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Online coaching is a $28.9B industry racing toward $120B by 2031 — but 71% of people who start an online fitness program quit within 90 days. In this episode, Eric sits down with Cody McBroom, founder of Tailored Coaching Method, who's built a 7-figure, ad-free coaching business over the past nine years without ever accepting a pay-in-full client. Cody breaks down the "identity-based coaching" model that took his team's client retention from 3-4 months to 14 months, why AI is reshaping how people find (and vet) a coach, and his genuinely nuanced take on GLP-1 drugs and the fitness industry's future. Key Takeaways:

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

Ottawa has selected Germany's TKMS as its preferred bidder for Canada's massive new submarine contract, a generational defence purchase that could be worth up to $120B and signals a deeper bet on European and NATO ties. Plus, Rogers is buying the remaining stake in MLSE, giving it control of nearly every major Toronto sports property and raising fresh questions about what consolidation — and possible private equity investment — could mean for fans.And in The Big Picture: Ontario and Alberta are pitching a new cross-country oil pipeline, Reddit is using AI to fight AI-generated marketing spam, and Alberta is issuing drilling permits at the fastest pace in more than a decade.The Peak Daily is produced in partnership with reframevid.com

Goldman Sachs has advised on more than $1 trillion of mergers and acquisitions in record time, highlighting the return of blockbuster dealmaking. We explain what's driving the surge in investment banking activity, why AI is fuelling both M&A and IPOs, and what it means for markets.We also break down the growing crisis at Thames Water, why nationalisation is back on the table, and how years of private equity ownership led to one of the UK's biggest corporate challenges. Finally, we analyse Snap's dramatic decline from a $130 billion company to under $10 billion, why its founder-led governance matters, and whether its bet on augmented reality glasses can ever pay off.(00:00) Coming Up(02:32) Goldman Sachs Hits $1 Trillion(06:08) M&A League Tables 1H26(09:08) IPO Markets Returns(17:29) What Next for M&A(19:11) Thames Water Explained(24:32) Special Administration Regime(32:27) Can Thames Water Be Saved?(36:54) Snap's $120B Collapse(40:07) The Founder Control Problem(45:24) Does ESG Still Matter?

My guest today is Vlad Barbalat, the Chief Investment Officer of Liberty Mutual Investments, the $120 billion investment platform that sits within one of the largest insurance companies in the world.  Vlad grew up in Soviet Moldova, came to America in 1990, and built a career that eventually led him to one of the most distinctive capital allocator seats anywhere in finance.  Today we talk about how the mutual insurance structure creates a unique investment platform, what Liberty looks for in a new deal or partner, and what it means to build a career and a life in a country that gave you opportunities you never would have had anywhere else.  Please enjoy my conversation with Vlad Barbalat. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:53) Vlad Barbalat (00:01:28) The Most Interesting Seat in the Market (00:05:53) Breaking Down the $120B (00:10:41) How the Portfolio Is Constructed (00:11:00) The House View (00:13:49) What Liberty Looks for in a GP (00:16:32) Why Not Just Buy Bonds (00:18:30) Benefits of the Mutual Structure (00:23:40) The Luxury of the American Citizen Through Immigrant Eyes (00:30:26) How Immigration Shaped His Worldview (00:32:45) Direct Deals vs. GP Allocations (00:35:23) Branded Capital (00:39:07) Geopolitics & Investing (00:43:48) AI's Impact on Investing (00:46:22) The Valuation Debate (00:50:47) Public vs. Private Markets (00:53:53) Lessons from Goldman (00:54:41) Why Excellence Matters (00:57:30) Managing Permanent Capital (01:03:54) The Kindest Thing 

What does it look like when one of the world's most longstanding institutional investors ($120B) decides to go deeper into venture capital? And what happens when one of the most respected LP teams in the business joins forces with them?Thomas Kristensen, who is responsible for the venture capital business at LGT Capital Partners, joins hosts Nick and Beezer for a wide-ranging conversation that doubles as an announcement: the Sapphire Partners team – including Beezer, Laura and Nate – have joined LGT Capital Partners. Thomas explains why the fit made sense.LGT Capital Partners manages over $120 billion on behalf of more than 700 institutional clients and is owned by the Princely Family of Liechtenstein. That ownership brings a long-term perspective, often measured in decades rather than years, and it shapes how Thomas and his team approach venture: with patience, close partnership and a willingness to be both buyer and seller in private markets.Together with Nick and Beezer, Thomas unpacks the firm's recently published white paper on the case for increasing venture allocation, built on three pillars: lifecycle diversification, an innovation hedge against AI-driven displacement and the maturation of secondary markets as a liquidity tool. He also offers a frank assessment of the current market, noting that the pace of deployment feels reminiscent of 2020-2021, and that a valuation dip may be coming regardless of how transformative AI ultimately proves to be.From the endowment model's stress test to the temptation of clinging to a single fund-returner, this is a thoughtful conversation about long-term thinking from an investor who has spent more than two decades refining his approach.Quotes“It's not new that incumbents are challenged by new entrants. I think what's new is the speed at which this is happening. In the age of AI, it feels like there is a risk that many incumbents could be displaced quite quickly. And so including venture capital in your portfolio is a way of hedging against this.”“Are LPs gonna run out of capital? I don't know. Sometimes I wonder if the world is going to run out of capital to fund CapEx for AI right now. We always say, ‘Listen, if you come into venture capital as an asset class, if you're an allocator, you can go in when it's hot, you can go in when it's not. You will only really figure out in hindsight.' The most important thing if you start committing to venture is, make sure that you can commit at a steady pace over a long, long period of time because it's a market you cannot time. There's just no way.”Time Stamps04:13 Meet Thomas Kristensen, Head of Venture Capital at LGT Capital Partners05:52 The Princely Family of Liechtenstein and the Long-Term Mindset08:37 The White Paper: Why LGT Capital Partners Is Increasing Its Venture Allocation09:45 Three Pillars: Lifecycle Diversification, Innovation Hedge, and Secondary Markets12:35 Big News: The Sapphire Partners Team Joins LGT Capital Partners17:35 LP Consolidation: What It Means for GPs23:47 GP Advice: Keep First Things First on LP Alignment32:03 Engineering a More Liquid Private Portfolio37:07 AI Market Heat and Fundraising Pace45:44 Closing Remarks and What's NextLinksConnect with the guest and hosts on LinkedIn!• Thomas Kristensen• Beezer Clarkson• Nick ChirlsLearn more about:• LGT Capital Partners• Asylum Ventures• OpenLP• LGT Venture Capital White Paper

