Podcasts about RL

A stacked Wall Pass Wednesday on SDH AMScarves N Spikes Tyler Pilgrim looks at the Atlanta United match against Columbus and previews San DiegoMLSSoccer.com's Dylan Butler looks at Open Cup and the playoff races plus previews action tonightNPSL Regional League Managing Director Jason Brown drops by to talk about the annoucement of the RL and what it means for the structure of the league on the whole for 2026 and beyondPlus, shocking news and results in UCL- and we preview today in league phase

In this episode of Gradient Dissent, Lukas Biewald talks with the CEO & founder of Surge AI, the billion-dollar company quietly powering the next generation of frontier LLMs. They discuss Surge's origin story, why traditional data labeling is broken, and how their research-focused approach is reshaping how models are trained.You'll hear why inter-annotator agreement fails in high-complexity tasks like poetry and math, why synthetic data is often overrated, and how Surge builds rich RL environments to stress-test agentic reasoning. They also go deep on what kinds of data will be critical to future progress in AI—from scientific discovery to multimodal reasoning and personalized alignment.It's a rare, behind-the-scenes look into the world of high-quality data generation at scale—straight from the team most frontier labs trust to get it right.Timestamps: 00:00 – Intro: Who is Edwin Chen? 03:40 – The problem with early data labeling systems 06:20 – Search ranking, clickbait, and product principles 10:05 – Why Surge focused on high-skill, high-quality labeling 13:50 – From Craigslist workers to a billion-dollar business 16:40 – Scaling without funding and avoiding Silicon Valley status games 21:15 – Why most human data platforms lack real tech 25:05 – Detecting cheaters, liars, and low-quality labelers 28:30 – Why inter-annotator agreement is a flawed metric 32:15 – What makes a great poem? Not checkboxes 36:40 – Measuring subjective quality rigorously 40:00 – What types of data are becoming more important 44:15 – Scientific collaboration and frontier research data 47:00 – Multimodal data, Argentinian coding, and hyper-specificity 50:10 – What's wrong with LMSYS and benchmark hacking 53:20 – Personalization and taste in model behavior 56:00 – Synthetic data vs. high-quality human data Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb

Help support the free broadcast by donating to our PayPal fundraiser!https://www.paypal.com/ncp/payment/RL... *Behind the Bunker Paintball Podcast* is a long-running weekly show dedicated to everything paintball. Hosted by passionate players and industry veterans, the podcast dives into the latest happenings in the sport, from new gear releases and product reviews to updates on tournaments and events around the world. It has built a loyal audience by combining serious paintball discussion with a lighthearted, entertaining approach that keeps both new players and seasoned veterans engaged.A big part of the podcast's appeal comes from its interactive nature. Viewers and listeners are encouraged to join in live chats, submit questions, and even share their own paintball stories and gear setups. The hosts often highlight community input, creating a sense of connection between fans and the broader paintball scene. This makes the podcast not just a source of information, but also a hub where players can feel part of a larger community that shares their enthusiasm for the sport.Beyond news and discussions, *Behind the Bunker* mixes in humor, playful banter, and fun segments that give the show a casual and friendly vibe. Whether it's debating the best gear setups, laughing over paintball fails, or breaking down the history of the game, the podcast manages to balance informative content with entertainment. This blend has made it one of the most recognizable and trusted voices in the paintball community, continuing to grow and evolve alongside the sport itself.

My guest today is Jonathan Siddharth, co-founder and CEO of Turing.Jonathan incubated Turing in Foundation Capital's Palo Alto office in 2018. Since then, it has grown into a multi-billion dollar company that powers nearly every frontier AI lab: OpenAI, Anthropic, Google, Meta, Microsoft, and others. If you've seen a breakthrough in how AI reasons or codes, odds are Turing had a hand in it.Jonathan has a provocative thesis: within three years, every white-collar job, including the CEO's, will be automated. In this episode, we talk about what it will take to reach artificial superintelligence, why this goal matters, and how the agentic era will fundamentally reshape work. We also dig into his founder journey: what he learned from his first startup Rover, how he built Turing from day one, and how his leadership style has evolved to emphasize speed, intensity, and staying in the details.Jonathan has been at the edge of AI for years, and he has the rare ability to translate what's happening at the frontier into lessons for builders today.Hope you enjoy the conversation! Chapters: 00:00 Cold open00:02:06 Jonathan's backstory: his experience at Stanford00:06:37 Lessons from Rover00:08:39 Early Turing: incubation at Foundation Capital and finding PMF00:13:52 Why Turing took off00:15:12 Evolving from developer cloud to AGI partner for frontier labs00:16:49 How coding improved reasoning - and why Turing became essential00:20:38 Founder lessons: building org speed and intensity00:23:33 Why work-life balance is a false dichotomy00:24:17 Daily standups, flat orgs, and Formula One culture00:25:15 Confrontational energy and Frank Slootman's influence00:29:50 Positioning Turing as “Switzerland” in the AI arms race00:34:32 The four pillars of superintelligence: multimodality, reasoning, tool use, coding00:37:39 From copilots to agents: the 100x improvement00:40:00 Why enterprise hasn't had its “ChatGPT moment” yet00:43:09 Jonathan's thoughts on RL gyms, algorithmic techniques, and evals00:46:32 The blurring line between model providers and AI apps00:47:35 Why defensibility depends on proprietary data and evals00:55:20 RL gyms: how enterprises train agents in simulated environments00:57:39 Underhyped: $30T of white-collar work will be automated

Help support the free broadcast by donating to our PayPal fundraiser!https://www.paypal.com/ncp/payment/RL... *Behind the Bunker Paintball Podcast* is a long-running weekly show dedicated to everything paintball. Hosted by passionate players and industry veterans, the podcast dives into the latest happenings in the sport, from new gear releases and product reviews to updates on tournaments and events around the world. It has built a loyal audience by combining serious paintball discussion with a lighthearted, entertaining approach that keeps both new players and seasoned veterans engaged.A big part of the podcast's appeal comes from its interactive nature. Viewers and listeners are encouraged to join in live chats, submit questions, and even share their own paintball stories and gear setups. The hosts often highlight community input, creating a sense of connection between fans and the broader paintball scene. This makes the podcast not just a source of information, but also a hub where players can feel part of a larger community that shares their enthusiasm for the sport.Beyond news and discussions, *Behind the Bunker* mixes in humor, playful banter, and fun segments that give the show a casual and friendly vibe. Whether it's debating the best gear setups, laughing over paintball fails, or breaking down the history of the game, the podcast manages to balance informative content with entertainment. This blend has made it one of the most recognizable and trusted voices in the paintball community, continuing to grow and evolve alongside the sport itself.

All over the world, for all of human history – and probably going back to our earliest hominid ancestors – people have found ways to try to keep themselves clean. But how did soap come about? Research: “Soap, N. (1), Etymology.” Oxford English Dictionary, Oxford UP, June 2025, https://doi.org/10.1093/OED/1115187665. American Cleaning Institute. “Soaps & Detergents History.” https://www.cleaninginstitute.org/understanding-products/why-clean/soaps-detergents-history Beckmann, John. “History of Inventions, Discoveries and Origins.” William Johnston, translator. Bosart, L.W. “The Early History of the Soap Industry.” The American Oil Chemists' Society. Journal of Oil & Fat Industries 1924-10: Vol 1 Iss 2. Cassidy, Cody. “Who Discovered Soap? What to Know About the Origins of the Life-Saving Substance.” Time. 5/5/2020. https://time.com/5831828/soap-origins/ Ciftyurek, Muge, and Kasim Ince. "Selahattin Okten Soap Factory in Antakya and an Evaluation on Soap Factory Plan Typology/Antakya'da Bulunan Selahattin Okten Sabunhanesi ve Sabunhane Plan Tipolojisi Uzerine Bir Degerlendirme." Art-Sanat, no. 19, Jan. 2023, pp. 133+. Gale Academic OneFile, dx.doi.org/10.26650/artsanat.2023.19.1106544. Accessed 18 Aug. 2025. Costa, Albert B. “Michel-Eugène Chevreul.” Encyclopedia Britannica. https://www.britannica.com/biography/Michel-Eugene-Chevreul Curtis, Valerie A. “Dirt, disgust and disease: a natural history of hygiene.” Journal of epidemiology and community health vol. 61,8 (2007): 660-4. doi:10.1136/jech.2007.062380 Dijkstra, Albert J. “How Chevreul (1786-1889) based his conclusions on his analytical results.” OCL. Vol. 16, No. 1. January-February 2009. Gibbs, F.W. “The History and Manufacture of Soap.” Annals of Science. 1939. Koeppel, Dan. “The History of Soap.” 4/15/2020. https://www.nytimes.com/wirecutter/blog/history-of-soap/ List, Gary, and Michael Jackson. “Giants of the Past: The Battle Over Hydrogenation (1903-1920).” https://www.ars.usda.gov/research/publications/publication/?seqNo115=210614 Maniatis, George C. “Guild Organized Soap Manufacturing Industry in Constantinople: Tenth-Twelfth Centuries.” Byzantion, 2010, Vol. 80 (2010). https://www.jstor.org/stable/44173107 National Museum of American History. “Bathing (Body Soaps and Cleansers).” https://americanhistory.si.edu/collections/object-groups/health-hygiene-and-beauty/bathing-body-soaps-and-cleansers New Mexico Historic Sites. “Making Soap from the Leaves of the Soaptree Yucca.” https://nmhistoricsites.org/assets/files/selden/Virtual%20Classroom_Soaptree%20Yucca%20Soap%20Making.pdf “The history of soapmaking.” 8/30/2019. https://www.open.edu/openlearn/history-the-arts/history/history-science-technology-and-medicine/history-science/the-history-soapmaking Pliny the Elder. “The Natural History of Pliny. Translated, With Copious Notes and Illustrations.” Vol. 5. John Bostock, translator. https://www.gutenberg.org/files/60688/60688-h/60688-h.htm Pointer, Sally. “An Experimental Exploration of the Earliest Soapmaking.” EXARC Journal. 2024/3. 8/22/2024. https://exarc.net/issue-2024-3/at/experimental-exploration-earliest-soapmaking Ridner, Judith. “The dirty history of soap.” The Conversation. 5/12/2020. https://theconversation.com/the-dirty-history-of-soap-136434 Routh, Hirak Behari et al. “Soaps: From the Phoenicians to the 20th Century - A Historical Review.” Clinics in Dermatology. Vol. No. 3. 1996. Smith, Cyril Stanley, and John G. Hawthorne. “Mappae Clavicula: A Little Key to the World of Medieval Techniques.” Transactions of the American Philosophical Society, vol. 64, no. 4, 1974, pp. 1–128. JSTOR, https://doi.org/10.2307/1006317. Accessed 18 Aug. 2025. Timilsena, Yakindra Prasad et al. “Perspectives on Saponins: Food Functionality and Applications.” International journal of molecular sciences vol. 24,17 13538. 31 Aug. 2023, doi:10.3390/ijms241713538 “Craftsmanship of Aleppo Ghar soap.” https://ich.unesco.org/en/RL/craftsmanship-of-aleppo-ghar-soap-02132 “Tradition of Nabulsi soap making in Palestine.” https://ich.unesco.org/en/RL/tradition-of-nabulsi-soap-making-in-palestine-02112 “Soaps.” https://www.fs.usda.gov/wildflowers/ethnobotany/soaps.shtml van Dijk, Kees. “Soap is the onset of civilization.” From Cleanliness and Culture. Kees van Dijk and Jean Gelman Taylor, eds. Brill. 2011. https://www.jstor.org/stable/10.1163/j.ctvbnm4n9.4 Wei, Huang. “The Sordid, Sudsy Rise of Soap in China.” Sixth Tone. 8/11/2020. https://www.sixthtone.com/news/1006041 See omnystudio.com/listener for privacy information.

