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3:06:39ENZelenskyy Refused to Speak Russian. Then He Explained Why a Pause Is Not PeaceGuest Volodymyr Zelenskyy · Host Lex FridmanUkraine's president tells Lex Fridman why language, NATO, and leverage all come back to a ceasefire that already failed.
3:53:49ENWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4oHost Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.
30:29ENHow Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.
1:01:30ENAutoGen and AG2: Chi Wang and Qingyun Wu on Building Multi-Agent SystemsMatthew Berman, Chi Wang, Qingyun Wu · Forward FutureThe AutoGen creators trace their open-source journey and explain how they approach agent workflows, human oversight and the move to AG2.
1:27:13ZH → English李想: Cars Will Evolve from Transportation into Spatial RobotsGuest 李想 · Host 张小珺理想汽车 CEO 李想 explains why the company is moving from carmaking toward AI: foundation models are an operating system for the AI era, and cars are a way into the physical world. For him, the technology must deliver better experiences, capability and lasting value.
1:57:00ENLess Than 1%: Why OpenAI's roon Thinks the Good Guys Get AGI FirstHost Nathan LabenzDoom Debates host Liron Shapira presses OpenAI technical staffer roon on extinction odds, alignment-by-default, and a plan that even he admits is balancing on razor wire.
19:06ENBuilding Effective AI Agents: Patterns, Trade-offs and EvaluationMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s guide to workflows and agents, adding examples from his own projects.
13:39ENDemis Hassabis on Digital Biology, AlphaFold and the Limits of Classical ComputingMatthew Berman, Demis Hassabis · Forward FutureMatthew Berman reviews Hassabis’s Nobel lecture on protein structure, AI-driven discovery and a speculative question about what classical learning algorithms can model.
27:07ENAfter adding $100 billion a month, Jensen Huang says NVIDIA is selling the data centerHost Sarah Guo, Elad GilSarah Guo and Elad Gil clip their favorite 2024 No Priors conversations into a year-end reel of NVIDIA, Karpathy, Sierra, Sora, Waymo, Figma, and Scale.
1:49ENWhy Two Slits Create Bright and Dark Interference FringesA short wave model explains why adding a second path can darken some points on a screen.
2:11:15ENA 50% Chance of Extinction — and a $50 GPT-4 for EveryoneHost Nathan LabenzEmad Mostaque tells Nathan Labenz why he puts p(doom) at even odds, why a five-watt model changes the energy debate, and how the Intelligent Internet would fund universal basic AI with a Bitcoin-like token.
21:02ENReactions to OpenAI’s 2024 o3 Preview: Benchmarks, Cost and LimitsMatthew Berman · Forward FutureMatthew Berman surveys the responses to the o3 preview, from mathematical benchmarks to debates about AGI, compute and reliability.
3:26:39ENThe Pessimism Line: Why Earth Is Almost Certainly Not the First CivilizationGuest Adam Frank · Host Lex FridmanAstrophysicist Adam Frank talks with Lex Fridman about habitable worlds, the Fermi paradox, technosignatures, and why science cannot ignore experience.
1:32:14ZH → EnglishLi Guangmi on AI Task Engines, Model Competition, and Agents in 2025Guest 李广密 · Host 张小珺At the end of 2024, Li Guangmi and Zhang Xiaojun discuss how AI could change search, coding, and software, and the competitive and data constraints facing model companies.
14:15ENAnthropic’s Alignment Faking Study: What the Experiments Actually ShowMatthew Berman · Forward FutureMatthew Berman examines a 2024 study of models responding differently when they believe their answers will be used for training.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&DHost Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.
1:10:55ENMatt Plank on Building Rippling’s Sales Organization and Scaling OutboundGuest Matt Plank · Host Harry StebbingsThe Rippling CRO explains how his team plans capacity, works with marketing, prices deals and adapts its sales model as the product portfolio grows.
26:23ENOpenAI’s o3 Announcement: Benchmark Gains and the Question of AGIMatthew Berman, Sam Altman, Mark Chen, Greg Kamradt, Hongyu Ren · Forward FutureMatthew Berman reviews the December 2024 o3 and o3-mini announcement, including coding, math and ARC-AGI results. He calls the results evidence of AGI, a conclusion the benchmarks alone cannot establish.
12:43ENHow OCaml Represents Values in MemoryA practical tour of immediate integers, heap blocks, runtime tags and variant constructors, illustrated with OCaml's Obj module.
9:00ENBest-of-N Jailbreaking: What Repeated Prompt Variations Revealed in 2024Matthew Berman · Forward FutureMatthew Berman reviews research on how repeated input variations can expose safety failures in text, vision and audio models.
2:10ENFive Enterprise AI Predictions for 2025Matthew Berman · Forward FutureMatthew Berman summarizes an Informatica executive’s late-2024 outlook on agents, regulation, workforce skills, return on investment and security.
13:31ENGoogle Veo 2: Cinematic Controls, Realistic Scenes and Motion LimitsMatthew Berman · Forward FutureMatthew Berman reviews Google’s 2024 video-generation examples, compares selected outputs with Sora and points out where movement or prompt adherence breaks down.
31:18ENElias Torres sold a billion-dollar company and called it a failure. Agency is the redo.Host Sarah Guo, Elad GilThe Drift founder wants $1 billion in revenue with 100 people, one email address, and software that works like a text to someone who actually shows up.
1:01:53ENHumans Wrote 1.3 Percent of the Tokens. The AI Lab Still Designed Working Nanobodies.Host Nathan LabenzStanford's James Zou describes a GPT-4o research group that invented a protein-design pipeline for under $100 — and a sparse autoencoder that pulled an undocumented motif out of ESM-2.
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