Nvidia's RTX Spark Targets Apple's M-Series as a New AI Laptop Super ChipThe script argues Nvidia has escalated competition with Apple by unveiling the RTX Spark, an ARM-based “super chip” combining Blackwell GPU architecture with Grace, positioned for on-device AI agents. Citing reports from The Telegraph and Mac Rumors, it claims RTX Spark can run 120B-parameter local LLMs, handle 12K video editing, and play AAA games at 1440p with ray tracing, and will appear in a Microsoft Surface Laptop Ultra plus high-end HP, Dell, and Lenovo systems. It frames this as a direct threat to Apple's M-series efficiency advantage and Mac “walled garden,” while noting Apple is banking on an N5 chip and rumored “Project Q” to build data-center-class AI chips to reduce reliance on Nvidia. The script highlights Nvidia's ~86% AI accelerator share and urges viewers to watch adoption over the next six months.00:00 Nvidia Challenges Apple00:29 Meet RTX Spark01:22 Three Big Advantages01:51 Windows Laptops Get It02:39 Apple Plays Defense03:04 Project Q Rumors03:45 Market Share And Bets04:41 Stay Winning Mindset05:02 Subscribe And Wrap Up________________________________________________________________FOLLOW ME ON X: https://twitter.com/staywinningusdFOLLOW ME ON INSTAGRAM: https://www.instagram.com/staywinningusd/SUBSCRIBE ON YOUTUBE: www.youtube.com/@staywinningusdDOWNLOAD ON SPOTIFY: https://open.spotify.com/show/2lPyA19keI2fpr0xZrEKxMNEWSLETTER SIGNUP: https://stay-winning-wealth.kit.com/806fb337d7SUBSCRIBE TO THE BLOG: https://medium.com/@staywinningusd________________________________________________________________

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

Canada's coming submarine super-deal turns into a full-on bidding war as South Korea dangles “Project Beaver” — a massive hydrogen-trucking investment — to win a 12-sub contract potentially worth $120B over 70 years. Plus, famous short seller Andrew Left is found guilty of securities fraud in a “short-and-distort” scheme, raising fresh questions about market manipulation in the social media era. In The Big Picture: a proposed new U.S. tariff on Canada tied to forced-labour imports, Ottawa backs off its Cancon streamer rules, and Meta launches an AI agent for businesses.The Peak Daily is produced in partnership with reframevid.com

Grant McDowell is in London and Tim Buckley is in Sydney recording the Spark Club Podcast on the 23rd March 2026 Highlights – Draft AER Default Market Offer Brilliant to see the Australian Energy Regulator has today flagged draft default market offer (DMO) electricity pricing down ⬇️ 1% to ⏬ 10% for residential consumers, and between ⬇️ 8% to ⏬ 21% for small business consumers The DMO sets an efficiently priced safety-net for households and small businesses on standing offer electricity plans and acts as a reference price to help consumers compare market offers. This is the draft ruling, with the final ruling released May 2026 for effect for the 12 months starting 1 July 2026. This is consistent with Australian Energy Market Operator (AEMO)'s quarterly energy dynamics highlighting Australia hit a record high 51% RenewableEnergy share in the 4QCY2025, and wholesale electricity prices fell by >40% yoy as a result. Highlights – PRRT reform - Petroleum Resource Rent Tax - Dodge The ACTU this week is calling for a flat 25% tax on Australian LNG to replace the entirely failing PRRT, to capture the wind fall war-profits being generated, and to then use the massive tax revenues of up to $40bn to fund energy poverty relief across Australia. Highlights – CATL CY2025 results highlight their global leadership and scale Nothing short of staggering to watch the rise and rise of China's CATL to supremacy in battery manufacturing. Their speed & scale of technology innovation is amazing to see.

Recorded live at NVIDIA GTC 2026 in San Jose, Corey sits down with returning guest Kari Briski—VP of Generative AI Software for Enterprise at NVIDIA—to unpack their biggest open-source model yet: Nemotron 3 Super. Kari breaks down why a 120B-parameter model runs as fast as a 12B one, how multi-agent systems are going from science fiction to production, and why Jensen Huang is calling this "a new operating system." We also dig into NVIDIA's work on Open Claw security, the 35x explosion in open-model token generation, and where omni-modal AI is heading next.Subscribe to The Neuron newsletter: https://theneuron.aiRelevant links:NVIDIA Build (try Nemotron): https://build.nvidia.comNemotron on Hugging Face: https://huggingface.co/nvidiaOpen Router: https://openrouter.aiKari's previous Neuron episode (Oct 2025): https://youtu.be/p0INn_w7TYo

Our 237th episode with a summary and discussion of last week's big AI news!Recorded on 03/13/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:* Perplexity announced “Personal Computer,” a local Mac-based AI agent positioned as a safer alternative to OpenAI's computer-use agents, while Anthropic added GitHub PR code review pricing reviews at $15–$25 and Cursor launched trigger-based “Automations” for always-on coding agents.* ChatGPT introduced interactive math/science visuals and Anthropic added in-chat interactive charts/diagrams; Nvidia released open weights for its 120B-parameter Natron Free Super hybrid Transformer–Mamba latent-MoE model trained natively at 4-bit for Blackwell GPUs.* Nvidia halted H200 production for China amid customs blocks and domestic chip pressure; xAI saw major co-founder departures; Anthropic previewed a Claude Marketplace for enterprise procurement; Yann LeCun's aMI raised $1.3B; humanoid robot maker Sanctuary reached a $1.15B valuation.* Anthropic sued the Pentagon over a “supply chain risk” designation as memos ordered removal within 180 days; research covered models resisting activation steering, limits of chain-of-thought control, inference-scaling boosting cyber-task success, low-probability risky actions, weaknesses in SWE-bench, multimodal pretraining, long-context RNN memory caching, context-parallel training efficiency, RL for CUDA kernel optimization, and latent introspection detecting concept injection.A thank you to our current sponsors:Box - visit Box.com/AI to learn moreODSC AI - go to odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - head to factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps:(00:00:10) Intro / Banter(00:01:23) Response to listener commentsTools & Apps(00:02:06) Perplexity's Personal Computer turns your spare Mac into an AI agent | The Verge(00:04:22) Anthropic launches code review tool to check flood of AI-generated code | TechCrunch(00:08:08 ) Cursor is rolling out a new kind of agentic coding tool | TechCrunch(00:11:14) ChatGPT can now create interactive visuals to help you understand math and science concepts | TechCrunch(00:11:56) Anthropic's Claude AI can respond with charts, diagrams, and other visuals now | The VergeProjects & Open Source(00:13:54) Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning | NVIDIA Technical BlogApplications & Business(00:21:22) Nvidia halts H200 production as China backs Huawei AI chips(00:28:33) Another XAI Cofounder Has Left, and Another Says He's Leaving. - Business Insider(00:34:04) Anthropic's Claude Marketplace allows customers to buy third-party cloud services | TechRadar(00:37:57) Yann LeCun's AMI Labs raises $1.03 billion to build world models | TechCrunch(00:44:52) Humanoid robotics maker Sunday reaches $1.15B valuation to build household robots | TechCrunchPolicy & Safety(00:46:09) Anthropic Sues Department of Defense Over ‘Supply Chain Risk' Label - The New York Times + Google and OpenAI Just Filed a Legal Brief in Support of Anthropic (00:53:24) Internal Pentagon memo orders military commanders to remove Anthropic AI technology from key systems - CBS News(00:58:15) Endogenous Resistance to Activation Steering in Language Models(01:06:27) Reasoning Models Struggle to Control their Chains of Thought(01:09:52) ‘It means missile defence on datacentres': drone strikes raise doubts over Gulf as AI superpower(01:14:57) Evidence for inference scaling in AI cyber tasks: Increased evaluation budgets reveal higher success rates(01:18:24) Frontier Models Can Take Actions at Low ProbabilitiesResearch & Advancements(01:24:20) Research note: Many SWE-bench-Passing PRs Would Not Be Merged into Main(01:28:26) [2603.03276] Beyond Language Modeling: An Exploration of Multimodal Pretraining(01:40:09) Memory Caching: RNNs with Growing Memory(01:48:47) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking(01:58:41) CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation(02:08:57) Latent Introspection: Models Can Detect Prior Concept Injections(02:16:45) Physics of RL: Toy scaling laws for the emergence of reward-seekingSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hey, Alex here

