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슬라이드 7장18:56EN
AI Engineer· 2026년 10월 27일John Feminella: Five Controls for AI Agents, and the $900 Million Bank ErrorJohn Feminella · Two SigmaA payment mistake shows why good intentions and approval prompts are not enough. Feminella describes practical controls for giving agents useful autonomy.안전과 거버넌스에이전트 엔지니어링금융 AI+2
슬라이드 6장23:22EN
AI Engineer· 2026년 10월 27일Noyan 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.AI platform engineering평가(Evals)AI cost & performance+2
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
26:41EN
Latent Space· 2026년 10월 22일팟캐스트 · 2명Anekha Sokhal on Moshi, a Recommendation Engine for Commodity Traders게스트 Anekha Sokhal · 진행자 swyxThe Moshi founder tells swyx how she would rebuild a trader’s workflow: connect market data, private files and conflicting sources, then push the second-order effects people would otherwise miss. The hard part is not the dashboard. It is getting someone to actually use it.금융 AIRecommendation systemsData quality & curation+2