Podcasts about etched

Today, a thorough look at the commodity space, especially crude oil as hopes for relief from geopolitical tensions fade and how it may be weighing on broader risk sentiment. With Saxo Head of Commodity Strategy Ole Hansen, we also take a look at copper market dynamics and grain and soft developments as geopolitical and El Niño risks weigh. We also sift through broader equity market dynamics and the risks that we are heading into a bearish correction here. Today's pod hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links Coverage of Cerebras' latest chip and claims of huge potential advances in inference computing efficiency. As well, WSJ covers a new semiconductor startup Etched, which shows that technical disruption risks in the AI compute hardware space are significant.  Polemic Paine with a thorough look at factors that have him concerned we face a significant equity market correction in the US. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.

Neil O'Connell, Executive Director of the Mt. Soledad National Veterans Memorial, is joined byTrevor Wessman-Lavelle, the Memorial's Marketing and Communications Director, to discuss its "250 Years of Valor" campaign, that honors  “honor above all,” 250 years of valor etched in stone, and how personal stories of service inspire San Diego's business and civic leaders. They explore record-breaking attendance, school and youth “living classroom” programs, strategic expansion plans, and how executives can align philanthropy, brand, and community impact. Listen Where You Live!About Spotlight and Cloudcast Media  "Spotlight On The Community" is the longest running community podcast in the country, continuously hosted by Drew Schlosberg for 20 years.  "Spotlight" is part of Cloudcast Media's line-up of powerful local podcasts, telling the stories, highlighting the people, and celebrating the gravitational power of local.   For more information on Cloudcast and its shows and cities served, please visit www.cloudcastmedia.us. Cloudcast Media | the national leader in local podcasting.   About Mission Fed Credit Union  A community champion for over 60 years, Mission Fed Credit Union with over $6 billion in member assets, is the Sponsor of Spotlight On The Community, helping to curate connectivity, collaboration, and catalytic conversations.  For more information on the many services for San Diego residents, be sure to visit them at https://www.missionfed.com/

Moritz Baier-Lentz is one of the foremost investors in gaming, AI, and frontier technologies—including companies like OpenAI, SpaceX, Palantir, Etched, Decart, and Recursive. He founded Goldman Sachs's global gaming practice and was previously a partner, IC member, and head of gaming at Lightspeed. Moritz is part of the founding team of General Intuition, an AI research lab training agents on video game data, which has raised a $133M Seed round and a $320M Series A. He is also a senior advisor to OpenAI, McKinsey & Co., and TPG Capital.In this episode of Summation, Moritz and Auren discuss:Why video games are the best data set for the next phase of AIThe idea that humans only truly learn during playWhy Germany's fear of risk is culturalGetting into Stanford with an essay about Diablo IIYou can find Auren Hoffman on X at @auren and Moritz Baier-Lentz on LinkedIn or his website.

AGENDA: 05:00 Jensen's Open-Weights Letter Puts Anthropic on an Island 10:00 OpenAI's Model Targets Hugging Face in a Cyber-Security Scare 18:00 America's Open-Model Push Accelerates as China Looms Large 30:00 Etched Raises $300M to Take on Nvidia 34:00 Google Cloud Grows 82%—So Why Did the Market Panic? 41:00 Travis Kalanick Raises $1.7B for Atoms and Physical AI 51:00 Francisco Partners Raises $21B: Can Private Equity Still Save SaaS? 1:00:00 Mark Pincus Says Quit When It's Too Hard—Jason Lemkin Erupts 1:07:00 Stripe vs Revolut: Which $100B+ Fintech Would You Own?    

Adam and Adir discuss the Labor Party conference, Ed Husic, Jacinta Allan’s political problems, Victoria’s unemployment rate, a 1998 celebrity earnings quiz, Etched and the AI chip race, Nvidia, OpenAI, KPMG’s Lendlease audit scandal, Waymo vs Uber, Revolut, bank fees, Jim Chalmers’ housing policy and Australia’s property risk. Watch the video Adam mentions: https://www.instagram.com/reels/DbNXhESCeXM/ 2:06 - Ed Husic and the Gaza Debate8:46 - Jacinta Allen and Victorian Labor13:45 - Work From Home Laws19:19 - Highest Earning Celebrities of 199835:33 - Nvidia's Margin Problem57:11 - KPMG1:00:36 - Waymo1:09:53 - Mid Roll1:10:17 - Revolut1:17:53 - First Home Buyers Join us on Substack for articles, news and more: https://www.thecontrarianspod.com/ See omnystudio.com/listener for privacy information.See omnystudio.com/listener for privacy information.

Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says. Also, Imagi announced a $4.5 million seed round, with investors including Brighteye Ventures, Day One Capital, and artist Wil.i.am. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

I have absolutely zero self-control around anything sweet!! Do these names belong on the Stanley Cup? I didn't have many whoopsies this week!! So I went back into the vault and got a Weekly Whoopsies from October 2025 when Jess was still here. All this and more on The Mark and Jess Replay!

Our 251st episode with a summary and discussion of last week's big AI news!Recorded on 07/01/2026Hosted 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:Anthropic redeploys Claude Fable 5 after talks with the US government, adding new cybersecurity classifiers, drafting a jailbreak-severity framework with major partners, and expanding model-testing coordination; broader concerns remain about the inevitability of jailbreaks and uneven release constraints versus OpenAI.Anthropic launches Claude Sonnet 5 with time-limited discounted pricing, improved agentic coding and benchmark performance, reduced misaligned behavior, and default cyber safeguards despite relatively weaker cybersecurity capability than top-tier models.New tools and apps include Google NotebookLM generating TikTok-style vertical video summaries of uploaded research and Google releasing Nano Banana 2 Lite, a faster, cheaper image generator available via API.Business and research updates span Etched's push toward full-stack inference hardware with major funding and contracts, Baidu's AI chip unit IPO ambitions, Agility Robotics' SPAC plan, DeepSeek's hiring expansion, and China's open-source Longcat 2.0 MoE model with notable large-scale training and efficiency techniques alongside new long-horizon agent benchmarks.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:02:07) News PreviewTools & Apps(00:02:32) Trump drops restrictions on Anthropic's Mythos and Fable models | TechCrunch(00:16:08) Anthropic launches Claude Sonnet 5 as a cheaper way to run agents | TechCrunch(00:20:35) Google's NotebookLM can sum up your research in a TikTok-style clip | The Verge(00:22:08) Google introduces a faster, cheaper image generator with Nano Banana 2 Lite | TechCrunchApplications & Business(00:22:50) Etched Pulls 400+ Engineers From NVIDIA, TSMC & More to Build a New Frontier Inference Cluster For AI Which Is Already Worth $1B in Demand(00:31:17) Baidu Rallies on AI Chip IPO Report(00:33:54) Agility Robotics plans to go public via SPAC in a $2.5B deal | TechCrunch(00:37:06) China's DeepSeek plans to at least double staff in all departments | ReutersProjects & Open Source(00:40:44) Introducing LongCat-2.0(00:57:42) OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks(01:01:33) TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents(01:04:29) SWE-Together: Evaluating Coding Agents in Interactive User SessionsPolicy & Safety(01:07:38) Taiwan raids Supermicro and two supply-chain partners in widening Nvidia smuggling probe — nine sites hit as six people summoned for questioning | Tom's HardwareResearch & Advancements(01:11:53) Autodata: An agentic data scientist to create high quality synthetic data(01:17:13) Reinforcement Learning without Ground-Truth Solutions can Improve LLMsSynthetic Media & Art(01:22:54) Neon Buys ‘Artificial,' a Film About OpenAI, After Amazon Dropped It - The New York Times(01:26:32) Tidal won't pay royalties on AI-generated music, but isn't banning it outright | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Political commentator James Mennie is a reporter, columnist and editor at the Montreal Gazette. He can be heard weekdays at 4:05 p.m. on Montreal Now with Aaron Rand.

