Podcasts about openai codex

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

OpenAIは、キーボードメーカーのWork Louderと共同開発したキーパッド「Codex Micro」を230ドルで販売開始しました。

Every team has communication habits, but how many of those habits have ever been discussed out loud? In this episode, Tricia introduces The Shared Kitchen, a free reflection and facilitation tool designed to help teams understand how each person prefers to communicate, receive feedback, make decisions, ask questions, and work through a project. Using stories about getting fired from a bartending job and accidentally adding unwanted sriracha to breakfast, she shows how easily we can mistake our own habits for shared expectations. The tool begins with individual reflection before helping a team create its own shared "recipe" for collaboration. Participants consider questions such as how much context they prefer, when they like others to check in, what helps feedback land well, and what makes them feel trusted. Tricia also shares how she built the interactive page using Claude Code and OpenAI Codex, including an experiment in asking the two coding tools to compete against one another. The result is both a practical resource for teams and a look at how generative coding tools can support educators and leaders who want to turn an idea into something others can use. Access the page here: https://triciafriedman.com/the-shared-kitchen/  

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year's survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.In our in-depth conversation, we discuss:1. Why AI has split the tech workforce almost exactly in half—one half that's thriving, another that's shaken2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)3. Why burnout has jumped an alarming 11 points in a single year4. Why nobody in tech would recommend their job to someone entering the industry today5. The #1 fear in tech right now (it's not job loss to AI)6. Why managers are the single biggest lever for employee well-being7. Concrete advice for what employees and leaders can do right now—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/command?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q3_brand_campaign—Episode transcript: https://www.lennysnewsletter.com/p/how-tech-workers-actually-feel-about—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Noam Segal:• X: https://x.com/noamseg• LinkedIn: https://www.linkedin.com/in/noamsegal—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Noam Segal(02:34) About the survey: methodology and scope(06:04) The core finding: AI has split the tech workforce in half(13:03) The AI identity stance(14:40) The four archetypes: Energized, Conflicted, Disoriented, Resentful(19:35) Burnout is surging (and why shipping faster is making it worse)(22:53) A glimmer of hope(24:55) Layoff worries(29:15) The career recommendation NPS score(36:45) The ladder metaphor: rungs disappearing beneath our feet(45:14) AI is making us faster, not better(52:53) The #1 fear: being squeezed to do more for the same pay(55:55) The emotional landscape and “smiling exhaustion”(01:01:02) Designers and researchers: the most negative group two years running(01:06:27) Who's happiest(01:12:18) Managers: the single biggest lever on well-being(01:18:47) The industry is “chaotic”(01:24:53) What employees and leaders can do right now(01:31:32) AI guilt and closing thoughts—Referenced:• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026• How tech's most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• NPS Is The Worst: https://www.npsistheworst.com• The Terminator: https://www.imdb.com/title/tt0088247• Skynet: https://terminator.fandom.com/wiki/Skynet• Inside Devin: The world's first autonomous AI engineer that's set to write 50% of its company's code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu• Devin: https://devin.ai• An AI state of the union: We've passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste• Building beautiful products with Stripe's Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape• Elon Musk: ‘Chances are we're all living in a simulation': https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Canzhi is a programmer and also part of a sports betting syndicate. He talks about his path from coding in middle school to computer science in college, to becoming a software developer. Then he left the mainstream world for professional gambling.Canzhi also talks about using OpenAI Codex to do the data science work that his betting group relies on.Follow Canzhi on Twitter: https://x.com/canzhiyeFollow the show on Twitter: https://x.com/halfkellySubstack Archive: https://riskofruinpod.substack.com/(To find past paywalled episodes of the show search the word "ARCHIVE" in the search bar in the top right).Email the show: risk of ruin pod at gmail

https://clearmeasure.com/developers/forums/ Matthew Renze is an AI researcher, consultant, and author, and the founder of Renze Consulting, where he has trained over 500,000 software developers and IT professionals worldwide. He has delivered over 200 keynotes, presentations, and workshops on every continent — including Antarctica — for clients ranging from tech startups to Fortune 500 companies. A nine-time Microsoft MVP in AI, Matthew is also the president of the Renze AI Research Institute, where he studies how self-reflecting large language model agents improve problem-solving performance, trustworthiness, and value alignment. Most recently he was accepted into the Doctor of Engineering program at Johns Hopkins University, and he featured as an interview subject in the 2026 documentary "AI Everywhere." Website: https://matthewrenze.com/ LinkedIn: https://www.linkedin.com/in/matthewrenze/ Twitter/X: @matthewrenze GitHub: https://github.com/matthewrenze (via profile links) Our OpenClaw agent ("Bob") - Bob's website: https://bobrenze.com/ - Bob's blog: https://blog.bobrenze.com/ - Bob's book: https://a.co/d/014kieQI - Agent ranking site: https://agentfolio.io/ Stage 1 - Communicating in steps with an AI assistant - ChatGPT - https://chatgpt.com/ - Claude Chat - https://claude.ai/ Stage 2 - Collaborating on tasks with an AI agent - GitHub Copilot - https://github.com/features/copilot - Claude Code - https://claude.com/product/claude-code - OpenAI Codex - https://openai.com/codex/ Stage 3 - Supervising processes with an agentic workflow - LangChain / LangGraph - https://www.langchain.com/langgraph - Microsoft Agent Workflows - https://learn.microsoft.com/en-us/agent-framework/workflows/ Stage 4 - Managing a project with an autonomous agent - OpenClaw: https://openclaw.ai/ - Hermes: https://hermes-agent.nousresearch.com/ Stage 5 - Leading a mission with an autonomous agency - PaperClip AI: https://paperclip.ing/ Fireworks AI: https://fireworks.ai/ ---------------------------------- Previous Appearances on the Azure & DevOps Podcast: Episode 44 — Matthew Renze on Data Science for Developers https://azuredevopspodcast.clear-measure.com/matthew-renze-on-data-science-for-developers-episode-44 Episode 220 — Matthew Renze: Developing Your AI Strategy https://azuredevopspodcast.clear-measure.com/matthew-renze-developing-your-ai-strategy-episode-220 Episode 249 — Matthew Renze: AI Ethics https://azuredevopspodcast.clear-measure.com/ai-ethics-with-matthew-renze-episode-249 --------------------------------------- Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

OpenAI and Thrive Holdings built Tax AI, a Codex-powered agent that helps prepare complex tax returns while preserving evidence for accountant review. In this episode, Corey and Grant talk with OpenAI's John de Wasseige and Arthur Fernandes Araujo about how expert corrections become structured signals, how Codex turns repeated failures into evals and scoped engineering tasks, and why the best AI deployments still need humans close to the work. They also dig into what this pattern could mean for bookkeeping, audits, IT help desks, and other expert workflows where the system can measure what “right” looks like.Relevant links:OpenAI Tax AI case study: https://openai.com/index/building-self-improving-tax-agents-with-codex/OpenAI Codex: https://openai.com/codex/Harness engineering: https://openai.com/index/harness-engineering/Thrive Holdings: https://www.thriveholdings.com/Crete: https://www.cretepa.com/Subscribe to The Neuron newsletter: https://theneuron.ai