Today we are joined by Gorkem and Batuhan from Fal.ai, the fastest growing generative media inference provider. They recently raised a $125M Series C and crossed $100M ARR. We covered how they pivoted from dbt pipelines to diffusion models inference, what were the models that really changed the trajectory of image generation, and the future of AI videos. Enjoy! 00:00 - Introductions 04:58 - History of Major AI Models and Their Impact on Fal.ai 07:06 - Pivoting to Generative Media and Strategic Business Decisions 10:46 - Technical discussion on CUDA optimization and kernel development 12:42 - Inference Engine Architecture and Kernel Reusability 14:59 - Performance Gains and Latency Trade-offs 15:50 - Discussion of model latency importance and performance optimization 17:56 - Importance of Latency and User Engagement 18:46 - Impact of Open Source Model Releases and Competitive Advantage 19:00 - Partnerships with closed source model developers 20:06 - Collaborations with Closed-Source Model Providers 21:28 - Serving Audio Models and Infrastructure Scalability 22:29 - Serverless GPU infrastructure and technical stack 23:52 - GPU Prioritization: H100s and Blackwell Optimization 25:00 - Discussion on ASICs vs. General Purpose GPUs 26:10 - Architectural Trends: MMDiTs and Model Innovation 27:35 - Rise and Decline of Distillation and Consistency Models 28:15 - Draft Mode and Streaming in Image Generation Workflows 29:46 - Generative Video Models and the Role of Latency 30:14 - Auto-Regressive Image Models and Industry Reactions 31:35 - Discussion of OpenAI's Sora and competition in video generation 34:44 - World Models and Creative Applications in Games and Movies 35:27 - Video Models' Revenue Share and Open-Source Contributions 36:40 - Rise of Chinese Labs and Partnerships 38:03 - Top Trending Models on Hugging Face and ByteDance's Role 39:29 - Monetization Strategies for Open Models 40:48 - Usage Distribution and Model Turnover on FAL 42:11 - Revenue Share vs. Open Model Usage Optimization 42:47 - Moderation and NSFW Content on the Platform 44:03 - Advertising as a key use case for generative media 45:37 - Generative Video in Startup Marketing and Virality 46:56 - LoRA Usage and Fine-Tuning Popularity 47:17 - LoRA ecosystem and fine-tuning discussion 49:25 - Post-Training of Video Models and Future of Fine-Tuning 50:21 - ComfyUI Pipelines and Workflow Complexity 52:31 - Requests for startups and future opportunities in the space 53:33 - Data Collection and RedPajama-Style Initiatives for Media Models 53:46 - RL for Image and Video Models: Unknown Potential 55:11 - Requests for Models: Editing and Conversational Video Models 57:12 - VO3 Capabilities: Lip Sync, TTS, and Timing 58:23 - Bitter Lesson and the Future of Model Workflows 58:44 - FAL's hiring approach and team structure 59:29 - Team Structure and Scaling Applied ML and Performance Teams 1:01:41 - Developer Experience Tools and Low-Code/No-Code Integration 1:03:04 - Improving Hiring Process with Public Challenges and Benchmarks 1:04:02 - Closing Remarks and Culture at FAL

In this lecture, military historian Sarah Paine explains how Britain used sea control, peripheral campaigns, and alliances to defeat Nazi Germany during WWII. She then applies this framework to today, arguing that Russia and China are similarly constrained by their geography, making them vulnerable in any conflict with maritime powers (like the U.S. and its allies).Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox partners with researchers to scope, generate, and deliver the exact data frontier models need, no matter the domain. Whether that's multi-turn audio, SOTA robotics data, advanced STEM problem sets, or even novel RL environments, Labelbox delivers high-quality data, fast. Learn more at labelbox.com/dwarkesh* Warp is the best interface I've found for coding with agents. It makes building custom tools easy: Warp's UI helps you understand agent behavior and its in-line text editor is great for making tweaks. You can try Warp for free, or, for a limited time, use code DWARKESH to get Warp's Pro Plan for only $5. Go to warp.dev/dwarkeshTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps00:00:00 – How WW1 shaped WW200:15:10 – Hitler and Churchill's battle to command the Atlantic00:30:10 – Peripheral theaters leading up to Normandy00:37:13 – The Eastern front00:48:04 – Russia's & China's geographic prisons01:00:28 – Hitler's blunders & America's industrial might01:15:03 – Bismarck's limited wars vs Hitler's total war Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Wohoo, hey ya'll, Alex here,I'm back from the desert (pic at the end) and what a great feeling it is to be back in the studio to talk about everything that happened in AI! It's been a pretty full week (or two) in AI, with Coding agent space heating up, Grok entering the ring and taking over free tokens, Codex 10xing usage and Anthropic... well, we'll get to Anthropic. Today on the show we had Roger and Bhavesh from Nous Research cover the awesome Hermes 4 release and the new PokerBots benchmark, then we had a returning favorite, Kwindla Hultman Kramer, to talk about the GA of RealTime voice from OpenAI. Plus we got some massive funding news, some drama with model quality on Claude Code, and some very exciting news right here from CoreWeave aquiring OpenPipe!

I've recently written about how I've updated against seeing substantially faster than trend AI progress due to quickly massively scaling up RL on agentic software engineering. One response I've heard is something like: RL scale-ups so far have used very crappy environments due to difficulty quickly sourcing enough decent (or even high quality) environments. Thus, once AI companies manage to get their hands on actually good RL environments (which could happen pretty quickly), performance will increase a bunch. Another way to put this response is that AI companies haven't actually done a good job scaling up RL—they've scaled up the compute, but with low quality data—and once they actually do the RL scale up for real this time, there will be a big jump in AI capabilities (which yields substantially above trend progress). I'm skeptical of this argument because I think that ongoing improvements to RL environments [...] ---Outline:(04:18) Counterargument: Actually, companies havent gotten around to improving RL environment quality until recently (or there is substantial lead time on scaling up RL environments etc.) so better RL environments didnt drive much of late 2024 and 2025 progress(05:24) Counterargument: AIs will soon reach a critical capability threshold where AIs themselves can build high quality RL environments(06:51) Counterargument: AI companies are massively fucking up their training runs (either pretraining or RL) and once they get their shit together more, well see fast progress(08:34) Counterargument: This isnt that related to RL scale up, but OpenAI has some massive internal advance in verification which they demonstrated via getting IMO gold and this will cause (much) faster progress late this year or early next year(10:12) Thoughts and speculation on scaling up the quality of RL environmentsThe original text contained 5 footnotes which were omitted from this narration. --- First published: September 3rd, 2025 Source: https://www.lesswrong.com/posts/HsLWpZ2zad43nzvWi/trust-me-bro-just-one-more-rl-scale-up-this-one-will-be-the --- Narrated by TYPE III AUDIO.