NIO just reported its first quarterly profit in 11 years.They beat the top end of their own guidance. The stock closed at $5.70 — up 15.38% on the day. A week that started with NIO at $4.59 ended with a historic earnings report.Full breakdown in this episode:— The numbers: $178.9M non-GAAP operating profit, $40.4M GAAP net profit, 75.9% revenue growth, 17.5% gross margin— 124,807 Q4 deliveries — up 71.7% — ES8 carrying 32% of volume at ~20% gross margin— The cost story: R&D cut 44.3%, SG&A cut 27.5% — how they engineered the profit— William Li's pay package: 10 tranches, market cap targets from $30B to $120B — why it's a shareholder alignment signal— Q1 guidance: 80-83K vehicles, revenue more than doubling YoY— Full year 2026: 40-50% growth target and full-year profitability— Shenji chip 2 in mass production — and the robotics angle— $6.6B cash on hand. The liquidity question is answered.Courtside Financial. Hosted by Obi.NIO, NIO earnings, NIO Q4 2025, NIO first profit, NIO stock,NIO 2026, NIO analysis, NIO bull case, Chinese EV stocks,EV investing 2026, NIO gross margin, NIO William Li,NIO Shenji chip, Courtside Financial, EV podcast, NIO reaction

Another HUGE Week for Bitcoiners, with TONS of news from all over the world.

Another HUGE Week for Bitcoiners, with TONS of news from all over the world.

CSI is analyzing the semiconductor manufacturing supply chain: TSMC, Amkor, and Aehr Test Systems.Time to go back to our roots! In this episode, we're breaking down the semiconductor manufacturing supply chain to see exactly how money is moving through the industry right now.We are comparing three major players: the industry titan TSMC, the packaging specialist Amkor, and the small-cap testing favorite Aehr Test Systems.While Amkor has ripped higher recently and Aehr is a volatile favorite among traders, the data might surprise you on who the true long-term wealth compounder is. We dive into the operating margins, 2025 revenue projections, and why advanced packaging is the new battleground for chip revenues.In this video, we cover:The difference between "Set it and Forget it" stocks vs. those that need babysitting.--TSMC's massive $120B+ revenue year and 2026 outlook.--Why Amkor is picking up the work TSMC doesn't want.--The latest earnings update on Aehr Test Systems.Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters: 01:10 - The 3 Stocks: Amkor, TSMC, Aehr 02:15 - 3-Year & 5-Year Return Comparison 03:55 - The Advanced Packaging Opportunity (Amkor) 05:30 - TSMC Revenue Breakdown & 2025 Projections 08:20 - Comparing Operating Margins10:45 - Aehr Test Systems: Earnings & Cash Burn 14:15 - Summary: The Sleep Well at Night PickIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.#Semiconductors #TSMC #Amkor #AehrTestSystems #StockMarket#TSM #AMKR #AEHR #ChipStocks #SemiconductorSupplyChain #AdvancedPackaging #WaferFab #ChipManufacturing #LongTermInvesting #TechStocks #FinancialAnalysis #PortfolioManagement #ValueInvestingNick and Kasey own shares of TSM

Zevachim 120a-120b (Daf Yomi) by Rabbi Avi Zakutinsky

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Outline of the Sugyaסיום מסכת זבחים - יישר חילכם לאורייתא

Ho Ho Ho, Alex here! (a real human writing these words, this needs to be said in 2025) Merry Christmas (to those who celebrate) and welcome to the very special yearly ThursdAI recap! This was an intense year in the world of AI, and after 51 weekly episodes (this is episode 52!) we have the ultimate record of all the major and most important AI releases of this year! So instead of bringing you a weekly update (it's been a slow week so far, most AI labs are taking a well deserved break, the Cchinese AI labs haven't yet surprised anyone), I'm dropping a comprehensive yearly AI review! Quarter by quarter, month by month, both in written form and as a pod/video! Why do this? Who even needs this? Isn't most of it obsolete? I have asked myself this exact question while prepping for the show (it was quite a lot of prep, even with Opus's help). I eventually landed on, hey, if nothing else, this will serve as a record of the insane week of AI progress we all witnessed. Can you imagine that the term Vibe Coding is less than 1 year old? That Claude Code was released at the start of THIS year? We get hedonicly adapt to new AI goodies so quick, and I figured this will serve as a point in time check, we can get back to and feel the acceleration! With that, let's dive in - P.S. the content below is mostly authored by my co-author for this, Opus 4.5 high, which at the end of 2025 I find the best creative writer with the best long context coherence that can imitate my voice and tone (hey, I'm also on a break!