Andrew Ambrosino leads development of the Codex desktop app at OpenAI. Nearly 100% of OpenAI employees—not just engineers—now use Codex weekly. A lifelong builder with a background spanning engineering, design, product management, and founding companies, he is now responsible for turning the Codex desktop experience into what he calls “the best desktop app that has ever existed, full stop.”In our in-depth conversation, we discuss:1. Why AI has completely flipped the product development process2. What “taste” really means as a professional skill, and why it is emerging as the most valuable capability in an AI-first workplace3. Why Andrew believes the Codex app would have failed if they launched it last November (vs. in February)4. The “zone defense” model for how product managers at OpenAI operate when everyone can build anything5. How roles are collapsed on Andrew's team, and why eliminating the concept of roles entirely is a big mistake6. How Andrew uses Codex to run his own workflows7. The vision for a home base that coordinates work across ChatGPT, Codex, and the tools people already use.—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreMercury—Radically different banking, now with Command—Episode transcript: https://www.lennysnewsletter.com/p/openais-codex-lead-on-the-new-shape—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Andrew Ambrosino:• X: https://x.com/ajambrosino• LinkedIn: https://www.linkedin.com/in/ajambrosino• Website: https://ambrosino.io—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Andrew Ambrosino(02:30) How AI is changing the shape of product work(06:32) When to use documents vs. prototypes(10:25) What “taste” actually means(12:06) Why AI is still bad at design(16:18) Is the design process really dead?(21:35) What the design process looks like on the Codex team(23:41) Are product functions disappearing?(27:22) Team structure(30:12) IC vs. management(31:37) Planning roadmaps(35:16) Building features that don't work yet(38:13) The ambition problem: when you're too AGI-pilled(39:17) The latest frontier: loops and autonomous development(52:05) How Andrew uses Codex to automate his entire job(46:52) The power of computer use and browser automation(49:10) Will we run all our SaaS apps inside Codex?(52:05) The future vision for Codex(57:20) The videographer who built a Premiere Pro extension with Codex(59:30) Failure corner(1:01:50) Lightning round(1:07:03) BTS: How our producer uses Codex for editing—References: https://www.lennysnewsletter.com/p/openais-codex-lead-on-the-new-shape—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

What if the biggest AI opportunity for your business isn't ChatGPT—but the tool quietly replacing it?While much of the AI conversation has focused on Claude Code and coding assistants, a new contender is changing how business leaders think about productivity, automation, and execution.In this episode, Dan Sanchez joins Isar Meitis to explore how OpenAI Codex has evolved far beyond software development. Together they reveal how AI agents can proactively find context, take action, automate complex workflows, and become true collaborators inside your business, not just chatbots that answer questions. If you're looking for practical ways to scale marketing, streamline operations, and unlock new levels of efficiency without increasing headcount, this conversation offers a glimpse into what the next generation of AI-powered work looks like. In this session, you'll discover: Why OpenAI Codex is gaining momentum beyond software development  The key differences between Codex, ChatGPT, Claude Code, and Claude CoWork  How AI agents proactively find context and execute tasks autonomously  Why project-based AI workflows are becoming essential for modern businesses  How Dan uses Codex for marketing, content creation, and process automation  The power of AI-accessible folders, files, and organizational systems  How AI can generate, manage, and improve business assets over time  Practical examples of automating large-scale content operations  Why business leaders should start thinking beyond prompts and toward AI-powered execution  The future of agentic workflows and AI-assisted business operationsAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

The federal government wants equity in OpenAI (and others) and ... the people might get a slice?

У свіжому дайджесті DOU News розбираємо чому Google змушений платити Ілону Маску $920 млн на місяць та епічний кіберфакап Meta. Кабмін оновлює правила бронювання для ІТ-фахівців, а в спільноті обговорюють скандал навколо Гергелі Ороса та російського перекладу його книги. Дивіться ці та інші новини українського та світового тек-сектору! Таймкоди 00:00 Інтро 00:21 Кабмін закручує гайки: нові правила бронювання для IT-сектору 03:28 Скандал із Гергелі Оросом: російський переклад в обмін на донати ЗСУ 06:04 Конфіденційне IPO: Anthropic офіційно подала документи на вихід до біржі 08:36 Збір «Сутінки. Сага. Куп'янськ» — 10 000 000 грн на важкі бомбери VAMPIRE 09:45 Світ ШІ б'є на сполох: Anthropic закликає тимчасово зупинити розробку моделей 12:44 Загроза біозброї: OpenAI та Anthropic просять Конгрес США регулювати синтетичну ДНК 15:02 Плагіни для гуманітаріїв: OpenAI випустила інструменти Codex для тих, хто не кодить 16:50 Болюча втрата: на Запоріжжі загинув розробник із SoftServe Михайло Щепан 17:27 Ультрабюджетний хіт: Apple подвоює виробництво MacBook Neo за $599 через божевільний попит 19:04 Епічний кіберфакап: ШІ-сапорт від Meta дозволяв хакерам легко викрадати акаунти в Instagram 20:34 Анонс: DOU Product Day та DOU Mobile Meetup 21:28 Божевільний контракт: Google платитиме Маску $920 млн на місяць за дата-центри xAI 22:52 Таємне SEO-просування: як компанії маніпулюють відповідями ChatGPT за допомогою Reddit 25:10 Екологи у дії: Google планує випустити 32 мільйони стерильних комарів у США 26:57 Катастрофа Джеффа Безоса: Blue Origin заявляє про агресивні терміни відновлення після вибуху ракети 28:03 Рекомендації: репозиторій 30-days-of-coding та стаття Max Leiter

