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45:34EN
Citadel Securities· 2026년 10월 26일팟캐스트 · 2명Henry Paulson on AI Risk, U.S.–China Relations and Crisis Leadership게스트 Henry M. Paulson, Jr. · 진행자 Jim EspositoThe former Treasury secretary discusses today’s economic risks and lessons from the 2008 financial crisis.LeadershipDecision MakingInvesting+2
슬라이드 5장30:37EN
Theo - t3․gg· 2026년 10월 26일Theo’s Three Skills for Reviewing and Shipping with Coding AgentsTheo Browne · Ping LabsHow sol-loop, full-send and full-stop encode review, verification and different levels of merge authority.Agent skills코딩 에이전트Code review & verification+2
슬라이드 8장18:27EN
AI Engineer· 2026년 10월 26일Daksh 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 & verification코딩 에이전트개발 워크플로와 테스트+2
23:48EN
20VC with Harry Stebbings· 2026년 10월 26일팟캐스트 · 2명Satya Nadella on Zero Trust for AI and Why Every Company Should Own Its Intelligence게스트 Satya Nadella · 진행자 Harry StebbingsThe Microsoft CEO argues that capable agents need security boundaries even as alignment research remains unsettled, and explains why firms should retain the value created by their own AI use.엔터프라이즈 AIAI security평가(Evals)+2
슬라이드 3장30:11ZH
硅谷101· 2026년 10월 25일AI 如何重构跨境支付:从交易路由、风控到智能体商务게스트 Leo Li, Apple Gao · 진행자 陈茜硅谷101 从一笔跨境机票付款出发,拆解支付网络的复杂性,以及 Antom 如何把 AI 引入运营、风险判断和智能体交易。FintechAgentic commerce금융 AI+2
슬라이드 12장17:56EN
AI Engineer· 2026년 10월 25일How Bridgewater Keeps Its AI Analyst Inside Permission BoundariesNikhil Benesch, Aaron Linsky · turbopufferAaron Linsky and Nikhil Benesch explain why search over an enterprise vault must honor document permissions without sacrificing recall or speed. Their solution combines vector indexing with native filtering to search hundreds of millions of vectors in roughly 120 milliseconds.엔터프라이즈 AIPrivacy & data governanceSearch & retrieval+2
슬라이드 7장35:19EN
AI Engineer· 2026년 10월 25일SallyAnn 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.평가와 관측 가능성평가(Evals)에이전트 엔지니어링+2
0:26EN
Matthew Berman· 2026년 10월 25일Sam Altman on Human Values and Problem-Solving in an AI WorldSam Altman · OpenAIIn a short interview excerpt, Altman considers the enjoyment of solving hard problems and the human role in interpreting results.Human-AI CollaborationFuture of WorkAI Strategy
슬라이드 9장20:58EN
AI Engineer· 2026년 10월 25일Vinoo 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.금융 AI엔터프라이즈 AI검색 증강 생성(RAG)+2
슬라이드 10장18:31EN
AI Engineer· 2026년 10월 25일Janie Gu and Suyeol Yun: Why Frontier Models Fail as Buy-Side AnalystsJanie Gu, Suyeol Yun · Google DeepMindJanie Gu explains why benchmark scores can mislead, while Suyeol Yun presents MandateBench, which tests whether a model can turn a fund mandate into a defensible investment memo. The two talks show why real workflows, reproducible grading, and evidence-backed reasoning matter.금융 AIBenchmarks평가(Evals)+2
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
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
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
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
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