This Week In Startups is made possible by: Digital Ocean - do.co/twist Agree.com - agree.com Every.io - every.io.   Today's show: How many startups matter in tech? Fewer than you think. That's why venture capitalists are tripping over themselves to get onto their cap tables, no matter the cost. Why? Footwork's Nikhil Basu Trivedi argues that the Valley has never been more "power-law-pilled" than it is today. Basu Trivedi joined Cendana Capital's Michael Kim and TWiST's Alex Wilhelm to go deep on secondary markets, the state of startup M&A, why the SaaSpocalypse may be temporary, and what could trigger a retrenchment of the AI trade. It's Wednesday, so it's time for our venture capital roundtable to go deep on how VCs are investing today, and where on the horizon they have their eyes fixed! Guest links: Nikhil Basu Trivedi https://x.com/nbt Footwork https://www.footwork.vc/ Michael Kim https://x.com/MKRocks Cendana Capital https://www.cendanacapital.com/ Show links: The USVC-Anduril blowup https://x.com/ankurnagpal/status/2072701195714531398 Kline Hill Cendana Partners https://www.secondariesinvestor.com/kline-hill-and-cendana-raise-400m-for-second-vc-secondaries-fund/ GPTZero's exit https://gptzero.me/news/preserving-whats-human/ Salesforce buys Fin https://www.salesforce.com/news/press-releases/2026/06/15/salesforce-signs-definitive-agreement-to-acquire-fin/ Vercel buys Better Auth https://vercel.com/blog/vercel-acquires-better-auth Figma buys Bud https://techcrunch.com/2026/07/07/figma-acquires-team-behind-a-vibe-coding-app/ Protoge https://withprotege.ai/ Windborne https://windbornesystems.com/ Etched https://www.etched.com/ Lovable's reported raise https://sifted.eu/articles/lovable-300m-13-2bn-valuation Josh Browder https://x.com/Joshuabrowder   Timestamps: 0:00 Introduction: Nikhil Basu Trivedi (Footwork) & Michael Kim (Cendana Capital) 1:59 The Anduril vs. USVC secondary market blowup 4:08 Why Silicon Valley is 'power-law-pilled' 8:23 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off! 9:37 Information asymmetry in the secondary markets 9:45 Every.io — For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io 15:02 Is SPV fraud smoke or fire? 16:32 Superhuman acquires GPTZero 19:54 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 21:10 The M&A wave 27:19 The SaaSpocalypse debate 29:59 DigitalOcean - Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%. 30:44 Data's moment in the energy → compute → data loop 35:01 Where will AI value accrue? 40:04 What could cause an AI correction? 42:17 Why some companies are "too big to miss" 46:23 China's possible open-weight model ban 53:28 Young founders: Etched, Thiel Fellows, Z Fellows, Neo 55:33 Portfolio spotlight: WindBorne's weather balloons and data moat 58:47 Michael's favorite fund manager: Josh Browder Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

The guys kick off the Fourth of July week by arguing that high agency culture is still America's edge, then dive into why AI is officially too big to fail. Marty and John break down FERC forcing grid operators to fast track data center connections, OpenAI floating a five percent stake to the Trump administration, and Marc Andreessen landing on the Pentagon's defense policy board. They also dig into the memory bottleneck squeezing the chip buildout, why frontier models are not getting commoditized by open source, and what Saudi oil flows back at ninety percent mean for Iran's leverage. To close, they look at Trump's fifty million dollar Bitcoin stash, Strategy selling coin to fund dividends, and whether Washington might already be building a strategic position through the public markets.

Nvidia AI chip competitor Etched says it has already booked $1 billion under contract for the inference systems powered by its chip. Also, Neo is Bhavin Turakhia's fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI. Learn more about your ad choices. Visit podcastchoices.com/adchoices

My guests today are Gavin Uberti and Rob Wachen, the founders of Etched.  A few years ago, when they set out to build a better AI chip than the largest companies in the world, almost everyone I called told me it could not be done. They have since done it, taping out a working chip on their first attempt and becoming the first hardware company founded after ChatGPT to do so. They already have more than a billion dollars of customer demand for their first product, and have raised eight hundred million dollars to build it.  Etched builds chips and systems designed to run AI models faster and at lower cost. They started the company in 2023, and that product is a complete rack for inference, the chip along with the boards, the power delivery, the interconnects, and the manufacturing to produce it all. We talk about the technical bets behind their architecture, how they hired industry legends and paired them with elite 22 year-olds, and why they believe inference will become one of the largest markets in the world. I think you will find the story of what they have built hard to forget. Please enjoy my conversation with Gavin and Rob. 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 ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:07) Gavin Uberti and Rob Wachen (00:03:54) Two 21-Year-Olds Taking on NVIDIA (00:07:52) The Two Technical Bets Behind Their Architecture (00:14:15) Why Inference Becomes the Biggest Market (00:20:23) Rob and Gavin's Origins Stories (00:28:38) How They Recruit Industry Legends (00:36:30) Moving a Dozen Engineers to Bangalore for Six Months (00:38:01) Speed Wins (00:43:58) Getting More Concurrency Out of Every Megawatt (00:52:44) Vertical Integration (00:57:43) Hardest Obstacles to Overcome (01:01:09) Raising The Largest AI Chip Series A Ever (01:06:29) TSMC (01:13:20) Designing Gen 2 for Gigawatt-Scale Production (01:16:42) Why Machines Don't Think Like People (01:20:03) A Year of Compute Compressed Into a Month (01:23:44) The Trillion-Dollar Data Center (01:26:19) The Kindest Thing

Queer liberation is incompatible with capitalism, because queer oppression inscribed in capitalist production and reproduction. In this episode, we explain why, and how socialism can create the context for queer liberation and sexual liberation for all. Learn More: Queer Oppression is Etched in the Heart of CapitalismFor a Pride Against Capitalism, Imperialism, and the Far RightWhither Queer Liberation?The Supreme Court Is Endangering Far More than Trans Athletes — We Must Fight BackSupport this podcast on Patreon Follow us on social media! We're on Instagram, Twitter, and TikTok as @left_voice and Facebook as @leftvoice. Follow us on Bluesky at leftvoice.bsky.social. 