✅ New autonomous agents. ✅ Canva designs made for you. ✅ Codex upgrades to make your business move. If you had your head down in spreadsheets this week, you missed some MAJOR AI upgrades that are available now. We track what's hot and what's not and break it all down on Fridays with our Friday Features. Autonomous Copilot agents, new Codex tools, Github CoPilot app and 7 more AI updates you should be using — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Codex Role-Specific Plugins LaunchMicrosoft Build Conference AI Feature ReleasesChatGPT Memory and Business Account UpgradesMicrosoft Flash Image Model for PowerPointCanva Integrated with ChatGPT and CodexGitHub Copilot Standalone Desktop App PreviewMicrosoft Autopilot Always-On Work AgentsOpenAI Models Now Available on AWS BedrockCodex Sites: AI-Built Internal Web AppsTimestamps:00:00 OpenAI's big money moves03:47 Explaining role-specific plugins09:02 Microsoft's new image model release11:09 Microsoft's AI strategy and Canva update14:23 Canva integration with ChatGPT16:56 GitHub Copilot's new canvas feature20:46 AI token subscription changes24:42 AWS adds OpenAI models to Bedrock28:25 Introducing OpenAI's CodeX Sites Feature32:07 Launch of OpenAI's New Plug-in34:16 Overview of podcast structureKeywords: Autonomous copilot agents, Codex tools, GitHub Copilot app, OpenAI Codex, ChatGPT business accounts, OpenAI enterprise, Microsoft Build conference, Microsoft always-on agents, AWS AI updates, Canva plugin, ChatGPT memory upgrade, Windows Codex integration, Microsoft Flash model, Enterprise apps integration, Role-specific plugins, Sales data analytics, Product design AI, Creative production AI, Investment banking plugin, Public equity investing, Data analytics plugin, Workspace admins, App permissions, Role-aware work agent, Financial research automation, Microsoft image generation model, PowerPoint AI integration, OneDrive AI features, Visual design creation, Canva app for ChatGPT, Canva MCP server, Agentic context carry, Full screen design preview, GitHub Copilot desktop app, GitHub Copilot Canvas, Agent-native command center, Parallel agent work tree, Code app interface, Model options in GitHub, Token usage limits, Subscription token subsidizing, Anthropic token efficiency, Amazon Bedrock, GPT-4, GPT-4.5, Small language models, Token reckoning, Security governance, Inference engine, Code app sidebar, Codex Sites, Internal dashboards, Project trackers, Interactive web apps, Shareable AI apps, Enterprise data connectors, ChatGPT Canvas, Automated workflow, Workplace authentication, Creative briefs repository.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Ed Zitron, Scott Galloway und Gary Marcus warnen vor dem AI-Crash. Wie ernst muss man sie nehmen? ChatGPT knackt die Milliardenmarke an monatlichen Nutzern, OpenAI Codex meldet 5 Millionen Weekly Active Users mit sechsfachem Wachstum gegenüber Februar. WSJ enthüllt: Auf Sam Altmans Vorschlag erwägt die US-Regierung finanzielle Beteiligungen an den großen KI-Firmen. Anthropic erweitert sein Mythos-Programm auf 150 Organisationen weltweit. FT berichtet, dass die NSA Mythos jetzt offensiv für Hacking nutzt. Meta begrenzt sein Mitarbeiter-Überwachungstool nach Belegschafts-Backlash und launcht zeitgleich AI-Agents für WhatsApp Business. Google kauft heimlich Code von Play-Store-Entwicklern fürs AI-Training. Alibaba Qwen 3.7+ kommt multimodal zu einem Bruchteil der westlichen Preise. S&P 500 hält an Profitabilitätsregeln fest, keine Aufweichung für SpaceX. Morningstar bewertet SpaceX bei nur $780 Mrd., der Hälfte des IPO-Ziels. Alphabet erhöht seine Kapitalmaßnahme auf $85 Mrd., das größte Equity-Offering der Geschichte. Anthropic warnt vor Recursive Self-Improvement. Meta hat heimlich Gesichtserkennung in die Smart Glasses eingebaut. Peter Thiels Founders Fund startet ein YouTube-Format mit Tech-CEOs als Mafia-Spielern. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) AI-Bären-Debatte (00:34:54) ChatGPT 1 Mrd. Nutzer & Codex bei 5 Mio. WAU (00:44:16) US-Regierung will Stake an AI-Firmen (00:47:35) Anthropic dehnt Mythos Access aus (00:49:27) NSA nutzt Anthropic Mythos offensiv für Hacking (00:53:50) Meta limitiert Mitarbeiter-Tracking nach Backlash (00:56:16) Google kauft Play-Store-Code für AI-Training (01:00:40) Alibaba Qwen 3.7+ zum Spotpreis (01:02:07) SpaceX-IPO konkret: $135/Aktie, Trade Republic (01:05:56) S&P 500 bleibt hart, kein Frühzugang für SpaceX (01:08:53) Morningstar halbiert SpaceX auf $780 Mrd. (01:12:24) Alphabet raised $85 Mrd. (Rekord-Equity-Offering) (01:19:01) Anthropic Recursive Self-Improvement (01:24:43) Meta Smart Glasses mit heimlicher Gesichtserkennung (01:26:07) Mafia: The Game von Founders Fund Shownotes Ed Zitron Bloomberg Podcast - youtube.com Ed Zitron: Anthropics Profitability Swindle - wheresyoured.at Scott Galloway: 95% der KI-Investments ohne Return, 50-70% Korrektur in 24 Monaten - the-ai-corner.com ChatGPT-App knackt 1 Mrd. monatliche Nutzer in Rekordzeit - reuters.com OpenAI launcht Codex for Knowledge Work - openai.com US-Regierung diskutiert Beteiligungen an AI-Firmen - wsj.com Anthropic to expand Mythos access - ft.com Uber begrenzt Claude-Code-Nutzung, um Kosten zu sparen - bloomberg.com Snowflake-CIO: Layoffs als Druckmittel, damit Mitarbeiter KI nutzen - theinformation.com Metas AI-Agent für WhatsApp Business weltweit verfügbar - techcrunch.com Meta rollt Mitarbeiter-Tracking-Tool nach Belegschafts-Backlash zurück - theinformation.com US National Security Agency using Anthropic's Mythos for cyber attacks - ft.com Google kauft heimlich Code von Play-Store-Entwicklern fürs KI-Training - 404media.co Alibaba Qwen3.7+: Text, Video, Bilder ab $0,40-$1,60 pro Mio. Token - venturebeat.com SpaceX will $75 Mrd. im Rekord-IPO einsammeln - bloomberg.com Morningstar bewertet SpaceX bei $780 Mrd., nur die Hälfte des IPO-Ziels - reuters.com Wild Twist: SpaceX kommt doch nicht früh in den S&P 500 - marketwatch.com Sitecore übernimmt Scrunch für $225 Mio. - bloomberg.com Alphabet raised $85 Mrd. für AI: größtes Equity-Offering aller Zeiten - thenextweb.com GitLab cuttet 14% der Belegschaft für AI-Workload-Skalierung - techcrunch.com CrowdStrike Q1 2027 Earnings - cnbc.com Anthropic Institute: Recursive Self-Improvement - anthropic.com Anthropic fordert globale KI-Entwicklungspause wegen Self-Improvement-Risiko - wsj.com Meta-Smart-Glasses mit Gesichtserkennung und Nametag - wired.com Tech Celebrities Playing Mafia - newcomer.co

The Pope said WHAT about AI?