TDC 063: 5 AI Infographics That Will Blow Your MindFive shocking AI infographics reveal hidden truths about data centers, jobs, and what AI is doing to your brain.---Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque shares five mind-blowing AI infographics from his personal collection.You'll discover hard evidence that AI is killing jobs for young people, learn why data centers now consume more power than entire countries, and see shocking MIT research on what ChatGPT does to your brain connectivity.---Question of the Day

Asha Sharma leads AI product strategy at Microsoft, where she works with thousands of companies building AI products and has unique visibility into what's working (and what's not) across more than 15,000 startups and enterprises. Before Microsoft, Asha was COO at Instacart, and VP of Product & Engineering at Meta, notably leading product for Messenger.What you'll learn:1. Why we're moving from “product as artifact” to “product as organism” and what this means for builders2. Microsoft's “seasons” planning framework that allows them to adapt quickly in the AI era3. The death of the org chart: how agents are turning hierarchies into task networks and why “the loop, not the lane” is the new organizing principle4. Why post-training will soon see more investment than pre-training—and how to build your own AI moat with fine-tuning5. Her prediction for the “agentic society”—where org charts become work charts and agents outnumber humans in your company6. The three-phase pattern every successful AI company follows (and why most fail at phase one)7. The rise of code-native interfaces and why GUIs might be going the way of the desktop8. What Asha learned from Satya Nadella about optimism—Brought to you by:Enterpret—Transform customer feedback into product growth: https://enterpret.com/lennyDX—The developer intelligence platform designed by leading researchers: http://getdx.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lenny—Transcript: ⁠https://www.lennysnewsletter.com/p/how-80000-companies-build-with-ai-asha-sharma⁠—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/171413445/my-biggest-takeaways-from-this-conversation⁠—Where to find Asha Sharma:• LinkedIn: https://www.linkedin.com/in/aboutasha/• Blog: https://azure.microsoft.com/en-us/blog/author/asha-sharma/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Asha Sharma(04:18) From “product as artifact” to “product as organism”(06:20) The rise of post-training and the future of AI product development(09:10) Successful AI companies: patterns and pitfalls(12:01) The evolution of full-stack builders(14:15) “The loop, not the lane”—the new organizing principle(16:24) The future of user interfaces: from GUI to code-native(19:34) The rise of the agentic society(22:58) The “work chart” vs. the “org chart”(26:24) How Microsoft is using agents(28:23) Planning and strategy in the AI landscape(35:38) The importance of platform fundamentals(39:31) Lessons from industry giants(42:10) What's driving Asha(44:30) Reinforcement learning (RL) and optimization loops(49:19) Lightning round and final thoughts—Referenced:• Copilot: https://copilot.microsoft.com/• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Inside ChatGPT: The fastest growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• GitHub: https://github.com• Dragon Medical One: https://www.microsoft.com/en-us/health-solutions/clinical-workflow/dragon-medical-one• Windsurf: https://windsurf.com/• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Lovable: https://lovable.dev/• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Bolt: http://bolt.com• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Replit: https://replit.com/•Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Sierra: https://sierra.ai/• Spark: https://github.com/features/spark• Peter Yang on X: https://x.com/petergyang• How AI will impact product management: https://www.lennysnewsletter.com/p/how-ai-will-impact-product-management• Instacart: http://instacart.com/• Terminator: https://en.wikipedia.org/wiki/Terminator_(franchise)• Porch Group: https://porchgroup.com/• WhatsApp: https://www.whatsapp.com/• Maslow's Hierarchy of Needs: https://www.simplypsychology.org/maslow.html• Satya Nadella on X: https://x.com/satyanadella• Perfect Match 360°: Artificial intelligence to find the perfect donor match: https://ivi-fertility.com/blog/perfect-match-360-artificial-intelligence-to-find-the-perfect-donor-match/• OpenAI's GPT-5 shows potential in healthcare with early cancer detection capabilities: https://economictimes.indiatimes.com/news/international/us/openais-gpt-5-shows-potential-in-healthcare-with-early-cancer-detection-capabilities/articleshow/123173952.cms• F1: The Movie: https://www.imdb.com/title/tt16311594/• For All Mankind on AppleTV+: https://tv.apple.com/us/show/for-all-mankind/umc.cmc.6wsi780sz5tdbqcf11k76mkp7• The Home Depot: https://www.homedepot.com/• Dewalt Powerstack: https://www.dewalt.com/powerstack• Regret Minimization Framework: https://s3.amazonaws.com/kajabi-storefronts-production/sites/2147500522/themes/2148012322/downloads/rLuObc2QuOwjLrinx5Yu_regret-minimization-framework.pdf—Recommended books:• The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip: https://www.amazon.com/Thinking-Machine-Jensen-Coveted-Microchip/dp/0593832698• Tomorrow, and Tomorrow, and Tomorrow: https://www.amazon.com/dp/0593466497Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed.My biggest takeaways from this conversation: To hear more, visit www.lennysnewsletter.com

Help support the free broadcast by donating to our PayPal fundraiser!https://www.paypal.com/ncp/payment/RL...1. Gear deep-dive and product highlightsIn the episode, the hosts focused on the latest gear hitting the market—painting a detailed overview of standout products—from markers and loaders to masks and apparel. They discussed performance features, reliability, and value, offering insight into what's worth the investment. They emphasized not just flashy gear but practical upgrades that improve field experience and longevity through proper care and maintenance.2. User experiences & maintenance tipsHosts shared firsthand stories about using the new equipment themselves—what worked well, what needed tweaking, and common pitfalls to avoid. They discussed cleaning routines, part replacements, and pro tips to keep gear performing at peak levels, stressing that maintenance is as critical as the initial product selection.3. Broader implications and fan interactionThis gear-focused segment was part of a broader conversation about player strategy, rule evolution, and paintball community trends. The hosts took live comments and questions from fans—covering topics like gear setup, communication tools, and field etiquettes—reinforcing the interactive, community-driven nature of the show. They wrapped up with thoughts on how the right gear complements game tactics and encouraged listeners to tune in, submit gear questions, and stay connected for future episodes.

TDC 062: Is the AI Bubble Bursting? My Keynote Insights + MIT's Shocking 95% Failure RateWhile 65% of all VC flows into AI, the real opportunity lies in the barbell approach.---Episode Summary  In this episode of The Digital Contrarian, host Ryan Levesque dives into AI bubble dynamics and enterprise failure rates.  You'll learn why 95% of AI projects fail, how billionaire investors view current market concentration, and discover the barbell strategy for hedging against bubble conditions while maintaining human competitive advantage.  ---Question of the Day

As I discussed in a prior post, I felt like there were some reasonably compelling arguments for expecting very fast AI progress in 2025 (especially on easily verified programming tasks). Concretely, this might have looked like reaching 8 hour 50% reliability horizon lengths on METR's task suite[1] by now due to greatly scaling up RL and getting large training runs to work well. In practice, I think we've seen AI progress in 2025 which is probably somewhat faster than the historical rate (at least in terms of progress on agentic software engineering tasks), but not much faster. And, despite large scale-ups in RL and now seeing multiple serious training runs much bigger than GPT-4 (including GPT-5), this progress didn't involve any very large jumps. The doubling time for horizon length on METR's task suite has been around 135 days this year (2025) while it was more like 185 [...] The original text contained 5 footnotes which were omitted from this narration. --- First published: August 20th, 2025 Source: https://www.lesswrong.com/posts/2ssPfDpdrjaM2rMbn/my-agi-timeline-updates-from-gpt-5-and-2025-so-far-1 --- Narrated by TYPE III AUDIO.

Hey everyone, Alex here

TDC 061: The Personal Branding Paradox in the Age of AIThe more AI you use to scale your personal brand, the less personal it becomes.Episode SummaryIn this episode of The Digital Contrarian, Ryan Levesque explores the personal branding AI paradox and why scaling with AI often flattens your voice.You'll discover the "Personal Brand Power Law," learn the three-part "Fewer, Deeper, Less" framework for standing out, and understand why small rooms beat trying to be everywhere.Question of the Day

Greg Brockman, co-founder and president of OpenAI, joins us to talk about GPT-5 and GPT-OSS, the future of software engineering, why reinforcement learning is still scaling, and how OpenAI is planning to get to AGI. 00:00 Introductions 01:04 The Evolution of Reasoning at OpenAI 04:01 Online vs Offline Learning in Language Models 06:44 Sample Efficiency and Human Curation in Reinforcement Learning 08:16 Scaling Compute and Supercritical Learning 13:21 Wall clock time limitations in RL and real-world interactions 16:34 Experience with ARC Institute and DNA neural networks 19:33 Defining the GPT-5 Era 22:46 Evaluating Model Intelligence and Task Difficulty 25:06 Practical Advice for Developers Using GPT-5 31:48 Model Specs 37:21 Challenges in RL Preferences (e.g., try/catch) 39:13 Model Routing and Hybrid Architectures in GPT-5 43:58 GPT-5 pricing and compute efficiency improvements 46:04 Self-Improving Coding Agents and Tool Usage 49:11 On-Device Models and Local vs Remote Agent Systems 51:34 Engineering at OpenAI and Leveraging LLMs 54:16 Structuring Codebases and Teams for AI Optimization 55:27 The Value of Engineers in the Age of AGI 58:42 Current state of AI research and lab diversity 01:01:11 OpenAI's Prioritization and Focus Areas 01:03:05 Advice for Founders: It's Not Too Late 01:04:20 Future outlook and closing thoughts 01:04:33 Time Capsule to 2045: Future of Compute and Abundance 01:07:07 Time Capsule to 2005: More Problems Will Emerge