Etched into Peru's remote desert floor lies one of the strangest ancient mysteries on Earth: the Nazca Lines - massive geoglyphs only fully visible from above. Who really made them? Enigmatic elongated skulls, mysterious mummies & legends of the gods making contact with ancient man - what is it about this strange location? What's more? An emerging first hand witness account from a licensed tour guide of a "Nazca Slender Man," a mysterious figurines that was recently seen walking over the lines... In this video, we explore the many enigmas of Nazca.JOIN ME ON A TOUR

Jake Paul and Geoff Woo join the podcast to announce Anti Fund's new $100 million growth fund and discuss the evolution of their investment strategy. The conversation covers the fund's portfolio, including investments in companies such as SpaceX, OpenAI, Anthropic, Anduril, Cognition, Etched, and Modal, as well as the lessons they've learned backing founders and identifying emerging technologies. They discuss founder psychology, resilience, ambition, and why they believe attention, culture, and distribution are becoming increasingly important advantages in the AI era. Along the way, Jake reflects on his path from creator to entrepreneur, athlete, and investor, while Geoff shares his views on venture capital, technology, and how AI is reshaping opportunity for founders and builders.   Resources: Follow Jake Paul on X: https://x.com/jakepaul Follow Geoff Woo on X: https://x.com/geoffreywoo Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Willard & Dibs ponder where the Warriors could land when it comes to Tuesday's draft. They also listen back to Mike Dunleavy sound to figure out what he was referring to when he brought up "four players" that are already etched into their plans

I've discussed the self-made man before, as this is a common type that I get as a therapy or coaching client. This is about the self-made women! Some differences and some similarities. And how does this woman behave in intimate relationships?Resources I promised:Etched in Sand: https://amzn.to/4e03f5CWall St Journal article: https://www.wsj.com/lifestyle/careers/surprising-lessons-from-talking-with-americas-highest-earning-women-fb7c08e9My newest venture, including ALL BRAND NEW POSTS EVERY DAY: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://buymeacoffee.com/drpsychmom/posts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join my awesome Midlife Women's Group here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠drpsychmom.com/mwg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠To get over 220 more episodes, most recently "Borderline Personality Disorder in Marriage," subscribe here! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://creators.spotify.com/pod/show/drpsychmomshow/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For my secret Facebook group, the "best money I've ever spent" according to numerous members: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/drpsychmom⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠For coaching from DPM, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.drpsychmom.com/coaching/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠For therapy or coaching, contact us at info@bestlifebehavioralhealth.com or visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.bestlifebehavioralhealth.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