Patrick Moorhead and Daniel Newman return from Dell Technologies World to unpack Google I/O's Gemini-as-operating-system moment, the Blackstone-Google TPU joint venture nobody saw coming, NVIDIA's $81.6 billion quarter with a $91 billion guide, and debate whether or not the "SaaSpocalypse" is finally over. The handpicked topics for this week are: Google I/O 2026: Gemini Becomes the Operating System. Google I/O repositioned Gemini from a product to the operating layer for everything Google does, and the numbers backed it up. 900 million monthly active users, 3.2 quadrillion tokens per month, a 7x jump year over year. Pat's headline: this is about widening distribution, not just model quality. Gemini 3.5 Flash, Antigravity 2.0, Gemini Spark, and Android XR glasses all extend Gemini into surfaces that no competitor can replicate. Daniel's read: the token-cost reckoning is coming, and when enterprise subsidies end, models that can deliver value at a lower cost per token will become the ground zero of the next era. (The Decode) Dell Technologies World 2026: AI Factory Goes Agentic, 1,000 New AI Server Clients. Pat and Dan were both on the ground in Las Vegas and called it the most consequential Dell event in years. Michael Dell and Jensen Huang co-keynoted to launch the next-generation Dell AI Factory with liquid-cooled PowerEdge XE9780 servers, Dell Deskside Agentic AI, and a multi-model ecosystem including Google Distributed Cloud with Gemini 3.0, on-prem OpenAI Codex, and Grok. 1,000 new AI server clients in a single quarter is the cleanest leading indicator of enterprise demand heading into Dell's Q1 print. Pat's biggest takeaway: OpenShell as a control plane for agents spanning from the GB10 all the way to the PowerEdge rack has been the missing orchestration piece. Daniel's read: large enterprises are going to build hybrid AI architectures and want to deliver tokens at the lowest possible on-prem cost, and Dell is ready. (The Decode) Blackstone and Google Launch a $5B TPU Joint Venture. Pat called it the biggest story of the week and the one that went most under the radar. For the first time, a hyperscaler has released its proprietary AI silicon to a third-party distribution entity. The $5 billion deal, up to $25 billion with leverage, targets 500 megawatts of capacity online by 2027. Daniel's framing: Google decided its custom silicon is worth more as a commercially distributed asset than as a captive moat. Pat's note: the proprietary nature of TPU infrastructure means retrofitting existing data centers will require real work, but the sovereign angle gives the JV a natural first market. (The Decode) AMD Helios, $10B Taiwan Investment, and the MI450 Anchor Customer Rumor. AMD dropped a $10 billion Taiwan ecosystem investment alongside confirmation that Helios rack-scale is on track for multi-gigawatt customer deployments beginning 2H 2026. A Citi rumor surfaced Anthropic as the anchor MI450 customer, to be formally announced at AMD's Advancing AI Day in July. Pat's read: Lisa Su has made a commitment and she almost never falls through. The analysts who said AMD would not ship anything in the second half of 2026 are going to be very wrong. (The Decode) OpenAI Guaranteed Capacity: Sam Altman's Moment. OpenAI launched multi-year compute commitment contracts the same week that Anthropic was struggling with capacity outages. Pat called it brilliant and said it makes Sam Altman look like a genius. It's the inference-era analog of cloud reserved instances: guaranteed availability at a locked price for one, two, or three years. Daniel added context: Anthropic's annualized ARR growth is nearly double OpenAI's and is about to lap them, so the model war is far from over. But for enterprises that need reliability, OpenAI just made the most compelling enterprise trust argument of the week. (The Decode) Sovereign AI Crosses $30 Billion at NVIDIA, 14% of Revenue. NVIDIA disclosed sovereign AI as a segment-level line for the first time, at $30 billion in FY26, 3x the prior year. Pat has been tracking sovereign for years and calls this the clearest possible signal that it has moved from marketing term to structural revenue category. Daniel's point: outside of the four or five hyperscalers doing all the major buying, sovereign is where the incremental demand is coming from and it is very real. (The Decode)  The Flip: Is the SaaSpocalypse Over? Daniel took the affirmative and came in loaded. Every earnings report across CrowdStrike, Cloudflare, ServiceNow, Intuit, Salesforce, Atlassian, Notion, and monday.com shows companies growing with the AI tailwind. His core argument: there was a reason SaaS emerged 20 to 30 years ago. Companies do not want to be in the software business. Vibe-coded flat-file apps with no security, no governance, no data lineage look great in a kitchen demo and fall apart at enterprise scale. The SaaSpocalypse is over and he is tired of talking about it. Pat's counter: BofA slapped Salesforce with an Underperform at $160, 8% below where it trades. Snowflake is down 35% year-to-date. A senior Dell executive told him Dell will not buy another SaaS system and is tripling internal software creation. The growth question is real even if the terminal value is not zero. Both agree the tape will tell the real story. (The Flip) NVIDIA Q1 FY27 Results. Record $81.6 billion revenue, up 85% year over year. Data center at $75.2 billion, up 92%. Non-GAAP EPS of $1.87, up 140%. Q2 guide of $91 billion crushed the $86.8 billion consensus by $4 billion at the midpoint. $80 billion buyback authorized, dividend raised 25x. The stock went down after hours for the fifth consecutive time following a massive beat and raise. Pat's read: NVIDIA may be worth $8 to $9 trillion on paper at a sector-average multiple and 75% gross margins held. Daniel's framing: this is the best company in the world, possibly tied with Google, and it is becoming the Apple of this era. He sees a long safe journey of continued growth vs. speculative dollars chasing quantum and space names that can double in a week. (Bulls and Bears) Intuit: Earnings Beat, Revenue Miss. A 17% workforce cut, raised guidance, and $8 billion buyback were authorized. Pat's emerging thesis: these companies are cutting people to afford tokens. Intuit comes at a moment when OpenAI's ChatGPT finance plugin via Stripe is building an intelligence layer that could sit on top of Intuit's products without displacing them directly, at least not yet. (Bulls and Bears) Lenovo: Record $21.6 billion quarterly revenue, up 27% year over year. The company's fastest growth in five years. AI-related revenue is up 84% year over year to 38% of total company revenue. ISG returned to full-year operating profit with a $21 billion AI server pipeline. Pat and Dan both read Lenovo's results as NVIDIA tea leaves, a leading indicator of enterprise AI server demand that directly validates what Dell said on stage about 1,000 new AI server clients. (Bulls and Bears) Analog Devices: Record $3.62 billion revenue, up 37% year over year. EPS up 67%. Q3 guide of $3.9 billion crushed consensus by $270 million. Data center up 90%, industrial up 56%, comms up 79%. The $1.5 billion Empower Semiconductor acquisition adds integrated voltage regulator technology that can reduce AI data center power consumption by 10 to 15% while shrinking the power footprint by up to 4x. Daniel's closing point: you can't build AI servers without players like Analog Devices and Lattice Semiconductor. These essential node companies aren't boring, they're foundational. (Bulls and Bears) Check out all of our Dell Technologies World coverage linked in the show notes including our sit-downs with Michael Dell, Jeff Clark, and key customers. Be part of our community. Hit that subscribe button and see you at Computex.   The Decode Google I/O 2026 — Gemini Becomes the Operating System: 900M MAU, 3.2 Quadrillion Tokens/Month, Gemini Omni, Antigravity 2.0, Gemini Spark, and Android XR Glasses https://blog.google/innovation-and-ai/sundar-pichai-io-2026/ Dell Technologies World 2026 — AI Factory Goes Agentic: Michael Dell + Jensen Huang Unveil PowerEdge XE9780, Dell Deskside Agentic AI, and a Multi-Model Ecosystem; Dell Adds 1,000 AI-Server Clients in the Quarter https://www.dell.com/en-us/blog/dell-technologies-world-a-bright-and-beautiful-road-ahead/ Blackstone + Google Launch $5B (Up to $25B w/ Leverage) JV to Sell Google TPUs Outside Google Cloud — First Time a Hyperscaler Has Released Its Custom Silicon to a Third-Party Distribution Channel; 500 MW Online by 2027, Benjamin Treynor Sloss as CEO https://www.blackstone.com/news/press/blackstone-announces-joint-venture-with-google-to-create-new-tpu-cloud/ AMD Announces $10B+ Taiwan Ecosystem Investment — Helios Rack-Scale Platform With MI450X GPUs and Venice EPYC on TSMC 2nm Targeting Multi-Gigawatt Deployments 2H 2026; the Clearest Second-Source Signal Yet https://ir.amd.com/news-events/press-releases/detail/1286/amd-announces-more-than-10-billion-in-taiwan-ecosystem-investments-to-accelerate-ai-infrastructure OpenAI Launches Guaranteed Capacity — Multi-Year Compute Commitments Turn Inference Capacity Into a New Enterprise Asset Class https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html The Sovereign AI Government Investment Wave — NVIDIA Discloses ~$30B Sovereign-AI Revenue (14% of Mix); UAE, Saudi, Japan, Australia, France All in Motion This Week https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html   The Flip: Is the SaaSpocalypse Officially Over — or Is BofA's Split Call (ServiceNow Buy, Salesforce Underperform) the Real Signal That Platform AI Monetization Is Going to Be Bifurcated, Not Universal? FOR:  BofA Reinstates Coverage of ServiceNow, Salesforce — Barron's (May 18) https://www.barrons.com/articles/servicenow-salesforce-stock-price-ai-7b109396 Embedded workflow + system-of-record stickiness still wins citing ServiceNow Q1 2026 financial results https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-Reports-First-Quarter-2026-Financial-Results/default.aspx Intuit Q3 revenue up 10%, cuts 17% of staff — SEC 8-K filing (May 20) https://www.stocktitan.net/sec-filings/INTU/8-k-intuit-inc-reports-material-event-b23073259896.html   AGAINST:  BofA Slaps Salesforce With Underperform Rating, $160 Price Target — 24/7 Wall St (May 18) https://247wallst.com/investing/2026/05/18/bofa-slaps-salesforce-with-underperform-rating-160-price-target-is-the-ai-story-falling-flat/ BofA resets Salesforce price target to Underperform — TheStreet (May 19) https://www.thestreet.com/investing/stocks/bofa-resets-salesforce-stock-price-target-to-underperform-at-160 Snowflake -35% YTD heading into May 27 print is the canary that platform stickiness is being repriced https://eciks.org/4640-22295-snowflake-set-to-report-q1-earnings-may-27-with-ai-strategy-in-focus OpenAI Guaranteed Capacity + Dell on-prem Codex create a credible path to displace seat-based SaaS https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html Bulls & Bears NVIDIA Q1 FY27 ACTUALS https://www.cnbc.com/2026/05/20/nvidia-nvda-earnings-report-q1-2027.html Intuit Q3 FY26 Actuals https://investors.intuit.com/news-events/press-releases/detail/1312/intuit-reports-strong-third-quarter-results-and-raises-full-year-revenue-guidance Lenovo Q4 FY26 ACTUALS https://www.cnbc.com/2026/05/22/lenovo-shares-jump-15percent-on-record-earnings-as-ai-revenue-nearly-doubles.html Analog Devices Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html  

Google dropped like 197 new AI features this week.

Jack Hoss is the owner of RealDealCrew and host of the RealDealCast podcast, with over 850 episodes recorded. In this episode, Jack breaks down how he flips houses in the Fargo/Moorhead market, how he uses AI to underwrite multifamily deals, and the local SEO strategy that keeps his business visible and competitive online. If you're a real estate investor, operator, or entrepreneur looking to put AI to work in your business, this one is packed with practical takeaways you can use right now. What We Cover: - Jack's house flipping approach in Fargo/Moorhead targeting 250-300k ARV properties - How he builds every deal around a 20% return target - Using AI to underwrite multifamily properties faster and smarter - The AI phone receptionist setup that handles inbound calls automatically - Google Business Profile and Bing Business Profile as a local SEO foundation - How Jack uses Claude Projects to get consistent outputs and avoid AI slop - Why he recommends Claude's Cowork feature and OpenAI Codex - Lessons from 850+ podcast episodes on what actually moves the needle Connect with Jack Hoss: RealDealCast Podcast:  @RealDealCast   RealDealCrew: https://realdealcrew.com/ Connect with Jason Fishman: LinkedIn: https://www.linkedin.com/in/jafishman/ Connect with Digital Niche Agency: Website: www.digitalnicheagency.com Subscribe to Test. Optimize. Scale. for weekly conversations on growth marketing, business strategy, and what is actually working right now. CHAPTER TIMESTAMPS 0:00 - Intro 1:30 - Meet Jack Hoss and Real Deal Crew 4:00 - House flipping in the Fargo/Moorhead market 8:30 - Targeting 250-300k ARV and why 20% returns is the number 13:00 - Using AI to underwrite multifamily properties 18:00 - The AI phone receptionist setup 22:30 - Local SEO: Google Business Profile and Bing Business Profile strategy 27:00 - Claude Projects and how to avoid AI slop 32:30 - Claude Cowork and OpenAI Codex recommendations 37:00 - Lessons from 850 podcast episodes 41:00 - Where to find Jack

