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4,539 件
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スライド 2 枚34:48EN
Latent Space· 2026年11月3日ポッドキャスト · 2 人Zach Lloyd on the Infrastructure Behind a Software Factoryゲスト Zach Lloyd · ホスト Shawn Wang (swyx)Warp’s founder explains how teams can define, observe and improve agentic development workflows—and when the additional infrastructure may be excessive.コーディングエージェント開発ワークフローとテストAgent orchestration+2
スライド 4 枚15:50EN
Theo - t3․gg· 2026年11月3日Theo Compares Codex and Claude Subscription Value After Limit ChangesTheoTheo looks at estimated allowances, per-task costs and his own review experiments, while arguing that model quality remains decisive.コーディングエージェントOpenAIAI cost & performance
48:08EN
a16z· 2026年11月3日Andrew Huberman on Building a Sustainable Routine for Focus and HealthDaisy Wolf, Andrew Huberman · a16zThe neuroscientist describes his daily routine, his struggle with social media, and how he protects the hours when he does his best work.NeuroscienceProductivityWork-Life Balance
1:53:06EN
Theo Von· 2026年11月3日ポッドキャスト · 2 人Lindsay Owens on Price Experiments, Consumer Data and the Fight for Fair Pricesゲスト Lindsay Owens · ホスト Theo VonThe consumer advocate explains her investigations into changing prices and data-driven commerce, and what shoppers and lawmakers can do.Economics
スライド 4 枚16:29EN
AI Engineer· 2026年11月3日Why Retrieval Quality Matters for AI Agents Working with Large Document CollectionsAamir Shakir · MixedbreadMixedbread’s Aamir Shakir critiques a multi-stage retrieval pipeline and presents a customer case and multimodal search demo.Search & retrievalRAG とナレッジEmbeddings+2
スライド42:30EN
How I AI· 2026年11月2日ポッドキャスト · 2 人Inside Figma’s PM OS: Ezra Mechaber on Skills, Bug Bashes and Automated Experimentsゲスト Ezra Mechaber · ホスト Claire VoFigma growth PM Ezra Mechaber shows Claire Vo how a shared repository supports product work, browser testing and a human-reviewed experiment loop.AI Product ManagementProduct ManagementAI agents+2
1:37:08EN
Machine Learning Street Talk· 2026年11月2日ポッドキャスト · 2 人Joscha Bach on Consciousness, Causal Patterns and Machine Mindsゲスト Joscha Bach · ホスト Tim ScarfeA discussion of a speculative machine-consciousness hypothesis, the limits of behavioral tests and competing accounts of mind.ConsciousnessPhilosophy of MindCognitive Science+1
スライド 2 枚16:46EN
Theo - t3․gg· 2026年11月2日Theo Browne on Claude Ultra, Five-Hour Limits and SDK UsageTheo Browne · Ping LabsA breakdown of the renamed subscription tier, verification and reported differences between interactive and SDK usage.コーディングエージェントDeveloper productivity開発ワークフローとテスト
1:43:15EN
Chris Williamson· 2026年11月2日ポッドキャスト · 2 人Joe Hudson on Decisions, Fear and Living by Your Principlesゲスト Joe Hudson · ホスト Chris WilliamsonCoach Joe Hudson joins Chris Williamson to explore indecision, emotional avoidance, personal principles and the value of learning through action.Decision MakingPsychologyPersonal Growth+2
スライド 4 枚15:51EN
AI Engineer· 2026年11月2日Adam Nahari: Six Moves to Turn AI Pilots Into Financial ImpactAdam Nahari · ApolloAn airline crew-scheduling example shows why financial outcomes depend on workflow redesign, adoption and disciplined measurement.エンタープライズ AI企業と戦略Productivity
1:01:08EN
Unsupervised Learning: With Jacob Effron· 2026年11月2日ポッドキャスト · 2 人Nebius CTO Danila Shtan on AI Clouds, Inference and Competition with Hyperscalersゲスト Danila Shtan · ホスト Jacob EffronDanila Shtan tells Jacob Effron how Nebius builds AI infrastructure, where inference remains inefficient and why he expects larger clouds to compete more aggressively.インフラとデプロイInferenceDistributed training & inference+2
44:57EN
