Podcasts about RSI

In this week's stock market outlook, Matt breaks down the technical battle facing the S&P 500 and NASDAQ, reviews the biggest winners and losers of earnings season, and grades Kevin Warsh's performance as Federal Reserve Chair following the latest Fed meeting. The bulls reclaimed an important trend line, but the broader trend still needs confirmation. Matt identifies the resistance levels required for a legitimate breakout, the support zones traders must defend, and the signals coming from RSI and MACD heading into the new trading week. Earnings growth has been exceptional, but the market has not rewarded every company that beat expectations. Matt reviews the best and worst reports of the season, separates true operating strength from headline distortions, and explains what the results mean for technology and the broader market. The Federal Reserve held interest rates steady following a contentious meeting. Matt breaks down the decision, the dissents, Kevin Warsh's communication strategy, and whether the new Fed Chair has earned a passing grade so far. • The S&P 500 breakout test and key market levels • Earnings season growth, market reactions, and major surprises • The strongest and weakest earnings reports • The Federal Reserve's latest interest rate decision • Kevin Warsh's first report card as Fed Chair • Precision Trader and the upcoming Master Trader series

Une émission spéciale, ce lundi 3 aout. Date symbolique du centenaire de la radiodiffusion réguliere en Slovaquie, date a laquelle tous les circuits de la radio slovaque diffusent une émission spéciale consacrée a l'histoire et a l'actualité de la radio. RSI ne fait pas exception, en reprenant le premier épisode de notre série documentaire sur ce centenaire.

An overview of this week´s news combined with three excerpts from Monday´s, Tuesday´s and Wednesday´s magazines from RSI english section. More on Bratislava´s influential women, Bratislava´LGBTQ+ Pride march and an interview with young Slovak scientist Anna Podmanická.

An overview of this week´s news combined with three excerpts from Monday´s, Tuesday´s and Wednesday´s magazines from RSI english section. More on Bratislava´s influential women, Bratislava´LGBTQ+ Pride march and an interview with young Slovak scientist Anna Podmanická.

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

In this episode, we start with an RSI from David Karngba on anesthetic considerations for patients of Venezuelan descent. Next, I talk to veterinarian Mike Wenninger, Director of Animal Health at the Cincinnati Zoo, about what anesthesia looks like when your patients include fish, penguins, venomous snakes, and elephants.

This Week In Startups is made possible by: Northwest Registered Agent- NorthwestRegisteredAgent.com/twist Odoo - Odoo.com/twist MongoDB - MongoDB.com/ai   Today's show: The FCC's decision to ban Chinese humanoid robots over security concerns is a boon to American startups, which now face a narrower competitive market. But where should we draw the line on security over competition? Menlo Ventures' Deedy Das, Weisburd Pierce's David Weisburd, Plexo Capital's Lo Toney, and LAUNCH's Jason Calcanis broke down how they differentiate between legitimate security concerns and purported regulatory capture. Today's venture capital roundtable also dug into OpenRouter's possible sale to Stripe, changing tokenomics, the Indian market for startups, and even DoorDash's drone-delivery business taking flight. Guest Links: Deedy Das https://x.com/deedydas Menloe Ventures https://menlovc.com/ Lo Toney https://x.com/lo_toney Plexo Capital https://www.plexocap.com/ David Weisburd https://x.com/DWeisburd Weisburd Pierce https://www.weisburdpierce.com/ Jason Calcanis https://x.com/Jason LAUNCH https://launch.co/ Show Links: The FCC's decision regarding Chinese robots https://www.fcc.gov/document/fcc-adds-foreign-produced-power-inverters-and-robots-covered-list-0 Pacing the Frontier letter https://www.pacingthefrontier.com/ Cursor Start https://cursor.com/blog/cursor-start-india Stripe may buy OpenRouter https://www.axios.com/2026/07/24/stripe-openrouter-merger-ai-currency OpenRouter financials https://www.theinformation.com/articles/openrouter-financials-suggest-steep-price-possible-acquirer-stripe?rc=g3wfdp Kimi K3 license https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE Pangram https://www.pangram.com/ Tau Robotics https://www.tau-robotics.com/ Zipline https://www.zipline.com/ Manna https://www.manna.aero/ Kindred Ventures https://kindredventures.com/ Autolane https://goautolane.com/ Salmon Labs https://salmonrun.ai/   Timestamps: 0:00 The FCC bans Chinese humanoid robots 2:18 Waymo, Uber, Robotaxi, and the global BYD threat 10:30 MongoDB - AI-assisted and agentic coding is helping you build faster than ever. Start building at https://MongoDB.com/ai 15:45 The "Pacing The Frontier" letter (1,100+ AI staffers warn on RSI) 19:51 Odoo - The all-in-one business platform. Get started for free at https://Odoo.com/twist 21:07 Regulatory capture vs. genuine concern 30:09 Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://northwestregisteredagent.com/twist 37:57 Have we reached AGI? 38:35 Stripe eyes OpenRouter at $10B 39:19 Does OpenRouter have a moat? 47:13 Kimi K3's license and neocloud margins 49:34 Claude Tag and the future of AI-mediated workplaces 53:26 Cursor Start, ChatGPT Go, and the India market 1:02:03 Have we solved AI detection? 1:11:20 Tau Robotics $30/hour robotic housecleaning 1:12:49 Job loss, and the social safety net 1:18:25 DoorDash Air takes on Zipline, Manna 1:21:00 Portfolio shout-outs 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   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

