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슬라이드 2장0:36EN
Matthew Berman· 2026년 10월 25일Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.평가(Evals)코딩 에이전트Open-weight models
슬라이드 8장15:27EN
AI Engineer· 2026년 10월 25일What 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.에이전트 엔지니어링평가(Evals)관측 가능성과 안정성+2
슬라이드 7장19:37EN
AI Engineer· 2026년 10월 25일Jay 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.에이전트 엔지니어링Computer useWorkflow automation+2
1:25:47EN
Lenny's Podcast· 2026년 10월 25일팟캐스트 · 2명Rob Snyder’s PULL Framework for Finding Early Demand게스트 Rob Snyder · 진행자 Lenny RachitskyA practical conversation about customer urgency, alternatives, early sales and retention.Company BuildingProduct Strategy
슬라이드 4장19:24EN
AI Engineer· 2026년 10월 24일Building an Agent Regression Loop from Production TracesSofia Jakovcevic · Arize AISofia Jakovcevic demonstrates how to compare agent changes across safety, task completion and other dimensions before deployment.에이전트 엔지니어링평가와 관측 가능성개발 워크플로와 테스트+1
슬라이드 32장1:50:48EN
Y Combinator· 2026년 10월 24일Why Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.InferenceGPU programming & kernelsKV caching & prefix reuse+2
슬라이드 5장27:09EN
AI Engineer· 2026년 10월 24일Laurie Voss on the Product Engineer After AI Coding AgentsLaurie Voss · Arize AIAs code generation accelerates, Voss argues that review, operations and defining user needs become successive constraints—and that junior training must change.Software engineeringDeveloper productivityFuture of Work+2
슬라이드 2장0:21EN
Matthew Berman· 2026년 10월 24일An AI-Generated Kimi Benchmark Chart Used Invented ScoresMatthew Berman · Forward FutureMatthew Berman asks for a graph of Kimi K3.1 Pro benchmarks, then discovers that the displayed figures are illustrative placeholders.Benchmarks평가(Evals)Generative media
1:28:36EN
Cognitive Revolution "How AI Changes Everything"· 2026년 10월 24일팟캐스트 · 5명AI’s Pace: Interpretability, GPU Credit and the Physical Supply Chain게스트 Zvi Mowshowitz, Evan Conrad, Lonis Hamaili · 진행자 Nathan Labenz, Prakash NarayananA weekly highlights episode weighs uncertain AI research trajectories against finance, infrastructure, security and regulation.AI researchAI SafetyAI Regulation
21:52EN
PragerU· 2026년 10월 24일Josef 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.Mental HealthSocial MediaRelationships+2
슬라이드 5장16:44EN
AI Engineer· 2026년 10월 24일How Bridgewater Built the Vault for Human Researchers and AI AgentsSam Green, Aaron Linsky · Bridgewater AssociatesSam Green and Aaron Linsky explain how Bridgewater organized more than 100 million documents and built search and proactive research tools around them.금융 AISearch & retrieval에이전트 엔지니어링+2
슬라이드 6장25:50EN
Matthew Berman· 2026년 10월 24일Kimi 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
1:13:26EN
20VC with Harry Stebbings· 2026년 10월 24일팟캐스트 · 2명Naveen Rao on Energy, AI Hardware, Verification and the Future of Robotics게스트 Naveen Rao · 진행자 Harry StebbingsThe Unconventional AI founder argues that energy efficiency will shape AI hardware, while the growing volume of model output makes verification more valuable.EnergySemiconductorsInference+2
47:08EN
Y Combinator· 2026년 10월 23일How Farza Majeed Makes Product Demo VideosAaron Epstein, Farza Majeed · Y CombinatorA founder’s approach to frequent launches, storytelling and measuring the audience that converts.Company BuildingProduct Strategy
58:25EN
