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798 件
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スライド 7 枚19:43EN
AI Engineer· 2026年10月26日Jack 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.エージェント工学Agent orchestrationSandboxes & isolation+2
スライド 5 枚20:55EN
AI Engineer· 2026年10月26日Jesse 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.音声 AIエージェント工学評価(Evals)+2
スライド 8 枚18:27EN
AI Engineer· 2026年10月26日Daksh 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.Code review & verificationコーディングエージェント開発ワークフローとテスト+2
スライド 7 枚35:19EN
AI Engineer· 2026年10月25日SallyAnn 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.評価とオブザーバビリティ評価(Evals)エージェント工学+2
スライド 9 枚20:58EN
AI Engineer· 2026年10月25日Vinoo 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.金融 AIエンタープライズ AI検索拡張生成(RAG)+2
スライド 8 枚15:27EN
AI Engineer· 2026年10月25日What 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.エージェント工学評価(Evals)オブザーバビリティと信頼性+2
スライド 6 枚25:50EN
MBMatthew Berman· 2026年10月24日Kimi 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.Open-weight modelsBenchmarksAI cost & performance+1
スライド 2 枚32:20EN
MBMatthew Berman· 2026年10月23日Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
スライド 3 枚13:21EN
Adrian Twarog· 2026年10月21日Adrian 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.Vibe Codingエンタープライズ AIAI security+2
スライド 2 枚17:42EN
AI Engineer· 2026年10月21日Is 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?AI cost & performanceInferenceQuantization+2
スライド 13 枚16:22EN
How I AI· 2026年10月21日ポッドキャストClaire 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.Personal agentsAnthropicAI interface design+2
1:25:56EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月17日ポッドキャストClaude Changed the Password. The Steering Wheel Was Already Optionalホスト 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.エージェント安全性とガバナンスエージェント工学+3
2:17:48EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月14日ポッドキャストAgent IDs Are Empty, Rogue Agents Are Coming, and We Might Have to Bargainホスト 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.エージェント安全性とガバナンスAI security+3
スライド 5 枚12:14EN
MBMatthew Berman· 2026年10月14日Gemini 4 Argon Tested: 3D Demos, a Spreadsheet Prototype and Benchmark ResultsMatthew Berman · Forward FutureMatthew Berman tests Google’s model on games and visual projects, then compares reported coding and agent benchmarks.GeminiBenchmarksコーディングエージェント+1
スライド 5 枚10:36EN
MBMatthew Berman· 2026年10月13日Meta Muse Wonder Tested: Strong Benchmarks, Mixed Demos and Quota FrictionMatthew Berman · Forward FutureMatthew Berman tests Meta’s models on browser demos and reviews their published benchmark scores, availability and cost.モデルと学習BenchmarksAI cost & performance+1
スライド 2 枚17:37EN
MBMatthew Berman· 2026年10月10日AI 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.コーディングエージェントSoftware engineeringCreativity+1
2:00:51EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月10日ポッドキャストResample Every Token and You Can Watch the Model Decideホスト Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.エージェントPrompting & in-context learningReasoning & test-time compute+3
1:26:46EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月8日ポッドキャストThere Likely Is a Level of Intelligence We Shouldn't Go Pastホスト 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.企業と戦略安全性とガバナンスAGI Timelines+3
1:10:12EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月7日ポッドキャストOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anywayホスト 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.企業と戦略エンタープライズ AIコーディングエージェント+3
スライド 5 枚21:29EN
MBMatthew Berman· 2026年10月7日Mistral 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.Open-weight modelsBenchmarksAI security+1
スライド 5 枚25:39EN
MBMatthew Berman· 2026年10月7日Personal 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.Personal agentsWorkflow automationProductivity+1
56:31EN
Unsupervised Learning: With Jacob Effron· 2026年10月6日ポッドキャストPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Labホスト 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.モデルと学習Post-trainingReinforcement Learning+3
1:30:54EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月3日ポッドキャストHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2ホスト Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.エージェントRoboticsMultimodal AI+3
1:28:08EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月1日ポッドキャストThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missedホスト 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.企業と戦略安全性とガバナンスAI cost & performance+3
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