Tevaun Smith is a former professional football player and current financial services professional based in Toronto, Canada. After playing for the Indianapolis Colts, Jacksonville Jaguars, and in the Canadian Football League, Smith transitioned into financial services, helping individuals, business owners, and athletes navigate insurance, estate planning, and long-term wealth strategies.Drawing from his own experience as a professional athlete, Smith focuses on protecting income, reducing risk, and building long-term financial security. He is a graduate of the University of Iowa and has completed business and finance leadership programs through the University of Miami.$1 Trial Membership to SCNCEUs: https://strengthcoachnetwork.com/TrialSave on your re-certification to the NSCA and CSCCa with best price CEUsCEUs: https://strengthcoachnetwork.com/CEUFrom our sponsors:Hawkin Dynamicshttps://www.hawkindynamics.com/ Hawkin Dynamics is the world leader in force plate solutions. Wireless hardware, intuitive software, and powerful analysis make Hawkin Dynamics the best choice for any coach or trainer. Power Lifthttps://www.powerliftusa.com/Power Lift is an industry-leading designer and manufacturer of American-made strength and conditioning equipment. They understand that your Brand is important and offer several diverse ways to incorporate that Brand into your strength equipment. Dashrhttps://www.dashrsystems.com/Dashr is a performance testing company best known for its laser timing, but also integrates with Teambuildr and offers a free Player Profile app used by 6k+ athletes. Beyond timing, they provide a full suite of testing solutions including verticals, broad jumps, RSI, biometrics, reaction, and AMS. PlayerDatahttps://www.playerdata.com/en-usPlayerData is Strength Coach Network's trusted GPS provider and a FIFA Quality–certified system, used by coaches at every level to quantify training load and understand movement demands. PlayerData delivers reliable, actionable data at a strong value point, with seamless syncing that saves coaches precious time—so the focus stays on coaching, not managing technology. TeamBuildrhttps://www.teambuildr.com/enTeamBuildr is a platform for any coach in any setting. Every day, thousands of coaches log into TeamBuildr to write training programs, build questionnaires and access athlete training in a simple and manageable way. Thornehttps://www.thorne.com/Thorne is a global leader in high-quality nutritional supplements and health testing. Trusted by professional sports teams, the U.S. military, and practitioners around the world, Thorne is known for rigorous ingredient sourcing, third-party testing, and science-backed formulations. Whether supporting performance, recovery, or overall health, Thorne provides products coaches and athletes can trust.

Marty and John dig into the Houthis blocking the Bab el Mandeb strait, oil whipsawing on peace headlines, and why Lloyd's of London pulling war coverage matters for US insurers. They get into Trump's air defense stockpile problem, Chinese diesel demand falling off a cliff, and why the MOVE index staying subdued even as yields rise is the chart to watch. The bulk of the show is an extended breakdown of the open versus closed AI model debate, why the Hugging Face hack exposes the absurdity of current guardrails, and how the real axis of conflict is US versus China, not open source versus safety. They close with Bitcoin long term holder supply hitting 84 percent, a classic bottoming signal.

Prova Shopify ad 1 € - Vai su ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠shopify.it Uno dei grandi misteri legati alla fuga di Benito Mussolini negli ultimi, caotici giorni della RSI è legato al destino di alcuni dei suoi bagagli. Da decenni si dibatte sul destino di quelle casse che il convoglio del dittatore caricò sui veicoli dopo gli infruttuosi colloqui a Milano e che sparirono nel nulla. Una nuova traccia destinata a far luce su un autentico mistero italiano arriva dalla studiosa Valentina De Santis, già nostra ospite, che ha scritto un libro (qui il link Amazon: https://amzn.eu/d/0d3oJfaX ) sul tema. Nel corso della nostra piacevole chiacchierata abbiamo riscontrato aspetti inquietanti di una storia ancora non del tutto svelata.

Keith Belton has coached at nearly every level of sport—from the NFL and Power 4 football to the NBA and Olympic sports. In this episode, we discuss his journey into strength and conditioning, lessons from coaching Robert Griffin III during his Heisman season, working alongside Chip Kelly, the proper role of sports science, and why leadership is ultimately about serving others.___CONNECT WITH SCN:

Summer Reading with RSI is a series spotlighting Slovak literature in translation. Published in 2026, The Book of Bratislava brings together ten stories by contemporary Slovak writers—five women and five men—representing different generations and literary styles. Today's episode features "The Last Days of Stoka" by Lucia Piussi, translated by Janet Livingstone. Also this summer, as Pohoda—the longest-running multi-genre festival in Slovakia—celebrates its 30th anniversary, we're bringing you a series of laid-back interviews with both international and Slovak artists from the anniversary lineup. Today, meet Muovipussi from Finland.