What if one celebrity client could permanently change the trajectory of your entire career? In this episode, Dr. Catrise Austin, celebrity dentist turned brand authority strategist, shares how a bold move at a New York restaurant in the 1990s launched a career that would land her in hip hop history. She introduced herself to Isaac Hayes over dinner, became his dentist, went to the Oscars and Grammys, got a publicist through a barter deal, and eventually transformed Cardi B's smile on national television. The Cardi B episode aired as a season premiere on Love and Hip Hop, the song Bodak Yellow went number one, and TMZ ran the story. Her business tripled overnight. What she built over 30 years wasn't just a celebrity dental practice. It was a masterclass in how relationships compound when you protect them the way most people protect money.   [00:04:20] What She Does and Who She Serves Brand authority strategist helping entrepreneurs become the go-to in their industries Uses the FAME Formula to help clients climb the authority ladder Works across dental, medical, corporate, and entrepreneurial spaces [00:05:20] How She Got Here Two years after dental school, moved to New York and started networking Hit comedy clubs in the 90s and befriended a young Kevin Hart and Tracy Morgan Comedians opened for music acts; she started meeting people in the music industry Realized she could be the dentist to the stars and started passing out flyers at celebrity hotspots [00:07:40] The Night That Started Everything: Isaac Hayes Walked up to Isaac Hayes at P. Diddy's restaurant and gave him her elevator pitch He had never seen a dentist like her; he invited her to sit down for dinner She left with his phone number and his promise to become her first celebrity client That one bold move launched her career as a celebrity dentist [00:11:00] What Isaac Did Next Sent his assistant to vet her office before committing Once convinced, put her in his entourage and took her to the Oscars and Grammys Introduced her to Denzel Washington's wife, who gave her a home phone number she was too scared to call Encouraged her to get a publicist; her mom had a card for Eddie Murphy's publicist Terri Williams [00:13:00] The Publicist Who Changed the Game: Renee Foster Terri Williams loved the story but charged $50,000 a month; she referred her to Renee Foster Renee didn't have a dentist; they bartered services In exchange for cleanings, Renee got her on the Today Show and Good Morning America That third-party visibility put her on the map in a way self-promotion never could [00:15:40] What Inspires Her: The Power of a Smile Had terrible teeth growing up; her mom sacrificed as a single parent to get her braces Wearing braces for a year changed her confidence completely and made her want to become a dentist Seeing a smile transformation and knowing firsthand how it feels is the greatest feeling in dentistry The smile is your business card, your mood ring, and the first thing people notice about you [00:19:20] The Relationship That Etched Her in History: Mona Scott-Young Music executive Mona Scott-Young managed P. Diddy, LL Cool J, Missy Elliott, and 50 Cent They bonded after receiving an award together; Mona sent her the biggest music acts of the era Each high-profile referral added credibility and trust with the general public Fast forward to 2016: Mona was producing Love and Hip Hop on VH1 [00:21:40] Cardi B and the Billboard Number One Cardi B joined Love and Hip Hop with a big personality but teeth she was constantly mocked for Because of her relationship with Mona, Dr. Austin got the call to do the smile makeover The transformation aired as the season premiere and Cardi later referenced it in Bodak Yellow The song hit number one on Billboard; TMZ ran the story and her business tripled overnight [00:25:40] How She Protects Relationship Capital People constantly ask her to introduce them to Cardi B; she never asks Cardi for anything Her rule: only make introductions that further the relationship with both parties She lost friends over refusing to make introductions; she has no regrets Treat clients like family; that is the foundation of everything she has built [00:31:20] Client Impact: Omarosa's Mother Omarosa insisted her New York dentist fly to California to do her mother's smile makeover for a Discovery Channel show Her mother's teeth needed significant work; Dr. Austin coordinated four additional dentists Any Beverly Hills dentist could have done it; Omarosa's loyalty to their relationship made it happen Giving someone's mother a confident smile was one of the greatest honors of her career [00:35:00] Final Word: Bring Back the Personal Touch Learned from Terri Williams's book The Personal Touch to go beyond digital communication Pick up the phone, write a personal note, send a postcard when traveling The small things nobody else is doing anymore are what make you stand out Honor your clients; treat them like family and they will never leave   KEY QUOTES "You don't have to be the best at what you do to get the opportunities. Sometimes it's the best known that gets all the opportunities." - Dr. Catrise Austin "I am so protective of the relationship I don't ask for anything. If I asked her for something, she would know this is a big deal, because I don't ask." - Dr. Catrise Austin CONNECT WITH DR. CATRISE AUSTIN Website: https://www.celebritybrandingusa.com LinkedIn: https://www.linkedin.com/in/dr-catrise-austin Instagram: https://www.instagram.com/celebritybrandingusa Facebook: https://www.facebook.com/DrCatriseAustin   Thanks for tuning in! If you liked my show, please LEAVE A 5-STAR REVIEW, like, and subscribe! Find me on: Apple Podcasts | Spotify | iHeart Radio | Stitcher

Zack Pearson from Bear Wire returns to break down the Chicago Bears schedule release, rookie minicamp takeaways, and the latest on whether a new stadium project is finally moving forward. Plus, the crew gives out their Etched in Stone selections for tonight's Major League Baseball slate. Follow The Drive on X, Instagram, and Facebook!

The Visitor Always Left Tokens but My Name Was Etched FirstBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-the-strange-and-unusual-podcast-2026--5684156/support.Darkest Mysteries Online

Elias Schuster joins the show to break down the latest in the Crosstown Series between the Chicago Cubs and Chicago White Sox, plus the latest buzz surrounding the Chicago Bulls and the upcoming NBA Draft. The guys also discuss the best possible landing spot for Keaton Wagler as his basketball future takes shape. Plus, “Etched in Stone” returns and the crew wraps up the week with their Friday Toasts! Follow The Drive on X, Instagram, and Facebook!

Tristan Thomas joined the show to break down the returning Illini players and the impact they'll have heading into next season. He also reflected on Ty Rodgers' legacy, emphasizing his role as a true “culture guy” during his time at Illinois, while touching on Illini spring football storylines. The hour rounded out with NFL Draft discussion, what is the best fit for Luke Altmyer in the NFL, and the latest “Etched in Stone” bets.