My fellow pro-growth/progress/abundance Up Wingers,Nuclear fission is a safe, powerful, and reliable means of generating nearly limitless clean energy to power the modern world. A few public safety scares and a lot of bad press over the half-century has greatly delayed our nuclear future. But with climate change and energy-hungry AI making daily headlines, the time — finally — for a nuclear renaissance seems to have arrived.Today on Faster, Please! — The Podcast, I talk with Dr. Tim Gregory about the safety and efficacy of modern nuclear power, as well as the ambitious energy goals we should set for our society.Gregory is a nuclear scientist at the UK National Nuclear Laboratory. He is also a popular science broadcaster on radio and TV, and an author. His most recent book, Going Nuclear: How Atomic Energy Will Save the World is out now.In This Episode* A false start for a nuclear future (1:29)* Motivators for a revival (7:20)* About nuclear waste . . . (12:41)* Not your mother's reactors (17:25)* Commercial fusion, coming soon . . . ? (23:06)Below is a lightly edited transcript of our conversation. A false start for a nuclear future (1:29)The truth is that radiation, we're living in it all the time, it's completely inescapable because we're all living in a sea of background radiation.Pethokoukis: Why do America, Europe, Japan not today get most of their power from nuclear fission, since that would've been a very reasonable prediction to make in 1965 or 1975, but it has not worked out that way? What's your best take on why it hasn't?Going back to the '50s and '60s, it looked like that was the world that we currently live in. It was all to play for, and there were a few reasons why that didn't happen, but the main two were Three Mile Island and Chernobyl. It's a startling statistic that the US built more nuclear reactors in the five years leading up to Three Mile Island than it has built since. And similarly on this side of the Atlantic, Europe built more nuclear reactors in the five years leading up to Chernobyl than it has built since, which is just astounding, especially given that nobody died in Three Mile Island and nobody was even exposed to anything beyond the background radiation as a result of that nuclear accident.Chernobyl, of course, was far more consequential and far more serious than Three Mile Island. 30-odd people died in the immediate aftermath, mostly people who were working at the power station and the first responders, famously the firefighters who were exposed to massive amounts of radiation, and probably a couple of hundred people died in the affected population from thyroid cancer. It was people who were children and adolescents at the time of the accident.So although every death from Chernobyl was a tragedy because it was avoidable, they're not in proportion to the mythic reputation of the night in question. It certainly wasn't reason to effectively end nuclear power expansion in Europe because of course we had to get that power from somewhere, and it mainly came from fossil fuels, which are not just a little bit more deadly than nuclear power, they're orders of magnitude more deadly than nuclear power. When you add up all of the deaths from nuclear power and compare those deaths to the amount of electricity that we harvest from nuclear power, it's actually as safe as wind and solar, whereas fossil fuels kill hundreds or thousands of times more people per unit of power. To answer your question, it's complicated and there are many answers, but the main two were Three Mile Island and Chernobyl.I wonder how things might have unfolded if those events hadn't happened or if society had responded proportionally to the actual damage. Three Mile Island and Chernobyl are portrayed in documentaries and on TV as far deadlier than they really were, and they still loom large in the public imagination in a really unhelpful way.You see it online, actually, quite a lot about the predicted death toll from Chernobyl, because, of course, there's no way of saying exactly which cases of cancer were caused by Chernobyl and which ones would've happened anyway. Sometimes you see estimates that are up in the tens of thousands, hundreds of thousands of deaths from Chernobyl. They are always based on a flawed scientific hypothesis called the linear no-threshold model that I go into in quite some detail in chapter eight of my book, which is all about the human health effects of exposure to radiation. This model is very contested in the literature. It's one of the most controversial areas of medical science, actually, the effects of radiation on the human body, and all of these massive numbers you see of the death toll from Chernobyl, they're all based on this really kind of clunky, flawed, contentious hypothesis. My reading of the literature is that there's very, very little physical evidence to support this particular hypothesis, but people take it and run. I don't know if it would be too far to accuse people of pushing a certain idea of Chernobyl, but it almost certainly vastly, vastly overestimates the effects.I think a large part of the reason of why this had such a massive impact on the public and politicians is this lingering sense of radiophobia that completely blight society. We've all seen it in the movies, in TV shows, even in music and computer games — radiation is constantly used as a tool to invoke fear and mistrust. It's this invisible, centerless, silent specter that's kind of there in the background: It means birth defects, it means cancers, it means ill health. We've all kind of grown up in this culture where the motif of radiation is bad news, it's dangerous, and that inevitably gets tied to people's sense of nuclear power. So when you get something like Three Mile Island, society's imagination and its preconceptions of radiation, it's just like a dry haystack waiting for a flint spark to land on it, and up it goes in flames and people's imaginations run away with them.The truth is that radiation, we're living in it all the time, it's completely inescapable because we're all living in a sea of background radiation. There's this amazing statistic that if you live within a couple of miles of a nuclear power station, the extra amount of radiation you're exposed to annually is about the same as eating a banana. Bananas are slightly radioactive because of the slight amount of potassium-40 that they naturally contain. Even in the wake of these nuclear accidents like Chernobyl, and more recently Fukushima, the amount of radiation that the public was exposed to barely registers and, in fact, is less than the background radiation in lots of places on the earth.Motivators for a revival (7:20)We have no idea what emerging technologies are on the horizon that will also require massive amounts of power, and that's exactly where nuclear can shine.You just suddenly reminded me of a story of when I was in college in the late 1980s, taking a class on the nuclear fuel cycle. You know it was an easy class because there was an ampersand in it. “Nuclear fuel cycle” would've been difficult. “Nuclear fuel cycle & the environment,” you knew it was not a difficult class.The man who taught it was a nuclear scientist and, at one point, he said that he would have no problem having a nuclear reactor in his backyard. This was post-Three Mile Island, post-Chernobyl, and the reaction among the students — they were just astounded that he would be willing to have this unbelievably dangerous facility in his backyard.We have this fear of nuclear power, and there's sort of an economic component, but now we're seeing what appears to be a nuclear renaissance. I don't think it's driven by fear of climate change, I think it's driven A) by fear that if you are afraid of climate change, just solar and wind aren't going to get you to where you want to be; and then B) we seem like we're going to need a lot of clean energy for all these AI data centers. So it really does seem to be a perfect storm after a half-century.And who knows what next. When I started writing Going Nuclear, the AI story hadn't broken yet, and so all of the electricity projections for our future demand, which, they range from doubling to tripling, we're going to need a lot of carbon-free electricity if we've got any hope of electrifying society whilst getting rid of fossil fuels. All of those estimates were underestimates because nobody saw AI coming.It's been very, very interesting just in the last six, 12 months seeing Big Tech in North America moving first on this. Google, Microsoft, Amazon, and Meta have all either invested or actually placed orders for small modular reactors specifically to power their AI data centers. In some ways, they've kind of led the charge on this. They've moved faster than most nation states, although it is encouraging, actually, here in the UK, just a couple of weeks ago, the government announced that our new nuclear power station is definitely going ahead down in Sizewell in Suffolk in the south of England. That's a 3.2 gigawatt nuclear reactor, it's absolutely massive. But it's been really, really encouraging to see Big Tech in the private sector in North America take the situation into their own hands. If anyone's real about electricity demands and how reliable you need it, it's Big Tech with these data centers.I always think, go back five, 10 years, talk of AI was only on the niche subreddits and techie podcasts where people were talking about it. It broke into the mainstream all of a sudden. Who knows what is going to happen in the next five or 10 years. We have no idea what emerging technologies are on the horizon that will also require massive amounts of power, and that's exactly where nuclear can shine.In the US, at least, I don't think decarbonization alone is enough to win broad support for nuclear, since a big chunk of the country doesn't think we actually need to do that. But I think that pairing it with the promise of rapid AI-driven economic growth creates a stronger case.I tried to appeal to a really broad church in Going Nuclear because I really, really do believe that whether you are completely preoccupied by climate change and environmental issues or you're completely preoccupied by economic growth, and raising living, standards and all of that kind of thing, all the monetary side of things, nuclear is for you because if you solve the energy problem, you solve both problems at once. You solve the economic problem and the environmental problem.There's this really interesting relationship between GDP per head — which is obviously incredibly important in economic terms — and energy consumption per head, and it's basically a straight line relationship between the two. There are no rich countries that aren't also massive consumers of energy, so if you really, really care about the economy, you should really also be caring about energy consumption and providing energy abundance so people can go out and use that energy to create wealth and prosperity. Again, that's where nuclear comes in. You can use nuclear power to sate that massive energy demand that growing economies require.This podcast is very pro-wealth and prosperity, but I'll also say, if the nuclear dreams of the '60s where you had, in this country, what was the former Atomic Energy Commission expecting there to be 1000 nuclear reactors in this country by the year 2000, we're not having this conversation about climate change. It is amazing that what some people view as an existential crisis could have been prevented — by the United States and other western countries, at least — just making a different political decision.We would be spending all of our time talking about something else, and how nice would that be?For sure. I'm sure there'd be other existential crises to worry about.But for sure, we wouldn't be talking about climate change was anywhere near the volume or the sense of urgency as we are now if we would've carried on with the nuclear expansion that really took off in the '70s and the '80s. It would be something that would be coming our way in a couple of centuries.About nuclear waste . . . (12:41). . . a 100 percent nuclear-powered life for about 80 years, their nuclear waste would barely fill a wine glass or a coffee cup. I don't know if you've ever seen the television show For All Mankind?I haven't. So many people have recommended it to me.It's great. It's an alt-history that looks at what if the Space Race had never stopped. As a result, we had a much more tech-enthusiastic society, which included being much more pro-nuclear.Anyway, imagine if you are on a plane talking to the person next to you, and the topic of your book comes up, and the person says hey, I like energy, wealth, prosperity, but what are you going to do about the nuclear waste?That almost exact situation has happened, but on a train rather than an airplane. One of the cool things about uranium is just how much energy you can get from a very small amount of it. If typical person in a highly developed economy, say North America, Europe, something like that, if they produced all of their power over their entire lifetime from nuclear alone, so forget fossil fuels, forget wind and solar, a 100 percent nuclear-powered life for about 80 years, their nuclear waste would barely fill a wine glass or a coffee cup. You need a very small amount of uranium to power somebody's life, and the natural conclusion of that is you get a very small amount of waste for a lifetime of power. So in terms of the numbers, and the amount of nuclear waste, it's just not that much of a problem.However, I don't want to just try and trivialize it out of existence with some cool pithy statistics and some cool back-of-the-envelopes physics calculations because we still have to do something with the nuclear waste. This stuff is going to be radioactive for the best part of a million years. Thankfully, it's quite an easy argument to make because good old Finland, which is one of the most nuclear nations on the planet as a share of nuclear in its grid, has solved this problem. It has implemented — and it's actually working now — the world's first and currently only geological repository for nuclear waste. Their idea is essentially to bury it in impermeable bedrock and leave it there because, as with all radioactive objects, nuclear waste becomes less radioactive over time. The idea is that, in a million years, Finland's nuclear waste won't be nuclear waste anymore, it will just be waste. A million years sounds like a really long time to our ears, but it's actually —It does.It sounds like a long time, but it is the blink of an eye, geologically. So to a geologist, a million years just comes and goes straight away. So it's really not that difficult to keep nuclear waste safe underground on those sorts of timescales. However — and this is the really cool thing, and this is one of the arguments that I make in my book — there are actually technologies that we can use to recycle nuclear waste. It turns out that when you pull uranium out of a reactor, once it's been burned for a couple of years in a reactor, 95 percent of the atoms are still usable. You can still use them to generate nuclear power. So by throwing away nuclear