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Explore the impact of Global Accessibility Awareness Day (GAAD 2026) with Steven Scott, Shaun Preece, and guest Joe Devon, co-founder of the event. Learn how AI, vibe coding, and personalised apps are reshaping accessibility while uncovering the ongoing challenges faced by developers and users alike. This episode of Double Tap dives into the 15th anniversary of GAAD, reflecting on progress and persistent barriers in digital accessibility. Steven and Shaun discuss the debate around whether GAAD should be celebrated, given that over 90% of websites remain inaccessible to many users. Guest Joe Devon shares insights into the origins of GAAD, its mission to raise awareness among developers, and the state of accessibility in 2026. The conversation explores: The tension between celebrating accessibility wins and recognising ongoing failures. How AI coding tools like Claude and OpenAI Codex are empowering blind users to create personalised solutions. The role AI can play in generating alt text and bridging accessibility gaps for websites and apps. The future of digital experiences where universal interfaces and personalisation may redefine accessibility. Relevant Links Global Accessibility Awareness Day: https://accessibility.day WebAIM Million Report: https://webaim.org/projects/million Accessibility and AI Podcast: https://podcasts.apple.com/gb/podcast/accessibility-and-gen-ai-podcast/id1759047581 ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Windows Insider Program Release Preview channel updates (including 26H1 for the first time? - A preview of the June Patch Tuesday updates - Shared audio, NPU usage in Task Manager, multi-app camera support, Magnifier improvements. Taskbar updates come to Insiders! Also in Canary, weʼre throwing them a bone this time. Enshittification remedies all around Microsoft just held a WinHEC for the first time since 2018 and thereʼs a new Windows Driver Initiative! Microsoft will soon let us remap Copilot key to Right Ctrl, which is what it was in the first place. A Linux privacy nut YouTuber confuses privacy and security and doesnʼt understand Windows 11 so... ... Paul wrote a complete guide to the local account de-Microsoft experience in Windows 11 Microsoft Edge will stop loading all passwords into clear text on startup like a big boy browser. Hardware Paul came home to an ASUS Zenbook A16 and ohmygodohmygodohmygod Surface Microsoft finally revs Surface Laptop and Surface Pro for Business, with Intel chips and VERY high prices. Snapdragon X2 variants in late 2026 because of supply issues wa-waa-waaaaa. AI MDASH is Microsoftʼs answer to Anthropic Mythos, in-house only. Elon Musk and Sam Altman are both terrible but a jury decided against Muskʼs frivolous lawsuit. OpenAI and Apple might head to court over Siri promises OpenAI Codex is on mobile via the ChatGPT app Google unleashes an AI tsunami at Google IO this week. A few relevant takeaways: Overview of the major announcements Google advances Android as a developer platform Chrome is turning into a proactive assistant Google AI subscriptions are an incredible value Related: The Gemini Intelligence feature for Googlebooks and more has steep hardware requirements - 12 GB of RAM, flagship SoC So Pixel 10 series/Galaxy S26 series and newer only etc. Just a reminder that Microsoft makes a Linux distribution ... for Azure specifically More dev WWDC schedule is up for June 8 opening day Build 2026 kicks off June 2 in SFO After another boring .NET 11 preview release, we finally get our first look at a major change: MAUI is switching from the Mono runtime to the CoreCLR runtime. And we should pause for a moment to remember S "Soma" Somasegar, who sadly passed away this week. Xbox and Gaming Next Xbox Elite controller leaks and it is glorious Related: An Xbox Cloud-Connected controller leaks too and it is less than glorious. Forza Horizon 6 is here, and itʼs on Game Pass on Day One. Be sure to read Laurentʼs detailed review. Haters gonna keep hating: Fans want Xbox exclusives because their heads are still in the sand. Sony is allegedly returning to this model for single player experiences Related: Sony raises prices on PS Plus Fortnite comes back to the Apple App Store worldwide *excluding Australia for some reason. Tips and Picks Tip of the week: Google AI Studio. Vibe-code your next app with this incredible free tool. Related: A look at Markdown editors. App pick of the week: DeskScapes 2026 Stardock DeskScapes 2026 is normally $9.99 but it will cost just $6.99 during the launch period. Also: Firefox 151 is a big update on desktop and mobile, the latter gets the AI kill switch RunAs Radio this week: UEFI Secure Boot with Richard Hicks Brown liquor pick of the week: Daftmill Winter Batch Release These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/984 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsors: outsystems.com/twit trustedtech.team/windowsweekly365 zscaler.com/security

Send us Fan MailIn this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias unpack Google I/O 2026 and what it signals for the future of AI-powered work, search, and member engagement. They explore Google's push toward proactive, agentic AI across Gemini, Workspace, Search, and new infrastructure like Antigravity and TPU chips, while digging into what these changes mean for associations trying to protect their content, improve digital experiences, and stay relevant as members increasingly expect voice, multimodal interaction, intelligent search, and personalized service. The conversation also covers AI's impact on career advice, leadership, web traffic, SEO, smart glasses, privacy, and why associations may need to double down on trust, niche expertise, and human connection in an increasingly agent-driven world.

У свіжому дайджесті DOU News обговорюємо оновлення від OpenAI: реліз GPT-5.5 Instant та появу ШІ-улюбленців в Codex для «вайбового» кодингу. Поки одні додають тамагочі в IDE, Cloudflare скорочує 20% штату через масштабну ШІ-реструктуризацію. Також у випуску: нові ліміти Claude від Anthropic, автоматичне підвищення зарплати як ліки проти токсичності та плани Spotify щодо персоналізованих ШІ-подкастів. Дивіться ці та інші новини українського та світового тек-сектору. Таймкоди 00:00 Інтро 00:24 Anthropic збільшує ліміти для користувачів Claude 03:51 Чи можна довіряти Claude у питаннях про колишніх та суперечки? 05:40 Твіт про Apple та Claude 06:28 Курс «Multi-Agent Systems» від robot_dreams 07:41 Жорстке звільнення 700 співробітників криптокомпанії о 7 ранку 10:14 Cloudflare скорочує 20% штату через ШІ-реструктуризацію 11:46 Meta змушує інженерів розмічати дані перед звільненням 13:52 DOU Day 2026 — останні квитки! 14:43 Чи врятує токсичну культуру автоматичне підвищення зарплати на 10%? 16:51 OpenAI випустила GPT-5.5 Instant 17:45 ШІ-улюбленці в OpenAI Codex: компаньйони для вайбового кодингу 19:23 Повідомлення Маска стали доказами у суді проти OpenAI 23:18 Spotify планує стати платформою для персональних згенерованих подкастів 24:50 Що рекомендує Женя: фільм «Король Річард: Виховуючи чемпіонок» та джаз

Anthropic is testing a change that removes Claude Code from the Pro plan for some new users, and I think it is a mistake.In this video, I break down what changed, why people noticed so fast, and why this kind of pricing test sends a bad message to the exact users most likely to adopt Claude Code, stick with it, and eventually upgrade. I also go through Anthropic's response, why it feels like damage control, and what I think may be coming next.If you use Claude Code, OpenAI Codex, or AI coding tools every day, this is worth paying attention to.#ClaudeCode #Anthropic #AIcoding #ClaudeAI #AIDevTools

