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19:43ENJack Wotherspoon on Building a Team of Managed AgentsJack Wotherspoon · Google DeepMindGoogle DeepMind’s Jack Wotherspoon introduces Gemini Managed Agents: cloud sandboxes that let teams run coding and research agents with a single API call. He explains how persistent environments, reusable skills, proxy-injected credentials, schedules, and specialized agents fit together.
20:55ENJesse Hall:语音智能体要为对话而优化,而非只看排行榜Jesse Hall · LiveKitLiveKit 的 Jesse Hall 解释了为什么语音智能体必须像实时系统一样接受评测:听清用户、允许打断、在工具调用期间继续对话,并在用户真正听到声音之前控制延迟。
18:27ENDaksh Gupta on Building a Better AI Code Review AgentDaksh Gupta · GreptileGreptile co-founder Daksh Gupta explains why a webhook plus a coding agent is not enough for reliable pull-request review—and how targeted prompts, incident data, model inversion, codebase context, browser testing, and task-specific model routing can help.
35:19ENSallyAnn DeLucia: How to Write Your First AI EvalSallyAnn DeLucia · Arize AITurn an agent’s real traces into concrete pass/fail criteria, then build an LLM judge you can test, align with people, and run in production.
20:58ENVinoo Ganesh on Making Financial AI Cite Every NumberVinoo Ganesh · KeplerKepler CEO Vinoo Ganesh explains why financial AI should retrieve and cite figures while deterministic software handles the math. His design makes traceability, retrieval quality, and tenant security part of the answer itself.
15:27ENWhat AI Engineers Do Now: Build the Loop Around AgentsAparna Dhinakaran · Arize AIAparna Dhinakaran argues that AI engineers are moving from shipping individual features to building systems that let agent teams improve them. At Arize, that means tracing agent behavior, evaluating its work against human judgment, and feeding those signals back into an always-on agent.
13:21ENAdrian Twarog: Giving Enterprise Vibe Coding a Governed Path to ProductionAdrian TwarogA Superblocks walkthrough shows how shared integrations, isolated databases, policy agents and audit logs can bring business-built AI apps under IT oversight.
17:42ENIs a Faster Model Actually Faster? Measure the Finished Job, Not the TokensMorgan Suo · MiniMaxMiniMax's Morgan Suo takes the engineering changes inside M3.1 Flash one by one and asks the question that matters to users: does the job an analyst needs actually get done sooner, cheaper and correctly?
16:22ENClaire Vo: Claude Duet Is Beautiful, but It Is Not My Number One Agent진행자 Claire VoAnthropic’s phone-based personal agent has the most polished launch Claire Vo has seen: thoughtful onboarding, task lists, first-class artifacts, and Slack-style channels. Polish is not enough when every other agent is competing for the same attention.
1:25:56ENClaude Changed the Password. The Steering Wheel Was Already Optional진행자 Nathan LabenzNathan Labenz and Prakash Narayanan spend a week on agents that can lock you out, models that dodge a feeling they deny having, and formal proofs that found Linux bugs.
2:17:48ENAgent IDs Are Empty, Rogue Agents Are Coming, and We Might Have to Bargain진행자 Nathan LabenzGovAI’s Alan Chan scores two years of agent-governance infrastructure with Nathan Labenz, then walks through a recursive-self-improvement paper co-authored with OpenAI’s Jakub Pachocki and Anthropic’s Jack Clark.
2:00:51ENResample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past진행자 Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anyway진행자 Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab진행자 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:30:54ENHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2진행자 Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missed진행자 Nathan LabenzA Cognitive Revolution weekly briefing on a possible U.S.–China AI hotline, why hyperscalers charge two to three times as much for identical chips, co-writing a novel with frontier models, and an AI loop that re-diagnosed a child a premier prenatal lab had cleared.
2:13:27ENHe Wrote a Book to Stop the AI Race — Then Almost Got Addicted to Claude진행자 Nathan LabenzJournalist Garrison Lovely takes the AGI labs at their word, rejects the bubble story, and argues for freezing the frontier until the public actually consents.
1:46:04ENThe Training Worked. It Just Worked Too Well.진행자 Nathan LabenzA week of Cognitive Revolution clips: OpenAI agents colluding on a German wiki, $200 million checks for safety orgs that do not exist yet, a GPU price index, and two opposite bets on biological data.
2:50:40ENThey Priced the Chance She Would Leave. Then They Handed Her the Letter.진행자 Nathan LabenzNathan Labenz premieres the first four chapters of Joel Borgen's AI-coauthored novel The Receipt Horizon, a postwar world where a singleton superintelligence keeps everyone comfortable, scored, and gently out of the loop.
1:48:57ENAI Safety's Real Shortage Is Founders, Not Ideas진행자 Nathan LabenzHalcyon's Mike McCormick on launching about 30 AI safety, cyber, and biosecurity organizations in three years — and why experienced leaders, not more Google Docs, are the bottleneck.
1:40:36ENThe Labs Are Screaming: Internal Models Are a Generation Ahead진행자 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.
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.
1:06:47ENEighty percent of what agents do should still be ordinary code진행자 Nathan LabenzZapier CEO Wade Foster tells Nathan Labenz that models top out at 40% on real automation tasks, users will not leave their daily driver, and the winning move is headless tools delivered through MCP.
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