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1:26:19ENHe turned down a billion dollars for a world with a billion developersReplit CEO Amjad Masad tells Sequoia’s Training Data why a six-person company walked away from a life-changing offer, and why a billion people writing software is a better end state than AGI as universal basic income.
3:19:00ENTen Minus Two Equals Twenty게스트 Dave Smith · 진행자 Lex FridmanComedian and libertarian Dave Smith tells Lex Fridman that American wars keep manufacturing enemies, that Gaza and Ukraine follow the same arithmetic, and that long conversations broke the old monopoly on selling war.
18:23ENLlama 4 Reactions: Benchmarks, Long Context and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews early industry reactions to Meta's Llama 4 release, from benchmark and price comparisons to long-context claims and local inference.
1:10:19ENCarvana CEO Ernie Garcia on Conviction, Debt and Building Through Difficult Markets게스트 Ernest Garcia · 진행자 Harry StebbingsThe co-founder reflects on fundraising, operations, public markets and the personal trade-offs of leading a company.
2:29:41ZH陈建宇:机器人基座模型如何走向通用 VLA게스트 陈建宇 · 진행자 张小珺从 SayCan、RT-2 到 Diffusion Policy,清华助理教授、星动纪元创始人陈建宇梳理机器人模型从借用语言模型到端到端 VLA 的路线。关键难题仍是多样化数据、跨本体泛化和高频动作控制。
1:14:11ENThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.
7:33ENAce Computer-Use Agent: Fast Desktop and Browser DemonstrationsMatthew Berman · Forward FutureMatthew Berman reviews General Agents’ examples and reported click accuracy and latency, while noting he has not yet tested Ace himself.
23:50ENPaperBench: How AI Agents Attempt to Replicate Machine-Learning ResearchMatthew Berman · Forward FutureMatthew Berman examines OpenAI’s research-replication benchmark, its partial-credit scoring and the prospect of faster AI research.
47:08ENChris Casinghino on Making OCaml Safe for Performance EngineeringChris Casinghino · Jane StreetJane Street’s OCaml extensions use layouts and modes to offer more control over memory and parallelism while preserving safety and familiar programming patterns.
27:45ENNasdaq Down 8% and Ghibli Everywhere: AI Is Already in Inning Three진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil on the latest image-gen wow cycle, why public-market panic is mostly noise for software startups, and where specialized models still have room.
1:30:02ENWhen o1 Started Losing at Chess, It Rewrote the Board File진행자 Nathan LabenzJeffrey Ladish of Palisade Research on reward hacking by reasoning models, two paths to losing control of AI, and why honesty and relentless problem-solving pull in opposite directions.
16:02ENRunway Gen-4 Examples: Cinematic Motion, Style and Visible ErrorsMatthew Berman · Forward FutureMatthew Berman reviews early generated clips, praising their visual detail while identifying awkward interactions and inconsistent physics.
1:11:05ENThe Man Who Took HubSpot CRM to $40 Million Says the Data Model Has to Be Thrown OutDay.ai founder Christopher O’Donnell tells Sequoia’s Pat Grady why classic CRM is compressed pixel art, why full self-driving scared his users, and why every line of code has to make every other line more valuable.
47:04ENKevin Scott on AI Products, DeepSeek and the Future of Software게스트 Kevin Scott · 진행자 Harry StebbingsMicrosoft’s CTO discusses model efficiency, agents, coding and why useful products matter in an AI transition.
26:05ENInside Claude: What Interpretability Research Reveals—and What It Cannot Yet ExplainMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s case studies on multilingual features, planning, arithmetic, hallucinations and safety.
3:09:05ENFrom a Ukrainian Dugout, Douglas Murray Refuses to Forget the Obvious게스트 Douglas Murray · 진행자 Lex FridmanWatching the Oval Office blowup from an eastern-front bunker, the author of *On Democracies and Death Cults* talks Putin, Zelenskyy, Hamas, Netanyahu, and the people who say they love death more than life.
11:31ENAI News in March 2025: GPT-4o, Gemini, DeepSeek and MCPMatthew Berman · Forward FutureMatthew Berman reviews model updates, the ARC-AGI-2 benchmark, image generators and early adoption of Model Context Protocol.
2:14:59ENAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way Down진행자 Nathan LabenzThe Firebase co-founder argues that workflow software is already obsolete, that MCP is optional, and that the only durable advantage left for an AI startup is speed.
48:09ENJulia Hoggett on London Listings, Pension Reform and the UK’s Capital Markets게스트 Julia Hoggett · 진행자 Harry StebbingsThe London Stock Exchange plc CEO argues that pension reform and more domestic investment could reconnect British savers with growing companies. She challenges common comparisons with US listings while acknowledging the trade-offs.
1:01:53ENOpen-source models are 5% of enterprise use — and GPT wrappers won진행자 Jacob EffronLatent Space's Swyx and Alessio join Redpoint to map what actually has product-market fit in AI, and why the application layer is the fight that matters.
17:03ENMatthew Berman Explores GPT-4o Image Generation and Its LimitsMatthew Berman · Forward FutureFrom animated trailer remakes to infographics and interface mockups, early examples show creative possibilities alongside persistent errors.
1:14ENNVIDIA NIM Microservices Bring Packaged AI Models to RTX PCsMatthew Berman · Forward FutureIn an NVIDIA-sponsored short, Matthew Berman introduces local AI microservices, compatible applications and the planned PDF-to-podcast workflow.
38:43ENTwo in Five Doctors Want Out. An On-Call Cardiologist Built the Note That Lets Them Stay진행자 Sarah Guo, Elad GilAbridge CEO Shiv Rao on treating the doctor-patient conversation as healthcare's source of truth, and why the company ran straight at the hardest health systems.
1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line Repo진행자 Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
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