Alex Kyi from Armchair Illini joins us t0 break down Illinois' win over Penn and looking ahead to their matchup with VCU. He shares key insights on what carried the Illini in the opening round and what challenges await in the next game. The conversation also dives into expectations for Illinois moving forward in the tournament. Plus, we debate a fun hypothetical: would you rather the Illini win one national title and be mediocre for five years, or make the Elite Eight every season? Evan has your tourney bets with Etched in Stone and our Friday Toasts!

Josh Hanes, whose work at Uplift Aerospace focuses on expanding access to space through student-centered education and immersive experiences, returns to the show with major updates on what's happened since STORIES of Space submitted its community database at the end of 2025. https://www.upliftaerospace.com Beth and Josh celebrate two landmark achievements: the successful laser engraving the story database from STORIES of Space into a 5D crystal along with over 3,000 messages from students, teachers, and community storytellers around the world, an extraordinary archival medium capable of preserving data for potentially millions of years, and the integration of that crystal onto Astrolab's FLIP rover on March 2nd, 2026. Josh explains how 5D crystal technology works, using femtosecond lasers to modify atomic structures within crystal, storing terabytes of data in a piece no larger than a quarter. He walks through the mission patch artwork created by Dr. Sian Proctor, who implemented a word cloud of student messages into a design now displayed prominently on the rover itself. The FLIP rover, a pathfinder mission for Astrolab's larger FLEX rover planned for the Artemis program, is targeting the lunar South Pole and is expected to launch in the middle to late part of 2026. When it arrives, it will carry the dreams, hopes, and words of STORIES of Space contributors as part of what Josh describes as a living archive of humanity at this pivotal moment in history. This episode is a must-listen for anyone following the STORIES of Space journey, or for anyone who wants to understand just how close we are to a genuine paradigm shift in humanity's relationship with space. The future is not a promise on the horizon anymore, it is already being built. Follow along at https://www.upliftaerospace.com and across their socials at @UpliftAerospace on Instagram, X, and LinkedIn.  

Isaac Ambrose joins the show to break down the NCAA Tournament bracket and assess the odds for the Illinois Fighting Illini men's basketball. The conversation dives into key matchups, potential upset alerts, and how the Illini stack up against the field. The guys also reveal their Final Four picks and debate which teams have the best path to a title run. Plus, another edition of “Etched in Stone” locks in the day's best bets.

We preview Illinois vs Wisconsin in the Big Ten Tournament and what the Illini need to do to advance. The guys break down the key matchups, including how Illinois can attack Wisconsin's defense. Later, they dive into a Chicago Bears Mock Draft and discuss potential targets at DE & DT. The hour wraps up with another edition of Etched in Stone.

Alex Kyi from Armchair Illini joins the show to talk Illinois basketball and preview the Big Ten Tournament. He also shares his thoughts on Bret Bielema's updated coaching staff and what it could mean for Illini football. Later, the guys dive into another edition of Etched in Stone with Evan's bets for tonight. Plus they reveal the winner of the Tuesday Draft. And they wrap up by debating Bam Adebayo's 83-point performance versus Kobe Bryant's legendary 81-point game. Evan explains why Bam's game was NOT that impressive.

WCIA 3's Sports Director Glenn Kinley joins the show to discuss the impact that Shauna Green has had on Illini women's basketball. He also shared insight on the Big Ten Tournament and the excitement surrounding local high school hoops. Later in the hour, the crew runs another round of Etched in Stone. We wrap up the week with our Friday Toasts! Cheers!

The guys debated what will matter more for Illinois basketball in the postseason: knocking down three-pointers or limiting costly turnovers. The conversation looked at how each factor could impact the Illini's chances of making a deep tournament run. Also, how did this debate lead to our first On-Air Injury?! Evan and Coop also learned about the legendary “snipe hunt,” leading to some BIG laughs  from listeners.  The hour wrapped up with another edition of Etched in Stone.

Kyle Tausk from Illini Inquirer breaks down how Illinois Fighting Illini men's basketball cruised past Oregon Ducks and what clicked for Andrej Stojakovic in the dominant performance. He also previews Illinois' upcoming matchup against the Maryland Terrapins and the Big Ten Tournament. Evan dives into another edition of Etched in Stone with the latest betting angles and insights. Plus, we reveal the winner of the Best Jersey Draft and debate whether the right pick took home the title. 