Hey folks

Wes Kirk is a Sports Performance Coach. In this episode of Iron Game Chalk Talk 2.0, Coach Kirk talks to us about: Why you should always bet on yourself even when things seem difficult The importance of checking in with athletes and getting their input What a high performance model can look like in a high school setting Visit our website at https://isaiahcastilleja.podbean.com/ Please visit our sponsors and show them some appreciation for their support. Visit Teambuildr at www.teambuildr.com Visit The Strength and Conditioning Co at https://thestrengthandconditioningco.com/ Visit BetterHelp at https://www.betterhelp.com/  

Originally uploaded September 18, reloaded October 10th. Jeffrey Mosher welcomes back Julie Pingston, President & CEO, Choose Lansing, Lansing, MI. Questions covered: Economic Impact – How does positioning Greater Lansing as an accessible destination strengthen the region's competitiveness in the $120B accessible travel market? Business Engagement – What role did local hotels, attractions, and restaurants play in the accessibility mapping initiative, and how are businesses responding? Tourism Strategy – How does accessibility tie into Choose Lansing's broader strategy to grow visitor spending and attract new markets to the region? Return on Investment – What measurable outcomes—like increased bookings, extended stays, or repeat visitors—do you expect from verified accessibility listings? Future Growth – How can other Michigan businesses partner with Choose Lansing to build on this momentum and expand accessible travel opportunities statewide? » Visit MBN website: www.michiganbusinessnetwork.com/ » Watch MBN's YouTube: www.youtube.com/@MichiganbusinessnetworkMBN » Like MBN: www.facebook.com/mibiznetwork » Follow MBN: twitter.com/MIBizNetwork/ » MBN Instagram: www.instagram.com/mibiznetwork/ Choose Lansing® Partners with Wheel the World to Expand Accessible Travel Opportunities LANSING, MICH. – Sept. 2, 2025 – Choose Lansing® is proud to announce the successful completion of its accessibility mapping initiative in collaboration with Wheel the World, a leading accessible travel platform. This project marks a significant milestone in making Lansing a more accessible destination for travelers with disabilities by providing verified accessibility information for local businesses and attractions. Through the Destination Verified program, 55 key tourism listings including 14 hotels, 30 attractions and 11 restaurants across the region have been mapped, ensuring travelers with disabilities have reliable accessibility information. These listings are now available on Wheel the World's platform, offering detailed insights, allowing visitors to plan their trips with confidence. Lansing's Commitment to Accessible Travel Lansing has been a leader in accessible travel, continually working to enhance the visitor experience for all. This latest initiative with Wheel the World reinforces the region's dedication to inclusivity and its proactive approach to removing barriers for travelers with disabilities. “We are excited to partner with Wheel the World to enhance accessible travel in our region,” said Julie Pingston, President & CEO of Choose Lansing®. “This initiative is a natural extension of our commitment to making greater Lansing a welcoming destination for all travelers. By ensuring reliable accessibility information, we empower all visitors to enjoy the attractions, restaurants, hotels and other establishments that our destination has to offer.” The Power of Accessible Travel Accessible travel represents a $120 billion market, and destinations that prioritize accessibility position themselves as leaders in tourism innovation. Wheel the World's global community of over 150,000 travelers actively seeks destinations that provide transparent and reliable accessibility information. For more information about the initiative and how Choose Lansing® continues to champion accessible travel, visit www.lansing.org or call 517-487-0077. About Choose Lansing® The vision of Choose Lansing is to inspire everyone to celebrate and love Greater Lansing as much as we do! This goes hand in hand with our mission, to positively impact our community's quality of life by developing the region as a visitor destination. Learn more at www.lansing.org. ###

Amanda Morin of Understood joins Debbie for a special back-to-school episode to help families prepare for easing into the coming school year with as much confidence and serenity as possible. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Youtube Channel: https://www.youtube.com/@GenerativeAIMeetup Mark's Travel Channel: https://www.youtube.com/@kumajourney11 Mark's Channel: https://www.youtube.com/@markkuczmarski896 Gen AI Meetup: https://genaimeetup.com/ Shashank Linked In: https://www.linkedin.com/in/shashu10/  Mark Linked in: https://www.linkedin.com/in/markkuczmarski/  Join hosts Shashank and Mark in this electrifying episode of the Gen.ai Meetup Podcast, where they unpack a whirlwind week of AI advancements reshaping the future of technology. From Anthropic's Claude 4.1—a subtle yet powerful upgrade boosting coding prowess and multi-file edits for enterprise dominance—to OpenAI's long-awaited open-source comeback with GPT-OSS models (a beefy 120B parameter beast and a tiny laptop-friendly version rivaling proprietary giants), the duo dives into benchmarks, real-world applications, and how tools like Ollama make deployment a breeze. They explore Gemini's DeepThink, a reasoning powerhouse solving Olympiad-level math puzzles through extended inference, and Google's groundbreaking “world model”—a seamless blend of video generation and game engine tech that lets you control characters in hyper-realistic, physics-aware simulations. Along the way, Shashank and Mark share candid insights on vibe coding pitfalls, side projects built with AI agents, OpenAI's staggering valuations, and the open-source ecosystem's role in driving innovation. Whether you're a developer wrestling with agentic workflows, an enterprise leader eyeing LLM integrations, or an AI enthusiast dreaming of interactive worlds, this episode delivers expert analysis, practical tips, and forward-thinking speculation. Tune in for a fun, far-flung chat (Mark's broadcasting from a Canadian road trip en route to the Arctic!) and discover why AI's evolution is accelerating faster than ever. Drop your questions in the comments—we'll tackle them next time! Timestamps: 00:00:00 - Introduction: Shashank welcomes listeners and introduces Mark, who's road-tripping in Canada to the Arctic Ocean. 00:03:50 - Episode Overview: A quick rundown of the week's major AI announcements. 00:07:44 - Claude 4.1 from Anthropic: Discussing the incremental improvements of Claude Opus 4.1, its coding strengths, and enterprise adoption. 00:16:32 - Claude's Enterprise Impact: Why Claude leads in enterprise LLMs and its role in tools like Cursor for vibe coding. 00:28:38 - Gemini's DeepThink Feature: Deep dive into Gemini's reasoning capabilities for complex math and problem-solving. 00:29:28 - OpenAI's GPT-OSS Release: OpenAI's open-source models (120B and 20B parameters), their performance, and community implications. 00:44:94 - OpenAI's Valuation Debate: Exploring OpenAI's $300B valuation and the strategic benefits of open-source releases. 00:45:18 - Google's World Model Announcement: Exploring the steerable 3D environments blending video generation and game engine tech. 00:50:32 - World Model Applications: Potential uses in robotics, self-driving, and synthetic data generation. 00:54:86 - Coding Agents and Side Projects: Shashank and Mark share experiences with vibe coding and AI-powered side projects. 00:58:74 - Amazon's Spec-Driven Development: Insights on Amazon's Kero tool and the importance of detailed software specifications. 00:58:94 - Ollama and Ollama Turbo: How Ollama simplifies model deployment and the new cloud-based Ollama Turbo service. 01:07:26 - Prompt Engineering Tips: Practical advice on crafting effective prompts and iterating with LLMs for better outputs. 01:11:50 - Closing and Call for Questions: Wrap-up and a call for listener questions in the YouTube comments. Subscribe and leave a comment with your questions for the next episode! #AI #GenAI #Claude4.1 #OpenAI #GPTOSS #GeminiDeepThink #WorldModels #Ollama #CodingAgents #TechPodcast

