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1:48:29ENHow AI Is Changing Programming: John Krazit and Ron MinskyRon Minsky, John Krazit · Jane StreetJane Street’s AI tooling lead and the Signals and Threads host examine agent infrastructure, security, code review and the changing experience of writing software.
1:05:45ENCyrus Omar on Live Programming and a Computational Commons for Humans and AICyrus OmarA demonstration of Hazel shows how immediate feedback, structured collaboration and carefully scoped permissions could support people and AI agents working together.
1:25:00ENEric Mannes on Turning Messy External Data Into Trading ResearchRon, Eric Mannes · Jane StreetFrom commodity futures and an Excel risk system to point-in-time datasets and AI, Eric Mannes explains the domain knowledge behind data engineering at Jane Street.
1:16:29ENGrant Sanderson and Alok Puranik: Positional Encodings Through Group TheoryGrant Sanderson, Alok Puranik · 3Blue1BrownStarting with assumptions about attention, they derive matrix exponentials and connect rotations, decay and Jordan blocks to RoPE and ALiBi.
1:34:29ENJacob Baskin on Building Superstore and Scheduling the HiveYaron Minsky, Jacob Baskin · Jane StreetThe Jane Street engineer traces the design choices behind a distributed analytical database and the challenge of allocating a growing research cluster.
1:34:36ENNate Foster: Treating the Network as a ProgramYaron Minsky, Nate Foster · Jane StreetFrom programmable switches to BGP, a programming-languages researcher explains how higher-level policies and formal models can make networks easier to change.
1:48:21ENWill Wilson on Deterministic Simulation and the Hard Problems of Software TestingYaron Minsky, Will Wilson · Jane StreetThe Antithesis co-founder explains how replayable simulations explore complex software, why properties matter and where testing meets its limits.
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.
1:00:15ENArjun 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.
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.
52:05ENCorwin de Zahr: Exploring OCaml Runtime Safety Through a CTF ChallengeCorwin de Zahr · Jane StreetA deliberately restricted OCaml sandbox becomes a way to examine type safety, callbacks, memory representation and binary debugging.
1:12:58ENChris Lattner on Mojo and the Search for Portable GPU PerformanceYaron Minsky, Chris Lattner · Jane StreetRon Minsky and Chris Lattner discuss the language and compiler design behind Modular’s effort to make accelerator programming both portable and fast.
1:01:23ENWill Crichton on Making Rust More UnderstandableWill Crichton · Brown UniversityOwnership visualizations, interactive trait diagnostics and program slicing illustrate an evidence-based approach to developer tools.
58:47ENDaniel Pontecorvo on Cooling Trading Infrastructure and WorkspacesYaron Minsky, Daniel Pontecorvo · Jane StreetFrom dense GPU racks to movable desks, Jane Street’s physical engineering team designs around heat, power, failure and human comfort.
1:27:41ENRon Minsky on the Saga of Multicore OCaml at Jane StreetRon Minsky · Jane StreetA production migration exposed garbage-collector regressions, led to a new pacing design, and illustrated the difficulty of turning research into reliable infrastructure.
47:08ENChris Casinghino on Making OCaml Safe for Performance EngineeringChris Casinghino · Jane StreetJane Street’s OCaml extensions use layouts and modes to offer more control over memory and parallelism while preserving safety and familiar programming patterns.
59:46ENIn Young Cho on Trading Research and Finding Signal in Financial DataRon Minsky, In Young Cho · Jane StreetIn Young Cho and Ron Minsky discuss the blurred lines between trading, research, and engineering, and why machine learning in markets is difficult.
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.
40:33ENCharlie Marsh on uv: Python Dependency Resolution and PerformanceCharlie Marsh · AstralThe Astral founder explains universal lockfiles, Python packaging constraints, and the data structures and cache design behind uv.
1:06:23ENSylvain Gugger on Accelerating ML Models and Reproducing ResultsRon Minsky, Sylvain Gugger · Jane StreetFrom learning-rate schedules to GPU bottlenecks and noisy market data, Sylvain Gugger explains how he approaches practical machine-learning performance.
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.
1:03:36ENErin Murphy on Designing Tools for Expert UsersRon Minsky, Erin Murphy · Jane StreetFrom NASA scientists to Jane Street engineers, a UX designer explains how observation, drawing and collaboration reveal what complex tools need to do.
1:02:17ENPeter Bogart-Johnson on Product Management, Poetry and ListeningYaron Minsky, Peter Bogart-Johnson · Jane StreetA poet and program manager explains how to earn a team’s trust, preserve what works and coordinate change without separating engineers from their users.
59:38ENRichard Eisenberg on Dependent Types, OCaml, and AI-Assisted ProgrammingYaron Minsky, Richard Eisenberg · Jane StreetHow type systems can express correctness and performance, and why precise, readable specifications still matter when AI writes code.
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