Dr. Timothy Mahr is Professor Emeritus at St. Olaf College, and a prolific composer for wind band. Dr Mahr joins the podcast for a conversation that encompasses advice for young composers, discussion of ways we can improve our bands as a conductor, and a focus on some of his music!Highlighted is Etched in Stone, Commissioned by Kyle Smith and the Westbrook High School Wind Ensemble for the 50th Anniversary of the assassination of Martin Luther King, Jr.Support the Show HereTo gain access to all show notes and audio files please Subscribe to the podcast and consider supporting the show on Patreon - using the button at the top of thegrowingbanddirector.comOur mission is to share practical  advice and explore topics that will help every band director, no matter your experience level, as well as music education students who are working to join us in the coming years.Connect with us with comments or ideasFollow the show:Podcast website : Thegrowingbanddirector.comOn Youtube The Growing Band Director Facebook-The Growing Band Director Podcast GroupInstagram @thegrowingbanddirectorTik Tok @thegrowingbanddirectorIf you like what you hear please:Leave a Five Star Review and Share us with another band director!

Carson Bounds from Orange and Blue News joins the show to preview the huge game between the Illinois Fighting Illini and the Michigan Wolverines on Friday night. Carson, a former member of the Orange Krush, talks about some of the criticisms the student section has faced this year. We also chat about Altmyer, Jacas, and Davis at the NFL Combine & plenty more. We also discuss where the St. Louis Cardinals are landing in early season rankings and what to expect heading into the year. And we wrap things up with Etched in Stone and our best bets.

Tristan Thomas from WCIA breaks down Illinois vs UCLA Bruins men's basketball and shares the highlights from the IHSA Wrestling State Finals. He highlights key matchups, postseason implications, and what to watch as the Illini continue their Big Ten push. Kurtis then takes over Etched in Stone with his top picks and best bets. Plus, Cooper O'Kelly from The Champaign Room joins the show to fill in for Evan and discuss his biggest takeaways surrounding Illinois athletics.

PA announcer Tim Sinclair joins the show to reflect on his broadcasting career and share some of his favorite moments in Illini history. Bob McCoppin of the Chicago Tribune breaks down the latest on the Chicago Bears and the possibility of a move to Indiana. We also give our score predictions for Illinois vs. the Bruins and highlight the key matchups that could decide it. Plus, we wrap it up with another edition of Etched in Stone and our Friday Toasts.

Bracket expert Brad Evans from The Athletic breaks down Illinois' path to a potential National Championship and what the Illini need to do to make a deep March run. He discusses favorable matchups, key players, and the biggest obstacles standing in the way. The conversation also looks at how Illinois stacks up against other contenders across the country. Plus, we wrap up with Etched in Stone bets and the top picks heading into the weekend.

WCIA 3's Tristan Thomas breaks down the matchup between the Illinois Fighting Illini and the USC Trojans, along with the biggest Illinois High School Association basketball headlines this week. He also discusses the new IHSA football rule changes and what they could mean for teams moving forward. Evan's out for today, so Kurtis takes a crack at Etched in Stone. Plus, we reveal the winner of the Oscars-themed Tuesday Draft..

Kyle Tausk from Illini Inquirer previews Illinois vs. USC and breaks down the biggest matchups to watch. We dive into what the Illini must do to control the tempo and come away with a statement win. It's also time for the Tuesday Draft and since the Illini went to Hollywood, so are we! We pick the best movies ever to win Best Picture at the Oscars. Plus, we need money! Evan gives his bets to Etch in Stone.

Another award rolls in for Keaton Wagler, as his standout season continues to earn well-deserved recognition. What did he win now? We also highlight Sarah Warren and her performance on the Olympic stage, representing Team USA with pride. Why did she tell her family to NOT wear Illini orange & blue? Plus, Evan shares his latest picks in Etched in Stone as he looks to keep the hot streak going. And don't miss our Tuesday Draft announcement, where we reveal this week's topic and get the debate started.

More highlights rolled in from Bobby Hauck's press conference, where he mixed serious football talk with a few personality-filled moments. We also dove into another edition of Etched in Stone, tossing around takes, predictions, and a little friendly debate on tonight's money lines. And we got to know "Newphew" Cooper O'Kelly beyond the bylines, including some fun stories about his path into covering Illinois athletics. Plus, we talked about his work with The Champaign Room and why Illini fans might want to keep an eye on what he's writing next.

Carson Gourdie from Fox Illinois and WICS-TV joins us this hour to talk Illinois vs Michigan State. We also hear from Chris Solari of the Detroit Free Press. Get both perspectives ahead of the Illini vs Sparty. And it's Super Bowl Weekend! Kurtis tests your knowledge with Super Bowl trivia, Evan's "Etched in Stone" features Super Bowl prop bets, and we give our score predictions. Plus our Friday Toasts! Cheers!