nvestors keep score with growth rates, but this quarter you had to read the footnotes and the fine print. Microsoft put up eye-popping Azure growth again, but a big slice of that acceleration is AI inference — notably ChatGPT — now embedded in the revised Azure definition - great for headlines but not conducive to apples-to-apples comparisons. Meanwhile, AWS delivered the largest dollars and a very clear message that demand exceeds supply. Meaning growth is capped by power and components, not pipeline. That creates a weird optics penalty — AWS showing growth in the high teens growth on a $120B-plus run rate and it's a “concern.” But it also telegraphs future upside as capacity lands and depreciation cycles through.  The stealth story is Google. Google Cloud posted a strong print with solid top-line growth and steadily improving operating margin — and GCP (the IaaS/PaaS core) is growing materially faster than Cloud overall - our estimate is nearly 40%. Backlog is building at more than $250M and $1B+ deals are real. As the mix shifts toward AI-heavy, infrastructure-centric workloads, it becomes a tailwind for Google, continues to lag the scale of AWS and Azure. Let's call it a disciplined scale strategy with less noise.The other common thread is a capex arms race that faces real constraints. All three players said the quiet part out loud — power, sites, servers, power and lead times will dictate who wins AI inference and who monetizes it. Microsoft is capacity-constrained, AWS says “several quarters” to rebalance, and Google raised capex again. This is not a one-quarter story; it's a multi-year land-and-power grab that will determine margin structures for the next cycle. Meanwhile, the Big three cloud players are on a pace to spend about $240B this year on CAPEX with AI revenue coming in at about 10% of that figure. We clearly have a big hurdle before that massive investment pays back. 

「Apple、独自のAI “アンサーエンジン” を開発中か — ChatGPT 風機能をSiriやSafariに統合へ」Appleは「Answers, Knowledge, and Information（AKI）」という新チームを設立し、ウェブをクロールして一般知識の質問に応答するAIアンサーエンジンを構築中と報じられています。「Google、Pixel 10広告にてApple Intelligenceの延期を皮肉」GoogleはPixel 10シリーズのティザー広告（8月20日発表予定）にて、Apple IntelligenceによるSiri機能強化が1年以上も “coming soon” のままである点をユーモラスに批判し、自社製品への乗り換えを促しています。「Google、Geminiに“Guided Learning”モードを追加」GoogleはGeminiに新たな「Guided Learning」モードを導入し、答えを提示するだけでなく、ステップごとに理解へ導く対話型サポートを提供し始めました。「Genie 3：DeepMindが目指す次世代“世界モデル”」Google DeepMindは、テキストプロンプトからリアルタイムに3D環境を生成し操作できるAIモデル「Genie 3」を発表しました。AGI（汎用人工知能）への鍵となる革新的技術として注目されています。「OpenAI、ローカル実行可能なオープンモデル『GPT-OSS』を公開」OpenAIは2025年8月、6年ぶりにオープンウェイトの大規模言語モデル「gpt-oss-120B」と「gpt-oss-20B」をリリースしました。商用利用も可能なApache 2.0ライセンスで、Windows PCなどでのローカル実行にも対応しています。「Anthropic、OpenAIのClaudeアクセスを停止」Anthropicは2025年8月、OpenAIが自社モデルの開発にClaude APIを利用していたことを理由に、利用規約違反としてアクセスを遮断しました。AI業界における競合間の緊張がさらに高まっています。「Perplexity AI、robots.txtを無視してWebクロールを実行か」Cloudflareは、Perplexity AIが明示的にクロール禁止とされたサイトを「ステルス手法」で巡回していると非難しました。Perplexityはこれを否定し、AIによる情報収集の在り方に議論が広がっています。「Instagram、新機能“リポスト”と“マップ機能”を導入」Instagramが「リポスト」機能と「マップ」機能を新たに導入しました。友人の投稿やおすすめスポットの発見がしやすくなり、Reelsには「Friends」タブも追加されました。他SNSの人気機能を取り入れ、より“つながり”と“発見”にフォーカスしたアップデートが進行中です。「男性向けTeaアプリ『TeaOnHer』、個人情報と運転免許証を含む重大なデータ漏洩が発覚」女性の情報を匿名投稿する目的でリリースされた新興アプリ『TeaOnHer』において、約53,000人分のユーザーデータや運転免許証画像などが誰でもアクセスできる状態で放置されていたことが判明しました。「スクリームクラブ シカゴ＆カンザスシティ」= = = = = = = = = = = = = = = = = = = = = = = = =【ユカスタポッドキャスト // Podcast by Yuka Studio】ユカスタポッドキャストは、テックとクリエイティビティがもっと身近になる、トーク番組です。ニューヨークを拠点に、テック系クリエイターとして活動する大石結花がメインホストとして、テックニュースや、インタビューコンテンツをお届けします。

This ChatGPT announcement is more important than GPT-5.Seriously.This week, OpenAI (kinda) quietly released its first open-source model since 2019.Us AI dorks are talking about it… but the business landscape is crickets.(As everyone gets hyped for GPT-5 today.)But…. Hot take on a Thursday shorties: ChatGPT's new Open Source Model will be a bigger step forward for AI tech than GPT-5 and it's not even close.Join us to find out why, how business development could change, and who will be the winners and losers.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.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 Releases GPT OSS Open Source ModelComparison: GPT OSS vs GPT-4 Level ReasoningImpact on AI Industry Competitors & StrategyApache 2.0 License vs Meta Llama RestrictionsBusiness Benefits: Local, Secure, Free AI DeploymentTechnical Specs: 20B and 120B Parameter VersionsAI Model Customization, Fine-Tuning, and Edge UseWinners and Losers: Nvidia, Google, API ProvidersEdge Computing and On-Device AI FutureOpen Source AI Risks and Safety ConcernsGlobal AI Race: US vs China Open SourceAcceleration of AI Innovation and Model DevelopmentTimestamps:00:00 "ChatGPT's Game-Changing Open Source"05:27 Open Source AI Models Explained07:23 OpenAI's New Open-Source Model11:30 Affordable High-Performance Language Models15:54 Meta's Shift Toward Proprietary Models17:56 "AI Model Customization and Deployment"20:38 Leveraging AI for Cost Efficiency26:13 OpenAI's Strategic Competitive Advantage27:58 OpenAI's Strategic Dominance Forecast31:33 "Anticipating Google's Gemma 4 Impact"35:18 Apple's Future in AI-Powered Phones39:11 AGI: The New Global SuperpowerKeywords:GPT OSS, OpenAI, ChatGPT open source, GPT-OSS, GPT4O level reasoning, Open source AI model, Apache 2.0 license, Reasoning model, Local AI models, AI edge computing, On-device AI, Downloadable AI model, 21B parameter model, 120B parameter model, AI model fine tuning, Commercial use AI, Chain of thought, Agentic tasks, Tool use AI, Secure AI deployment, Data privacy, API providers, AI innovation, Chinese open source AI, Meta Llama, MMLU benchmark, Nvidia GPU, Microsoft Azure, AWS Bedrock, Hugging Face, Cloud AI, AI business strategy, AI market disruption, AI mid tier competitors, AI scalability, Patent protection, Cybersecurity, Bioweapon risks, Global AI race, AGI acceleration, Model weights releaSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