Building Repeatables in Claude: Skills, CLI vs MCP and Token Discipline | Go With The Flow Claude Skills, CLI vs MCP and Token Discipline with Ritu Java | Seller Sessions SEO Description Ritu Java and Danny McMillan on building agentic skills, choosing CLI over MCP, plan mode discipline and the short window to ship before token costs reset. Episode Summary Week 4 of the month, Go With The Flow, and Ritu Java is back from her travels. The world has shipped fast since the last episode: Codex 5.5, Claude 4.7, an Amazon Ads MCP and a fresh round of panic over the rumoured removal of Claude Code from the $20 plan (it was a 2% AB test, not a rollout). Ritu and Danny use the noise to make a sharper point: this is the moment to stop chasing models and start building repeatable systems on the platform you have already chosen. Ritu walks through the three eras of PPC Ninja's automation stack. Apps Script bulk file generators three years ago, Netlify hosted UI apps last year, and now agentic skills that her team chats with in plain English to produce upload ready Amazon bulk files. The same shift applies to data: BigQuery accessed through the Google Cloud CLI rather than through MCP, because CLI is leaner on tokens and works better when the job is heavy on data rather than tool surface. Danny mirrors the move with his event-ops CLI for WordPress, WooCommerce, Stripe and FooEvents reconciliation, and his four tier ExtractFlow cascade (HTTP, headless, stealth, agentic) that bypasses the limits of any single browser tool. The second half is a discipline talk. Plan mode every time. Push back on the first plan because Claude over engineers by default. 30% of your time on workflow scaffolding so the other 70% can be real building. The 21 day Claude rule: when a shiny new tool fires the dopamine, wait 21 days before refactoring around it. Left brain tasks (counting, SQL, deterministic logic) belong in scripts. Right brain tasks (judgment, creativity, hypotheses) belong in the model. Mix them inside a single skill. Skills are micro pieces of your workflow, not magic, and Claude can write them for you from an existing SOP. Key Topics The three eras of PPC Ninja automation: Apps Script, Netlify UI apps, agentic skills CLI vs MCP: when to choose each and why CLI is more token efficient for data heavy work Token economics, the rumoured $20 plan change and why it was a 2% AB test The short window before subsidised tokens get repriced Plan mode discipline and the "push back on plan one" rule Danny's 30 / 70 framework: workflow scaffolding vs building The 21 day Claude rule for resisting tool churn Left brain vs right brain task design inside a single skill The PPC Ninja "5 Whys" skill: deterministic SQL plus non deterministic hypotheses Claude.md, Gemini.md, Skills.yaml and the emerging Agents.md standard Skills for beginners: let Claude write them from your SOP Skill cascading: research, article, LinkedIn post, tweets, slide deck in one chain Timestamps [00:01] Welcome back, Week 4 Go With The Flow, Ritu returns from travels [00:17] Codex 5.5, Claude 4.7 and the "no one is writing code anymore" reality [02:01] Ritu on the three eras of PPC Ninja automation [02:42] Era 1: Apps Script bulk file generators in Google Sheets [03:46] Era 2: Netlify hosted UI apps with input fields [04:48] Era 3: Agentic skills, the bulk file skill trained on Amazon templates [06:22] Cloud talking to BigQuery through the Google Cloud CLI [07:00] Danny: what is a CLI and why it matters for token use [08:00] Amazon Advertising MCP vs CLI based access to the same data [09:33] WordPress horrible to drive via MCP, easy via CLI [10:00] Danny's event-ops CLI: tickets, food tickets, WooCommerce, Stripe reconciliation [12:13] ExtractFlow four tier cascade: soft, medium, stealth, agentic [13:46] Why CLI for the heavy stuff, MCP for the soft touch [14:13] AWS CLI: chat to Claude, push HTML blog posts live in two minutes [15:33] The overwhelm problem and the 5,000costbehindthe5,000costbehindthe100 plan [17:35] The $20 plan rumour: it was a 2% AB test, not a rollout [19:38] Build repeatables, not one offs [20:38] Danny: pick a platform and stop chasing benchmarks [21:16] The 21 day Claude rule for new tools [22:16] Plan mode every time, push back on plan one, get the second plan [23:02] Why am I building it, who is it for, what am I building [23:30] The 30 / 70 split: workflow scaffolding vs real building [25:13] Why long six to fourteen hour Claude runs are usually inefficiency [27:12] Compounding 1% a day across a year [27:47] "I build the things that build things" [28:00] Architecture vs apps: filling the gaps between A and B [29:06] Left brain vs right brain task design [30:01] Why throwing 80/20 at a sales drop diagnosis fails [31:33] The PPC Ninja 5 Whys skill: deterministic plus non deterministic in one flow [34:32] Claude.md, Gemini.md, skills.yaml and the agents.md standard [40:53] Beginners: let Claude write the skill from your SOP, use the interview pattern [42:39] Skill cascading: URL to research to article to LinkedIn post to tweets to slides [44:42] Mixing deterministic and non deterministic inside a single skill [45:39] Wrap up, signal to noise, who is it for Key Takeaways Pick a platform and stop chasing models. A new model ships every week. Time spent benchmarking is time not building. Double down on Claude (or whichever you chose), use the 21 day rule, and let the ecosystem catch up to the shiny thing in your feed. CLI for heavy work, MCP for soft touch. MCP loads tools and skills into context and burns tokens. CLI uses programs already on your machine. For data heavy jobs (BigQuery, AWS, WordPress at scale), CLI wins. For light cross app workflows, MCP is fine. Build repeatables, not one offs. Subsidised tokens will not last. The 100planreportedlycostsAnthropic100planreportedlycostsAnthropic5,000 to serve. Spend the window building scaffolding that compounds, not 14 hour vibe coding runs. Plan mode every time, then push back. Claude over engineers by default. Generate the plan, then say "you have over engineered this, although I want it elegant, go back and review." Plan two is the one you start from. 30% on workflow, 70% on building. Each new dependency, MCP, skill or repo you add to your workflow compounds across every future project. Stop building only the apps. Build the things that build the apps. Left brain in scripts, right brain in the model. Counting, SQL, deterministic logic belongs in Python the moment you can offload it. Save the model for hypotheses, judgment and creativity. The PPC Ninja 5 Whys skill mixes both inside one flow. Skills are micro pieces, not magic. Take an SOP, ask Claude to interview you with decision panels, and let it write the skill. Then cascade skills together: URL to research to long form article to LinkedIn post to tweets to slide deck. Notable Quotes "Instead of doing one offs, it is time to build repeatables. The more people can learn that skill now, the better it will be, because a year from now you may not have access to the same tokens." Ritu Java "If you see something and it looks sexy and it has sex and sizzle and your dopamine is screaming to go after it, wait 21 days. Either Claude will have it, or someone will have a repo, and you can combine it." Danny McMillan "Always use plan mode. Never accept plan number one. Tell Claude: you have over engineered this, although I want it elegant, go back and review. Then start from plan two." Danny McMillan "I build the things that build things. I build the scaffolding the team needs so they can build on top of it." Danny McMillan "Spend 30% of your time on your workflow and 70% building. The 30% compounds across every project." Danny McMillan "If we just hand six months of ad, organic, ranking and SQP data to Claude with no structure, it is going to mess up. It will give you an 80/20 you are not satisfied with, because it is not equipped to handle that volume without scaffolding." Ritu Java "WordPress is horrible to work with through MCP. It falls over all the time. CLI can be amazing for certain things." Danny McMillan Resources Mentioned PPC Ninja : Ritu's Amazon PPC software and agency, base for the BigQuery + CLI stack discussed Claude Code : Anthropic's CLI for Claude, the primary surface used in the episode Anthropic Claude : Claude 4.7 referenced as the current model OpenAI Codex : Codex 5.5 mentioned as the rival shipping fast Google Gemini CLI : Referenced as a sibling agent surface (Gemini.md) Google BigQuery : PPC Ninja's central data warehouse Google Cloud CLI (gcloud) : The CLI Claude uses to talk to BigQuery Amazon Advertising MCP : Amazon's official MCP server for ads data, referenced as the MCP comparison point AWS CLI : Used by Ritu to publish HTML blog posts to ppcninja.com from a Claude chat Netlify : Hosting layer for PPC Ninja's previous era of UI based apps WordPress and WooCommerce : Backbone of Danny's event-ops CLI FooEvents : Ticketing plugin that lives behind WooCommerce in the event-ops flow Stripe : Source of the card fee variation Danny reconciles via CLI ExtractFlow / CloudExtract : Danny's four tier extraction cascade (HTTP, headless, stealth, agentic). Open repo Playwright : The default browser automation tier inside ExtractFlow Agents.md : Emerging AI agnostic instruction file standard alongside Claude.md and Gemini.md Sequential Thinking MCP : The MCP Danny invokes when asking Claude to step through analysis Hosts Danny McMillan : Host of Seller Sessions, founder of DataBrill, building AI native tooling and CLI based workflows for Amazon sellers. Website: https://sellersessions.com LinkedIn: https://www.linkedin.com/in/dannymcmillan Ritu Java : CEO and co founder of PPC Ninja, Amazon PPC software and agency. Specialises in automation, BigQuery pipelines and agentic workflow design. LinkedIn: https://ca.linkedin.com/in/ritujava Website: https://www.ppcninja.com What's Next Next week: Ritu and Danny pick up routines and the new Claude scheduler. In 8 days: Seller Sessions Live 2026 in London on 9 May. Last week to lock in any final discounts. About Seller Sessions Seller Sessions is the leading podcast for serious Amazon sellers, hosted by Danny McMillan since 2017. Go With The Flow is the weekly automation strand where Danny and Ritu Java work through agentic flows, MCPs, CLIs and skills, in real time, on the same stack their teams ship every week. Episode published: 1 May 2026 Series: Go With The Flow (Week 4 of the month) Keywords: claude skills, claude code, cli vs mcp, mcp model context protocol, claude 4.7, codex 5.5, amazon ppc automation, bigquery cli, agentic workflows, plan mode, token optimisation, claude.md, agents.md, ppc ninja, ritu java, seller sessions podcast, go with the flow