Our guy the All-American J Leman joins us this hour to discuss Aaron Henry's departure from Illinois football. Henry is headed to Notre Dame to be the new Co-Defensive Coordinator. Who will replace him? J discusses possible replacements and how much it might cost Illinois to pay a new coordinator. Plus, Illinois Athletics had an incredible weekend with ranked wins in basketball, track & field, tennis, and wrestling. This led to an all-timer moment from Evan Stone. Plus, Evan is on a roll with Etched in Stone. Will that continue tonight?

We've had great guests on this week ahead of the Illinois vs Washington game, with a few people calling it a definite trap game. So how confident are YOU about tonight's game? We answer your texts at 217-359-2255. Have you seen ONE person agree with the decision to keep Bill Belichick out of the Hall of Fame? Neither have we. Evan got things back on track with Etched in Stone last night. He's got 3 new bets for tonight. Lets go!

Brad Underwood's squad is coming off their biggest win of the season and an all-time performance from Keaton Wagler at Purdue. Will that success carry over to tomorrow night at State Farm Center against Washington? We talk with Roman Tomashoff from Huskie Wire if this is a Trap Game for Illinois. We take another look at the Illinois 2026 football schedule, who has the easiest and the toughest schedules in the Big Ten. Evan's heater came to a screeching halt last night with Etched in Stone. Let's see if he cooks tonight. And who won the Tuesday Draft for our Super Bowl Spreads?!  

The Super Bowl is now set! The New England Patriots and the Seattle Seahawks are headed to Santa Clara. Who ya got?! We discuss. Plus, Evan is on a HEATER with Etched in Stone! It's a 20-4 run last week. Will he cash in again?

Nick Rodecap covers Indiana athletics for WIUX-FM, The Hoosier Network, and Inside The Hall. Nick was in Miami for their National Championship win and gives us his view of the game. Plus, where does Cignetti & CO go from here? A great listen! Evan was out of the studio, but still called in for "Etched in Stone." He's on a 8-2 heater over the last two days. Will that continue tonight? Plus we preview Championship Weekend in the NFL. Who's going to The Super Bowl? And Friday Toasts! Cheers!

Illini wrestling legend Zane Richards joins us to talk the free RTC clinic after Illinois vs. Rutgers. Evan checks in with his latest Etched in Stone picks after going 4–1, and we debate what the Bears should do with their first-round pick. Like ESPN 93.5 on Facebook and follow us on Twitter & Instagram.

Our guy Ravi Lulla from Hurrdat Sports joins us this hour to discuss the National Championship Game. Where does Indiana football go from here? How will Miami reload their roster? We also chat about Big Ten basketball and the NFL Playoffs. Who won our Tuesday Draft and why did they do a victory lap around the radio station? Evan has FIVE bets for Etched in Stone. And we give our score predictions for Illinois vs Maryland.

In the blistering summer heat of 1971, a quiet village in southern Spain became home to one of the strangest haunting cases ever recorded. When María Gómez Cámara found a face staring back at her from her kitchen floor, it launched a decades-long mystery that blurred the line between miracle, hoax, and full-on nightmare. Scientists, priests, and parapsychologists all came knocking—but the faces kept coming. Join us as we scrub through the mystery of the Bélmez Faces, where spirits seem to breathe through concrete, and the dead refuse to stay buried. Ancient graves, psychic energy and faces that morph before your eyes, this episode is part ghost story, part mystery, and 100% spooky fun. And if haunted kitchen floors weren't chilling enough, we've got a listener story about a demonic encounter and possession that turned one skeptic into a believer in the paranormal. Watch the video version here. Have ghost stories of your own? E-mail them to us at twogirlsoneghostpodcast@gmail.com New Episodes are released every Thursday and Sunday at 12am PST/3am EST (the witching hour, of course). Corinne and Sabrina hand select a couple of paranormal encounters from our inbox to read in each episode, from demons, to cryptids, to aliens, to creepy kids... the list goes on and on. If you have a story of your own that you'd like us to share on an upcoming episode, we invite you to email them to us!  If you enjoy our show, please consider joining our Patreon, rating and reviewing on iTunes & Spotify and following us on social media! Youtube, Instagram, TikTok, Facebook, and Discord. Edited by Jaimi Ryan and produced by Emma Leventer and Jaimi Ryan, original music by Arms Akimbo! Disclaimer: the use of white sage and smudging is a closed practice. If you're looking to cleanse your space, here are some great alternatives! Learn more about your ad choices. Visit podcastchoices.com/adchoices