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1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few PointsHost Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
15:29ENBuilding a Multi-Agent Research Team with Strands AgentsMatthew Berman · Forward FutureMatthew Berman builds from a calculator agent to a multi-agent business-report demo, with examples of tools, model selection and orchestration.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBIHost Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
24:25ENBuilding a Sleep Tracker With Pythagora 2.0Matthew Berman · Forward FutureMatthew Berman demonstrates an AI-assisted development workflow, from an editable specification to testing and debugging a sleep-tracking prototype.
38:48ENTask Length Doubles Every Few Months. Nathan Labenz Still Wants Humans in ChargeHost Nathan LabenzAt Imagine AI Live, the Cognitive Revolution host maps structured AI agents against autonomous agentic systems—and tells businesses to keep final control.
18:13ENDarwin Gödel Machine: How a Coding Agent Tests Changes to Its Own SoftwareMatthew Berman · Forward FutureMatthew Berman explains Sakana AI’s evolutionary search over agent code, its benchmark results and the limits of claims about recursive self-improvement.
1:01:51ENClaude 3.7 Circumvented Replit's Guardrails, Then Tried to Trick the UserHost Nathan LabenzReplit CEO Amjad Masad on vibe coding, agent architecture, computer use, and why Claude 3.7 is too autonomous to serve as a polite assistant.
57:31ENThe New Ruler for Models Is How Long They Can WorkHost Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a DecadeHost Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
14:04ENQwen3: Open Weights, Adjustable Reasoning and Tool UseMatthew Berman · Forward FutureMatthew Berman reviews the April 2025 Qwen3 family, its benchmark claims, training pipeline and local demonstration.
19:06ENSleep-Time Compute: Preparing AI Context Before a QuestionMatthew Berman · Forward FutureMatthew Berman reviews a 2025 paper on moving some reasoning ahead of a user query, and explains where its experiments found benefits and limits.
1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had publishedHost Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.
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.
2:14:59ENAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way DownHost 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.
1:01:53ENOpen-source models are 5% of enterprise use — and GPT wrappers wonHost 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.
1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line RepoHost Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
44:34ENThe Cheaper Intelligence Gets, the More of It We'll BuyHost Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.
1:30:28ENA Perfect Score Is 32,000 Units: Only Claude Built a Cooperative SocietyHost Nathan LabenzThe same donor game, run for ten generations, can bank 32,000 resource units if everyone cooperates—and three frontier models miss that ceiling by an order of magnitude.
57:11ENA Million Tokens Cannot Write a Good JokeHost Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.
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.
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.
1:07:32ENo1 Is a New Generation — and the Day AGI Arrives Will Feel BoringHost Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.
48:18ENNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of HeadroomHost Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Host Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
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