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1:50:48EN
Y Combinator· 24. Okt. 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.
56:49EN
Stanford AI Club· 15. Okt. 2026Alfred Lin on Building in the AI Era: The Diffusion Gap, Founder Conviction, and Fire and IceAlfred Lin, Jason Zhang, Tanvir Bhathal · Sequoia CapitalAt Stanford AI Club, Sequoia’s Alfred Lin explains where he sees opportunity in AI and why learning, human understanding, and operating discipline still shape enduring companies.
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Google DeepMind· 1. Okt. 2026From deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.
27:39EN3Blue1Brown and Jane Street on Trading, Machine Learning and CultureGrant Sanderson, Nitya, In Young, Craig, Alok · 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 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.
15:48ENInside 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.
1:00:15ENArjun Guha on How Language Models Represent Code and How People Prompt ThemArjun Guha · Northeastern UniversityExperiments with activation steering and student-written prompts reveal how programming models behave—and why evaluating them requires more than a single benchmark score.
47:43ENMaking GPU Training Faster: Pipelines, Fusion and Custom KernelsCorwin de Zahr · Jane StreetTwo Jane Street speakers explain how to diagnose idle GPUs, reduce memory traffic and build a parallel reduction kernel.
55:03ENNeil Mitchell: How Pyrefly Checks Python at ScaleNeil Mitchell, Roman · MetaA tour of Python's type system and the incremental architecture behind a fast, open-source checker and language server.
2:27ENWhat Jane Street Interns and Recruiters Say About ApplyingInterns and recruiters describe the application process, problem-solving interviews, and how they think about finance experience.
5:13ENHow Jane Street’s Accounting Team WorksCarmen, Ava, Hannah · Jane StreetAccounting staff describe shared responsibilities, automation, early involvement in decisions and growth in a flat organization.
4:51ENHow Jane Street’s Creative Engagement Team Shapes Company CultureTala, Lana · Jane StreetTeam members describe how events, design and digital projects bring colleagues together and introduce the firm to new people.
2:13ENInterns Describe Jane Street's 2022 Internship ProgramParticipants reflect on learning, project ownership, colleagues, and discovering possible career paths during their internships.
4:11ENInside Production Engineering at Jane StreetMark, Liora, Joseph · Jane StreetThree engineers describe the balance between resolving live issues and building software that makes support more effective.
25:42ENGraham 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.
1:02:52ENInside Jane Street’s Software Engineering InternshipYaron Minsky, Jeanne Van Briesen, Matt Else, Grace Zhang · Jane StreetThree former interns describe the real projects, mentorship and decisions that shaped their transition to full-time engineering.
1:05:06ENRaph Levien: GPU Compute, Functional Design, and a 2D RendererRaph LevienA tour of GPU execution and memory explains why parallel rendering needs more than simply assigning one thread to every pixel.
1:33:13ENDon Jones on PowerShell, Career Growth, and the First FollowerDon Jones · PluralsightPowerShell educator Don Jones reflects on writing small composable tools, building a resilient career, and helping others join a community.
1:03:45ENJane Street and the OCaml CompilerYaron Minsky · Jane StreetRon Minsky traces an accidental language adoption into a long-term commitment to compiler engineering and a shared open-source ecosystem.
1:06:51ENMarius Eriksen: Reflow for Incremental Cloud Data ProcessingMarius Eriksen · GRAILA functional language and runtime turn data workflows into typed programs, reuse prior results and provision cloud resources on demand.
1:13:25ENNate Foster: Verifying Programmable Network Data PlanesNate Foster, Ron · Cornell UniversityHow p4v applies program verification to packet-processing code, and why its conclusions depend on control-plane assumptions.
1:38:23ENYudkowsky and Hanson Debate Whether an Intelligence Explosion Would Be LocalEliezer Yudkowsky, Robin HansonA historical debate about self-improving AI, accumulated knowledge, innovation and whether a first mover could gain a decisive lead.
59:14ENArjun Guha on Verifying System ConfigurationsArjun Guha · UMass AmherstPuppet manifests can be declarative yet nondeterministic or non-idempotent. A verification tool models resource interactions to find those problems.