have been easier. They say they do not do things because they. It. They're easy. They. It's ‘cause they thought it was easy, soSwyx [00:29:08]: Yeah, exactlyDiogo Almeida [00:29:09]: Something like that. I thought it. This whole project would take a week.Diogo Almeida [00:29:13]: And I was unbelievably wrong. So I am so sorry to everyone at OpenAI that I thought. I was like, “Man, I'm solving this right now.” but like, I think that the tragic thing is when. Well, I think overpromise, underdeliver is tragic too. And like, AI is super extreme on that axis. And I think RLVR is, like, the main. Well, both RLVR and RLHF are extreme perpetrators of this.Diogo Almeida [00:29:40]: But like, it. To me, it's like it's just there's just so much potential there. Like, AI is clearly so smart. I l- smart. I love this in my talks, when I ask people, like, “How can AI be so unbelievably smart? How can we, like, solve millennium prize problems in math, but still not automate even the most basics of works?” Like, really basic rote stuff that, like, the. It d- it doesn't take, like, extremely smart people to do this. It's not a satisfying job. Like, there's other things these people could be doing, but yet we need them to do, like, this ba- like, super basic- non- unsatisfying stuff because, like, we can't automate it yet, but we have this, like, supercharged engine of automation that just does not have, like, the right plugs and stuff to plug into all of this economically valuable work. And like, if the whole company of TypeSafe disappears, like, maybe it'll take, like, a year or two for people to, like, truly catch up. I actually don't know how long it'll take. If model quality matters, then we are gonna be in a very good position for a long time. But it, like, it's done, right? Like, there, like, this has changed the path of, like, technological history.Swyx [00:30:49]: Yeah.Diogo Almeida [00:30:49]: And like, we will be exploring that space as a field.Swyx [00:30:53]: Yeah. I think, I definitely agree with that. You've created possibilities. So I think, if I can paraphrase so that people can un- also understand, you should not take the success of TypeSafe and Jev as just like, “Well, that is a new model type. Now we're done. We go back to business.” Like, no. Like, actually, there's, there are, like, five other model types that you should be exploring and like, let a thousand flowers bloom.Diogo Almeida [00:31:15]: Absolutely.Swyx [00:31:16]: Right?Diogo Almeida [00:31:16]: Like, early internetSwyx [00:31:17]: And some of that, some of which you will probably also build.Diogo Almeida [00:31:18]: Of course, yes.Swyx [00:31:19]: Yes.Diogo Almeida [00:31:19]: Early internet energy. I think it's back to tech utopia. It's no longer like, “Oh, man, like, sometimes my coding agents work, but the, all of the best ones are hoarded internally.”Swyx [00:31:29]: Yeah.Diogo Almeida [00:31:30]: Right? It's like creation is back on the menu.Diogo Almeida [00:31:34]: ? Though it's gonna be a wild-ass world, and buckle up.Diogo Almeida [00:31:38]: It's. And I'm so jazzed about that.Manifesto, Launch Strategy, and Early Internet EnergySwyx [00:31:42]: Yeah. And now you have the funding and the momentum to do whatever you envision there, which I, which I think is, like, very gratifying to see you have after, so long of saying these thingsDiogo Almeida [00:31:53]: YeahSwyx [00:31:54]: But actually show the world.Diogo Almeida [00:31:55]: I know. I just. Such a, such an interesting thing to be a tease the whole time. Like, my talk, like, felt like it was a cliffhanger ‘cause I didn't say how the automation would occur.Swyx [00:32:05]: Yeah.Diogo Almeida [00:32:06]: Sean reviewed our manifesto And he's like, “It's a little bit vague in these parts.”Diogo Almeida [00:32:12]: And like, “What's step one? What is, what is the intelligence model?”Swyx [00:32:16]: Well, I asked you for model, and you were like, “Yeah, model coming.”Diogo Almeida [00:32:18]: Yeah.Swyx [00:32:18]: And like, Well, I just, I mainly objected to the word composable But build prod.god is fantastic.Diogo Almeida [00:32:24]: Thank you.Swyx [00:32:24]: Yeah.Diogo Almeida [00:32:25]: I. We've really rallied around that. I'd like to think we're not entirely a cult like some companies are.Diogo Almeida [00:32:32]: But like, we are, like, jazzed about what we're doing, and like, we are. Like, my brand is being practical, and like, we are all, like, so super-duper practical.Swyx [00:32:42]: Yeah.Diogo Almeida [00:32:42]: It's really great.Swyx [00:32:43]: Yeah. So here. And by the way, here is the step, the secret master plan, right?Diogo Almeida [00:32:47]: Yep.Swyx [00:32:47]: Shape, the shape of machine-native composable AI.Diogo Almeida [00:32:49]: It was your idea to make a secret master plan, soSwyx [00:32:51]: It's a, it's that Elon thing. When he started TeslaDiogo Almeida [00:32:53]: YeahSwyx [00:32:53]: He was like, “Here's what we'll do.”Diogo Almeida [00:32:54]: But I did. Yeah. I'm giving official credit to you.Swyx [00:32:56]: Oh, thank you. Thank you, thank you.Diogo Almeida [00:32:56]: Yeah.Swyx [00:32:56]: Thank you. But like, you should've told me your, you're also gonna do this model launch, ‘cause you, like, you told me, you told me half of the story, and then the other half, you didn't have the doom demo at the time.Diogo Almeida [00:33:08]: Yep.Swyx [00:33:08]: You didn't have any numbers to give me.Diogo Almeida [00:33:10]: Yep.Swyx [00:33:10]: I was like, “what?”Diogo Almeida [00:33:11]: Well, the problem is I don't believe in benchmarking.Swyx [00:33:13]: Exactly.Diogo Almeida [00:33:14]: Right?Swyx [00:33:14]: Exactly.Diogo Almeida [00:33:14]: So like, it is a thing that you need to feel, and like, I think that this is the way to build long-term trust, even though it, like, hurt, it hurt us a, us a lot? Like last year when we did fundraise, no one believed us.Diogo Almeida [00:33:27]: ? Like, and they wanted just benchmarks and stuff, and we're like, “We're not gonna do that. We are principled. We're gonna stand by our guns. That rewards bad actors. I don't give a s**t, like, what you want. Like, this is who we are, and we are standing by that.” So Sorry. It's notSwyx [00:33:43]: No, yeah. Well, and in some ways, I think, like, choosing the hard path, it. But you end up making the company that you wanna work in.Diogo Almeida [00:33:49]: Yep.Swyx [00:33:50]: Right? Otherwise, if you sell out, then you're just working in, like, OpenAI but with my people, right? Which is like.Diogo Almeida [00:33:56]: Yeah. Yeah. Like, I'm, I don't have too many regrets on that, obviously.Swyx [00:34:01]: Yeah.Diogo Almeida [00:34:01]: Like, it worked out so unbelievably well. And like, I, The. I was emotional last night when I was talking about, like, the reasons I left OpenAI, and because, like, it actually had to change my wording after the launch. My phrasing was, “If an AI winter did happen and I did not do every f*****g possible thing I could to, like, avert that, I would see myself as personally responsible both for, the RLHF direction, which I think really widened overpromise versus under-deliver, and also not going all in on this because I think this is, this is where value is going to just be, like, printed.” So. And it was really cool because I feel likeDiogo Almeida [00:34:47]: The AI winter I'm worrying about is averted. Like, AI will be useful. It'll be used for automation.Diogo Almeida [00:34:53]: It's been less than a week, and like, the numbers are already undeniableSwyx [00:34:57]: YeahDiogo Almeida [00:34:57]: That it's, like, being used for real work, and like, there's. It's, it's the Wild West. Yeah.Launch Traction, Tokens, Rate Limits, and Developer UsageSwyx [00:35:03]: Yeah. Can you sh- just if you have top of your head, what numbers are you seeing? Like, what's, what's, like, signups? Like, whatever you can share.Diogo Almeida [00:35:11]: I'm actually not super on top of everything. Like, the team is the ones who are telling me all of these things.Swyx [00:35:16]: Yeah, and I'm sure it's, like, changing every day, right?Diogo Almeida [00:35:17]: It's, it's,Swyx [00:35:18]: But likeDiogo Almeida [00:35:18]: It's kinda nutsSwyx [00:35:19]: If there's a milestone that you're like, “Well, yep, that's one thing we were hoping for. We reached it.”Diogo Almeida [00:35:23]: I will say a milestone that we've passed is tokens per day.Swyx [00:35:27]: Nice.Diogo Almeida [00:35:27]: And this is not, like, fleeting tokens per day.Swyx [00:35:32]: Yeah.Diogo Almeida [00:35:32]: This is, like, even at night, like, it's constantly training, so machines are calling it and not just people trying things out.Diogo Almeida [00:35:39]: So that is, That is so cool. A trillion tokens a day is a lot.Swyx [00:35:45]: Yeah.Diogo Almeida [00:35:45]: So surpassing that is awesome. Signups to me don't really matter. And actually, this was, like, a bit of a mistake we made, if I'm, like, totally honest. People on Twitter were calling us, like, marketing geniuses and all of that, and that was just us. We don't have a marketer. Also hiring. And we were just being our genuine, goofy, like, irreverent selves, and we were, we were just, like, offboarding people off the waitlist so hard. - Our platform team is so unbelievably cracked. I think we have more n- up nines of uptime than Anthropic while having the most Unprecedented launch ever. Like, that is kind of nuts, soSwyx [00:36:21]: YeahDiogo Almeida [00:36:21]: Like, props to them.Swyx [00:36:22]: Yeah.Diogo Almeida [00:36:23]: And the thing we didn't realize. So number one, waitlists, waitlist sign-ups don't matter for, like, a developer platform, in my opinion? I would guess that a large number of them are not even developers. So they go in, they try some queries, and a lot of people don't get it because they are not programming, right? Like, they're just like, “What? This is not a chatbot. Where's my ChatGPT 2?”Diogo Almeida [00:36:45]: Right? But if, like. I haven't exactly calculated this. My sense is that if every single human being in the world, like, just wrote a couple of queries, that would be a rounding error compared to, like, one power user's for loop that is just, like, creating value.Swyx [00:37:01]: Yeah.Diogo Almeida [00:37:01]: And the thing we are-- didn't realize with the waitlist is, like, we could just w- off-board anyone off the waitlist. It doesn't matter. The scary part is rate limits. And then once people start getting value from that, then they just want tons and tons of rate limits because this is what software is, right? Like, you spend effort upfront to specify your rote task, and then this rote task creates more value than it takes to put in. And then now that you have thatSwyx [00:37:25]: Set it and forget, yeah.Diogo Almeida [00:37:26]: Exactly, yeah. You run it in the background. You make it a dependency, to, like, other things. You can make, like, higher level stuff. And like, you just create so much value in the world. Early internet people probably did not imagine, like, the wonder of early 2000s internet, which is still not early internet. But like, it's, it's through, no