Hard Fork· 2026년 10월 23일팟캐스트 · 4명The Theranos Documentary, Reporting and What the Film Leaves Open게스트 Rob Copeland, Erin Griffith, John Carreyrou · 진행자 Max ReadHard Fork discusses You Can See Everything, a new venture and the evidence behind the Theranos story.Company Building기업과 전략
슬라이드 2장0:43EN
Theo - t3․gg· 2026년 10월 23일Theo Distinguishes Security Vulnerabilities from Active AttacksTheo Browne · Ping LabsA short explanation of why disclosed software flaws and reported attacks are different kinds of security news.CybersecuritySoftware engineering
슬라이드 2장32:20EN
Matthew Berman· 2026년 10월 23일Who 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
0:54EN
Matthew Berman· 2026년 10월 23일Sam Altman: Seek Major Life Advice From People, Not AISam Altman · OpenAIThe OpenAI CEO distinguishes practical AI assistance from consequential life advice and argues for assistants that can push back.Human-AI CollaborationDecision MakingAI Alignment
슬라이드 5장34:11EN
GOTO Conferences· 2026년 10월 23일Anatoliy Bilenko and Alberto Carretero: Making Database Protocols Reliable with Formal Methods and AIAnatoliy Bilenko, Alberto Carretero · CanonicalCanonical’s database engineers connect executable specifications, model-based tests, failure exploration, and reconstructed execution histories—and explain why none of these techniques proves a real system correct on its own.개발 워크플로와 테스트Code review & verification관측 가능성과 안정성+2
슬라이드 6장1:03:37EN
Matthew Berman· 2026년 10월 23일Matthew Berman’s Team Tests Kimi K3.1 on Animation, Games, and ChartsMatthew Berman, Alex, Brian · Forward FutureA live team AMA turns into an informal test of Kimi K3.1, from personal-agent workflows to motion graphics, a browser game, and a benchmark chart.코딩 에이전트Generative media평가(Evals)+2
슬라이드 2장1:43:22EN
All-In Podcast· 2026년 10월 23일팟캐스트 · 4명AI Security, Investment and the Debate Over France’s Future게스트 Nikesh Arora · 진행자 Jason Calacanis, Chamath Palihapitiya, David FriedbergThe All-In hosts and Nikesh Arora discuss cybersecurity and AI spending, then debate France’s unrest, public services and US policy.AI security엔터프라이즈 AIInvesting+2
슬라이드 3장34:35EN
SemiAnalysis· 2026년 10월 23일팟캐스트 · 3명SemiAnalysis on ASML's Hybrid-Bonding Patents, HBM Economics and the Next Tool Ecosystem게스트 Sherry Zhang, Howie · 진행자 Jordan NanosJordan Nanos, Sherry Zhang and Howie examine a track-based bonding design that brings the wafer to the die, then explain why adoption depends on precision, cleanliness, economics and a changing supply chain.Semiconductors인프라와 배포Geopolitics
1:00:39EN
a16z· 2026년 10월 23일팟캐스트 · 3명Amazon CEO Andy Jassy on the $220 Billion AI Buildout게스트 Andy Jassy · 진행자 Ben Horowitz, Erik TorenbergEven at roughly $220 billion of 2026 CapEx, Jassy says Amazon is still turning away AI demand. He tells Ben Horowitz and Erik Torenberg why AWS could become a trillion-dollar run-rate business, and why inference cost, custom chips, and smaller teams matter more than squeezing old customers.AI Strategy인프라와 배포Semiconductors+2
슬라이드 19장1:09:00EN
Dwarkesh Patel· 2026년 10월 22일팟캐스트 · 9명Dwarkesh Patel: What will computer chips look like in 2045?게스트 Chris Auth, Kevin Fischer, Jack Kavalieros, Tyler Osborn, Pushkar Ranade, Gary Dickerson, Peter Wennink, Elon Musk · 진행자 Dwarkesh PatelAfter two days inside Intel’s most advanced fab, Dwarkesh Patel walks through gate-all-around, backside power, High-NA EUV, ruthenium, CFET, and hybrid bonding — and asks Intel’s leadership how the industry can possibly meet AI’s 3×-a-year compute demand when the world’s chipmaking tools cannot.SemiconductorsAI cost & performance인프라와 배포+2
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