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12 diapositivas17:56EN
AI Engineer· 25 oct 2026How 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.IA empresarialPrivacy & data governanceSearch & retrieval+2
7 diapositivas35:19EN
AI Engineer· 25 oct 2026SallyAnn 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.Evaluaciones y observabilidadEvaluacionesIngeniería de agentes+2
0:26EN
Matthew Berman· 25 oct 2026Sam 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
9 diapositivas20:58EN
AI Engineer· 25 oct 2026Vinoo 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.IA en finanzasIA empresarialGeneración aumentada por recuperación+2
10 diapositivas18:31EN
AI Engineer· 25 oct 2026Janie 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.IA en finanzasBenchmarksEvaluaciones+2
2 diapositivas0:36EN
Matthew Berman· 25 oct 2026Kimi 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.EvaluacionesAgentes de programaciónOpen-weight models
8 diapositivas15:27EN
AI Engineer· 25 oct 2026What 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.Ingeniería de agentesEvaluacionesObservabilidad y fiabilidad+2
7 diapositivas19:37EN
AI Engineer· 25 oct 2026Jay 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.Ingeniería de agentesComputer useWorkflow automation+2
1:25:47EN
Lenny's Podcast· 25 oct 2026Pódcast · 2 vocesRob Snyder’s PULL Framework for Finding Early DemandInvitado: Rob Snyder · Presenta: Lenny RachitskyA practical conversation about customer urgency, alternatives, early sales and retention.Company BuildingProduct Strategy
4 diapositivas19:24EN
AI Engineer· 24 oct 2026Building 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.Ingeniería de agentesEvaluaciones y observabilidadFlujos de desarrollo y pruebas+1
32 diapositivas1:50:48EN
Y Combinator· 24 oct 2026Why 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
5 diapositivas27:09EN
AI Engineer· 24 oct 2026Laurie 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
2 diapositivas0:21EN
Matthew Berman· 24 oct 2026An 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.BenchmarksEvaluacionesGenerative media
1:28:36EN
Cognitive Revolution "How AI Changes Everything"· 24 oct 2026Pódcast · 5 vocesAI’s Pace: Interpretability, GPU Credit and the Physical Supply ChainInvitado: Zvi Mowshowitz, Evan Conrad, Lonis Hamaili · Presenta: 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· 24 oct 2026Josef 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
5 diapositivas16:44EN
AI Engineer· 24 oct 2026How 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.IA en finanzasSearch & retrievalIngeniería de agentes+2
6 diapositivas25:50EN
Matthew Berman· 24 oct 2026Kimi 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· 24 oct 2026Pódcast · 2 vocesNaveen Rao on Energy, AI Hardware, Verification and the Future of RoboticsInvitado: Naveen Rao · Presenta: 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· 23 oct 2026How 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· 23 oct 2026Pódcast · 4 vocesThe Theranos Documentary, Reporting and What the Film Leaves OpenInvitado: Rob Copeland, Erin Griffith, John Carreyrou · Presenta: Max ReadHard Fork discusses You Can See Everything, a new venture and the evidence behind the Theranos story.Company BuildingEmpresa y estrategia
2 diapositivas0:43EN
Theo - t3․gg· 23 oct 2026Theo 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
2 diapositivas32:20EN
Matthew Berman· 23 oct 2026Who 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· 23 oct 2026Sam 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
5 diapositivas34:11EN
GOTO Conferences· 23 oct 2026Anatoliy 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.Flujos de desarrollo y pruebasCode review & verificationObservabilidad y fiabilidad+2
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