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35:19EN
AI Engineer· 25. Okt. 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.
1:20:57ENMatt Godbolt: Inside Skylake’s Out-of-Order PipelineMatt GodboltA guided tour of instruction decoding, register renaming, execution and memory ordering, grounded in reverse engineering and careful measurements.
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.
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.
57:57ENSebastian Funk on a Fast, Safe and Maintainable Packet ProcessorSebastian Funk · Jane StreetA single-core market-data case study shows how latency targets, allocation control and generated code can coexist with high-level abstractions.
1:08:44ENNathan Linger on Ontology: A Language-Oriented Permissions SystemNathan Linger · Jane StreetHow Jane Street represents permissions as typed declarations and presents them through an English-language interface.
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.
1:10:18ENShriram Krishnamurthi on Designing Computing Curricula for Real ClassroomsShriram Krishnamurthi · Brown UniversityThe Bootstrap project treats curriculum design as an engineering problem: start with goals and constraints, then support teachers and measure learning.
1:06:49ENBryan Cantrill on Finding the Butterfly Behind a Performance HurricaneBryan Cantrill · JoyentSmall defects can produce vast performance failures across layers of abstraction. Bryan Cantrill explains how to investigate them with questions, instrumentation and visualization.
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.
51:35ENYaron Minsky on Effective ML: Interfaces, Invariants and Code ReadersYaron Minsky · Jane StreetIn the first part of a 2011 Harvard lecture, Jane Street’s Yaron Minsky shows how OCaml interfaces and types can make long-lived code easier to understand and change.