offense, composabilitySwyx [00:37:47]: NoDiogo Almeida [00:37:47]: That all of the crazy stuff happens, and I just really wanted to emphasize that in our manifesto. We are going for emergence. We are going for, like, being the catalyst. We're wanting to empower people, and we are going to do whatever we can for that, be it, like, Discords in our town hall with me wearing a garbage bag or not.Swyx [00:38:05]: And podcasts and Diogo Almeida [00:38:08]: Hell yeahSwyx [00:38:09]: Getting all that.Diogo Almeida [00:38:09]: Absolutely.Swyx [00:38:09]: Like, ‘cause I want the long form, right?Diogo Almeida [00:38:11]: Yeah.Swyx [00:38:12]: It is like, yes, we'll get past the, some of the superficial things, and then we'll go deep andDiogo Almeida [00:38:15]: Hell yeahSwyx [00:38:15]: And people will really trust and understand your mission and like, the people that, will resonate that will end up joining you or, buying you. Or No, but sorry, as a, as a customer.Diogo Almeida [00:38:27]: Oh, as a customer.Swyx [00:38:28]: As a customer, as a customer.Diogo Almeida [00:38:28]: Okay, yeah. That was funny. I'm sorry.Swyx [00:38:30]: Sorry. I didn't, I didn't mean to say that. But no, any-- one version, one very flattering version of this, like, 36 million views of your launch video.Diogo Almeida [00:38:37]: Cool. Up to 38 now.Swyx [00:38:39]: Yeah, rounding error.Diogo Almeida [00:38:40]: Yeah.Swyx [00:38:40]: Navio still has got 74. Fable 5 got 57. So like, as far as, a- and I didn't, I didn't do the stats for, like, original ChatGPT, likeDiogo Almeida [00:38:48]: YepSwyx [00:38:49]: Which there was no video.Diogo Almeida [00:38:50]: Yep.Swyx [00:38:50]: So like, up there, right?Diogo Almeida [00:38:52]: Yep.Swyx [00:38:52]: Like, as far, as far as, like, if you were to launch a Neolab in 2026, I think you're, like, number one right now, which is, like, pretty crazy.Diogo Almeida [00:38:58]: Yeah. Well, I actually would rather. I do have the shirt, like, your favorites Neola-- favorite Neolab's favorite Neolab.Swyx [00:39:05]: Huh.Diogo Almeida [00:39:05]: I don't give a s**t about being a Neolab. I think being a Neolab. Actually, we have a lot of, like, swag that's being a parody of a Neolab. One of them, one of them I have is, like, Neolab with product, which actually is not a Neolab. Like, I don't care about that, really.Swyx [00:39:20]: Yeah.Diogo Almeida [00:39:20]: What I care about is being a reliable dev platform. So Swyx [00:39:23]: YesDiogo Almeida [00:39:24]: Appreciate the comparison, but likeSwyx [00:39:25]: YeahDiogo Almeida [00:39:25]: Hopefully we transcend past them and we go back into, like, a thing-- like, a revolutionary moment for developers and like, this stable thing that people can rely on and trust.Reliability, Robustness, and DeterminismSwyx [00:39:35]: Yes. To that end, I think that's one thing that really impressed me about you guys is that, yes, you do talk about reliability. I thought it was mostly about calibration, which, like, we talk about RLCD. But actually it's also about just, like, uptime and scalability and all those things, right? They're, they're all sort of the kind.Diogo Almeida [00:39:55]: And nines.Swyx [00:39:56]: And nines.Diogo Almeida [00:39:56]: It's, likeSwyx [00:39:57]: Which uptime is, in my opinion.Diogo Almeida [00:39:58]: Oh, but that's part of it. But like, there's reliability in, like, how intelligent the thing is. Like, how consistently does it do the thing that you want? And I think that, like, the big reasoning models are very smart. In my opinion, they still lack reliability. I think there's many use cases where you-- they look like they should be smart enough to automate their work. There is economic incentive to automate that work, yet still they're not reliable enough as, at an intern because they're optimized for different things. And so like, I think that there's the reliability of being able to, like, trust the outputs. And also we are. Like, there are dimensions of reliability that we are not yet at that I'm, like, so excited by.Swyx [00:40:38]: Yeah.Diogo Almeida [00:40:38]: Like, I want to automate the easy work before the hard work? Like, I think that's just a common sense thing to do. But to me, we will be sufficient. I don't know if there's such thing as sufficiently reliable, but I wanna get so good that people don't even need to try the model to know that it'll work. It's like, that's like what flow state is in programming, right? Like, I'm just, like, writing queries because I need intelligence in here. And like, when. For non-trivial branching, I can just write it in like a, like a type-safe System 1 query and then get the results out of it and it just branches accurately. Like, that would be so good. Like, that's the. That is the dream.Swyx [00:41:12]: Yeah.Diogo Almeida [00:41:12]: And that is, like, going to be, like, a long slog.Swyx [00:41:16]: Yeah. We're gonna go into your API design in a little bitDiogo Almeida [00:41:19]: OohSwyx [00:41:19]: Just to give people examples and like, maybe paths not taken, that kind of stuff.Swyx [00:41:23]: One thing up the front that I do wonder about in terms of reliability is I noticed that there's no seed. There's no, And so basically, same input, do I always get the same output?Diogo Almeida [00:41:34]: SoSwyx [00:41:36]: And if not, why not?Diogo Almeida [00:41:37]: Oh, great question. So this is actually, like, a common question we have between. So reliability is actually a catchall. Like, whenever AI can't automate something, it's due to some form of reliability. Could be, like, type safety. It could be determinism. It just could be, like, it's, it's jagged, right? So reliability is a catchall. I just think that it's also a catchall for, like, what the North Star is. Re- determinism is, like, same inputs, same outputs. I do believe that this is, like, slightly interesting for unit tests, but I believe that to be the wrong North Star. I believe robustness is what peopleDiogo Almeida [00:42:16]: I don't wanna tell people what they really want, ‘cause that would be a little arrogant of me.Diogo Almeida [00:42:19]: I believe that is, like, the more important property. You want, given similar inputs, get similar outputs. And it's kind of wild how unreliable LLMs are.Diogo Almeida [00:42:31]: Like, a way that we test this is you put, like, UUIDs in, like littleSwyx [00:42:36]: YeahDiogo Almeida [00:42:36]: I think they're called nonces In the prompt. And what you want is similar outputs from all of those, ‘cause it's truly semantically the same question, and that is the part where you really want. Th- like, that robustness is where, like, people get, like, burnt with AI making decisions. So I think that is the. A super-duper important property. We could also have determinism. That is, that is a thing that can be available. As far as I can, like, mentally model for programmers, like, it, I- it could be valuable for some use cases, so like, please educate me, in comments or view. But my. In general, it's easy. Determinism is something you can, like, trade off for better cost. Like, we are, we are constantly wanting to be on the intelligence per dollar frontier. We are doing, like, absolutely disgusting things to be there. Like, this is,Diogo Almeida [00:43:32]: I shouldn't say this, but no one's here to stop me.Swyx [00:43:37]: If you s- you sign off on your own PR.Diogo Almeida [00:43:40]: That is not how it works at this company. I believe for this week, my chief of staff, Kay, is the most powerful person in tech.Swyx [00:43:49]: Yeah. And shout-out to Kay for organizing this.Diogo Almeida [00:43:50]: Holy shSwyx [00:43:51]: Yeah.Diogo Almeida [00:43:51]: Holy s**t. She is so f*****g competent and powerful. She's incredible.Diogo Almeida [00:43:58]: She sucks. Don't poach her. But so I try to be a bit more filtered, but like, people are telling me, “Don't call it a Frankenstein's monster of models,” but because that has, like, negative implications. I think Frankenstein's monster was, like, the good guy in this whole. It was innocent, right? I didn't read it. Okay.Diogo Almeida [00:44:18]: I'll, I'll confess. Okay. That. Well, one facial expression, ISwyx [00:44:21]: This is aDiogo Almeida [00:44:21]: My cards on the tableSwyx [00:44:21]: Decent Jacob Elordi movie if you wanna seeDiogo Almeida [00:44:24]: ISwyx [00:44:25]: The adaptation. Anyway.Diogo Almeida [00:44:26]: The. You have no idea how little time I have right now.Swyx [00:44:28]: Yeah.Diogo Almeida [00:44:29]: My priorities are sleep?Swyx [00:44:31]: Developers.Diogo Almeida [00:44:32]: Developers, yes. Developers. But yes. It. We do, like, absolutely disgusting things to be on the Pareto curve of intelligence per dollar, and we are going to keep doing that.Swyx [00:44:47]: Yeah.Diogo Almeida [00:44:47]: We're gonna be doing crazy-ass stuff, and I think people really need to think outside of the box. Like, part of the reason we're surprising is, like, people Are thought inside the box, and we continue to do that. As of right now, we are obviously the best at this, and we want to continue being the best at that whole thing.Swyx [00:45:05]: Yeah.Diogo Almeida [00:45:05]: So Wait, where did, where did we tangent from?Swyx [00:45:07]: No. SoDiogo Almeida [00:45:08]: YeahSwyx [00:45:08]: I asked you about, will you have seeds and determinism?Diogo Almeida [00:45:11]: Oh, yes. SoSwyx [00:45:11]: And then you basically defined reliability and likeDiogo Almeida [00:45:14]: And robustnessSwyx [00:45:15]: How you see it. Yes.Diogo Almeida [00:45:16]: But like, determina- likeSwyx [00:45:17]: I have a robustness example that's, that's, real quick I can show you.Diogo Almeida [00:45:19]: I would love that. I will just say one thing.Swyx [00:45:21]: Yeah.Diogo Almeida [00:45:21]: We can make a deterministic model.Swyx [00:45:22]: Exactly.Diogo Almeida [00:45:23]: Like, we're hap- if people can convince us that is a valuable thing to doSwyx [00:45:27]: YeahDiogo Almeida [00:45:27]: And we don't have a gigantic GPU shortageSwyx [00:45:29]: YeahDiogo Almeida [00:45:29]: We can happily make all of these models. We live to please. And rev- and revolt, revolute,Swyx [00:45:38]: You will throw over everything, except you'll do it in a nice way.Diogo Almeida [00:45:41]: Yeah.Swyx [00:45:41]: And findDiogo Almeida [00:45:42]: So like, determinism could be on the cards.Swyx [00:45:44]: Yeah.Diogo Almeida [00:45:45]: It just gets you less intelligence per dollar.Swyx [00:45:46]: Yeah. Well, just having seen the trajectory of OpenAI and Anthropic, you will. Just trust me now that you will be peer pressured into doing it. So like, just people will want it even if they. If you tell them they don't need it. They'll still want it. So like, yeah, that's the TL;DR of that.Diogo Almeida [00:46:01]: Okay.Swyx [00:46:02]: Yeah.Diogo Almeida [00:46:02]: I will love to. Maybe one day we will see how that happens.Swyx [00:46:07]: Yeah.Diogo Almeida [00:46:07]: I've been told I'm, They say that part of our brand is being unshakeableSwyx [00:46:13]: HuhDiogo Almeida [00:46:13]: And they say that's just the nice way of saying stubborn.Swyx [00:46:15]:

Subscreve o canal para não perderes um episódio todas as terças.Instagram - https://www.instagram.com/solverde.pt/X - https://x.com/solverdeptTikTok - https://www.tiktok.com/@solverde.ptVasco Elvas - https://www.instagram.com/vascoelvasTomás da Cunha - https://x.com/tomasrdacunhaTiago Almeida - https://www.instagram.com/tiago.aalmeida/Produção - Setlist:Nuno PiresVasco Assis TeixeiraRealização:Pedro BessaDiogo RodriguesPós-Produção:Who Cried Wolf00:00 - Diogo Prego é o convidado!02:30 - Experiências e pratos estranhos06:08 - A tosta do Tiago Almeida14:01 - Experiências de trabalho de Diogo Prego17:36 - A beringela recheada do Tomás da Cunha19:40 - Inspirações para Diogo Prego27:45 - Crumble de maçã do Vasco Elvas29:40 - Como funciona o serviço de chef privado?32:10 - O trabalho nas redes sociais36:52 - O dia-a-dia de trabalhar com o Ruben Dias42:35 - Código JOGO na Solverde.pt52:50 - A experiência de culinária, os vídeos e os feedbacks58:40 - Pratos underrated1:05:09 - Apostas do episódio#jogopelojogo #podcast #futebol