On this episode of The Horizon, John discusses a potential game-changing executive order from President Trump that could allow U.S. retirement accounts like 401(k)s to invest in private market assets, including commercial real estate. He explores how this influx of capital could significantly reshape cap rates—not because of interest rates, but due to shifts in capital flows. Using historical data, John challenges the commonly held belief that cap rates track closely with interest rates, demonstrating instead that transaction volume and investor demand play a much stronger role. He concludes by urging investors to consider positioning themselves ahead of a possible capital wave that could drive property values higher. Get a 4-week trial, free postage, and a digital scale at ⁠https://www.stamps.com/cre⁠. Thanks to Stamps.com for sponsoring the show! Post your job for free at https://www.linkedin.com/BRE. Terms and conditions apply. Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at ⁠www.bestevercommunity.com⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

What if the market chaos we're experiencing today isn't as unprecedented as it seems? In this episode, we unpack what today's economic volatility has in common with the 2008 financial crisis and how smart investors can adapt. Host Jeannette Friedrich is joined by Jeff Traister, Managing Director of Systematic Portfolio Investing at Guggenheim Partners. With decades of experience spanning crisis-era mortgage trading to building systematic strategies for a $120B portfolio, Jeff offers rare insight into how to navigate uncertainty, manage risk, and stay grounded in disciplined strategy.  Key takeaways from the episode: - The most resilient investment strategies are systematic but also adaptive, continuously evolving based on incoming data rather than rigid models - Human behavior is often the biggest challenge to disciplined investing, especially during high volatility - Liquidity risk is often invisible until it's too late, and many investors fail to account for it properly - Lessons from the 2008 crisis remain critical today, particularly around risk modeling and the importance of conducting in-house analysis - Extreme market behavior today, such as dislocation in Treasury markets, may require Federal Reserve intervention similar to 2008 - Long-term investment strategies should be designed to weather political uncertainty, tariffs, and recession risk - Private real estate stands out in today's environment for its stability, insulation from daily volatility, and asset-specific fundamentals - Emotional discipline and diversified thinking are essential, and investors should never rely solely on credentials or single sources of information Contact Jeff https://www.linkedin.com/in/jeff-traister-los-angeles/  Timestamps 00:00 Introduction and Guest Introduction 01:59 Navigating Investment Strategies in Uncertain Times 04:59 Lessons from the 2008 Financial Crisis 19:55 The Fed's Potential Actions and Market Impact 21:35 Comparing 2008 Crisis to Current Market Conditions 24:06 Personal Insights and Investment Strategies Are you REady2Scale Your Multifamily Investments? Learn more about growing your wealth, strengthening your portfolio, and scaling to the next level at www.bluelake-capital.com. Credits Producer: Blue Lake Capital Strategist: Syed Mahmood Editor: Emma Walker Opening music: Pomplamoose *

1 section- debate regarding causation to extinguish a fire and related points of opening doors to flame and erasing Name Hashem

1 section- debate regarding causation to extinguish a fire and related points of opening doors to flame and erasing Name Hashem

Bava Basra 120a-120b (Daf Yomi) by Rabbi Avi Zakutinsky

Insights on the past, present and future of the crypto industry with Tether CEO Paolo Ardoino.Follow the podcast here.Tether CEO Paolo Ardoino joins Bullish CEO Tom Farley at CoinDesk Spotlight to discuss the rise of USDT, the largest stablecoin with a nearly $120 billion market cap. Plus, the secret behind Tether's profitability and its role in revolutionizing the world of finance. Paolo also answers some of the challenging questions that the crypto community has about the third largest crypto asset.-This content should not be construed or relied upon as investment advice. It is for entertainment and general information purposes.-This episode was hosted by Tom Farley. “CoinDesk Spotlight” is produced by Sam Ewen, Jennifer Sanasie, Melissa Montañez, and edited by Victor Chen.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In April 2023 we released an episode named “Mapping the future of *truly* open source models” to talk about Dolly, the first open, commercial LLM. Mike was leading the OSS models team at Databricks at the time. Today, Mike is back on the podcast to give us the “one year later” update on the evolution of large language models and how he's been using them to build Brightwave, an an AI research assistant for investment professionals. Today they are announcing a $6M seed round (led by Alessio and Decibel!), and sharing some of the learnings from serving customers with >$120B of assets under management in production in the last 4 months since launch. Losing faith in long context windowsIn our recent “Llama3 1M context window” episode we talked about the amazing progress we have done in context window size, but it's good to remember that Dolly's original context size was 1,024 tokens, and this was only 14 months ago. But while understanding length has increased, models are still not able to generate very long answers. His empirical intuition (which matches ours while building smol-podcaster) is that most commercial LLMs, as well as Llama, tend to generate responses

Get ready to explore Citadel and BlackRock's epic $120B Texas Stock Exchange launch, NVidia's skyrocketing $3 trillion market cap and upcoming stock split, and the NYSE glitch that halted major stocks. We'll uncover Ford's SUV recall saga, booming discount retailers, potential $76B NBA media deals, and introduce the first woman worth over $100B. Plus, snag hurricane season tips, smart options trading advice, and the latest tech buzz with Joe Grand. Subscribe for your weekly dose of finance, tech, and trends! Subscribe to our YouTube page and listen on Spotify, Apple, or your favorite podcast platform.

