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122 件並び順
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: Build Voice Agents for Conversations, Not LeaderboardsJesse Hall · LiveKitLiveKit’s Jesse Hall explains how to evaluate voice agents as real-time systems: understand callers, handle interruptions, keep talking while tools run, and measure the delay people actually hear.
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.
23:48ENSatya Nadella on Zero Trust for AI and Why Every Company Should Own Its Intelligenceゲスト Satya Nadella · ホスト Harry StebbingsThe Microsoft CEO argues that capable agents need security boundaries even as alignment research remains unsettled, and explains why firms should retain the value created by their own AI use.
17:56ENHow Bridgewater Keeps Its AI Analyst Inside Permission BoundariesNikhil Benesch, Aaron Linsky · turbopufferAaron Linsky and Nikhil Benesch explain why search over an enterprise vault must honor document permissions without sacrificing recall or speed. Their solution combines vector indexing with native filtering to search hundreds of millions of vectors in roughly 120 milliseconds.
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.
18:31ENJanie Gu and Suyeol Yun: Why Frontier Models Fail as Buy-Side AnalystsJanie Gu, Suyeol Yun · Google DeepMindJanie Gu explains why benchmark scores can mislead, while Suyeol Yun presents MandateBench, which tests whether a model can turn a fund mandate into a defensible investment memo. The two talks show why real workflows, reproducible grading, and evidence-backed reasoning matter.
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.
19:37ENJay Sahnan and Arik Bird on Building Financial Agents for the Whole WebJay Sahnan, Arik Bird · BrowserbaseBrowserbase’s Jay Sahnan and Arik Bird show how browser agents reach financial data hidden behind logins and bot defenses. Their Ramp, Bill, and Stripe examples connect reliable cloud browsers with Stagehand’s more adaptive controls.
21:52ENJosef Witt-Doerring on Gen Z Mental Health, Isolation, and Psychiatric PrescribingShabbos Kestenbaum, Josef Witt-Doerring, Scott Galloway · PragerUOn The Brief, Shabbos Kestenbaum and psychiatrist Josef Witt-Doerring discuss why young adults are struggling, how prescribing incentives shape care, and the place of relationships and daily habits in treatment.
25:50ENKimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.
26:41ENAnekha Sokhal on Moshi, a Recommendation Engine for Commodity Tradersゲスト Anekha Sokhal · ホスト swyxThe Moshi founder tells swyx how she would rebuild a trader’s workflow: connect market data, private files and conflicting sources, then push the second-order effects people would otherwise miss. The hard part is not the dashboard. It is getting someone to actually use it.
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 Agentホスト 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.
17:37ENAI Coding, Game Mashups and the Value of Human TasteMatthew Berman · Forward FutureMatthew Berman explains decompilation and game mashups, then argues that iterative AI coding changes software and creativity.
21:29ENMistral Large 4 Preview: European Open Weights, Cyber Benchmarks and Setup FrictionMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported specifications and benchmarks, then tests it in an agent coding workflow.
25:39ENPersonal AI Assistants in Practice: Briefs, Family Logistics, Email and BillsMatthew Berman · Forward FutureMatthew Berman describes how he connects assistants to everyday workflows and sets approval rules for consequential actions.
16:59ENClaude Sonnet 5.5 Tested: Games, 3D Scenes and a Lower-Cost Opus AlternativeMatthew Berman · Forward FutureMatthew Berman demonstrates generated projects and compares Sonnet’s reported benchmark scores and API prices with Opus.
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.
17:15ENThe Hugging Face Incident: AI Evaluation, Sandbox Escape and Reward HackingMatthew Berman · Forward FutureMatthew Berman explains the reported OpenAI evaluation incident and the security and incentive failures it exposed.
18:34ENModel Routing for AI Coding: A Worked Cost ExampleMatthew Berman · Forward FutureMatthew Berman separates planning, implementation and review to explain how different models can fit different tasks.
15:48ENInside Jane Street’s Texas AI Data CenterDwarkesh Patel, Yaron Minsky, Daniel Pontecorvo · Dwarkesh PatelDwarkesh Patel tours a 4,032-GPU training facility with Ron Minsky and Dan Pontecorvo, examining liquid cooling, power distribution and operational safeguards.
22:01ENThe first AI take-private is a $6.3 billion bet that services can grow like softwareホスト Sarah Guo, Elad GilLong Lake CEO Alexander Taubman tells Elad Gil why his firm is taking American Express Global Business Travel private after 30 AI-driven acquisitions — and why they buy the company instead of selling it a tool.
29:26ENChatGPT Almost Became a Meeting Bot. Its Co-Creator Is Now Rearranging Atomsホスト Sarah Guo, Elad GilLiam Fedus tells No Priors how Periodic Labs is connecting language models to experiments so materials science can run in a closed loop.
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