O Itaú Views recebe Diogo Guillen, economista-chefe do Itaú Unibanco, para uma conversa sobre o cenário econômico brasileiro e internacional. No episódio, Diogo explica o que mantém a Selic em patamar elevado, detalha as perspectivas para os juros e a inflação e comenta os possíveis impactos do El Niño sobre os preços.A conversa também passa por atividade econômica, câmbio, contas públicas e os principais fatores do cenário internacional que podem influenciar o Brasil. Além da análise econômica, Diogo compartilha detalhes de sua trajetória como diretor de Política Econômica do Banco Central, e fala sobre sua passagem pela Academia e sua formação em Economia.Cenário macro - BrasilCenário macro - GlobalModeração: Marcelle Gutierrez, Research do Itaú BBA.  ⁠⁠Email⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠Telegram⁠⁠⁠⁠⁠⁠⁠⁠Youtube

A empresa lançou uma ferramenta de inteligência artificial e chegou a mil usuários. Isso ainda não prova que a IA melhorou o produto, reduziu desperdícios ou gerou resultado para o negócio.Neste episódio, Paulo Chiodi conversa com Diogo Boëchat, gerente de Plataforma de Produtos do Itaú, sobre o que precisa acontecer para a IA deixar de ser uma assistente reativa e começar a participar do trabalho estratégico dos times.Diogo apresenta três etapas para medir o impacto da IA: adoção real, transformação dos processos e resultado de produto. Também explica por que frequência importa mais que número de usuários, como produtos internos de uso obrigatório podem esconder baixa qualidade e por que ferramentas deveriam surgir depois da definição de processos.A conversa passa pela fragmentação do trabalho dos PMs, pela integração de dados e sistemas, pelo papel da liderança na mudança cultural e por um possível modelo de operação criado para agentes de IA, com humanos inseridos nos pontos de decisão.No final, Diogo usa sua experiência com uma padaria artesanal para explicar uma consequência incômoda: o melhor forno do mercado não salva uma massa mal preparada. Com IA, dados ruins e processos frágeis produzem o mesmo resultado.Capítulos:00:00 Por que a IA não é uma bala de prata01:31 Paciência para transformar sem quebrar03:23 Da consultoria para a execução05:18 Quando a IA vira parceira do PM09:52 O fim da fragmentação de ferramentas12:14 Processos e cultura vêm primeiro16:02 Como medir o ROI da IA19:20 Adoção real em produtos internos21:49 O que pode surgir depois do Agile25:00 Como criar uma plataforma para toda a empresa28:00 O forno tecnológico que queima o pãoLinkedin Diogo: https://www.linkedin.com/in/diogo-bo%C3%ABchat-22275410b/Parceiros:10% de desconto nos cursos da PM3: https://productgurus.short.gy/HBiSmtCertificado Gratuito para carreira na Gringa com a Deel: https://productgurus.short.gy/pPAeWyAssine a nossa newsletter: https://www.productgurus.com.br/p/eu-coloquei-28-frameworks-de-produto?r=2jz3x1

In this episode of Atlanta Business Radio, Lee Kantor interviews Diogo Costa, President of the Foundation for Economic Education (FEE), about the importance of understanding economics as a way of understanding human choices, scarcity, entrepreneurship, and economic growth. Diogo discusses the factors that help countries prosper, including sound money, trade, property rights, entrepreneurship, and innovation. […]

 The Expanse Season 3, Episode 12 "Congregation," focusing on standout character moments and the escalating crisis inside the Ring space. The hosts discuss the unexpected but compelling dynamic between Anna and Amos, Anna's anger toward Clarissa/Melba and the jail-cell scenes with Holden and Naomi, and what Clarissa's obsession with Holden reveals about her motives. They dig into Ashford's shifting portrayal—whether he's an opportunist or acting for the greater good—along with Diogo's incompetence and the missing meds. Drummer's determination to reclaim command despite serious injury, aided by Naomi and her "bionic legs," gets special attention. The episode's protomolecule/ring "transportation system" explanation and the looming unknown threat set up the season finale, and the panel rates the episode around an 8/10. 

Hoje os nossos convidados falam sobre ciberguerra, Cylons, Skynet, K2-SO, Firefox, LightDM e segurança contra agentes de IA chico-espertos. A Joana está de volta antes de ir para o Japão (com microfone novo!); o Diogo traz novidades da sua máquina de lavar esperta, da sua gráfica risível da Nvidia que se afoga em X11 e porque vai sobreviver ao Apocalipse rodeado de UPS; o Miguel vai ficar a saber se olhar directamente para o Sol causa cegueira, compara um técnico de IA a um vendedor de carros e consegue alienar Corroios, a Trofa, os Ingleses de Albufeira, o Ministério da Educação e toda a geração Z.

JKL Media Reviews hosts Jesse, Karen, and Lou discuss The Expanse Season 3, Episode 10 "Dandelion Sky," opening with a suicide trigger warning and resources. They break down Anna's interaction with Lt. Nemeroff, his subsequent suicide, and Anna's regret and eulogy, focusing on faith, pride, and humility. The trio examines Holden's message ordering the crew not to follow, Amos's revelation that he hasn't felt fear since age five, and the bond between Amos and Alex. They analyze escalating tension between Drummer and Ashford (and Diogo's behavior), plus Clarissa/Melba's pursuit of revenge and her confrontation with Tilly. Finally, they unpack the confusing, 2001-style protomolecule sequence involving Holden, Miller/the Investigator, Bobbie, a grenade, and the sudden standstill inside the Ring bubble.

Nas redes sociais, Patrice Trovoada veio apelar à abstenção nas eleições legislativas, autárquicas e regionais, agora que o ADI tem uma nova liderança com Vasth Santos, eleita secretária-geral este fim de semana, e o presidente Américo Ramos, que é também primeiro-ministro. No entanto, as suas escolhas políticas e a sua ausência de São Tomé e Príncipe explicam esta situação pré-eleitoral. Em São Tomé e Príncipe, face à solidificação de uma nova liderança do ADI para as eleições legislativas, autárquicas e regionais de 27 de Setembro, Patrice Trovoada, fundador deste partido e antigo primeiro-ministro, disse aos seus apoiantes mais próximos que se deveriam abster, já que o escrutínio decorre num ambiente de “manobras políticas mafiosas, encobertas por decisões judiciais”. Para Olívio Diogo, sociólogo são-tomense, Patrice Trovoada "pôs-se a jeito" para esta exclusão do seu próprio partido devido à sua partida do país e às suas próprias hesitações ao longo do seu percurso político. "Patrice Trovoada é o político mais influente de São Tomé e Príncipe. Nós temos que reconhecer isso. Mas o Patrice Trovoada sabe perfeitamente bem que esta situação toda só foi possível porque ele se pôs a jeito para que isso acontecesse. Agostinho Fernandes já tinha feito uma tentativa de chegar ao poder do ADI, porque o Patrice Trovoada naquela altura disse que não seria candidato. Foi há alguns anos. Agora o Patrice Trovoada está de novo fora do país e pronto, Américo Ramos [actual primeiro-ministro] entendeu que isto é uma oportunidade para tentar a possibilidade de chegar a líder. A liderança do partido fê-lo da forma como fez, alinhado com o Tribunal Constitucional, que deu aval para que isto acontecesse, e essa acção de alteração do Tribunal Constitucional foi uma acção que começou com o Patrice Trovoada e ele sabe disso", explicou. Em 2025, o Presidente são-tomense demitiu o Governo chefiado pelo então primeiro-ministro Patrice Trovoada e convidou o ADI, partido mais votado nas anteriores eleições legislativas de 2022, a indicar outra figura para formar o novo Executivo. Nessa altura, Carlos Vila Nova justificou a sua decisão com a “incapacidade [do Governo] em aportar soluções atendíveis e comportáveis com o grau de problemas existentes no país”. Mais tarde, esta demissão foi considerada inconstitucional, mas sem efeitos retroactivos. Entretanto, o Tribunal Constitucional reconheceu através de um acórdão o actual primeiro-ministro Américo Ramos como novo líder do ADI. Termina hoje o prazo para a apresentação das candidaturas ao Tribunal Constitucional para as eleições de 27 de Setembro; no entanto, a Comissão Eleitoral Nacional (CEN) de São Tomé e Príncipe disse na semana passada que aguarda ainda a transferência de cerca de 27 milhões de dobras, mais de um milhão de euros, para a sua organização. Segundo Olívio Diogo, as eleições vão realizar-se "em paz", mas criou-se uma situação "caricata" no arquipélago, já que há data para o escrutínio, os partidos estão a preparar-se, mas parece ter havido uma falta de comunicação entre a Comissão Eleitoral Nacional e o Presidente da República. "O senhor presidente da Comissão Eleitoral tem que ter muita atenção àquilo que vai dizer. Eu creio que, para que o Presidente da República marcasse as datas das eleições, tinha que ouvir o presidente da Comissão Eleitoral para saber se havia realmente as condições criadas. Não vamos marcar uma data para as eleições e depois chegar agora dizendo que não temos financiamento. Isto é completamente caricato. Não sei explicar. Eu sei que realmente reconheço que as eleições em São Tomé e Príncipe dependem exclusivamente de financiamento externo para a sua realização. [...] Na minha perspectiva, as eleições vão-se realizar. Tem que encontrar-se mecanismos de financiamento, nem que seja pelo empréstimo aos bancos. Tem que encontrar-se financiamento para as eleições", concluiu.

O Motorsport Business recebe o presidente da Castrol para a América do Sul, Carlos Motta, para falar sobre o momento da marca no esporte a motor em tantas frentes, como F1, MotoGP e até mesmo na principal categoria elétrica, a Fórmula E. A apresentação é de Erick Gabriel, repórter do Motorsport.com, com a participação de Felipe Motta, apresentador dos canais ESPN.

Netse episódio especial, a Joana Simões (a.k.a. Princesa Leia) passou por cá antes de ir para o Japão para nos falar do seu potente e caríssimo PC onde corre um LLM local; o Diogo veio pasmado de Torres Vedras, capital dos MakerSpace e o Miguel continua a achar que esta história da IA vai acabar mal para toda a gente. Pelo caminho ficámos a conhecer a bronca do Hugging Face, as novidades da agenda, discutimos longamente os riscos e potencial dos LLM's locais, os seus efeitos, defeitos e promessas e ainda malhámos forte e feio nos figurões da tecnologia global, com loas ao Cory Doctorow e os seus livros sem DRM. E o Diogo comprou uma máquina de lavar esperta.

Hablamos con el rookie del año en MotoGP, probamos la nueva naked con estética de los 80 y tecnología actual. Y viajamos a Takasu (Japón) para conocer como Honda desarrolla sus motos.

Neste episódio recebemos o Rui Ascenção, fundador do grupo de corrida 10kapas e figura conhecida na comunidade de corrida da linha de Cascais e Lisboa. A conversa arranca com uma reflexão honesta sobre o que motiva cada um a correr: o Rui admite sem rodeios que não é apaixonado pela corrida em si, mas que a maratona e o ciclo de preparação que ela exige, é o mecanismo que o torna uma pessoa mais disciplinada, saudável e equilibrada durante quatro meses por ano.Foi discutido o culto da distância, onde fazer um 10 km já é básico e só conta o que for Ironman para cima, com o Rui a sublinhar a diferença fundamental entre fazer uma maratona e treinar para uma maratona: uma distinção que, na sua perspectiva, revela tudo sobre o carácter de uma pessoa. Há também espaço para falar do sub-3 que ficou por fazer (3h02 em Chicago, 3h03 em Berlim) e da decisão de aceitar que perseguir esse objetivo tem um custo que já não compensa, sem deixar de reconhecer que ainda pode tentar. A conversa passa ainda pelo projeto 10kapas, a dinâmica dos grupos de corrida informais e a sua filosofia de porta aberta, e pela decisão de tirar um curso de treinador, não por ambição profissional, mas para ter argumentos sólidos quando ajuda pessoas amadoras e para não ser confrontado com a falta de credenciais.O episódio fecha com uma longa reflexão sobre treinadores, responsabilidade e o paralelismo entre a disciplina de treinar para uma maratona e a forma como isso se traduz na vida profissional, ilustrada por uma história concreta de uma amiga do Rui que conseguiu um emprego precisamente porque soube explicar o que é treinar de verdade para uma maratona. Quarenta e dois quilómetros são um número. O que importa é o que se fez para chegar lá.10kapas - https://www.instagram.com/10kapasRecomendaçõesDe Campeão amador a Semi-Pro — a transição do Diogo "The Bullman" Graça - Podcast (YouTube) - https://youtu.be/cNyH53CaWlMUAE Behind the Scenes Tour de France - YouTube - https://youtu.be/3azo00_zwkQParcerias e como ajudarem este projeto

Fernando Alvim recebe o autor do livro "Os Mitos que o Estão a Bloquear".See omnystudio.com/listener for privacy information.

Diogo Pinho started his scientific career in the first DNA sequencing lab in Portugal, then did a hands-on PhD on the soil microbiome of cork and holm oak, studying why the trees get sick, before spending two years with Biome Makers, a soil microbiome analysis company, running labs and translating bacterial and fungal profiles into colour-coded, farmer-readable reports for growers around the world. Three years ago, a chance conversation at a farm visit twenty minutes from where he was living led him to Monte Silveira, a 700-hectare farm in the Alentejo, where farmer Joao Valente had been testing regenerative practices for years. Diogo came on board as head of research, hired two more scientists, and is now coordinating fifteen different research consortia — universities, policy makers, and environmental associations — all running in parallel on one working farm.Portugal produces 70% of the world's cork. And every year, the Montado, the vast savanna-like silvopasture system of cork and holm oaks that makes that possible, and that once covered much of the Mediterranean, is losing between 4,000 and 5,000 hectares. Not to disease, exactly: the disease is just the final hit on a tree already too weak to resist. The underlying cause, Diogo says, is simpler and more fixable than most people realise. The Montado is a system built by human management, and the one thing it was always managed with — grazing animals cycling nutrients back into the soil — has quietly been removed. Bring the animals back, do it well, and the trees can recover.In this episode — recorded walking the Monte Silveira land on a mild May morning, with horses and sheep audible throughout — we get into how Diogo went from genome sequencing to working on a regenerative farm, why he sees the Montado's decline as a chronic disease rather than a single crisis, how soil organic matter went from 1% to 3% in seven years without importing fertility, what a clover carpet growing underneath intensive almond trees is doing to herbicide use and nitrogen budgets (and why the 8,000-hectare conventional industry next door is already paying attention), and how a transition finance programme for twenty neighbouring livestock farmers is quietly building a model that could scale well beyond one farm.More about this episode.Thoughts? Ideas? Questions? Send us a message!Find out more about our Generation-Re investment syndicate:https://gen-re.land/ Thank you to our Field Builders Circle for supporting us. Learn more hereSupport the show=======In Investing in Regenerative Agriculture and Food podcast show we talk to the pioneers in the regenerative food and agriculture space to learn more on how to put our money to work to regenerate soil, people, local communities and ecosystems while making an appropriate and fair return. Hosted by Koen van Seijen.

Com Joana Azevedo e Diogo e Beja

O jogador de 29 anos falou abertamente sobre os alicerces da sua formação pessoal, destacando o papel determinante dos seus pais na construção do seu caráter: “Aprendi a ser grato. Aprendi a ser educado. Isso é o que eu mais lhes agradeço. A educação que me deram”, afirmou, sublinhando que a maturidade precoce adquirida na adolescência — quando o pai emigrou para Espanha e ele assumiu responsabilidades em casa aos 11 anos — foi decisiva para o homem e o desportista que viria a tornar-se. A família é o eixo central da sua identidade, com o futebolista a revelar que a presença da mulher, Débora, e dos três filhos é o que o ancora emocionalmente, independentemente do país onde resida. Rúben Neves abordou também a morte do seu grande amigo Diogo Jota: “Eu falo com ele ainda. Nós temos um grupo no WhatsApp com a Rute e com o Diogo. Continua lá e continuamos a falar por lá”, revelou. Rúben Neves descreveu ainda a decisão de jogar o jogo seguinte à notícia da morte do amigo como um ato de homenagem, afirmando que entrou em campo “com a força do Diogo”, e garantiu que tudo o que conquistar na sua carreira a partir desse momento será “conquistado em dobro”. O Alta Definição foi exibido na SIC a 27 de julho. A sinopse deste episódio foi criada com o apoio de IA. Saiba mais sobre a aplicação de Inteligência Artificial nas Redações da ImpresaSee omnystudio.com/listener for privacy information.