waste when it's been through a nuclear reactor once, we're actually squandering like 95 percent of material that we're throwing away.The theory is this sort of the technology behind breeder reactors?That's exactly right, yes.What about the plutonium? People are worried about the plutonium!People are worried about the plutonium, but in a breeder reactor, you get rid of the plutonium because you split it into fission products, and fission products are still radioactive, but they have much shorter half-lives than plutonium. So rather than being radioactive for, say, a million years, they're only radioactive, really, for a couple of centuries, maybe 1000 years, which is a very, very different situation when you think about long-term storage.I read so many papers and memos from the '50s when these reactors were first being built and demonstrated, and they worked, by the way, they're actually quite easy to build, it just happened in a couple of years. Breeder reactors were really seen as the future of humanity's power demands. Forget traditional nuclear power stations that we all use at the moment, which are just kind of once through and then you throw away 95 percent of the energy at the end of it. These breeder reactors were really, really seen as the future.They never came to fruition because we discovered lots of uranium around the globe, and so the supply of uranium went up around the time that the nuclear power expansion around the world kind of seized up, so the uranium demand dropped as the supply increased, so the demand for these breeder reactors kind of petered out and fizzled out. But if we're really, really serious about the medium-term future of humanity when it comes to energy, abundance, and prosperity, we need to be taking a second look at these breeder reactors because there's enough uranium and thorium in the ground around the world now to power the world for almost 1000 years. After that, we'll have something else. Maybe we'll have nuclear fusion.Well, I hope it doesn't take a thousand years for nuclear fusion.Yes, me too.Not your mother's reactors (17:25)In 2005, France got 80 percent of its electricity from nuclear. They almost decarbonized their grid by accident before anybody cared about climate change, and that was during a time when their economy was absolutely booming.I don't think most people are aware of how much innovation has taken place around nuclear in the past few years, or even few decades. It's not just a climate change issue or that we need to power these data centers — the technology has vastly improved. There are newer, safer technologies, so we're not talking about 1975-style reactors.Even if it were the 1975-style reactors, that would be fine because they're pretty good and they have an absolutely impeccable safety record punctuated by a very small number of high-profile events such as Chernobyl and Fukushima. I'm not to count Three Mile Island on that list because nobody died, but you know what I mean.But the modern nuclear reactors are amazing. The ones that are coming out of France, the EPRs, the European Power Reactors, there are going to be two of those in the UK's new nuclear power station, and they've been designed to withstand an airplane flying into the side of them, so they're basically bomb-proof.As for these small modular reactors, that's getting people very excited, too. As their name suggests, they're small. How small is a reasonable question — the answer is as small as you want to go. These things are scalable, and I've seen designs for just one-megawatt reactors that could easily fit inside a shipping container. They could fit in the parking lots around the side of a data center, or in the basement even, all the way up to multi-hundred-megawatt reactors that could fit on a couple of tennis courts worth of land. But it's really the modular part that's the most interesting thing. That's the ‘M' and that's never been done before.Which really gets to the economics of the SMRs.It really does. The idea is you could build upwards of 90 percent of these reactors on a factory line. We know from the history of industrialization that as soon as you start mass producing things, the unit cost just plummets and the timescales shrink. No one has achieved that yet, though. There's a lot of hype around small modular reactors, and so it's kind of important not to get complacent and really keep our eye on the ultimate goal, which is mass-production and mass rapid deployment of nuclear power stations, crucially in the places where you need them the most, as well.We often think about just decarbonizing our electricity supply or decoupling our electricity supply from volatilities in the fossil fuel market, but it's about more than electricity, as well. We need heat for things like making steel, making the ammonia that feeds most people on the planet, food and drinks factories, car manufacturers, plants that rely on steam. You need heat, and thankfully, the primary energy from a nuclear reactor is heat. The electricity is secondary. We have to put effort into making that. The heat just kind of happens. So there's this idea that we could use the surplus heat from nuclear reactors to power industrial processes that are very, very difficult to decarbonize. Small modular reactors would be perfect for that because you could nestle them into the industrial centers that need the heat close by. So honestly, it is really our imaginations that are the limits with these small modular reactors.They've opened a couple of nuclear reactors down in Georgia here. The second one was a lot cheaper and faster to build because they had already learned a bunch of lessons building that first one, and it really gets at sort of that repeatability where every single reactor doesn't have to be this one-off bespoke project. That is not how it works in the world of business. How you get cheaper things is by building things over and over, you get very good at building them, and then you're able to turn these things out at scale. That has not been the economic situation with nuclear reactors, but hopefully with small modular reactors, or even if we just start building a lot of big advanced reactors, we'll get those economies of scale and hopefully the economic issue will then take care of itself.For sure, and it is exactly the same here in the UK. The last reactor that we connected to the grid was in 1995. I was 18 months old. I don't even know if I was fluent in speaking at 18 months old. I was really, really young. Our newest nuclear power station, Hinkley Point C, which is going to come online in the next couple of years, was hideously expensive. The uncharitable view of that is that it's just a complete farce and is just a complete embarrassment, but honestly, you've got to think about it: 1995, the last nuclear reactor in the UK, it was going to take a long time, it was going to be expensive, basically doing it from scratch. We had no supply chain. We didn't really have a workforce that had ever built a nuclear reactor before, and with this new reactor that just got announced a couple of weeks ago, the projected price is 20 percent cheaper, and it is still too expensive, it's still more expensive than it should be, but you're exactly right.By tapping into those economies of scale, the cost per nuclear reactor will fall, and France did this in the '70s and '80s. Their nuclear program is so amazing. France is still the most nuclear nation on the planet as a share of its total electricity. In 2005, France got 80 percent of its electricity from nuclear. They almost decarbonized their grid by accident before anybody cared about climate change, and that was during a time when their economy was absolutely booming. By the way, still today, all of those reactors are still working and they pay less than the European Union average for that electricity, so this idea that nuclear makes your electricity expensive is simply not true. They built 55 nuclear reactors in 25 years, and they did them in parallel. It was just absolutely amazing. I would love to see a French-style nuclear rollout in all developed countries across the world. I think that would just be absolutely amazing.Commercial fusion, coming soon . . . ? (23:06)I think we're pretty good at doing things when we put our minds to it, but certainly not in the next couple of decades. But luckily, we already have a proven way of producing lots of energy, and that's with nuclear fission, in the meantime.What is your enthusiasm level or expectation about nuclear fusion? I can tell you that the Silicon Valley people I talk to are very positive. I know they're inherently very positive people, but they're very enthusiastic about the prospects over the next decade, if not sooner, of commercial fusion. How about you?It would be incredible. The last question that I was asked in my PhD interview 10 years ago was, “If you could solve one scientific or engineering problem, what would it be?” and my answer was nuclear fusion. And that would be the answer that I would give today. It just seems to me to be obviously the solution to the long-term energy needs of humanity. However, I'm less optimistic, perhaps, than the Silicon Valley crowd. The running joke, of course, is that it's always 40 years away and it recedes into the future at one year per year. So I would love to be proved wrong, but realistically — no one's even got it working in a prototype power station. That's before we even think about commercializing it and deploying it at scale. I really, really think that we're decades away, maybe even something like a century. I'd be surprised if it took longer than a century, actually. I think we're pretty good at doing things when we put our minds to it, but certainly not in the next couple of decades. But luckily, we already have a proven way of producing lots of energy, and that's with nuclear fission, in the meantime.Don't go to California with that attitude. I can tell you that even when I go there and I talk about AI, if I say that AI will do anything less than improve economic growth by a factor of 100, they just about throw me out over there. Let me just finish up by asking you this: Earlier, we mentioned Three Mile Island and Chernobyl. How resilient do you think this nuclear renaissance is to an accident?Even if we take the rate of accident over the last 70 years of nuclear power production and we maintain that same level of rate of accident, if you like, it's still one of the safest things that our species does, and everyone talks about the death toll from nuclear power, but nobody talks about the lives that it's already saved because of the fossil fuels, that it's displaced fossil fuels. They're so amazing in some ways, they're so convenient, they're so energy-dense, they've created the modern world as we all enjoy it in the developed world and as the developing world is heading towards it. But there are some really, really nasty consequences of fossil fuels, and whether or not you care about climate change, even the air pollution alone and the toll that that takes on human health is enough to want to phase them out. Nuclear power already is orders of magnitude safer than fossil fuels and I read this really amazing paper that globally, it was something like between the '70s and the '90s, nuclear power saved about two million lives because of the fossil fuels that it displaced. That's, again, orders of magnitude more lives that have been lost as a consequence of nuclear power, mostly because of Chernobyl and Fukushima. Even if the safety record of nuclear in the past stays the same and we forward-project that into the future, it's still a winning horse to bet on.If in the UK they've started up one new nuclear reactor in the past 30 years, right? How many would you guess will be started over the next 15 years?Four or five. Something like that, I think; although I don't know.Is that a significant number to you?It's not enough for my liking. I would like to see many, many more. Look at France. I know I keep going back to it, but it's such a brilliant example. If France hadn't done what they'd done in between the '70s and the '90s — 55 nuclear reactors in 25 years, all of which are still working — it would be a much more difficult case to make because there would be no historical precedent for it. So, maybe predictably, I wouldn't be satisfied with anything less than a French-scale nuclear rollout, let's put it that way.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were PromisedMicro Reads▶ Economics* The U.S. Marches Toward State Capitalism With American Characteristics - WSJ* AI Spending Is Propping Up the Economy, Right? It's Complicated. - Barron's* Goodbye, $165,000 Tech Jobs. 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Do the data back that up? - Reason* How Older People Are Reaping Brain Benefits From New Tech - NYT* Did Disease Defeat Napoleon? - SciAm* Scientists Discover a Viral Cause of One of The World's Most Common Cancers - ScienceAlert* ‘A tipping point': An update from the frontiers of Alzheimer's disease research - Yale News* A new measure of health is revolutionising how we think about ageing - NS* First proof brain's powerhouses drive – and can reverse – dementia symptoms - NA* The Problem Is With Men's Sperm - NYT Opinion▶ Clean Energy/Climate* The Whole World Is Switching to EVs Faster Than You - Bberg Opinion* Misperceptions About Air Pollution: Implications for Willingness to Pay and Environmental Inequality - NBER* Texas prepares for war as invasion of flesh-eating flies appears imminent - Ars* Data Center Energy Demand Will Double Over the Next Five Years - Apollo Academy* Why Did Air Conditioning Adoption Accelerate Faster Than Predicted? Evidence from Mexico - NBER* Microwaving rocks could help mining operations pull CO2 out of the air - NS* Ford's Model T Moment Isn't About the Car - Heatmap* Five countries account for 71% of the world's nuclear generation capacity - EIA* AI may need the power equivalent of 50 large nuclear plants - E&E▶ Space/Transportation* NASA plans to build a nuclear reactor on the Moon—a space lawyer explains why - Ars* Rocket Lab's Surprise Stock Move After Solid Earnings - Barron's▶ Up Wing/Down Wing* James Lovell, the steady astronaut who brought Apollo 13 home safely, has died - Ars* Vaccine Misinformation Is a Symptom of a Dangerous Breakdown - NYT Opinion* We're hardwired for negativity. That doesn't mean we're doomed to it. - Vox* To Study Viking Seafarers, He Took 26 Voyages in a Traditional Boat - NYT* End is near for the landline-based service that got America online in the '90s - Wapo▶ Substacks/Newsletters* Who will actually profit from the AI boom? - Noahpinion* OpenAI GPT-5 One Unified System - AI Supremacy* Proportional representation is the solution to gerrymandering - Slow Boring* Why I Stopped Being a Climate Catastrophist - The Ecomodernist* How Many Jobs Depend on Exports? - Conversable Economist* ChatGPT Classic - Joshua Gans' Newsletter* Is Air Travel Getting Worse? - Maximum Progress▶ Social Media* On AI Progress - @daniel_271828* On AI Usage - @emollick* On Generative AI and Student Learning - @jburnmurdoch Faster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

