絞り込み結果
26 件並び順
18:56EN
AI Engineer· 2026年10月27日John 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.
18:12ENAI Engineer· 2026年10月27日Tim Tyler: An Accountability Framework for AI Agents as a Digital WorkforceTim Tyler · GraviteeThe Gravitee speaker applies identity, sponsorship, authority and oversight to agents, drawing on how organizations manage human work.
23:22ENAI Engineer· 2026年10月27日Noyan 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.
16:38ENAI Engineer· 2026年10月27日Aiden 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.
15:33EN
Jane Street· 2026年10月26日Figgie Strategy: Reading Hands, Winning Majorities and Pricing CardsBradley, Mike · Jane StreetJane Street traders Bradley and Mike analyze three games to show how private information, market competition and timing affect card prices.
19:43ENAI Engineer· 2026年10月26日Jack Wotherspoon on Building a Team of Managed AgentsJack Wotherspoon · Google DeepMindGoogle DeepMind’s Jack Wotherspoon introduces Gemini Managed Agents: cloud sandboxes that let teams run coding and research agents with a single API call. He explains how persistent environments, reusable skills, proxy-injected credentials, schedules, and specialized agents fit together.
20:55ENAI Engineer· 2026年10月26日Jesse 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.
18:27ENAI Engineer· 2026年10月26日Daksh 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.
17:56ENAI Engineer· 2026年10月25日How 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.
20:58ENAI Engineer· 2026年10月25日Vinoo 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.
18:31ENAI Engineer· 2026年10月25日Janie 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.
15:27ENAI Engineer· 2026年10月25日What 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.
19:37ENAI Engineer· 2026年10月25日Jay 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.
17:42ENAI Engineer· 2026年10月21日Is 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?
27:39ENJane Street· 2026年8月21日3Blue1Brown and Jane Street on Trading, Machine Learning and CultureGrant Sanderson, Nitya, In Young Cho, Craig, Alok Puranik · 3Blue1BrownGrant Sanderson talks with Jane Street colleagues about market making, shared compute, research and the paths that brought them to the firm.
29:36ENJane Street· 2026年5月21日Jane Street on GPU Infrastructure, Trading and HiringDwarkesh Patel, Yaron Minsky, Daniel Pontecorvo · Dwarkesh PatelDwarkesh Patel speaks with Ron Minsky and Dan Pontecorvo about trading at different speeds, building data centers and expanding compute and teams.
15:48ENJane Street· 2026年5月15日Inside Jane Street’s Texas AI Data CenterDwarkesh Patel, Yaron Minsky, Daniel Pontecorvo · Dwarkesh PatelDwarkesh Patel tours a 4,032-GPU training facility with Ron Minsky and Dan Pontecorvo, examining liquid cooling, power distribution and operational safeguards.
27:16ENJane Street· 2024年11月15日Tudor Brindus on Finding and Reducing System JitterTudor Brindus · Jane StreetA memory ping-pong benchmark reveals how virtualization, interrupts, CPU frequency changes and speculation can disrupt low-latency systems.
15:59ENJane Street· 2024年5月7日Tail Calls and Local Regions in OCamlRichard Eisenberg · Jane StreetA list-counting example reveals why a local closure can escape at a tail call, and how regional mode and [@nontail] affect the result.
23:25ENJane Street· 2024年2月20日Richard Eisenberg: How OCaml Locals Reduce Garbage-Collected AllocationRichard Eisenberg · Jane StreetAn annotated list-processing example shows why locality must be preserved both when values are created and when functions consume them.
22:27ENJane Street· 2024年2月7日How OCaml Infers LocalityRichard Eisenberg · Jane StreetA look at local and global mode inference, module signatures, mode crossing for integers, and conversions between function types.
15:17ENJane Street· 2024年1月31日OCaml Locals, Modes, and SubmodingRichard Eisenberg · Jane StreetA worked explanation of how local and global modes control escaping values, and how those modes interact with function types.
25:42ENJane Street· 2022年11月8日Graham and Andrea Work Through a Trading Mock InterviewMichael, Graham, Andrea · Jane StreetA 20-sided die game shows how a trading interview can develop through clarification, quantitative estimates and collaborative problem-solving.
25:44ENJane Street· 2017年3月9日Why Functional Programming Matters at Jane StreetSebastian Funk · Jane StreetA Jane Street developer revisits John Hughes’s classic argument and explains why expressive static types rank highest for trading systems.もっと見る26 件中 24 件を表示