We are more or less done with the daughters of Zelophehad, (though are we ever really done with the daughters of Zelophehad?), but we are continuing with a midrashic sugya dedicated to the sanctification of the month/shabat/the curious holiday of the fifteenth of Av and what that is all about—and it seems to be about a lot of stuff. All in this week's daf.This week's daf can found in the following places:1. Vilna page (Hebrew and Aramaic) from Hebrewbooks.org2. Hebrew and English from Sefaria.org3. Hebrew and Aramaic with many commentaries from Alhatorah.orgPlease be in touch with any comments, criticisms, or witticisms at thewidowandthebrothers@gmail.comBecome a Patron of Daf Shvui @ Patreon. 

We go in-depth on our week one experience of Apple Vision Pro, Google's transitions Bard to Gemini and inserts their LLM across its services, Disney invests $1.5 billion into Epic Games, and most importantly, mouse or trackpad?Sponsored by:Hello There Greeting Cards: Primary Tech listeners can use offer code to receive 1 free year of HelloThere+ unlocks features like iCloud Sync, unlimited entries, app customizations, and lots more! Code: HELLOPRIMARYTECH and download the app here.Watch on YouTube!Subscribe and watch our weekly episodes plus bonus clips at: youtube.com/@primarytechshowSupport the showJoin our member community and get an ad-free versions of the show, plus exclusive bonus episodes every week! Subscribe directly in Apple Podcasts or here: primarytech.memberful.com/joinReach out:Stephen's YouTube Channel@stephenrobles on Threads@stephenrobles on XStephen on MastodonJason's Inc.com Articles@jasonaten on Threads@JasonAten on XJason on MastodonWe would also appreciate a 5-star rating and review in Apple Podcasts and SpotifyPodcast artwork with help from Basic Apple Guy.Those interested in sponsoring the show can reach out to us at: podcast@primarytech.fmLinks from the showExperiences That Changed My Mind on Apple Vision Pro - YouTubeT-Pain Wearing Vision Pro - TikTokDave meets Apple Vision Pro • Threadsthe thing no one will say about Apple Vision Pro - YouTubei wore Apple Vision Pro for 24 hours - YouTubeStephen Robles shows developer strap on ThreadsVision Pro Teardown Part 2: What's the Display Resolution? | iFixitGoogle Just Killed Bard and Replaced it With Gemini | Inc.comDisney to take $1.5 billion stake in Epic GamesWWDC 2023 — June 5 | Apple - YouTubeESPN, Fox and Warner Team Up to Create Sports Streaming PlatformWhere's USHER? | The Call | Apple Music Super Bowl LVIII Halftime Show (Official Teaser) - YouTubeApple reports nearly $120B quarter - Six Colors (00:00) - Intro (04:53) - One Week with Apple Vision Pro (13:41) - Spatial Computing (22:25) - Vision Developer Strap (29:39) - Who Is It For? (33:06) - Vision Pro Reviews (48:01) - Sponsor: Hello There (50:08) - Google Gemini (56:54) - Disney Invests $1B in Epic (01:04:42) - Sports Streaming Conglomerate (01:10:24) - Apple Earnings (01:14:59) - Mouse or Trackpad ★ Support this podcast ★

Jason, Alex, and guest Ray Maxwell have spent time with their Apple Vision Pros and share their thoughts on the device so far. Who would have thought the Vision Pro would scratch easily in a durability test? And an OLED iPad Pro is rumored to come out later this year with an expected price increase, but how much will the price of the iPad go to? Jason Snell, Alex Lindsay, and guest Ray Maxwell share their thoughts on the Vision Pro after spending the weekend with the device. This hospital system just bought 30 Vision Pro units and launched a new 'Spatial Computing Center of Excellence'. Apple Vision Pro front glass easily scratched in durability test. How to get an Apple Vision Pro demonstration. Apple reports nearly $120B quarter: full charts. Apple quadrupled its autonomous driving testing miles last year. OLED iPad Pro price increase won't be as painful as initially rumored. "Just peer-reviewed a forensic analysis in a case. The suspect mailed a package with a hidden Apple AirTag in it to a victim's old home address..." Picks of the Week: Jason's Picks: Juno and Runestone for Vision Pro Andy's Pick: Ensemble for Vision Pro Alex's Pick: Jig Space Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Guest: Ray Maxwell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: babbel.com/macbreak ecamm.com/twit or use Promo Code TWIT Wildgrain.com/MACBREAK or use code MACBREAK rocketmoney.com/macbreak

Jason, Alex, and guest Ray Maxwell have spent time with their Apple Vision Pros and share their thoughts on the device so far. Who would have thought the Vision Pro would scratch easily in a durability test? And an OLED iPad Pro is rumored to come out later this year with an expected price increase, but how much will the price of the iPad go to? Jason Snell, Alex Lindsay, and guest Ray Maxwell share their thoughts on the Vision Pro after spending the weekend with the device. This hospital system just bought 30 Vision Pro units and launched a new 'Spatial Computing Center of Excellence'. Apple Vision Pro front glass easily scratched in durability test. How to get an Apple Vision Pro demonstration. Apple reports nearly $120B quarter: full charts. Apple quadrupled its autonomous driving testing miles last year. OLED iPad Pro price increase won't be as painful as initially rumored. "Just peer-reviewed a forensic analysis in a case. The suspect mailed a package with a hidden Apple AirTag in it to a victim's old home address..." Picks of the Week: Jason's Picks: Juno and Runestone for Vision Pro Andy's Pick: Ensemble for Vision Pro Alex's Pick: Jig Space Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Guest: Ray Maxwell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: babbel.com/macbreak ecamm.com/twit or use Promo Code TWIT Wildgrain.com/MACBREAK or use code MACBREAK rocketmoney.com/macbreak

Jason, Alex, and guest Ray Maxwell have spent time with their Apple Vision Pros and share their thoughts on the device so far. Who would have thought the Vision Pro would scratch easily in a durability test? And an OLED iPad Pro is rumored to come out later this year with an expected price increase, but how much will the price of the iPad go to? Jason Snell, Alex Lindsay, and guest Ray Maxwell share their thoughts on the Vision Pro after spending the weekend with the device. This hospital system just bought 30 Vision Pro units and launched a new 'Spatial Computing Center of Excellence'. Apple Vision Pro front glass easily scratched in durability test. How to get an Apple Vision Pro demonstration. Apple reports nearly $120B quarter: full charts. Apple quadrupled its autonomous driving testing miles last year. OLED iPad Pro price increase won't be as painful as initially rumored. "Just peer-reviewed a forensic analysis in a case. The suspect mailed a package with a hidden Apple AirTag in it to a victim's old home address..." Picks of the Week: Jason's Picks: Juno and Runestone for Vision Pro Andy's Pick: Ensemble for Vision Pro Alex's Pick: Jig Space Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Guest: Ray Maxwell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: babbel.com/macbreak ecamm.com/twit or use Promo Code TWIT Wildgrain.com/MACBREAK or use code MACBREAK rocketmoney.com/macbreak

