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64 件
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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 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 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 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
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
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
スライド 4 枚10:00EN
MBMatthew Berman· 2026年9月30日OpenAI DevDay: Dots, Faster Inference, Codex and ChatGPT SpaceMatthew Berman · Forward FutureMatthew Berman reviews the event’s personal assistant, model, developer tooling and collaboration announcements.OpenAIPersonal agentsコーディングエージェント+1
1:46:04EN
Cognitive Revolution "How AI Changes Everything"· 2026年9月27日ポッドキャストThe Training Worked. It Just Worked Too Well.ホスト 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.エージェントMulti-agent systems安全性とガバナンス+3
スライド 5 枚16:57EN
MBMatthew Berman· 2026年9月22日Grok 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.BenchmarksAI cost & performanceコーディングエージェント+1
スライド 5 枚12:31EN
MBMatthew Berman· 2026年9月18日Jev's Fast Decisions: Demos, Tradeoffs and Reliability ClaimsMatthew Berman · Forward FutureMatthew Berman explores a decision-oriented AI model through routing, simulated worlds, chess and game-control examples.AI agentsAI cost & performance評価とオブザーバビリティ+1
1:06:47EN
Cognitive Revolution "How AI Changes Everything"· 2026年9月17日ポッドキャストEighty percent of what agents do should still be ordinary codeホスト 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.MCP とツールMCPWorkflow automation+3
スライド 5 枚16:50EN
MBMatthew Berman· 2026年9月3日Gemini 3.8 Flash: Benchmarks, Cost and Hands-On Coding TestsMatthew Berman · Forward FutureMatthew Berman compares Google's September 2026 model announcement with several practical visual and web-building examples.GeminiBenchmarksAI cost & performance+2
スライド 5 枚20:46EN
MBMatthew Berman· 2026年8月26日Open-Weight AI Models: Token Share, Spending and Strategic TradeoffsMatthew Berman · Forward FutureMatthew Berman examines a Vercel AI Gateway snapshot and argues that model usage and spending can tell very different stories.Open-weight modelsAI cost & performanceAI Strategy+2
2:06:11EN
Cognitive Revolution "How AI Changes Everything"· 2026年8月14日ポッドキャストLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Bannedホスト Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.エージェントAgent memoryコンテキストエンジニアリング+3
スライド 5 枚52:29EN
MBMatthew Berman· 2026年7月24日Claude Opus 5: Benchmarks, Cost per Task, and Early ImpressionsMatthew Berman · Forward FutureMatthew Berman reacts to the model launch, examines reported benchmark and pricing data, and explains why the cost of completing a task matters more than token price alone.AnthropicBenchmarksAI cost & performance+2
1:13:48EN
Unsupervised Learning: With Jacob Effron· 2026年7月16日ポッドキャストCarriers Spend $200 Billion a Year. The Money Went Up the Stackホスト Jacob EffronBenedict Evans tells Jacob Effron why analogizing AI to past platforms is a dead end, and why looking at how those platforms actually made money is not.企業と戦略AI Strategyエンタープライズ AI+3
50:57EN
Unsupervised Learning: With Jacob Effron· 2026年7月9日ポッドキャストA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen Schmidhuberホスト Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.モデルと学習AGI(汎用人工知能)AI research+3
スライド 3 枚18:34EN
MBMatthew Berman· 2026年7月7日Model 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.AI cost & performanceエージェント工学Developer productivity
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月4日ポッドキャスト19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chipホスト Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.モデルと学習Local inferenceScaling Laws+3
1:06:36EN
Unsupervised Learning: With Jacob Effron· 2026年6月12日ポッドキャストBills Doubled Overnight. Next, the APIs Might Vanish.ホスト Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.企業と戦略AI StrategyOpen-source ecosystems+3
1:34:54EN
Cognitive Revolution "How AI Changes Everything"· 2026年5月15日ポッドキャストSix Months, Eighty Percent Churn, Five Times the Tokensホスト Nathan LabenzTasklet CEO Andrew Lee tells Cognitive Revolution how he rewrote the stack, why most lost users leave for Anthropic, and why he thinks only three kinds of software company survive.エージェントエージェント工学コンテキストエンジニアリング+3
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