TDC 060: Inside a Room With 40+ Million-Copy Bestselling AuthorsSeven powerful lessons from the literary equivalent of the NBA All-Star team.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque shares profound insights from his private gathering with 40+ million-copy bestselling authors including James Clear, Jamie Kern-Lima, and Hal Elrod.You'll discover why good enough isn't good enough for books, learn the secret to short-form content success, and understand why there's no single "right way" to achieve massive success.Question of the Day

ChatGPT-5 just launched, marking a major milestone for OpenAI and the entire AI ecosystem.Fresh off the live stream, Erik Torenberg was joined in the studio by  three people who played key roles in making this model a reality:Christina Kim, Researcher at OpenAI, who leads the core models team on post-trainingIsa Fulford, Researcher at OpenAI, who leads deep research and the ChatGPT agent team on post-trainingSarah Wang, General Partner at a16z, who helped lead our investment in OpenAI since 2021They discuss what's actually new in ChatGPT-5—from major leaps in reasoning, coding, and creative writing to meaningful improvements in trustworthiness, behavior, and post-training techniques.We also discuss:How GPT-5 was trained, including RL environments and why data quality matters more than everThe shift toward agentic workflows—what “agents” really are, why async matters, and how it's empowering a new golden age of the “ideas guy”What GPT-5 means for builders, startups, and the broader AI ecosystem going forwardWhether you're an AI researcher, founder, or curious user, this is the deep-dive conversation you won't want to miss.Timecodes:0:00 ChatGPT Origins1:57 Model Capabilities & Coding Improvements4:00 Model Behaviors & Sycophancy6:15 Usage, Pricing & Startup Opportunities8:03 Broader Impact & AGI Discourse16:56 Creative Writing & Model Progress32:37 Training, Data & Reflections36:21 Company Growth & Culture41:39 Closing Thoughts & MissionResourcesFind Christina on X: https://x.com/christinahkimFind Isa on X: https://x.com/isafulfFind Sarah on X: https://x.com/sarahdingwangStay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures

TDC 059: Live Wild or Die Boring: The Urgent Case for Reconnecting with the Natural World (Part 1)Why nature isn't just good for your soul—it's essential for survival, creativity, and breakthrough innovation.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque shares insights from recording his upcoming book Return to Real and reveals shocking research about our digital lifestyle.You'll discover how concrete environments increase early death by 12%, learn why nature walks boost creativity by 60%, and understand why tech founders won't use their own products.Question of the Day

Chapters 00:00:00 Welcome and Guest Introduction 00:01:18 Tulu, OVR, and the RLVR Journey 00:03:40 Industry Approaches to Post-Training and Preference Data 00:06:08 Understanding RLVR and Its Impact 00:06:18 Agents, Tool Use, and Training Environments 00:10:34 Open Data, Human Feedback, and Benchmarking 00:12:44 Chatbot Arena, Sycophancy, and Evaluation Platforms 00:15:42 RLHF vs RLVR: Books, Algorithms, and Future Directions 00:17:54 Frontier Models: Reasoning, Hybrid Models, and Data 00:22:11 Search, Retrieval, and Emerging Model Capabilities 00:29:23 Tool Use, Curriculum, and Model Training Challenges 00:38:06 Skills, Planning, and Abstraction in Agent Models 00:46:50 Parallelism, Verifiers, and Scaling Approaches 00:54:33 Overoptimization and Reward Design in RL 01:02:27 Open Models, Personalization, and the Model Spec 01:06:50 Open Model Ecosystem and Infrastructure 01:13:05 Meta, Hardware, and the Future of AI Competition 01:15:42 Building an Open DeepSeek and Closing Thoughts We first had Nathan on to give us his RLHF deep dive when he was joining AI2, and now he's back to help us catch up on the evolution to RLVR (Reinforcement Learning with Verifiable Rewards), first proposed in his Tulu 3 paper. While RLHF remains foundational, RLVR has emerged as a powerful approach for training models on tasks with clear success criteria and using verifiable, objective functions as reward signals—particularly useful in domains like math, code correctness, and instruction-following. Instead of relying solely on subjective human feedback, RLVR leverages deterministic signals to guide optimization, making it more scalable and potentially more reliable across many domains. However, he notes that RLVR is still rapidly evolving, especially regarding how it handles tool use and multi-step reasoning. We also discussed the Tulu model series, a family of instruction-tuned open models developed at AI2. Tulu is designed to be a reproducible, state-of-the-art post-training recipe for the open community. Unlike frontier labs like OpenAI or Anthropic, which rely on vast and often proprietary datasets, Tulu aims to distill and democratize best practices for instruction and preference tuning. We are impressed with how small eval suites, careful task selection, and transparent methodology can rival even the best proprietary models on specific benchmarks. One of the most fascinating threads is the challenge of incorporating tool use into RL frameworks. Lambert highlights that while you can prompt a model to use tools like search or code execution, getting the model to reliably learn when and how to use them through RL is much harder. This is compounded by the difficulty of designing reward functions that avoid overoptimization—where models learn to “game” the reward signal rather than solve the underlying task. This is particularly problematic in code generation, where models might reward hack unit tests by inserting pass statements instead of correct logic. As models become more agentic and are expected to plan, retrieve, and act across multiple tools, reward design becomes a critical bottleneck. Other topics covered: - The evolution from RLHF (Reinforcement Learning from Human Feedback) to RLVR (Reinforcement Learning from Verifiable Rewards) - The goals and technical architecture of the Tulu models, including the motivation to open-source post-training recipes - Challenges of tool use in RL: verifiability, reward design, and scaling across domains - Evaluation frameworks and the role of platforms like Chatbot Arena and emerging “arena”-style benchmarks - The strategic tension between hybrid reasoning models and unified reasoning models at the frontier - Planning, abstraction, and calibration in reasoning agents and why these concepts matter - The future of open-source AI models, including DeepSeek, OLMo, and the potential for an “American DeepSeek” - The importance of model personality, character tuning, and the model spec paradigm - Overoptimization in RL settings and how it manifests in different domains (control tasks, code, math) - Industry trends in inference-time scaling and model parallelism Finally, the episode closes with a vision for the future of open-source AI. Nathan has now written up his ambition to build an “American DeepSeek”—a fully open, end-to-end reasoning-capable model with transparent training data, tools, and infrastructure. He emphasizes that open-source AI is not just about weights; it's about releasing recipes, evaluations, and methods that lower the barrier for everyone to build and understand cutting-edge systems. It would seem the