Jason, Alex, and guest Ray Maxwell have spent time with their Apple Vision Pros and share their thoughts on the device so far. Who would have thought the Vision Pro would scratch easily in a durability test? And an OLED iPad Pro is rumored to come out later this year with an expected price increase, but how much will the price of the iPad go to? Jason Snell, Alex Lindsay, and guest Ray Maxwell share their thoughts on the Vision Pro after spending the weekend with the device. This hospital system just bought 30 Vision Pro units and launched a new 'Spatial Computing Center of Excellence'. Apple Vision Pro front glass easily scratched in durability test. How to get an Apple Vision Pro demonstration. Apple reports nearly $120B quarter: full charts. Apple quadrupled its autonomous driving testing miles last year. OLED iPad Pro price increase won't be as painful as initially rumored. "Just peer-reviewed a forensic analysis in a case. The suspect mailed a package with a hidden Apple AirTag in it to a victim's old home address..." Picks of the Week: Jason's Picks: Juno and Runestone for Vision Pro Andy's Pick: Ensemble for Vision Pro Alex's Pick: Jig Space Hosts: Leo Laporte, Alex Lindsay, Andy Ihnatko, and Jason Snell Guest: Ray Maxwell Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: babbel.com/macbreak ecamm.com/twit or use Promo Code TWIT Wildgrain.com/MACBREAK or use code MACBREAK rocketmoney.com/macbreak

Mon, 05 Feb 2024 22:00:00 GMT http://relay.fm/upgrade/498 http://relay.fm/upgrade/498 Leap the Uncanny Valley 498 Jason Snell and Myke Hurley After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. clean 7289 After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. This episode of Upgrade is sponsored by: Fitbod: Get stronger, faster with a fitness plan that fits you. Get 25% off your membership. ExpressVPN: High-Speed, Secure & Anonymous VPN Service. Get an extra three months free. Squarespace: Save 10% off your first purchase of a website or domain using code UPGRADE. Links and Show Notes: Get Upgrade+. More content, no ads. Submit Feedback Myke met Tim and Joz – Instagram Apple reports nearly $120B quarter: Full charts – Six Colors This is Tim: Full transcript of Apple's Q1 2024 analyst call – Six Colors Tim Cook teases AI, but won't pick favorites – Six Colors Introducing Juno for Apple Vision Pro Why Tim Cook Is Going All In on the Apple Vision Pro | Vanity Fair NBA Apple Vision Pro Plans Could Include Additional Immersive Video – Sportico.com

Mon, 05 Feb 2024 22:00:00 GMT http://relay.fm/upgrade/498 http://relay.fm/upgrade/498 Jason Snell and Myke Hurley After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. clean 7289 After a brief bit of DMA follow-up and a recap of Apple's quarterly financial results, the rest of the episode is all about our first few days with the Apple Vision Pro. This episode of Upgrade is sponsored by: Fitbod: Get stronger, faster with a fitness plan that fits you. Get 25% off your membership. ExpressVPN: High-Speed, Secure & Anonymous VPN Service. Get an extra three months free. Squarespace: Save 10% off your first purchase of a website or domain using code UPGRADE. Links and Show Notes: Get Upgrade+. More content, no ads. Submit Feedback Myke met Tim and Joz – Instagram Apple reports nearly $120B quarter: Full charts – Six Colors This is Tim: Full transcript of Apple's Q1 2024 analyst call – Six Colors Tim Cook teases AI, but won't pick favorites – Six Colors Introducing Juno for Apple Vision Pro Why Tim Cook Is Going All In on the Apple Vision Pro | Vanity Fair NBA Apple Vision Pro Plans Could Include Additional Immersive Video – Sportico.com

Hay muchas razones para mantener un empleo y no hacer un emprendimiento. El detalle esta cuando caemos en la rutina diaria y comenzamos a dejar pasar por alto muchas cosas que son realmente importantes para continuar el desarrollo personal y profesional.Esto no es una cruzada en contra de los empleos 9-5. Es un llamado a sacarle el máximo de la oportunidad laboral que tienes hoy.En este episodio te hablamos sobre esos puntos ciegos que la rutina diaria no nos permite ver.0:00 Intro 2:16 ¿Que está pasando? Los intereses ¡se queda igual! 6:32 Apple reporta ganancias de $120B 8:56 META paga dividendos por primera vez 11:25 ¿Mas riesgo para el inversionista o para el empleado de META? 16:22 Buscando seguridad en vez de libertad 27:25 Para el Empleado la Libertad es un Sueño 32:20 No ves que cambias Tiempo por Dinero 41:10 Usar el Dinero en Liabilities te limita como Empleado 42:36 El Empleado que busca la Comodidad 47:50 Qué puedes hacer si no estas a gusto con tu carrera o empleo? 52:50 Outro Enlaces: ☕ LISTA DE ESPERA: añádete a la lista de esperar para comprar y vender tu propiedad con Suhailly en: https://www.cafeonabudget.com/contact☕ CONSULTAS: Agenda una consulta con nosotros donde le haremos una radiografía en detalle a tus finanzas y sabrás cual es el próximo paso a seguir para lograr tus metas financieras...Dale al enlace para más detalles. Recuerda que son pocos espacios. https://www.cafeonabudget.com/offers/ALLEZ2Mn

Uber CEO Dara Khosrowshahi dropped by the Acquired studio for an Eats delivery, so we broke out the cameras and asked him to hang out for a wide-ranging conversation. :) We talk about his 20 years working with Barry Diller, starting his career at Allen & Company, how the Uber CEO search process ACTUALLY went down… and oh yeah, the massive transformation that's happened at Uber over the past few years. When Dara took over the company it was bleeding huge sums of cash, losing share to competitors and embroiled in one of the biggest corporate controversies in recent memory. Fast forward to today and it's turned cashflow positive while also having tripled revenue to over $30B (on $120B in GMV) and solidified its rideshare dominance in the US. And in perhaps the biggest change, it's done it all while staying out of the headlines. Tune in!ACQ2 Show + LP Program: Subscribe to our interview show, ACQ2! Become an LP and support the show. Help us pick episodes, Zoom calls and more. Sponsors:Thanks to our fantastic partners, any member of the Acquired community can now get: 20% off Common Room's Team plan for 2023 Up to 10% off your first year of business insurance with Vouch A free trial of PitchBook + links to research reports! LinksBen & David on My First MillionNote: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.