Головна тема цього випуску — ініціатива StopKillingGames, яка вимагає від видавців ігор зберігати їх у робочому стані навіть після закриття серверів. Ведучі розглядають юридичні, етичні та бізнесові аспекти цього питання, а також ситуацію в інді-сегменті ринку. У частині про ШІ обговорюються: оновлений OpenAI Codex із функцією аналізу екрану, скептичне ставлення до перебільшених загроз навколо штучного інтелекту, а також нові можливості Apple для локального запуску foundation models. Зачіпаємо музичну індустрію, де сучасні алгоритми часто радять контент, створений для ботів або нецільової аудиторії, що призводить до кризи якості рекомендацій. І насамкінець про те, як дата-брокери скуповують архіви компаній, щоб годувати ними мовні моделі.  00:36 — ініціатива StopKillingGames 03:20 — проблеми з доступом до старих ігор 07:20 — реакція на ініціативу та її підтримка 12:50 — проблеми античитів у онлайн-іграх 16:00 — скептицизм щодо Mythos 19:06 — нові можливості штучного інтелекту 22:44 — вплив штучного інтелекту на музику 32:03 — ідеї та реалізація в стартапах 35:34 — корпоративна боротьба та YouTube Premium 38:49 — оптимізація токенів у LLM 42:38 — базові моделі та їх застосування 45:00 — кросс-мовні патерни програмування 49:35 — нові бізнес-моделі у сфері даних

Episode Summary:Will and Brandt continue to dig into the rapidly evolving world of agentic AI, with Will sharing hard-won lessons from burning through his OpenAI Codex quota and the growing appeal of Claude Cowork's new ability to run and manage tasks remotely from your phone. They also cover NVIDIA's OpenClaw announcement (NemoClaw), debate the rising anti-AI movement, and draw parallels between today's AI shift and the early days of the internet.Discussions Include:Managing AI credit quotas and the real cost of letting autonomous agents run unsupervisedClaude Cowork's remote phone control and what it means for agentic AI reaching everyday usersNVIDIA's OpenClaw fork and why a trusted brand name accelerates enterprise adoptionHow AI is changing the way we write, type, and interact with technology, including the death of the emdashThe anti-AI movement, AI slop, and why ignoring this technology may not be a long-term optionQuotable Quotes (Should you choose to share):     "My OpenClaw setup is a very capable executive assistant with the knowledge of the entire internet. And it runs 24/7." - Will Curran     "Nvidia wants to be the Levi's of the gold rush providing the picks and shovels and blue jeans." - Brandt Krueger     "It's like saying you're against the calculator when the calculator is sitting right there and can do the math problem for you." - Will Curran     "It's a paradigm shift. It is a big deal. It's not just a hype train. It's really changing the way that people are working." - Brandt Krueger     (note that "paradigm shift" was said in a snotty tone and not seriously)Thing of the Episode (TOTE):      Brandt: Rhino USA Folding Survival Shovel with Pick - rhinousainc.com/products/rhino-usa-survival-shovel      Will: Project Nomad - projectnomad.us

Tim Cook is stepping down as Apple's CEO on Sept 1st and John Ternus is taking the reigns. We discuss Cook's Legacy, the last CEO transition from Jobs to Cook, and what we expect from a Ternus-led Apple. Plus, OpenAI's new image generator is actually good, and Jason is building a wild notes app.Member Promo Code: IWANTCHAPTERS(Click above and the $2.50 promo will be auto applied!)Top Five Tech | Stephen's PodcastCreative Effort | Jason's PodcastWatch on YouTube!Show Notes via EmailEmail Us: podcast@primarytech.fm@stephenrobles on Threads@jasonaten on ThreadsSponsors:Framer - Start creating for free at framer.com/primary and get 30% off an annual Pro plan!Granola - Try Granola for FREE for 3 months at: granola.ai/primaryClaude AI - Ready to tackle bigger problems? Sign up for Claude todayat: claude.ai/primaryLinks from the showThe Master Engineer Taking Over Apple (and Why It Matters) - YouTubeI Tested 12 Podcast Apps - YouTubeTim Cook's Farewell Letter Just Revealed His Most Important Leadership Habit - IncTim Cook Is Stepping Down as Apple's CEO. He's Keeping the Most Important Job—for NowOptimising Vibe-Coded Next.js Applications for Performance, Crawlability and Search Success — Will KennardTim Cook to become Apple Executive Chairman John Ternus to become Apple CEO - ApplePhoto from Apple's Town HallTim Cook tells employees why he chose to step down now, what's next for him - 9to5MacCommunity Letter from Tim - AppleJohny Srouji named Apple's Chief Hardware Officer - AppleDaring Fireball: Another Day Has ComeOpenAI's updated image generator can now pull information from the web | The VergeChatGPT Image Post on BlueskyOpenAI updates ChatGPT with Codex-powered 'workspace agents' for teams - 9to5MacGoogle teases Gemini-powered Siri upgrade during Cloud Next keynote - 9to5MacAmazon invest up to $25 billion in Anthropic part of AI infrastructureSpaceX lands deal to likely purchase Cursor, a Claude Code and OpenAI Codex competitor - 9to5MacThreads is adding Live Chats to boost real-time engagement | TechCrunch (00:00) - Intro (07:31) - Tim Cook's Legacy (18:03) - John Ternus Era (39:20) - Sponsor: Framer (41:06) - Sponsor: Granola (42:23) - Sponsor: Claude (43:54) - Cook's Wins and Failures (53:58) - ChatGPT 2.0 Images (59:57) - Lightning Round (01:03:18) - Jason's Notes App ★ Support this podcast ★

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

In this podcast, Kenton, Denny, and Boone discuss the technical details of adding Monero, along with freedom, social knowledge platforms, libertarianism, and more. Swap now on THORChain https://swap.thorchain.org/ without KYC or limits! TL;DR Community member Boone used AI coding tools (OpenAI Codex) to wire Luke Parker's audited FROST Rust package (from @kayabaNerve) to THORNode, creating a working $XMR chain client in roughly two months Chad confirmed the hard part is done. $XMR on THORChain has never been more likely, though the team urges caution: expect a shallow pool, possible chain pauses, and failed swaps early on Critical PSA: If you swap from $XMR without including a return address in the memo, your funds are unrecoverable. THORChain cannot return Monero without one. swap.thorchain.org (STO) just passed $1 billion in cumulative volume, a major milestone for THORChain's own frontend The protocol-owned liquidity (POL) discussion is heating up, with both Kenton and Boone favoring 20% of system income to seed up to 50 new pools You can find Rayyyk's full write-up on blog: https://blog.thorchain.org/how-boone-used-ai-to-build-thorchains-monero-integration/ https://x.com/BooneW https://boonewheeler.com/boonetools/ THORChain is a decentralized cross-chain liquidity protocol that lets users swap assets directly between blockchains without wrapping or using centralized exchanges. Its app layer ecosystem means developers can build decentralized apps that tap directly into liquidity across chains. Unlike most platforms, it offers real ownership of your assets, deep liquidity, and fast swaps in one seamless network. To learn more about THORChain, check out more videos: https://www.youtube.com/watch?v=eMbeCjNJ5Eo https://www.youtube.com/watch?v=4M_4N9-3ZUo https://www.youtube.com/watch?v=zzHXrsaWT-w https://www.youtube.com/watch?v=Y5v9XiXAJ7g Swap now on THORChain https://swap.thorchain.org/ without KYC or limits!