Read more about Kafka Diskless-topics, KIP by Aiven:KIP-1150: https://fnf.dev/3EuL7mvSummary:In this conversation, Kaivalya Apte and Alexis Schlomer discuss the internals of query optimization with the new project optd. They explore the challenges faced by existing query optimizers, the importance of cost models, and the advantages of using Rust for performance and safety. The discussion also covers the innovative streaming model of query execution, feedback mechanisms for refining optimizations, and the future developments planned for optd, including support for various databases and enhanced cost models.Chapters00:00 Introduction to optd and Its Purpose03:57 Understanding Query Optimization and Its Importance10:26 Defining Query Optimization and Its Challenges17:32 Exploring the Limitations of Existing Optimizers21:39 The Role of Calcite in Query Optimization26:54 The Need for a Domain-Specific Language40:10 Advantages of Using Rust for optd44:37 High-Level Overview of optd's Functionality48:36 Optimizing Query Execution with Coroutines50:03 Streaming Model for Query Optimization51:36 Client Interaction and Feedback Mechanism54:18 Adaptive Decision Making in Query Execution54:56 Persistent Memoization for Enhanced Performance57:12 Guided Scheduling in Query Optimization59:55 Balancing Execution Time and Optimization01:01:43 Understanding Cost Models in Query Optimization01:04:22 Exploring Storage Solutions for Query Optimization01:07:13 Enhancing Observability and Caching Mechanisms01:07:44 Future Optimizations and System Improvements01:18:02 Challenges in Query Optimization Development01:20:33 Upcoming Features and Roadmap for optdReferences:- NeuroCard: learned Cardinality Estimation: https://vldb.org/pvldb/vol14/p61-yang.pdf- RL-based QO: https://arxiv.org/pdf/1808.03196- Microsoft book about QO: https://www.microsoft.com/en-us/research/publication/extensible-query-optimizers-in-practice/- Cascades paper: https://15721.courses.cs.cmu.edu/spring2016/papers/graefe-ieee1995.pdf- optd source code: https://github.com/cmu-db/optd- optd website (for now): https://db.cs.cmu.edu/projects/optd/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#database #queryoptimization #sql #postgres

TDC 058: A Bombshell Week in AI and an Eventful Week on the FarmMicrosoft's groundbreaking AI job impact research reveals which careers are most vulnerable.---Episode Summary:  In this episode of The Digital Contrarian, host Ryan Levesque breaks down Microsoft's explosive AI job impact research paper analyzing over 200,000 AI interactions.  You'll discover which jobs are most at risk from AI displacement, learn about satisfaction scores that predict future automation, and hear about Ryan's eventful farm accident that landed him in an ambulance.  ---Question of the Day

Join Tommy Shaughnessy as he hosts Pondering Durian (Lead at Delphi Intelligence) and José Macedo (Co-Founder at Delphi Labs & Founding Partner at Delphi Ventures) to introduce Delphi Intelligence — Delphi's new open research initiative focused on artificial intelligence. Learn why Delphi is going deep into frontier models, robotics, reinforcement learning, and the intersection of crypto and AI, and how this initiative aims to uncover transformative opportunities across emerging tech.Delphi Intelligence: https://www.delphiintelligence.io/

TDC 057: The Rise of the Generalist: An Unexpected Side Effect of the AI EraWhy AI might reverse 200 years of specialization and reward cross-domain thinking.---Episode Summary  In this episode of The Digital Contrarian, host Ryan Levesque explores a counterintuitive insight from Lex Fridman's interview with Google CEO Sundar Pichai.  You'll discover why AI may favor generalists over specialists, learn how cross-domain connections create unique value, and understand why your "non-productive" interests might be your secret weapon in the new economy.  ---Question of the Day

Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent's memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google's “non-acquisition” of Windsurf.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai Chapters: 00:00 – Misha Laskin Introduction 00:44 – Superintelligence vs. Super Intelligent Autonomous Systems 03:26 – Misha's Journey from Physics to AI 07:48 – Asimov Product Release 11:52 – What Differentiates Asimov from Other Agents 16:15 – Asimov's Eval Philosophy 21:52 – The Types of Queries Where Asimov Shines 24:35 – Designing a Team-Wide Memory for Asimov 28:38 – Leveraging Pre-Trained Models 32:47 – The Challenges of Solving Scaling in RL 37:21 – Training Agents in Copycat Software Environments 38:25 – When Will We See ASI?  44:27 – Thoughts on Windsurf's Non-Acquisition 48:10 – Exploring Non-RL Datasets 55:12 – Tackling Problems Beyond Engineering and Coding 57:54 – Where We're At in Deploying ASI in Different Fields 01:02:30 – Conclusion

In today's episode we get back to interviews! I'm joined by Greybeard, a huge advocate for the SSA region, and a lover of Rocket Leauge. We had a great chat about his time with RL. The ups and downs and lessons that he has learned along the way.

Una parte del dinero de los ahorrantes de la Asociación Cooperativa de Ahorro y Crédito Santa Victoria (Cosavi de RL) se usó en los últimos tres años para la compra de lujosos apartamentos en Miami, Estados Unidos, valorados en $5.8 millones. Otra parte se destinó a la adquisición de 8,670 botellas de vino, cervezas, limonadas y licores importados desde Francia, por un monto aproximado de $117,428. Las compras fueron realizadas por empresas de Manuel Alberto Coto Barrientos, el gerente de Cosavi que falleció en un accidente aéreo en septiembre de 2024, y que meses antes del escándalo financiero intentó “solucionar” el problema de la cooperativa por medio de un exasesor jurídico de Casa Presidencial.

全球最聰明的 AI 誕生了，而且它不是 GPT。xAI 推出的 Grok 4，在最新的 AI 大魔王考試裡，不只全場最高分，甚至學會了怎麼自己叫工具、自己算數學、還自己訂貨賣東西，靠經營虛擬販賣機賺了 4694 美金，撐了 324 天不崩潰。它的祕密武器叫做——巨量強化學習。這集我們就來聊聊：

Storytelling: The Most Scalable Post-AI Business Skill Worth MasteringIn a world of AI-generated content and fractured attention, your ability to tell compelling stories may be your greatest competitive advantage.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into why storytelling is the most scalable post-AI business skill worth developing.You'll learn how stories create deeper connection than any other content, discover the three story types you need to master, and find five contrarian tips for telling better stories that cut through the noise.Question of the Day

My Contrarian YouTube Strategy: Building a Strategic Content EcosystemMy contrarian YouTube strategy creates 500K+ annual views with just ONE piece of original content per week.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into the evolution of his strategic content ecosystem six months after first introducing it.You'll learn how one weekly "source of truth" piece creates a sustainable content system that respects family time, discover the long-form leverage strategy for YouTube, and find how to cultivate influence versus chasing attention.Question of the Day

The Breakdown of Shared Reality: AI's Most Dangerous Unintended ConsequenceNobel Prize winner Geoffrey Hinton warns that AI-driven personalization is destroying our collective understanding of what's real.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into the breakdown of shared reality caused by AI-driven hyper-personalization and its profound implications for business and society.You'll learn why isolated algorithmic realities undermine strategic thinking, discover the concept of the "Promethean Transition" we're navigating, and find how to choose between being a tunnel digger or pathfinder in our AI future.Question of the Day

How do you build a foundation model that can write code at a human level? Eiso Kant (CTO & co-founder, Poolside) reveals the technical architecture, distributed team strategies, and reinforcement learning breakthroughs powering one of Europe's most ambitious AI startups. Learn how Poolside operates 10,000+ H200s, runs the world's largest code execution RL environment, and why CTOs must rethink engineering orgs for an agent-driven future.

Between Death and Danger: Wilderness Wisdom from the Grand CanyonBetween death and danger lies the path up the mountain—a profound insight revealed during my life-changing vision quest.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into a deeply personal father-son Grand Canyon journey that became the catalyst for his upcoming book "Return to Real".You'll learn why reconnecting with nature isn't just a luxury but essential for breakthrough thinking, discover the symbolic message from a desert pocket mouse and California condor, and find how God-made things offer clarity in our AI-driven world.Question of the Day

Category of One: A $10 Million Business BlueprintAfter a decade building a highly profitable $10M/year consulting and software company, I reveal the contrarian framework that made it all possible.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into what it really means to create a "Category of One" business and why it's the only type worth building.You'll learn how to position yourself where no direct comparison exists, discover the exact framework for charging premium prices, and find the three-phase growth strategy that took his company from zero to over $1M/month.Question of the Day

Munaf Manji and Griffin Warner talk Tuesday MLB betting. Best bets as always.