Summer Reading with RSI is a series spotlighting Slovak literature in translation. Published in 2026, The Book of Bratislava brings together ten stories by contemporary Slovak writers—five women and five men—representing different generations and literary styles. Today's episode features "The Last Days of Stoka" by Lucia Piussi, translated by Janet Livingstone. Also this summer, as Pohoda—the longest-running multi-genre festival in Slovakia—celebrates its 30th anniversary, we're bringing you a series of laid-back interviews with both international and Slovak artists from the anniversary lineup. Today, meet Muovipussi from Finland.

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

Whitney Lee, MS, AT, LAT, CSCS is a dual-credentialed athletic trainer and strength coach currently supporting the U.S. Air Force's Operational Health and Wellness (OHWS) program in Okinawa, Japan. With more than 15 years of experience in athletic training, performance, and rehabilitation, she has worked across military, collegiate, high school, and private-sector settings.Lee is also the owner of Athletic Medicine LLC and previously served as an athletic trainer at Grand Canyon University, California University of Pennsylvania, and multiple Arizona high schools. She earned her bachelor's degree from Grand Canyon University and her master's degree in Athletic Training from California University of Pennsylvania$1 Trial Membership to SCN

RSI s'est rendue au Parlement européen de Bruxelles pour un séminaire journalistique sur le theme des "violences numériques faites aux femmes". Marie-Ondine Vidal vous a ramené des interviews exclusives de femmes évoluant en politique et qui partagent leurs difficultés. Quelles solutions sont nées de ce séminaire ? Reprise.

Lecturas de verano con RSI

Blood Flow Restriction (BFR) is much more than a rehab tool.In this episode of Cheeky Mid Weeky, Mark Mitchell shares how he integrates BFR into warm-ups, conditioning, in-season lifting, recovery, and travel. He also explains why great coaches don't chase one method—they build a toolbox and use the right tool for the right athlete.Learn more about Hytro BFR here:https://hytro.com/pages/basketball?utm_source=podcast&utm_medium=paid-media&utm_campaign=basketball&utm_content=journal&utm_term=mens-performance___Save on your re-certification to the NSCA and CSCCa with best price CEUs

In this episode of Supertraining in a Year, Justin Lima and Yosef Johnson continue their journey through Supertraining, covering pages 376–420.Pages 376-405 were covered last week when Justin was at NSCA National but Yosef's camera stopped working again - they cover those pages briefly then they move on.This week's discussion dives into one of the most information-dense sections of the book, covering Proprioceptive Neuromuscular Facilitation (PNF), functional training, accommodating resistance, accelerated eccentric methods, and hypertrophy. Along the way, they explore why many "new" training concepts are actually decades old, how coaches should think about exercise selection, and why results—not trends—should drive programming decisions.___Download The Supertraining Reading Planhttps://strengthcoachnetwork.com/st___Buy Supertraining to Read Along with Us - use code STRONGER20 to save 20%https://uaconcepts.com/product/supertraining___From our sponsors: Hawkin Dynamics

Mark Mitchell has spent 15 years coaching Division I men's basketball, and in this conversation he shares the principles that have shaped his approach to developing elite athletes. Rather than following one training system, Mark explains why he's become principle-based instead of method-based, blending traditional strength training with modern performance methods to meet the demands of today's basketball athlete.___$1 Trial Membership to SCN

Dr. Ken Clark is an Assistant Professor in the Department of Kinesiology at West Chester University, where he teaches biomechanics, motor learning, and kinetic anatomy. His research focuses on sprint performance, movement mechanics, injury mechanisms, and skill acquisition, with publications in many of the field's leading journals.Clark has more than a decade of strength and conditioning coaching experience across the private, high school, and collegiate levels, including stops at Summit Sports, CES Performance, Dickinson College, Haverford College, and Villanova University. He earned his PhD in Applied Physiology and Biomechanics from Southern Methodist University and is a former collegiate football player at Swarthmore College.$1 Trial Membership to SCN

1. Новости 2. Тема дня. 3. Бюджет ЕС должен помочь регионам 4. Летние чтения в RSI: словацкий писатель, поэт, переводчик и политический деятель Янко Есенский 5.Архитектор Артур Салатнай-Слатинский - Часть 2.

Summer Reading with RSI is a new series featuring Slovak literature in translation. This summer, we're bringing you stories from The Book of Bratislava, an anthology of contemporary Slovak fiction published by Comma Press, a not-for-profit publisher based in the UK. Every other Friday, you'll hear an excerpt from a different story in the collection. We will also talk to representatives of Slovak journals united in PUČ.

✔️ BTC could be printing a new ATH✔️ 9 out of 11 Bitcoin bottom Indicators have hit so far✔️ Bitcoin monthly RSI just matched the lowest recorded level… ever…✔️ Bitcoin "Electrical Cost" has almost dropped to $47,500✔️ The price of Bitcoin continues to make higher lows✔️ Bitcoin is in DEEP VALUE territory for just the 4th time in history ✔️ BTC is more oversold right now than during COVID crash✔️ Exactly this!✔️ This pattern will probably break eventually✔️ Fast money has been drained out of the market.✔️ I'm very confident that BTC is exactly at this point.✔️ Barclays just initiated Strategy MSTR with a Buy rating ✔️ Clarity Act advancement: one issue stands in the way!✔️ New Hampshire votes are in for the 100 million dollar bitcoin bond proposal. ✔️ STOP DAC8!✔️ Historical node charts

Nicholas Lee, BS, CSCS, RSCC, USAW-L1 is the H2F Lead Tactical Strength and Conditioning Coach at Fort Bragg, North Carolina. With more than 20 years of military service, including time in Army Special Operations and conventional forces, Lee brings a unique blend of tactical leadership and human performance expertise to his coaching.Prior to his current role, Lee worked with EXOS as an H2F Tactical Strength and Conditioning Coach and served as Director of Strength and Conditioning at Freedom Christian Academy. He earned his bachelor's degree in Sports and Exercise Science from Methodist University and is currently pursuing a master's degree in Athletic Development Management.Save on your re-certification to the NSCA and CSCCa with best price CEUs

Bulletin d´informations. Le prix CO2 Free contribue a renforcer la coopération scientifique et technologique entre la France et la Slovaquie et soutient le développement de la recherche dans des domaines essentiels a la transition écologique de l'Europe. RSI a assisté a seconde édition de ce prix et a rapporté des entretiens exlusifs des lauréats mais aussi de l´entreprise Framatome et du ministere slovaque de l´Education

Sean Hayes has built performance programs at every level—from playing at Harvard University to coaching at Penn State University, the Houston Texans, WWE, and now serving as Director of Player Performance for the UFL.In this episode, Sean shares lessons from his mentors, building WWE's first comprehensive strength program, coaching NFL stars like J. J. Watt, and preparing UFL players for NFL opportunities._$1 Trial Membership to SCN

Actualité, gros plan, nature, écologie, régions. Les vergers de sureaux sont plutôt rares en Slovaquie ; RSI a visité l'un d'eux. Nous vous emmenons a Dolná Krupá, a l'occasion de l'ouverture de la saison des rosiers

Matt Durant is the Head Strength and Conditioning Coach at University of La Verne, where he oversees performance training for all 20 varsity sports. A 1999 La Verne graduate, Durant returned to his alma mater in 2002 and has spent more than two decades developing student-athletes and building a highly respected Division III strength and conditioning program.In 2017, Durant was named the National Sports Performance Association Coach of the Year. During his tenure, numerous athletes have earned conference, regional, and national recognition, including multiple National Strength and Conditioning Association All-America Strength and Conditioning Athlete of the Year honors.Before returning to La Verne, Durant coached at College of the Canyons and Wingate University. He earned his bachelor's degree from La Verne and a master's degree from Azusa Pacific University, and remains active in both the NSCA and USA Weightlifting.From our sponsors: Hawkin Dynamics

What do probation work and theoretical chemistry have in common? More than you might think. In this edition of RSI, we meet two remarkable Slovak women whose careers may seem worlds apart, yet both are driven by the same goal – helping people. Probation officer and author Alena Gulánová explains why justice should never lose its human face, while young chemist Viktória Pohorelská shows how curiosity, science and lifelong learning can improve lives and bring complex knowledge closer to everyone.

Un peu de farine, un peu d'eau, un peu de sel et un peu de levain, on pétri, on leve et on cuit au four. Voila le pain! Aliment indispensable d'un repas, il se doit d'etre présent en toutes circonstances. Toutes? Meme pendant la guerre? Meme pendant le guerre! Aujourd'hui encore, les soldats slovaques sur le terrain disposent d'une boulangerie de campagne militaire qui fait l'envie de presque toutes les armées partenaires. RSI se penche aujourd'hui sur l'évolution de cette boulangerie sur pattes au travers des nombreux mouvements géopolitique qu'a connu la Slovaquie.