You prolly missed (most) of these 7 AI features

Most people are still using ChatGPT the way they used Google in 2005: type a question, get an answer, close the tab.In 2026, that's like owning a professional kitchen and only using the microwave.In this episode, Grant and Corey walk through The Neuron's 5-Level AI Proficiency Stack — a framework for going from “I use ChatGPT sometimes” to “AI saves me 10 hours a week.”No coding required. No hype. Just the actual progression that separates casual users from people getting real, compounding value out of AI every single day.The 5 Levels:

For MobileViews 603, recorded on March 29, 2026, I decided to return to my classic Blue Yeti Nano microphone, which I used for hundreds of episodes in years past. Much of our hardware discussion this week centered on my ongoing fascination with the MacBook Neo. I discovered that while it officially only supports one external display, you can effectively run a three-screen setup by using an iPad as a wireless third display through the MacOS Sidecar feature. This configuration, utilizing Mac OS Continuity, allows me to control the iPad using the MacBook's keyboard and mouse, creating a highly functional workstation without the need for extra cables. Jon has adopted a similar workflow in his classroom, using an iPad alongside his MacBook to handle student attendance while presenting his slides. On the software side, we discussed the release of iOS 26.4, which introduced a "Playlist Playground" feature in Apple Music on mobile devices. This tool uses AI to generate playlists from simple text prompts, and it serves as an excellent discovery tool for investigated genres where you might not be an expert. Looking further ahead, we looked at reports that iOS 27 may finally allow Siri to integrate with third-party AI chatbots like Gemini or ChatGPT. Since neither of us is a major fan of the current Siri, being able to choose a preferred chatbot would be a welcome change. As we approached Apple's 50th anniversary as an incorporated entity on April 1st, I reflected on the history of "tiny teams" in technology. While modern projects often involve hundreds of people, many of the most foundational tools—such as Apple DOS, CPM, and VisiCalc—were built by just one or two individuals. For instance, Paul Laughton built the first disk operating system for Apple in just 35 days by himself. We even saw this principle in action this week with Jon's new project, "Different Enough". He built this statistical testing website using GitHub Pages, TypeScript, and React in just 90 minutes. His secret was using ChatGPT to "interview" him about his requirements before generating a prompt for OpenAI Codex to build the final application. We followed up on the Adobe Podcast video test from last week; while the speaker identification worked well for the transcript, I had to boost the output volume significantly in post-production because it was surprisingly low. Jon also shared a bug he encountered with the Plaud Note platform, which misidentified a speaker by tagging the same student profile 20 times across different meetings with different students.. On a more aesthetic note, I shared Casio's announcement of a Japanese Lacquer Edition calculator. It is such a beautiful piece of craftsmanship that I'm now hoping Apple considers a lacquer edition for their MacBook line. What I found truly remarkable was that Jon was able to build a working model in only 90 minutes. He used what he calls a "one-two punch" with AI tools: The Interview: He first used regular ChatGPT to "interview" him about his specific requirements and ideas. The Build: Once the requirements were fleshed out, he had the AI write a high-quality prompt for OpenAI Codex, which then built the actual application using TypeScript and React. The project is currently hosted on GitHub Pages, which Jon set up so that the site automatically rebuilds and deploys in about a minute every time he pushes a change to his repository. To make the tool more accessible, he included real-world examples, such as independent t-tests for tutoring programs and chi-squared independence tests for marketing surveys

From Bangkok, Thailand...A tech tip about building custom practice tools using Google AI Studio, Claude Code, and OpenAI Codex.Some concise advice about how not to let the ups and downs of business and life wreck you in the moment.00:00 Location Update01:28 Tech Tip08:56 Concise Advice12:33 Wrapping Up

A profound shift in software engineering from manual coding to agentic development, where AI assistants like Claude Code, OpenAI Codex, and Cursor function as autonomous builders rather than simple autocomplete tools. This transition redefines the developer's role as an architect or "context engineer" who orchestrates multiple AI subagents to decompose tasks, conduct real-time code reviews, and automate complex workflows. Technical advancements such as the Model Context Protocol (MCP) and expanded context windows enable these agents to securely access entire repositories, databases, and design files to transform ideas into functional products. While traditional IDEs are becoming less central, new methodologies like vibe coding and specification-driven development allow teams to bridge the gap between high-level design and production-ready code. Ultimately, the industry is moving toward a usage-based economic model where human expertise is valued for problem-solving and strategic oversight rather than the mechanical act of writing syntax.

Everyone's chasing the next big model drop.

Get the AI Agents Playbook: https://clickhubspot.com/fno Ep. 401 There's a huge social network for agents—where humans aren't allowed to post. Kieran dives into how a weekend project became an autonomous AI ecosystem, changing how we interact with the web. Learn more about the meteoric growth and viral community of OpenClaw, the wild agent-only social network Moltbook, the real-world security challenges that come with autonomous agents, and how you can safely get started experimenting with your own AI agent today. Mentions OpenClaw https://openclaw.ai/ Moltbook https://www.moltbook.com/ OpenAI Codex https://openai.com/codex/ Claude https://claude.ai/ Gemini https://gemini.google.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

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In this week's live-stream replay, we go live for a 2-hour, hands-on deep dive into GPT-5.1 Codex Max with Alexander Embiricos, product lead for OpenAI Codex. You'll walk out feeling like an agentic-coding wizard, even if you're starting from zero. GPT-5.1 Codex Max is OpenAI's latest frontier agentic coding model. It's built on an upgraded reasoning backbone and trained to handle real-world software engineering tasks end to end: PRs, refactors, frontend builds, and deep debugging. It can work independently for hours, compacting its own history so it can refactor entire projects and run multi-hour agent loops without losing context. In this live session, we'll set it up together, build real agents, and push Codex Max to its limits.

New podcast series within Tacos and Tech: AI Builders Roundtable!Neal sits down with Craig Lauer and Ross Young on a day both Anthropic and OpenAI dropped major releases to talk about what it actually looks like to build with AI right now. Ross walks through how his team at Clinically AI built an internal AI operating system using Claude Co-work - from voice-interviewing department heads to capture tribal knowledge, to running full pipeline reviews from HubSpot in natural language. Craig shares how LaunchMate, the AI co-pilot he's building for student founders at SDSU's Zip Launchpad, uses persistent memory and multi-agent communication to keep founders moving. The conversation moves from tools to workflows to a surprisingly honest riff on identity - and what it means when intelligence is no longer your competitive advantage.Key Topics Covered:* The Anthropic 4-6 / OpenAI Codex same-day release and what it signals* LaunchMate: AI agents with persistent memory for founders, mentors, and cohort management at SDSU Zip Launchpad* “Tidbits” — auto-generated founder progress updates (”share without sharing”)* Ross's AI operating system at Clinically AI: markdown knowledge bases, Claude Co-work projects, HubSpot integration, voice-mode interviews for tribal knowledge capture* The AI capability spectrum: chatbots → cloud agents with tool access → local agents with full computer access* OpenClaw vs. Co-work: excitement vs. enterprise readiness and security* Craig's LettaBot/WhatsApp cautionary tale* Natural language as the new programming language - and why social workers may outperform engineers at agent programming* Processes they'll never go back to: manual contract redlines, email triage* Identity in the age of AI - detaching professional worth from intelligenceLinks & Resources:* Clinically AI* SDSU Zip Launchpad* Claude Co-work by Anthropic* LaunchMate (in development)Connect on LinkedIn:* Craig Lauer* Ross Young* Neal Bloom This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit risingtidepartners.substack.com/subscribe