a16z· 2026年11月2日ポッドキャスト · 3 人Tutor Intelligence CEO Josh Gruenstein on Building Robots for Real Workゲスト Josh Gruenstein · ホスト Martin Casado, Raghu RaghuramJosh Gruenstein joins Martin Casado and Raghu Raghuram to discuss human tutors, robot manufacturing and the economics of factory automation.RoboticsAI agentsインフラとデプロイ+2
スライド 7 枚40:04EN
GOTO Conferences· 2026年11月2日Sophia Drossopoulou: Verifying Trusted Code in an Open WorldSophia Drossopoulou · Imperial College LondonCapabilities, scoped invariants and explicit trust assumptions help reason about effects when trusted code calls unknown objects.Code review & verificationProgramming LanguagesSoftware engineering
スライド 4 枚51:06EN
GOTO Conferences· 2026年11月2日Kent Beck: Build AI Programming Tools for Human NeedsKent BeckThe “genie” can amplify healthy or unhealthy development practices. Beck calls for tools that preserve learning, connection and future options.コーディングエージェントSoftware engineeringDeveloper productivity+1
1:07:22EN
Latent Space· 2026年11月2日ポッドキャスト · 3 人Building Legal AI Agents Without Seeing Client Data: Legora's Jacob Lauritzenゲスト Jacob Lauritzen · ホスト swyx, Basil ChathaLegora's CTO explains how its team evaluates legal agents, designs review workflows and builds around the confidentiality of client matters.AI agentsエンタープライズ AI評価(Evals)+2
スライド 3 枚20:39EN
AI Engineer· 2026年11月2日Sagar Batchu: Authorizing AI Agent Actions Before They Reach an APISagar Batchu · SpeakeasyA proposed governance path combines agent identity, permission ceilings, policy decisions and audit records.エージェント認証AI security安全性とガバナンス+1
スライド 2 枚20:15EN
AI Engineer· 2026年11月2日Will Brown on Training Models That Learn From Organizational WorkWill Brown · Prime IntellectPrime Intellect’s Will Brown describes how traces, evaluations and human decisions could support continual learning alongside frontier models.Continual learningAI Product Management評価(Evals)+2
スライド 5 枚18:33EN
AI Engineer· 2026年11月2日Zack Proser: Build a Factory to Help Others Tell Their StoriesZack Proser · WorkOSA WorkOS blog assistant and a bedtime-story app show how AI tools can share expertise while humans retain responsibility.CreativityContent StrategyAI agents+1
1:02:12EN
20VC with Harry Stebbings· 2026年11月2日ポッドキャスト · 2 人Modal CEO Erik Bernhardsson on Inference, Compute and Building an AI Platformゲスト Erik Bernhardsson · ホスト Harry StebbingsErik Bernhardsson discusses GPU capacity, open models, sandbox growth and why Modal is expanding beyond a single infrastructure product.InferenceAI platform engineeringインフラとデプロイ+2
1:32:16ZH
硅谷101· 2026年11月2日MaxInsights 袁宇光:如何生产机器人第一视角训练数据ゲスト 袁宇光 · ホスト 陈茜从头戴设备、全球采集网络到模型评估与部署,袁宇光解释机器人数据生意的扩张及质量挑战。RoboticsData quality & curationScaling Laws+1
スライド 2 枚27:55EN
AI Explained· 2026年11月2日AI Safety, Model Releases and the Limits of UnderstandingAI Explained examines lab forecasts, safety commitments and competing model results, then argues for multiple safeguards as capabilities advance.AI Safety安全性とガバナンスModel Interpretability+2
スライド 3 枚18:57EN
AI Engineer· 2026年11月2日Andrew Norrish: Code Context for AI-Era Incident ResponseAndrew Norrish · SourcegraphA Sourcegraph engineer describes review pressure and bounded workflows for investigating, preventing and fixing incidents.コーディングエージェントオブザーバビリティと信頼性開発ワークフローとテスト+1
スライド 5 枚15:44EN
AI Engineer· 2026年11月1日Yijia Liang on Building Office-Compatible Engines for Finance AgentsYijia Liang · Compound AICompound’s approach combines shared agent inboxes, document engines and cited access to large data rooms.エージェント工学Software engineeringDeveloper productivity
スライド 5 枚16:37EN
AI Engineer· 2026年11月1日Nick Nisi: What a Fruit Fly Code Reviewer Reveals About Human ReviewNick Nisi · WorkOSA simulated fruit fly becomes an unlikely code reviewer—and a starting point for a team-owned automated review pipeline.開発ワークフローとテストコーディングエージェントSoftware engineering+1
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