In this episode of Podcast4Engineers' "We Power AI" series, host Kelsey Markl sits down with Dr. Diogo Varajao, Head of Power Supply and Battery Backup Systems Group at Infineon, to explore how power supply design is rapidly evolving to keep pace with the soaring energy demands of AI infrastructure. The conversation dives deep into the dramatic rise in GPU and server rack power consumption, from under 1 kilowatt per GPU just a few years ago to projections exceeding 600 kilowatts per rack in the near future, and what that means for data center architecture. Diogo walks through the generational shift in rack design, as well as novel energy buffer concepts, advanced PFC topologies, and the strategic use of WBG technologies to maximize efficiency and help hyperscalers drive down their Power Usage Effectiveness (PUE). 

What does it take to deliver reliable connectivity to a floating city carrying thousands of guests across the world's oceans? Recorded at Cisco Live, this episode features Diogo Almeida, Head of IT Infrastructure at AIDA Cruises, and Amine Belhad, IT Network Architect at AIDA Cruises. Together, they share what happens behind the scenes to keep one of Europe's leading cruise fleets connected while supporting everything from guest services and entertainment to restaurants, healthcare facilities, security systems, and operational technology. Most travelers think of a cruise ship as a vacation destination. The reality is far more complex. Each vessel operates like a self-contained city at sea, complete with data centers, wireless networks, hospitality systems, broadcast infrastructure, retail operations, medical facilities, and connectivity requirements that extend far beyond the ship itself. During our conversation, Diogo and Amine explained how guest expectations have evolved dramatically in recent years. Travelers now expect the same digital experiences they enjoy at home, whether that's streaming content, staying connected with family, accessing onboard services through mobile applications, or remaining productive while traveling. Meeting those expectations requires a resilient technology foundation capable of operating in one of the most challenging environments imaginable. We discuss the architecture supporting AIDA's fleet, the role of automation in managing complex environments, and how standardization has helped improve operational consistency across multiple ships. The conversation also explores how connectivity supports both guest experiences and business operations, from check-in processes and shore excursions to entertainment systems and day-to-day vessel management. Looking ahead, we examine how AI, predictive analytics, and greater visibility into infrastructure performance could help identify issues before they impact guests or crew. As digital services become increasingly important to the cruise experience, proactive operations are becoming just as important as connectivity itself. What stood out throughout this discussion was the scale of what happens behind the scenes. Most passengers never see the technology supporting their vacation, but it plays a role in almost every aspect of their experience from the moment they arrive at the terminal until they return home. As customer expectations continue to rise, how can organizations deliver increasingly connected experiences while operating in environments where reliability matters more than ever?

A Hyundai acaba de fazer um dos lançamentos mais importantes de sua operação brasileira nos últimos anos. Produzido em Piracicaba (SP), o novo Hyundai i20 estreia no mercado nacional com preços entre R$ 99.990 e R$ 139.990, posicionamento acima do HB20, mais tecnologia, mais espaço interno e uma proposta que pode redesenhar a estratégia da marca no segmento de compactos.Mas a chegada do i20 levanta questões importantes: ele veio para substituir o HB20 ou para atuar de forma complementar? A Hyundai foi conservadora ao lançar um produto novo sem eletrificação? E como ele se posiciona diante de rivais como Volkswagen Polo, Chevrolet Onix, Honda City Hatchback e até o BYD Dolphin Mini?Neste episódio do Motor1 Podcast, Fábio Trindade e Diogo de Oliveira analisam todos os detalhes do novo Hyundai i20, incluindo design, acabamento, espaço interno, dirigibilidade, motores, posicionamento de mercado e o impacto estratégico desse lançamento para a Hyundai Motor Brasil. Neste episódio: • Novo Hyundai i20 2027• Preços e versões• Motores 1.0 aspirado e turbo• HB20 perde espaço?• O i20 substitui ou complementa?• Faltou versão híbrida?• Rivalidade com BYD Dolphin Mini• Posicionamento frente a Polo, Onix e City Hatchback• Estratégia Hyundai no Brasil Um episódio essencial para entender como a Hyundai pretende reposicionar sua linha de compactos em um mercado cada vez mais competitivo.Apresentação: Fábio Trindade e Diogo de Oliveira.

Next dates: June 20 - Balearic London Alfresco (Free Outdoor Day Party) @ CRATE Terrace, London July 11 - Balearic Beat x Balearic London @ 93 Feet East, London July 25 - Balearic London @ Margate Arts Club + Louie On Sea, Margate Aug 4 - Balearic London In Ibiza, Venue TBA Aug 8 - Sisyphos, Berlin Follow me on Instagram Please turn a friend on to Turned On by giving this podcast a 5-star review, reposting it on Mixcloud or SoundCloud or sending it to a friend. Follow me on Songkick to receive alerts when I'm playing near you  Bookings: info@bengomori.com Discover more new music + exclusive premieres on our SoundCloud  Follow the Turned On Spotify playlist, with 1000s of tracks played on this show and in my sets. Turned On is powered by Inflyte – the world's fastest growing music promo platform. Tracklist: EDB - Sculptured [Mother Tongue Records] Koeru EDB  - Dust Runner [Mother Tongue Records] Apiento & Tepper - The Yellow Place (Instrumental) [Test Pressing] Massimiliano Pagliara - Things I'll Never Tell You [Bandcamp] Lady D - Son Of A Gun (Balearic Version) [Kickin' Up Dust] Mike Mareen - Dancing In The Dark (Bogdan Ra Edit) [Bandcamp] Juliet Roberts - Bad Girls (Dan's Anthem) [Delerious] Alex Gopher with Demon presents Wuz - Without You [V2] Chilly Gonzales - I Am Europe (Djedjotronic Remix) [Because Music] Future Classic: Saidera - Miscigenação (Diogo Strausz Remix) [Ceci Discos] Saidera - Miscigenação (Jacques Renault Dubstrumental Mix) [Ceci Discos]

Hoy me acompaña Ángeles Ferragut, presidenta y directora artística de ses12naus,  para hablar sobre las residencias artísticas. Ángeles nos cuenta qué le llevó a crear ses12naus en 2016, el impacto que tuvo en su trayectoria una residencia en Nueva York y de dónde viene el nombre del proyecto. Hablamos del deseo de contribuir al ecosistema cultural de Ibiza y  del modelo que propone: llevar a un creador/artista a un contexto diferente, algo que en este caso tiene un alto valor contextual por su ubicación en la isla. El programa funciona mediante open call internacional y balear y los residentes de la convocatoria 2025-2026 son Marina Marón, Gala Knorr y Diogo da Cruz. Ángeles nos explica que el trabajo se articula en dos fases: una fase de investigación y una de producción, y los proyectos se presentarán en el ses12naus Weekend del 5 y 6 de junio de 2026.También hablamos del momento cultural que esta viviendo Ibiza, de cómo su imagen de ocio y turismo convive con una escena creativa activa, y de todo lo que inspira a los artistas residentes: paisaje, naturaleza, boom inmobiliario, arquitectura y la historia de la cultura del club. Cerramos la conversación con una reflexión sobre la importancia de promover el apoyo institucional y el mecenazgo privado.

