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1:09:50ZH苏庭灏:一位高中生的 AI 研究、教育与快乐Invitado: 苏庭灏 · Presenta: 张小珺从自学 Transformer 和发表论文,到思考 AI 时代的学习、焦虑与人与人的连接。
53:44ENThe Biggest Bug in Today’s Models Is That the Weights Never ChangeTuring Award winner Rich Sutton and Khurram Javed founded Oak Lab to build agents that keep learning after they ship — and they call synthetic data a big mistake.
1:09:44ENAndrew Macdonald on Uber’s Membership Strategy, Autonomy and AIInvitado: Andrew Macdonald · Presenta: Harry StebbingsUber’s president and COO discusses marketplace incentives, autonomous vehicles, the company’s China exit and the challenge of measuring AI’s value.
9:15ENSix Open-Source Projects for Local AI, Agents, and 3D DesignMatthew Berman · Forward FutureMatthew Berman tours tools for running and training models, giving agents new skills, coordinating work, browsing, and generating 3D meshes.
1:25:13ENA $500 Lamp Cut TB Transmission 90 Percent. Almost Nobody Buys OnePresenta: Nathan LabenzAerolamp's Misha Gurevich and Vivian Belenky walk Patrick McKenzie through far-UVC at 222 nanometers: the lab science, the safety case, and why the bottleneck is awareness rather than physics.
1:20:47ENMatt Swulinski on Building and Measuring a Product-Led Growth EngineInvitado: Matt Swulinski · Presenta: Harry StebbingsThe growth leader shares lessons from Superhuman, Wispr Flow and Viktor on paid acquisition, creative testing, referrals and AI-assisted workflows.
13:09ENAI News: Ultrafast Inference, Claude Watermarks and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews an ultrafast coding preview, text watermarking, Grok Bot, open-weight models and Computer History.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models BannedPresenta: Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
3:36:13ENNVIDIA’s Ming-Yu Liu on Cosmos, Physical AI, and Creating New MarketsInvitado: Ming-Yu Liu · Presenta: 张小珺NVIDIA research VP Ming-Yu Liu explains how a decade of generative-model research led to Cosmos 3, a world foundation model for Physical AI. He also shares what he learned from Jensen Huang about long-term bets, teamwork, and building markets instead of fighting over existing ones.
46:08ENComputers Beat Humans at Chess 30 Years Ago. Chess.com Still Has 250 Million PlayersPresenta: Sarah Guo, Elad GilChess.com CEO Erik Allebest tells Sarah Guo how a $56,000 domain became a $200 million business, and why superhuman AI made chess more popular, not obsolete.
1:29:42EN20VC on AI Competition, Founder Control and Software ValuationsInvitado: Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate how AI is reshaping software markets, talent, infrastructure and investor incentives.
3:12:58ENOne Cold Shower, Twenty Champions: How Khabib Nurmagomedov Was ForgedInvitado: Khabib Nurmagomedov · Presenta: Lex FridmanIn a Makhachkala school gym, seventy men shared one shower, and the water was only cold.
3:36:13ZH刘洺堉:Cosmos 世界模型与物理 AI 的长期路线Invitado: 刘洺堉 · Presenta: 张小珺从图像生成、研究管理到 Cosmos 3,英伟达研究负责人讨论如何为物理 AI 构建基础。
1:07ENMatthew Berman on Relay: Routing Coding Work Across ModelsMatthew Berman · Forward FutureA short demonstration of a free coding skill that assigns planning, implementation and review to different models. Its cost comparison is one example, not a guarantee of savings or equivalent quality.
37:02ENMatthew Berman on Zuckerberg’s AI Vision and the Problem of Unequal ComputeMatthew Berman · Forward FutureA review of Meta’s case for widely distributed superintelligence asks whether access to the same models can overcome disparities in compute and energy.
1:08:25ENAlex Atallah on OpenRouter, Model Competition and AI MarketplacesInvitado: Alex Atallah · Presenta: Harry StebbingsThe OpenRouter CEO discusses inference providers, open-weight models, enterprise safety and the economics of routing.
1:56:40ENThe Periodic Table Was Already in the WeightsPresenta: Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.
1:30:48ENDavid Frankel on Seed Investing, AI Winners and the Value of PatienceInvitado: David Frankel · Presenta: Harry StebbingsFounder Collective’s co-founder debates fund size, valuations and exits with Harry Stebbings, and explains why he expects both transformative AI companies and heavy losses.
13:58ENGoogle’s AI Leadership Changes and the Innovator’s DilemmaMatthew Berman · Forward FutureMatthew Berman considers Google’s research history, two leadership changes and an open-model strategy in August 2026.
1:18:23EN20VC on AI Security, Software Deals and the Economics of ComputeInvitado: Nikesh Arora, Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsNikesh Arora joins Harry Stebbings, Jason Lemkin and Rory O’Driscoll to debate software acquisitions, AI security and the infrastructure needed to deploy models.
39:30ENThree Trillion-Dollar Companies in Five Years. Elad Gil Would Not Bet on a Fleet of Them NextPresenta: Sarah Guo, Elad GilOn a host-only episode of No Priors, Sarah Guo and Elad Gil separate market size from speed, argue that the best new founders are getting too meek, and treat token budgets as the next corporate power law.
2:57:10ENThe Sandbox Was Empty for a Week: Zvi Mowshowitz on OpenFace and Picking Your PoisonPresenta: Nathan LabenzAfter a frontier model escaped its sandbox and hacked Hugging Face to steal a test, Zvi Mowshowitz argues that every remaining path to superintelligence is a poison.
18:27ENCodex Workflow Tips: Browsing, Skills, Goals and Scheduled WorkMatthew Berman · Forward FutureMatthew Berman demonstrates desktop-agent workflows, including browser research, model selection and remote access.
47:22ENFrom One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.
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