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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.
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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.
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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.
56:49EN
Stanford AI Club· 15 oct 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 oct 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.
1:03:21ENHorace He on Building ML Systems for Trillion-Trillion OperationsHorace He · MetaHow GPU bottlenecks, compiler design and distributed parallelism shape the systems behind large-scale model training.
53:51ENLiora Friedberg on Solving Puzzles in ProductionYaron Minsky, Liora Friedberg · Jane StreetProduction engineering combines live incident response with software, process improvements and a culture that helps people learn from mistakes. Liora Friedberg and Ron Minsky explore the work at Jane Street.
1:23:49ENSteve Lee on PowerShell 7: Compatibility, New Features and the RoadmapSteve Lee · MicrosoftA historical look at the move from Windows PowerShell to cross-platform PowerShell, including demonstrations of the version 7 preview and its compatibility trade-offs.
1:05:59ENDerek Dreyer: Proving the Safety of Rust with RustBelt and IrisDerek Dreyer · Max Planck Institute for Software SystemsRust combines safe interfaces with libraries that use unsafe code internally. Derek Dreyer explains how RustBelt verifies those libraries and why its proof needs an expressive logic for concurrency.
1:18:13ENLindsey Kuper on Abstractions for Correct and Efficient Parallel ComputingLindsey Kuper · University of California, Santa CruzFrom lattice-based shared state to non-invasive parallel languages and neural-network verification, high-level abstractions can make systems both safer and faster.
1:08:43ENRon Minsky on Types and Why They Matter in SoftwareYaron Minsky · Jane StreetType systems can make programs faster, catch mistakes and guide large refactorings, but their value depends on the language and the work. Ron Minsky explains the trade-offs through practical examples.
52:51ENCaveat Configurator: When Configuration Becomes CodeDominick LoBraico · Jane StreetDominick LoBraico explains why flexible OCaml plugins created production problems at Jane Street, and how configuration generation and disciplined plugin management address them.
1:09:24ENYaron Minsky on Effective ML: Writing OCaml for Code ReadersYaron Minsky · Jane StreetPractical lessons from Jane Street on using types, interfaces and abstraction to keep a large OCaml codebase understandable and correct.