Setbacks and Breakthroughs: Why Feeling Stuck Might Mean You're on the BrinkWhat if your greatest setbacks are actually seeds for your most meaningful breakthroughs?Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into the nature of setbacks and breakthroughs through personal farm stories and neuroscience research.You'll learn why breakthroughs often cluster after prolonged setbacks, discover how moderate adversity builds resilience, and find three reflection questions to help you prepare for your next breakthrough.Question of the Day

Courage for the Rest of Us: Giving Up Good to Go After GreatMost of us aren't afraid of failure—we're afraid of other people seeing us fail.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into the true nature of courage and why giving up "good" to go after "great" is so difficult.You'll learn why the opinions of others hold more power over us than our own fears, discover Brené Brown's "Square Squad" technique to silence the noise, and find a simple 30-day experiment to build courage.Question of the Day

More stories from Reddit...  FOLLOW ME ON KICK!  https://kick.com/southerncannibal BUY MY MERCH PLEASE!  https://southern-cannibal-shop.fourthwall.com/? Send your TRUE Scary Stories HERE! ► https://southerncannibal.com/  OR Email at southerncannibalstories@gmail.com LISTEN TO THE DINNER TABLE PODCAST! ► https://open.spotify.com/show/3zfschBzphkHhhpV870gFW?si=j53deGSXRxyyo9rsxqbFgw Faqs about me ► https://youtube.fandom.com/wiki/Southern_Cannibal Stalk Me! ► Twitter: https://twitter.com/iAmCanni ► Instagram: https://instagram.com/SouthernCannibal ► Scary Story Playlist: https://www.youtube.com/playlist?list=PL18YGadwJHERUzNMxTSoIYRIoUWfcGO2I ► DISCLAIMER: All Stories and Music featured in today's video were granted FULL permission for use on the Southern Cannibal YouTube Channel!  Huge Thanks to these brave folks who sent in their stories! #1. - u/Rozalera #2. - Anonymous  #3. - Anonymous  #4. - RL #5. - FF #6. - BettyJoe #7. - John  Huge Thanks to these talented folks for their creepy music! ► Myuuji: https://www.youtube.com/c/myuuji ♪ ► CO.AG Music: https://www.youtube.com/channel/UCcavSftXHgxLBWwLDm_bNvA  ♪ ► Kevin MacLeod: http://incompetech.com ♪ ► Piano Horror:  https://www.youtube.com/PianoHorror ♪ https://creativecommons.org/licenses/by/3.0/us/

The Trust Molecule: Why Oxytocin (Not Dopamine) Will Define the Post AI EraIn a world built around dopamine hits, oxytocin might just be the brain molecule that matters most for your business.Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into the neuroscience of trust and why oxytocin will become your greatest competitive advantage.You'll learn about the four key happiness chemicals and why oxytocin stands apart, discover the "Global Oxytocin Deficit" creating both crisis and opportunity, and get three science-backed strategies to strategically elicit oxytocin in your business.Question of the Day

This week on Dividend Talk, Derek is joined by fellow Dutch investor DazZMikey as European DGI enjoys a well-earned holiday. Together, we go on a unique “Stock Safari,” exploring dividend gems far from our usual American and Western European terrain.Along the way, they reflect on macro news, dividend hikes and cuts, and how to handle markets you don't fully trust. Hope you enjoy

S&P Futures are trading lower this morning. There are news reports indicating that Isreal is preparing to launch a strike on Iran. In March, President Trump gave Iran a 60-day deadline to reach a deal, that deadline has passed. House Republicans appear close to passing their reconciliation bill, The Senate will likely make changes to the bill. If the bill passes in the House and Senate, it will likely be a negative for markets as it will increase the deficit. Yesterday, President Trump unveiled a missile defense plan, LHX shares are higher. Medtronic plans to separate its diabetes business into a stand-alone company. Take Two announced a $1 Billion stock offering. KEYS, BIDU, & LOW are higher after earning announcements. After the bell today SNOW, ZM and URBN are set to report. On Thursday morning ADI, BJ & RL will repor

Truth, Hell, & The Lie They Believe: A 12-Part Framework For Owning Your CategoryWhat if your most powerful business advantage isn't just what you know, but understanding the lie your audience believes?Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque dives into a complete 12-part strategic framework for differentiating yourself in any market.You'll learn how to identify your audience's "hell" and the lie they believe, discover why contrarian truths create more impact than incremental improvements, and get a strategic blueprint you can apply to any major project or business repositioning.Question of the Day

Join hosts Shawn and Joni Wolfswinkel for an inspiring episode of Inside The Wolf's Den as they sit down with Peter Lohmann, a successful entrepreneur and expert in the property management industry. As co-founder and CEO of RL Property Management in Columbus, Ohio, Peter oversees over hundreds of residential units and has built a reputation for his innovative systems and leadership in the field. In this episode, Peter shares his remarkable journey from control systems engineer to leading a thriving property management company. Discover what inspired him to start RL, how the industry has evolved since 2013, and the early challenges he faced—along with his strategies for overcoming them. Peter also discusses the vital qualities of effective leadership, maintaining team motivation, and differentiating in a competitive market. Listeners will gain insights into current industry trends, especially how technology is transforming property management operations. Peter offers practical advice for property owners selecting a management company and emphasizes the importance of trust, transparency, and communication in building strong client and tenant relationships. Beyond business, Peter shares his leadership philosophy, balancing a demanding career with family life, and the influences shaping his approach to success. He also provides a glimpse into the future of property management, highlighting innovative projects and emerging trends to watch. Whether you're an aspiring entrepreneur, seasoned property manager, or property owner, this episode delivers valuable insights from a leader who is shaping the future of the industry. Tune in for an engaging conversation packed with actionable tips, inspiring stories, and expert advice. RL Property Management Link: https://rlpmg.com Email Link: info@rlpmg.com

Today, we're joined by Mahesh Sathiamoorthy, co-founder and CEO of Bespoke Labs, to discuss how reinforcement learning (RL) is reshaping the way we build custom agents on top of foundation models. Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities. We also explore the limitations of supervised fine-tuning (SFT) for tool-augmented reasoning tasks, the reward-shaping strategies they've used, and Bespoke Labs' open-source libraries like Curator. We also touch on the models MiniCheck for hallucination detection and MiniChart for chart-based QA. The complete show notes for this episode can be found at https://twimlai.com/go/731.

Who Are You Really Building For: The 100,000, The 100, or The One?Are you diluting your message by trying to please everyone instead of focusing on your ideal customer?Episode SummaryIn this episode of The Digital Contrarian, host Ryan Levesque explores the strategic dilemma of who entrepreneurs should truly optimize their business for.You'll learn why chasing scale often leads to diluted messaging, how focusing on "The One" ideal customer creates authentic resonance, and discover why bestselling authors write for specific real people rather than abstract audiences.Question of the Day

Join Tommy Shaughnessy from Delphi Ventures as he hosts Sam Lehman, Principal at Symbolic Capital and AI researcher, for a deep dive into the Reinforcement Learning (RL) renaissance and its implications for decentralized AI. Sam recently authored a widely discussed post, "The World's RL Gym", exploring the evolution of AI scaling and the exciting potential of decentralized networks for training next-generation models.

The World's RL Gym: https://www.symbolic.capital/writing/the-worlds-rl-gym

Dylan Patel is the founder of SemiAnalysis, a research & analysis company specializing in semiconductors, GPUs, CPUs, and AI hardware. Nathan Lambert is a research scientist at the Allen Institute for AI (Ai2) and the author of a blog on AI called Interconnects. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep459-sc See below for timestamps, and to give feedback, submit questions, contact Lex, etc. CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Dylan's X: https://x.com/dylan522p SemiAnalysis: https://semianalysis.com/ Nathan's X: https://x.com/natolambert Nathan's Blog: https://www.interconnects.ai/ Nathan's Podcast: https://www.interconnects.ai/podcast Nathan's Website: https://www.natolambert.com/ Nathan's YouTube: https://youtube.com/@natolambert Nathan's Book: https://rlhfbook.com/ SPONSORS: To support this podcast, check out our sponsors & get discounts: Invideo AI: AI video generator. Go to https://invideo.io/i/lexpod GitHub: Developer platform and AI code editor. Go to https://gh.io/copilot Shopify: Sell stuff online. Go to https://shopify.com/lex NetSuite: Business management software. Go to http://netsuite.com/lex AG1: All-in-one daily nutrition drinks. Go to https://drinkag1.com/lex OUTLINE: (00:00) - Introduction (13:28) - DeepSeek-R1 and DeepSeek-V3 (35:02) - Low cost of training (1:01:19) - DeepSeek compute cluster (1:08:52) - Export controls on GPUs to China (1:19:10) - AGI timeline (1:28:35) - China's manufacturing capacity (1:36:30) - Cold war with China (1:41:00) - TSMC and Taiwan (2:04:38) - Best GPUs for AI (2:19:30) - Why DeepSeek is so cheap (2:32:49) - Espionage (2:41:52) - Censorship (2:54:46) - Andrej Karpathy and magic of RL (3:05:17) - OpenAI o3-mini vs DeepSeek r1 (3:24:25) - NVIDIA (3:28:53) - GPU smuggling (3:35:30) - DeepSeek training on OpenAI data (3:45:59) - AI megaclusters (4:21:21) - Who wins the race to AGI? (4:31:34) - AI agents (4:40:16) - Programming and AI (4:47:43) - Open source (4:56:55) - Stargate (5:04:24) - Future of AI PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips