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23:22ENNoyan Tokgozoglu: How Optiver Builds an AI Platform for Fast, Measurable DecisionsNoyan Tokgozoglu · OptiverThe trading firm’s AI engineering lead explains same-day model adoption, on-premises infrastructure, cost controls and the limits of replaying agent evaluations.
16:38ENAiden Sochowski: Securing 300 Agents in a 60-Person CompanyAiden Sochowski · RunlayerRunlayer’s founding engineer explains what session monitoring revealed about agent permissions, prompt injection, tool errors and runaway cost.
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.
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.
32:20ENWho 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.
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?
10:36ENMeta 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.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go PastModeration: 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 AnywayModeration: 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.
1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missedModeration: 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.
10:00ENOpenAI DevDay: Dots, Faster Inference, Codex and ChatGPT SpaceMatthew Berman · Forward FutureMatthew Berman reviews the event’s personal assistant, model, developer tooling and collaboration announcements.
1:46:04ENThe Training Worked. It Just Worked Too Well.Moderation: 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.
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.
12:31ENJev'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.
1:06:47ENEighty percent of what agents do should still be ordinary codeModeration: 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.
16:50ENGemini 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.
20:46ENOpen-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.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models BannedModeration: 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.
15:37ENMatthew Berman on Qwen3.8-Max, AI Costs and Open-Weight CompetitionMatthew Berman · Forward FutureA 2026 review of reported benchmark results and research demonstrations leads to questions about deployment economics and geopolitical dependence.
52:29ENClaude 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.
27:13ENKimi K3 and the Debate Over Open-Weight AIMatthew Berman · Forward FutureMatthew Berman examines model economics, competition and security arguments after Moonshot AI’s Kimi K3 announcement.
1:52ENAnthropic, Compute Capacity and the Competition for AI SubscribersMatthew Berman · Forward FutureMatthew Berman weighs a claimed compute constraint against model quality, subscription limits and the possibility of future self-improvement.
1:13:48ENCarriers Spend $200 Billion a Year. The Money Went Up the StackModeration: 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.
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