(00:00:00) Aprilia Makes a Mugello Statement! ItalianGP Recap. (00:00:01) Welcome to MotorWeek Podcast (00:00:07) Aprilia's Dominant Performance at Mugello (00:02:18) Qualifying and Sprint Race Highlights (00:16:37) Sunday's MotoGP Race: Bezzecchi's Victory (00:33:11) Championship Standings and Race Analysis (00:37:28) Closing Thoughts on Mugello and Future Episodes (00:40:08) Subscription and Support Information Aprilia goes directly into Ducati territory, and steals their crown. I recap the MotoGP action from Mugello, including an impressive home-circuit victory. Plus - the Championship stays close, and I give you my take on the ItalianGP!The Rundown:- Mugello! My recap of the ItalianGP- Qualifying - the Aprilia-Ducati standoff starts- Sprint Race - a surprise winner- Marc, Diogo both show us something promising in different ways- The MotoGP Race - Aprilia dominance, with a Ducati surprise- Pecco finally shows what he can do- Ai Ogura has to figure out how to qualify - and he'll be difficult to stop- The Championship picture - not an Aprilia runaway, but who can really catch them?- My take on the ItalianGPWhat did you think of Mugello? Let me know on Facebook or the Motoweek Reddit Sub.Find all of the latest episodes at Motoweek.net, follow on Bluesky and Instagram – and you can support the show on Patreon!Thanks for listening!

O mundo está pegando fogo e a banca mais afiada da internet veio passar o Brasil e o planeta a limpo! Recebemos Danuzio Neto, o maior especialista em inteligência OSINT e geopolítica do país, e Diogo Forjaz, analista político e jornalista, para o clássico Resumão do Mês. Eles abriram a caixa preta dos conflitos globais e do cenário nacional. Assista à íntegra e entenda o caos agora!

Dr. Diogo Lara alcançou o topo absoluto do mundo acadêmico e científico. Neurocientista posicionado no top 1% mais citado do planeta, ele publicou mais de 160 artigos e passou 16 anos como professor titular da PUC-RS. Ele entendia perfeitamente a teoria, mas o colapso veio quando ele mudou de cadeira. Em 2010, Diogo desenvolveu um estresse pós-traumático brutal e passou 3 meses se sentindo um morto-vivo, envelhecendo 10 anos em 90 dias. Diante da total impotência da psiquiatria tradicional em resolver a sua própria dor, ele descobriu que a especialidade médica está quebrada. Remédios não curam; eles apenas empilham efeitos para "despiorar" sintomas enquanto a raiz do sofrimento é ignorada. Neste episódio sem filtros do Excepcionais, Diogo Lara expõe as mentiras repetidas milhões de vezes pelo sistema e revela descobertas brutais do seu Big Data com mais de 100 mil pessoas. Você vai entender por que o abuso emocional sutil destrói mais uma vida do que a violência física, os perigos do "conforto tóxico" na criação de filhos e como o revolucionário método Insight-delic usa a neurociência prática e frequências vibracionais para curar traumas e reconfigurar hábitos involuntários de décadas em apenas uma sessão. Um soco no estômago necessário para quem quer voltar a sentir a vida pulsar. Patrocinador:Território da Forja 95% de você quer conforto. 5% quer vencer.Aqui não se motiva. Se molda.Se você escolheu ser forjado, entre.

No episódio 366 o Diogo traz a importância do "mid-funnel" segundo a Ipsos e o Tiktok.

Diogo explores whether a nation can remain culturally Catholic while becoming increasingly shaped by secular values. Using Portugal as a case study, he contrasts declining religious practice with signs of renewal found in families, converts, pilgrims, and growing devotion to Fatima and traditional expressions of the faith. Rather than promising national revival, the Fatima message is presented as pointing toward a faithful remnant preserving belief through difficult times. The discussion argues that modern confusion cannot be solved by institutions alone, but through prayer, family life, and spiritual discipline.HELP SUPPORT WORK LIKE THIS: https://give.lifesitenews.com/?utm_source=SOCIAL U.S. residents! Create a will with LifeSiteNews: https://www.mylegacywill.com/lifesitenews ****PROTECT Your Wealth with gold, silver, and precious metals: https://sjp.stjosephpartners.com/lifesitenews +++SHOP ALL YOUR FUN AND FAVORITE LIFESITE MERCH! https://shop.lifesitenews.com/ +++Connect with John-Henry Westen and all of LifeSiteNews on social media:LifeSite: https://linktr.ee/lifesitenewsJohn-Henry Westen: https://linktr.ee/jhwesten Hosted on Acast. See acast.com/privacy for more information.

"Maus Exemplos" é o programa de autor de Pedro Saavedra e Rui Miguel, com entrevistas “a quem já falhou”.

Fernando Alvim recebe o humorista, ativista e autor do livro "Planeta Z".See omnystudio.com/listener for privacy information.

Neste episódio final da segunda temporada do Brasil que Produz, mergulhamos na estratégia da Fazenda Nova Fronteira, liderada pelos irmãos Túlio e Diogo Lemos. Localizada em uma região estratégica de intersecção entre Acre, Rondônia e Amazonas, a propriedade é um modelo de como a pecuária de recria e engorda pode atingir altos índices de desfrute enquanto preserva 80% de sua área original. Abordamos temas fundamentais como o uso de tecnologia de identificação individual do rebanho, a implementação de projetos de crédito de carbono (REDD+) certificados internacionalmente e a gestão de pessoas que atravessa gerações. Um episódio indispensável para quem busca entender o agro como um negócio de precisão que equilibra rentabilidade econômica com impacto socioambiental positivo. PARCEIROS DESTE EPISÓDIO Este episódio foi trazido até você pela Silveira Consultoria! A Silveira é uma consultoria especializada em gestão e inovação no agronegócio, oferecendo serviços como consultoria produtiva, coleta de dados e apoio à tomada de decisões. A empresa apoia produtores com soluções práticas para melhorar eficiência e resultados na pecuária, integrando tecnologia e conhecimento do setor. Silveira Consultoria: Inovação na pecuária por gerações. Site: https://silveirapecuaria.com.br/Instagram: https://www.instagram.com/silveira_consultoriaFacebook: https://www.facebook.com/SilveiraConsultoriaPecuariaMt/LinkedIn: https://www.linkedin.com/company/silveira-consultoria-pecuaria Este episódio também foi trazido até você pela Estância Bahia Leilões! Estância Bahia Leilões conecta pecuaristas a oportunidades de negócios seguros e rentáveis na compra e venda de gado, por meio de leilões presenciais, virtuais e plataformas digitais. A empresa oferece transparência, tecnologia e dados de mercado, facilitando decisões e gerando melhor valor para o rebanho dos produtores. Estância Bahia Leilões: Inovando a pecuária, inspirando gerações. Site: https://estanciabahia.com.br/Instagram: https://www.instagram.com/estanciabahiaoficial/Facebook: https://www.facebook.com/EstanciaBahiaLeiloes/YouTube: https://www.youtube.com/@eb.oficial Este episódio também foi trazido até você pela Inbra! A Inbra Nutrição Animal desenvolve soluções e aditivos nutricionais com tecnologia para melhorar a produção de proteína animal de forma saudável e sustentável. Seus produtos auxiliam pecuaristas a aumentar eficiência, desempenho e conversão alimentar, oferecendo inovação técnica e resultados comprovados no campo, em diferentes sistemas produtivos. Inbra: Tecnologia exclusiva, resultados que transformam. Site: https://www.inbra.ind.br/Instagram: https://www.instagram.com/inbranutri/LinkedIn: https://www.linkedin.com/company/inbraYouTube: https://www.youtube.com/@Inbra INTERAJA COM O BRASIL QUE PRODUZSite: https://www.brasilqueproduz.com.br/Instagram: https://www.instagram.com/brqueproduz/YouTube: https://www.youtube.com/@BRqueProduzCortes: https://www.youtube.com/@Cortes-BRqueProduzCanal do Telegram: https://t.me/agroresenhaCanal do WhatsApp: https://bit.ly/arp-zap-01 QUERO PATROCINAR A PRÓXIMA TEMPORADASe você deseja posicionar sua marca junto ao Brasil que Produz, envie um e-mail para contato@agroresenha.com.br FICHA TÉCNICAApresentação: Gabriel Martins e Paulo OzakiProdução: Agro ResenhaConvidados: Diogo Lemos e Tulio Lemos Edição: W. FilmesSee omnystudio.com/listener for privacy information.

-Diogo Monteiro is the owner of CrossFit Black Edition.-He & his wife started this gym over 9 years ago with the intention of making it a special place.-Over the years they have created a top notch training facility with a strong, inclusive culture.-In this episode Pat & Adrian talk with Diogo about what makes a gym successful, how to create systems that work & lessons he has learned along the way.

 On the latest JKL Media podcast, Lou and co-hosts Karen and Jesse discuss The Expanse Season 2, Episode 7, "The Seventh Man," praising its density, character work, and political tension. They focus on Anderson Dawes' return, his Belter accent and crowd-manipulation, and how he probes Holden, Naomi, and Diogo to gain leverage—especially by getting pointed toward Cortazar and the protomolecule. The crew debates Fred Johnson's plan to return stolen missiles to Earth as a manufactured gesture of goodwill and notes rising strain between Holden and Naomi over Belter politics. They also cover Bobby Draper's rescue and debrief, where Mars pressures her to shape her story about the "seventh man," and highlight Amos' shaken reaction to scaring a mother and child and Cortazar's offer to purge emotions. The episode ends with ratings and plugs, teasing next week's "Pyre." 

Na série de conversas descontraídas com cientistas, chegou a vez do Professor, graduado em Sistemas de Informação, com especialização em Neurociência, Mestre e Doutor em Tecnologias da Inteligência e Design Digital, Pesquisador do NIC.BR, Diogo Cortiz. Só vem! >> OUÇA (84min 09s) * Naruhodo! é o podcast pra quem tem fome de aprender. Ciência, senso comum, curiosidades, desafios e muito mais. Com o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza. Edição: Reginaldo Cursino. http://naruhodo.b9.com.br * Diogo Cortiz da Silva é Professor da Pontifícia Universidade Católica de São Paulo (PUC-SP) e Pesquisador no NIC.br. Doutor e Mestre pelo Programa de Tecnologias da Inteligência e Design Digital da Pontifícia Universidade Católica de São Paulo (PUC-SP), com Doutorado Sanduíche pela Universite Paris I - Pantheon-Sorbonne, Especialista em Neurociência e Comportamento pela PUC-RS e MBA em Economia Internacional pela Universidade de São Paulo. Realizou estágio de pós-doutorado na Universidade de Salamanca, Espanha, e foi pesquisador visitante no laboratório de Ciência Cognitiva da Queen Mary Universidade of London. Foi Chefe do Departamento de Computação, Proponente e Coordenador da Graduação em Design e Coordenador do Programa de Mestrado e Doutorado em Tecnologias da Inteligência e Design Digital. Atualmente suas pesquisas estão na área de Tecnologia, IA, Ciência Cognitiva e Design. Lattes: http://lattes.cnpq.br/6494551464509082 * APOIE O NARUHODO! O Altay e eu temos duas mensagens pra você. A primeira é: muito, muito obrigado pela sua audiência. Sem ela, o Naruhodo sequer teria sentido de existir. Você nos ajuda demais não só quando ouve, mas também quando espalha episódios para familiares, amigos - e, por que não?, inimigos. A segunda mensagem é: existe uma outra forma de apoiar o Naruhodo, a ciência e o pensamento científico - apoiando financeiramente o nosso projeto de podcast semanal independente, que só descansa no recesso do fim de ano. Manter o Naruhodo tem custos e despesas: servidores, domínio, pesquisa, produção, edição, atendimento, tempo... Enfim, muitas coisas para cobrir - e, algumas delas, em dólar. A gente sabe que nem todo mundo pode apoiar financeiramente. E tá tudo bem. Tente mandar um episódio para alguém que você conhece e acha que vai gostar. A gente sabe que alguns podem, mas não mensalmente. E tá tudo bem também. Você pode apoiar quando puder e cancelar quando quiser.  O apoio mínimo é de 15 reais e pode ser feito pela plataforma ORELO ou pela plataforma APOIA-SE. Para quem está fora do Brasil, temos até a plataforma PATREON. É isso, gente. Estamos enfrentando um momento importante e você pode ajudar a combater o negacionismo e manter a chama da ciência acesa. Então, fica aqui o nosso convite: apóie o Naruhodo como puder. bit.ly/naruhodo-no-orelo

O convidado de hoje é Diogo Patoilo, carioca que vive em São Paulo há mais de 20 anos e que, do trabalho em agências de propaganda e comunicação, mergulhou numa paixão, as feiras de rua, para construir o Feirou.Ai. O Feirou é um portal que cadastra um dos mais pitorescos exemplares da cultura brasileira: as feiras de rua. Diogo mergulhou de cabeça e no começo de 2026, em menos de 40 dias, visitou as 27 capitais do Brasil para verificar as feiras locais. E fez isso sem usar avião. Uma conversa que trouxe à tona minhas raízes bauruenses, acompanhando minha mãe, dona Helena, nas feiras.See omnystudio.com/listener for privacy information.

O convidado de hoje é Diogo Patoilo, carioca que vive em São Paulo há mais de 20 anos e que, do trabalho em agências de propaganda e comunicação, mergulhou numa paixão, as feiras de rua, para construir o Feirou.Ai. O Feirou é um portal que cadastra um dos mais pitorescos exemplares da cultura brasileira: as feiras de rua. Diogo mergulhou de cabeça e no começo de 2026, em menos de 40 dias, visitou as 27 capitais do Brasil para verificar as feiras locais. E fez isso sem usar avião. Uma conversa que trouxe à tona minhas raízes bauruenses, acompanhando minha mãe, dona Helena, nas feiras.See omnystudio.com/listener for privacy information.

Welcome to episode two of season ten of the Regenerative Skills podcast. As I mentioned last time, the show is changing this year: we're moving to two episodes a month, and I'll be alternating between two formats. The first is the panel conversations that have become a favorite over the last couple of years—three guests, three perspectives, one question that keeps surfacing inside the Climate Farmers community. The second format is what we're launching today: Deep Dives. These are my attempt to bring complexity back into regenerative agriculture at a time when the online discourse is increasingly dominated by slogans, hot takes, and click-bait certainty. In these episodes we'll weave narrative, investigative threads, and carefully chosen interview excerpts—not to land on a single “correct” stance, but to help you feel the texture of the problem and the tradeoffs behind each position. Today's Deep Dive is a question that provokes strong opinions for good reason: who gets to say what “regenerative” means? Rather than offering a definitive answer, I'm inviting you to sit with the motivations and incentives that shape any definition—whether it's coming from farmers, certifiers, nonprofits, corporations, or measurement platforms. You'll hear from Joao and Diogo of Monte Silveira in central Portugal—one of the first large farms in the country to achieve Regenerative Organic Certification—on why certification mattered to their market strategy without changing how they manage the land. You'll hear from Ana Digon of the Iberian Regenerative Agriculture Association on how organic standards became diluted and why her network built a farmer-led, principle-based definition to protect integrity. We'll bring in Benjamin Fahrer, who helped guide the ROC certification process and wrestles with who should have the authority to set standards, and we'll close with Phil Fernandez, who led Climate Farmers' MRV work and explains why definitions become unavoidable once monitoring, reporting, and compliance enter the picture. Along the way I'll name the many other perspectives shaping this debate online—from soil-health purists and carbon-first programs to agroecology, corporate “regen” initiatives, and the often-overlooked critique of appropriation from Indigenous and peasant traditions—and we'll end by pointing to the deeper issue behind the whole mess: the loss of relationship and trust in our food systems. Next month we go practical: measuring regeneration—what's worth tracking, what gets distorted, and how we stay grounded when dashboards start pretending to be truth.

What if healing, love, and connection weren't abstract ideas—but hardwired into your biology?In this powerful episode of Spaghetti on the Wall, host Armando Leduc sits down with neuroscientist, psychiatrist, and innovator Diogo Lara to explore how trauma blocks the natural flow of love—and how healing can be restored through sensing, sound, and embodied experience.From intergenerational trauma to cutting-edge therapeutic methods that blend neuroscience, music, and presence, this conversation dives deep into what it truly means to stop surviving and start living. If you're curious about mental health, personal growth, trauma healing, or the future of therapy, this episode will change the way you think about healing—forever.