I recently had a minor spat over someone misinterpreting my AI beliefs (see section marked "Update" at the bottom here), so I thought I would list them in one place, so I can refer people when they ask. Timelines1 Define AGI as AI intelligent enough to do 90% of knowledge work jobs. I think there's a 25% chance of AGI by 20272, a 50% chance by 2034, and a 75% chance by 2045. Basic argument: In a certain sense, AI is already "smart" enough for this (eg it can answer quantum physics problems, which require higher IQ than most knowledge work). Its remaining limitations are that it's confused, unagentic, lacks situational awareness, and tends to hallucinate. The METR time horizon graph, and several other related benchmarks/experiments/intuition pumps, suggest it's improving on time horizons at an (exponential) rate that lets it cross human-level performance sometime around the early end of the schedule above, and subjectively it feels like harder-to-measure constructs like situational awareness are improving about as fast. Arguments for earlier: recursive self-improvement causes a speedup compared to the trend. This is one of the biggest blank spots in my model: I don't know how fast RSI will progress, and I don't think anyone else does either. There's some function mapping a combination of AI talent and compute to progress, and we don't know how it behaves in the domain when there's far more talent than compute available. It could fizzle out completely for lack of compute, or it could go vertical. The AI Futures Project has done some of the best work trying to model this, but even they have low confidence.

In honour of Canada and the founding of the Canadian-Slovak Chamber of Commerce, Ambassador Extraordinary and Plenipotentiary of Canada to Slovakia, Karen Mollica, visits the RSI studio. On this special occasion Ben Pascoe and Jacques Hoflack, from the French section of RSI, talk Canada and Slovakia in English and French.

In this episode, I talk to Dr. Brad Budde about his philosophy on pain management, and what's new in the world of regional anesthesia. We don't have an RSI this month, but I still learned a lot from Brad!

Contributor: Travis Barlock, MD Educational Pearls:   First-pass success is critical to limit complications from apnea, hypoxia, and airway trauma.  Complication rate for patients intubated on the first pass is 14% Complication rates increase to 47% after two attempts, 64% after three, and 71% after the fourth attempt   How to improve likelihood of first-pass success: Use Video laryngoscopy (VL). VL increases chance of first-pass success to 85% from 71% Use a bougie, especially in patients with anatomically difficult or otherwise obstructed airways. The BEAM study cites a success rate in these patients of 96% with a bougie, compared to 82% without Use a Checklist mnemonic (SOAPME) Suction – On, ready, and within reach Oxygen – Patient is preoxygenated Adjuncts – Oral/nasal adjuncts and BVM ready Positioning - Patient positioned properly; consider obesity, using semi-Fowler/head-up positioning Medications – Rapid sequence intubation (RSI), sedation, vasopressor, and other medications prepared as necessary Equipment – Laryngoscope (blade), tube, bougie/stylet, syringe, scalpel/cric kit, others ready as necessary References Sakles, J.C., Chiu, S., Mosier, J., Walker, C. and Stolz, U. (2013), The Importance of First Pass Success When Performing Orotracheal Intubation in the Emergency Department. Acad Emerg Med, 20: 71-78. https://doi.org/10.1111/acem.12055 Prekker ME, Driver BE, Trent SA, et al. Video versus Direct Laryngoscopy for Tracheal Intubation of Critically Ill Adults. New England Journal of Medicine. 2023;389(5). doi:https://doi.org/10.1056/nejmoa2301601 Driver BE, Prekker ME, Klein LR, et al. Effect of Use of a Bougie vs Endotracheal Tube and Stylet on First-Attempt Intubation Success Among Patients With Difficult Airways Undergoing Emergency Intubation: A Randomized Clinical Trial. JAMA. 2018;319(21):2179–2189. doi:10.1001/jama.2018.6496 ‌Turner JS, Bucca AW, Propst SL, et al. Association of Checklist Use in Endotracheal Intubation With Clinically Important Outcomes: A Systematic Review and Meta-analysis. JAMA Netw Open. 2020;3(7):e209278. doi:10.1001/jamanetworkopen.2020.9278 Turner, Joseph S et al. "Feasibility of  upright patient positioning and intubation success rates At two academic EDs." The American journal of emergency medicine vol. 35,7 (2017): 986-992. doi:10.1016/j.ajem.2017.02.011   Summarized by Sam Pahl | Edited by Sam Pahl & Ahmed Abdel-Hafiz, NREMT-P   Donate: https://emergencymedicalminute.org/donate/   Join our mailing list: http://eepurl.com/c9ouHf  

Every July, Prairie operators make new-crop decisions that set their cash flow for the next twelve months, usually reading the same headlines as everyone else and reacting to the loudest one. In this conversation, grain marketing advisor Ryan Bonnett puts his daily client read on a stage: what is actually moving canola, wheat, corn, and soybean markets right now, and where the summer selling window sits inside the seasonal pattern. Colin Brisebois, VP of Products and Market Strategies at FCC, adds the lender's view, what disciplined marketing looks like once it shows up in an operation's cash flow. Topics and Timestamps 0:00 -- The spring everyone just lived, and why the marketing window opens at the worst possible time 3:30 -- Welcome to Ryan Bonnett and Colin Brisebois 6:00 -- Live poll: how much of your 2026 crop is still unpriced 10:00 -- Beat the average, not the top: Ryan's core marketing philosophy 13:00 -- Colin on discipline: know your cost of production before you build a plan 16:00 -- Seller's remorse, and the parameters that remove it 19:00 -- The 80/20 problem: why most operators miss the top of the market 21:00 -- Crop conditions across the Prairies and the US heading into summer 28:00 -- Know your unique ability: when to hire the discipline you don't have 31:00 -- Ryan's seasonal chart: why prices rally into seeding and fall after 34:00 -- The summer selling window, and put options as price insurance 39:00 -- Reading the charts: double tops, RSI, and shifting momentum 42:00 -- China, Iran, and the headlines that don't move price until money does 45:00 -- New biofuel regulation, the floor under canola, and the policy risk underneath it 49:00 -- Bullish or bearish, defend your answer: Ryan's discipline test 52:00 -- Domestic processing and the case for building crush capacity at home 55:00 -- Colin's three pillars at FCC: knowledge, advice, capital 58:00 -- The $50 billion wealth transfer, and who replaces the next generation of ag talent 1:01:00 -- The cohort program: Ryan and FCC's plan to bring this education to more operators 1:03:00 -- Close   Resources Mentioned Ryan Bonnett's grain marketing client cohort program, run in partnership with FCC   Connect with Ryan Bonnett Grain marketing advisor (public brand name pending -- link to follow once confirmed) Connect with Colin Brisebois VP, Products and Market Strategies, Farm Credit Canada (FCC)   Connect with Growing the Future Website: growingthefuture.ca  YouTube: Growing the Future  Instagram: @growingthefuturepodcast  LinkedIn: Growing the Future Register for the Convergence Conference at convergence.ag and stay updated by subscribing to the Growing the Future Podcast at growingthefuturepodcast.ca.

This week, Justin Lima and Yosef Johnson discuss why long-term athlete development often gets sacrificed for flashy training, how Verkhoshansky's long-term delayed training effect changes programming, and why the best coaches focus on serving athletes—not impressing social media.___Save on your re-certification to the NSCA and CSCCa with best price CEUs

✔️ Tell me why all the people who have always said it's a scam and going to zero are wrong✔️ Noobs have been celebrating every green Bitcoin candle since Feb 2026✔️ Bitcoin has entered a historic bounce zone. ✔️ This indicator has been remarkably effective at identifying optimal BTC accumulation windows✔️ The worst-case outcome for BTC over the next six months is a 59% gain.✔️ Listen to Warren Buffett✔️ Price printing higher lows while RSI sweeps lower lows✔️ You need a “maximalist” belief system to justify Bitcoin's value.✔️ Bitcoin clock is ticking✔️ BTC daily testing linear trendline from 2022 & 2023 lows✔️ I am not a "permabull" on Bitcoin, I have conviction.✔️ BlackRock says Bitcoin will likely see a "renewal"✔️ Franklin Templeton files for ETFs that reinvest stock dividends into Bitcoin✔️ Bitcoin Mining tax clarity bill gains industry backing/Clarity act update ✔️ The ilbtccouncil X account has been suspended ✔️ Bull Bitcoin has obtained the MICA license in France✔️ Sources:► https://x.com/stoolpresidente/status/2069833701966393770► https://x.com/stockmoneyl/status/2069438204806127707► https://x.com/ao_btc_analyst/status/2068706526936617259► https://x.com/washigorira/status/2069467091187753089► https://x.com/durdenbtc/status/2069421756662599924► https://x.com/frankafetter/status/2069600311400870171► https://x.com/gertvanlagen/status/2069664213874741547► https://x.com/BitcoinNewsCom/status/2069753036839203317► https://x.com/mithcoons/status/2069763050714943882► https://x.com/superbitcoinbro/status/2069827106092232992► https://x.com/jameslavish/status/2069855787548344406► https://x.com/bitcoinmagazine/status/2069105338540794163► https://x.com/bitcoinarchive/status/2067927356333797793► https://finance.yahoo.com/markets/crypto/articles/franklin-templeton-files-etfs-funnel-100157393.html► https://x.com/bitcoinnewscom/status/2069097357132640437► https://x.com/BitcoinMagazine/status/2069873512525779242► https://x.com/tim_niemeyer_/status/2069164048176820284► https://x.com/francispouliot_/status/2069404801662619925► DONATE TO HELP KEONNE AND BILL https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools✔️ Check out Our Bitcoin Only Sponsors!► https://archemp.co/Discover the pinnacle of precision engineering. Our very first product, the bitcoin logo wall clock, is meticulously machined in Maine from a solid block of aerospace-grade aluminum, ensuring unparalleled durability and performance. We don't compromise on quality – no castings, just solid, high-grade material. Our state-of-the-art CNC machining center achieves tolerances of 1/1000th of an inch, guaranteeing a perfect fit and finish every time. Invest in a product built to last, with the exacting standards you deserve.► Join Our telegram: https://t.me/theplebunderground#Bitcoin #crypto #cryptocurrency #dailybitcoinnews #memecoinsThe information provided by Pleb Underground ("we," "us," or "our") on Youtube.com (the "Site") our show is for general informational purposes only. All information on the show is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site. UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SHOW OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SHOW. YOUR USE OF THE SHOW AND YOUR RELIANCE ON ANY INFORMATION ON THE SHOW IS SOLELY AT YOUR OWN RISK.

Dr. Eric Cash serves as President of the National High School Strength Coaches Association and is the Head Strength & Conditioning Coach at Dorman High School. Widely recognized as one of the leading voices in high school strength and conditioning, Dr. Cash has dedicated his career to developing young athletes through evidence-based training, mentorship, and long-term athletic development.Prior to Dorman, he held head strength and conditioning positions at Presbyterian College, Eastern Illinois University, and Elon University. He earned his doctorate in Kinesiology from University of North Carolina at Greensboro and was named NHSSCA Coach of the Year in 2020.___$1 Trial Membership to SCN

In this episode of Supertraining in a Year, Justin Lima and Jeff Moyer break down pages 331–360 of Yuri Verkhoshansky's Supertraining and dive into some of the most misunderstood topics in strength and conditioning.___Save on your re-certification to the NSCA and CSCCa with best price CEUs

Nas respostas às perguntas do Expresso, o governo desmente a percepção que ajudou a criar de que a maioria dos pagamentos indevidos nos apoios sociais corresponde a fraude. E acrescenta que 90% dos 159 milhões já foram recuperados. Neste episódio, falamos com a jornalista Joana Ascensão.See omnystudio.com/listener for privacy information.

Crypto natives are starting to warm up to SPCX trading, achieving the most active day of trading following the IPO on June 12. With SpaceX behaving like a meme stock is the top meme chain (Solana) about to skyrocket? 00:10 SPCX meme stock 01:20 The best is yet to come (The Fall) 01:40 Too Late Coinbase 02:00 Coinbase vs DeFi 02:45 Jupiter beat Coinbase 03:30 Hype dominance vs Solana 03:50 Spot volume king 04:00 Sunrise 04:40 Solana dominates SPCX and stock holders 05:00 Ranking vs other stocks after just 3 days 05:15 IPO Season 05:30 Nine Months of Red 06:00 RSI worse than FTX 06:20 Sticky New vs Returning users 06:50 Sticky A.I. Agents 07:15 Solana Disinflation & Burn Coming!? 08:10 CLARITY - don't buy until clarity passes #Solana #spacex #Crypto ~SpaceX Boosting $SOL!?

Here is the third part in this series where Justin breaks down why coaches need to be called Strength and Conditioning Coaches and NOT something else like Sports Performance Coach. Watch the full video to find out why.Click below to read the 2 articles that Justin mentions in this video. Article 1:https://strengthcoachnetwork.com/blog/Strength-CoachArticle 2:https://strengthcoachnetwork.com/blog/Title-2___From our sponsorsHawkin Dynamics

Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI landscape that has shifted dramatically: coding agents crossing the long-time-horizon threshold has turned engineers into managers of agents, near-frontier open weight AI looks like it may be disappearing as Meta and the Chinese labs pull back, and Anthropic's restrictions on its newly released Fable model have its biggest supporters questioning whether safety framing is masking competitive positioning. The conversation runs through the full state of the lab wars, including Rob doubling down on his Sam Altman ouster prediction and the Bret Taylor succession theory, why Google's structural advantages remain intact despite falling behind on coding, what xAI's Cursor acquisition is really for, and Ari's claim that compute constraints could push labs to suspend their APIs entirely. The back half digs into the physical bottlenecks underneath it all, from atom and x-ray lithography startups challenging ASML to H100 prices reversing their decline, before closing with predictions: recursive self-improvement is closer than it was six months ago but slower than the takeoff narratives suggest, robotics is nearing its GPT-3 moment, and Anthropic's next chapter may be life sciences.   (0:00) Intro (1:40) Coding Agents Cross a Threshold (3:29) Is Open-Weight AI in Retreat? (7:37) Cost Crunch & Scaffolding (12:13) The "Apps Are Cooked" Debate (16:37) Sam Altman Under Scrutiny (19:44) Anthropic's Fable Backlash (23:24) How Big a Step Change Is Fable? (26:50) What's Going On at Google? (33:20) Could the APIs Go Away? (34:11) Breaking the Semiconductor Bottleneck (35:42) Beyond EUV: Atom & X-Ray Lithography (37:23) Implications of a Compute Shortage (40:20) Do Alt Chips Actually Help? (43:43) SpaceX, xAI & the Cursor Acquisition (48:50) How Close Are We to RSI? (52:21) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

Prova Shopify ad 1 € - Vai su ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠shopify.it Nemmeno tre settimane dopo la nascita della Repubblica Italiana attraverso il referendum, il nuovo Stato emana una normativa destinata a far discutere. Il Guardasigilli Palmiro Togliatti, che di lì a poco cederà il posto di ministro, firma una amnistia completata da indulto tesa a ridurre gli effetti penali delle condanne e dei provvedimenti istruttori in corso per migliaia di ex fascisti, collaborazionisti e persino di partigiani che si erano vendicati delle violenze del regime e della RSI. Il provvedimento normativo bypassa la necessità di una "Norimberga italiana" e reimmette in servizio funzionari, magistrati, poliziotti, militari precedentemente collusi col fascismo. Tra volontà di riappacificazione nazionale ed errori commessi, oggi analizziamo la storia di una amnistia che continua a far discutere.

Anthony Pompliano breaks down why bitcoin is down 50% from its highs and whether the bear market bottom is in. In this episode, he covers Jordi Visser's capital rotation thesis, institutional buying from Middle East sovereign funds, bitcoin hitting the 200-week moving average for the first time since 2023, and why the data suggests now may be one of the best buying opportunities in bitcoin's history.=====================Uphold is the easiest way to buy and sell crypto unlike any other platform allowing you to trade in just one step between any supported asset. Check them out at https://www.uphold.com/pomp/ This video includes a paid sponsorship with Uphold. I'm compensated by Uphold for promoting its products and services and may receive commissions from referrals. Terms apply. Not available in all jurisdictions. Digital assets are risky and may result in the total loss of your capital.=====================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.=====================0:00 - Intro0:23 - Jordi Visser: Capital rotation away from bitcoin?7:03 - Is crypto winter here? 9:25 - Bitcoin hits the 200-week moving average for the first time since 202310:35 - RSI at historic lows & short-term holder capitulation11:30 - More underwater holders than profitable ones — what it means12:08 - Final take: dollar cost averaging in big drawdowns

We are BACK with pages 301-330 from Supertraining. This will cover: early specialization, circuit training, genetics, periodization, and of course insights from Yosef.___Download The Supertraining Reading Planhttps://strengthcoachnetwork.com/st___Buy Supertraining to Read Along with Ushttps://uaconcepts.com/product/supertraining___From our sponsors: Hawkin Dynamics

Scott Caulfield is one of the most respected voices in collegiate strength and conditioning, currently serving as Director of Strength & Conditioning at Norwich University. In 2025, he was named the National Strength and Conditioning Association College Strength & Conditioning Coach of the Year, recognizing decades of impact as a coach, educator, and leader in the profession.Before returning to Norwich in 2021 as the school's first-ever Director of Strength & Conditioning, Caulfield spent more than a decade with the NSCA in Colorado Springs, where he served as Head Strength Coach, Coaching Education Manager, and Performance Center Manager. In that role, he helped shape coach education nationwide while collaborating with organizations across the NCAA, MLB, NBA, NFL, and Olympic sport systems.His coaching journey also includes stops at Dartmouth College and Colorado College, along with experience in private sector performance and military fitness. A U.S. Navy veteran and Vermont native, Caulfield is known for blending high standards, real-world leadership, and relationship-driven coaching.$1 Trial Membership to SCN

Mark Weisman is a great example of how strong coaching relationships can come full circle. I had the chance to coach and work with Mark earlier in his career, so it's been special to watch his path unfold from athlete to coach at the highest levels of sport.A former standout running back at University of Iowa, Mark became one of the most productive backs in program history. After college, he spent time with the Cincinnati Bengals before transitioning into strength and conditioning after a short career in medical salesMark then returned to Iow a as an Assistant Strength & Conditioning Coach for football, later became Director of Sports Performance at Southeast Missouri State University, and continued building his career in professional baseball. He now serves with the Chicago Cubs as Major League Strength & Conditioning Coach. $1 Trial Membership to SCN

This week wasn't just another wave of AI announcements.It may have been the week the industry quietly crossed into a different phase entirely.In this episode, Isar connects the dots behind one of the biggest weeks in AI so far—from Anthropic's explosive growth, to Google I/O, OpenAI's legal win, NVIDIA's record earnings, and Andrej Karpathy joining Anthropic to work on recursive self-improvement.Individually, each story matters.Together, they point to something bigger: accelerating AI capability, accelerating infrastructure buildout, and growing signals from the people closest to the frontier that we may be entering a very different era.The quote that framed the episode came from Demis Hassabis: “We were standing at the foothills of the singularity. It will be a profound moment for humanity.”This episode breaks down what that actually means—and why the implications go far beyond new models and product launches.In this session, you'll discover: - Why Anthropic's projected $44B annualized revenue shocked the industry - How Anthropic became more profitable per user than OpenAI, Google, and Microsoft - Why Andrej Karpathy joining Anthropic may be one of the year's biggest AI stories - What recursive self-improvement (RSI) means—and why labs are racing toward it - How OpenAI's legal win against Elon Musk clears the runway for a potential IPO - Why Google's AI strategy suddenly looks both confusing and incredibly ambitious - What Google's shift from “search” to autonomous AI agents means for websites and SEO - Why AI solving an 80-year-old math problem matters more than most people realize - How NVIDIA, SpaceX, and compute infrastructure are becoming central to the AI race - Why electricity—not chips—may become the biggest bottleneck in AI expansion - What Demis Hassabis means when he says we're at the “foothills of the singularity”About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!