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16:57ENGrok 4.7 Compared: Coding, Knowledge Work and Cost per TaskMatthew Berman · Forward FutureMatthew Berman examines reported benchmarks and prices while noting missing competitors and limited hands-on testing.
38:51ENClaire Vo’s Blind Test of AI Models for Product WorkA live comparison of writing, prototypes, agents and creative tasks.
1:59:37ENOpus 5.5 and GPT-6 Sol and Luna: A Launch-Day LivestreamMatthew Berman, Thariq Shihipar, Alex, Brian · Forward FutureMatthew Berman interviews Anthropic’s Thariq Shihipar, shows early demos and then reviews two OpenAI models announced during the stream.
41:32ENUsing Fable 5.1 for Longer Coding TasksTheo Browne · T3Theo Browne shares how he prompts coding agents, sets end states and uses reviews and tests to support longer-running work.
0:23ENWhy AI Safety Evaluators Need More Than a Black BoxTheo Browne · T3Theo Browne explains a proposal to give outside evaluators employee-like access inside AI companies.
0:37ENA Fruit-Fly Connectome, Online Simulations and Questions About ExperienceMatthew Berman · Forward FutureMatthew Berman shows playful simulated-fly demonstrations and asks whether a future complete simulation could suffer. A neural wiring map and those demonstrations do not establish a conscious digital animal.
1:19:04ENDaniel Dines on AI Agents, Enterprise Work and the Limits of AutomationGuest Daniel Dines · Host Harry StebbingsThe UiPath co-founder argues that companies need a map of work and reliable automation alongside AI, while Harry Stebbings challenges his assumptions about how fast jobs will change.
30:29ENJev: A Fast Classifier for Structured DecisionsTheo Browne · T3Theo Browne explores Typesafe AI’s Jev through classification, game and developer-workflow demos, and explains where he thinks a reasoning model is still needed.
0:51ENJev Demos: Fast AI for Search, Classification and BrowsingMatthew Berman · Forward FutureMatthew Berman highlights a collection of community-built demonstrations using Jev.
2:14:20ENAlexander Mattick on Inference, Constraints and World ModelsGuest Alexander Mattick · Host Tim ScarfeWhy generative modeling, reinforcement learning and robotics all depend on assumptions and costs.
0:24ENWhy Theo Worries About AI Harm Before ShutdownTheo Browne · T3Theo Browne argues that harmful effects could persist even after a system is switched off.
1:40:36ENThe Labs Are Screaming: Internal Models Are a Generation AheadHost Nathan LabenzA week of Cognitive Revolution live shows on Dario Amodei's call to pace the frontier, a Trump–Xi deal that may not be needed, an AI store manager that fired a worker, and a new pain axis inside language models.
1:19:54ENThomas Sohmers on AI Inference, Memory and Data-Center GrowthGuest Thomas Sohmers · Host Harry StebbingsPositron’s co-founder discusses the engineering economics of inference, his views on AI infrastructure and regulation, and the risks he sees in financing and concentration.
1:19:48ENAI Safety, Regulation and a Hard Fork FarewellHost Kevin Roose, Casey NewtonKevin Roose and Casey Newton discuss the debate and reflect on their show.
0:22ENA Needle-in-a-Haystack Game, One Piece at a TimeMatthew Berman · Forward FutureMatthew Berman shows a game he says hides one needle among five million pieces of hay and jokes about searching it on a 48-hour livestream.
27:09ENWhy Theo Values Reliable Coding Agents Over Peak PerformanceTheo Browne · T3Theo Browne argues that a model’s weakest moments matter when a coding task stretches from investigation through testing and review.
1:05ENNVIDIA PAIR Routes Local AI Requests Across ComputersMatthew Berman · Forward FutureMatthew Berman demonstrates NVIDIA's local-network router for distributing AI inference work among compatible machines.
51:55ENThe last IMO problem AI could not solveGrant Sanderson · 3Blue1BrownGrant Sanderson walks through IMO 2025 Problem 6 — a tiling puzzle that frontier models missed — and the visual patience it takes to prove a construction is optimal.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconHost Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
0:22ENAI Leaders Express Support for Slowing the FrontierTheo Browne · T3Theo Browne discusses public responses to Dario Amodei’s proposal for independent AI evaluators.
12:31ENJev's Fast Decisions: Demos, Tradeoffs and Reliability ClaimsMatthew Berman · Forward FutureMatthew Berman explores a decision-oriented AI model through routing, simulated worlds, chess and game-control examples.
36:29ENY Combinator’s View of Startups in 2026: Hard Tech, AI and Solo FoundersGarry Tan, Jared Friedman, Diana Hu, Harj Taggar · Y CombinatorThe Lightcone panel shares its own batch data and observations about robotics, agent workflows and founder experience.
1:19:28ENAI Safety, Personal Agents and the Venture Market’s Uneven ExitsGuest Jason Lemkin, Rory O’Driscoll · Host Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate oversight of advanced AI, the economics of personal assistants and what a weaker software exit market means for investors.
1:12ENA Sponsored Look at Higgsfield's Image and Video APIMatthew Berman · Forward FutureMatthew Berman uses a real-versus-AI video comparison to introduce Higgsfield's model catalog and generation-based API billing.
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