Signals and Threads

Jane Street

Listen in on Jane Street’s Ron Minsky as he has conversations with engineers who are working on everything from clock synchronization to reliable multicast, build systems to reconfigurable hardware. Get a peek at how Jane Street approaches problems, and how those ideas relate to tech more broadly. You can find transcripts along with related links on our website at signalsandthreads.com.

  1. 3日前

    The Network as a Program with Nate Foster

    Nate Foster is a professor at EPFL in Switzerland in the Networked Systems Abstractions Lab, and a visiting researcher at Jane Street on the Networking team. In this episode, he and Ron consider what happens when you bring a software mindset to network engineering. Can you use programming language theory and formal methods to realize the dream of software-defined networks? Along the way, they discuss how hyperscalers have shaped networking hardware; the return (or not) of multicast; the ways ML workloads are reshaping the networking layer; and the success Jane Street has had using an early Internet protocol, BGP, together with a more declarative high-level specification language. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: P4 (Programming language Lenses (bidirectional transformation) OpenFlow Kleene algebra with tests NetKAT End-to-end principle Border Gateway Protocol “Stable Internet routing without Global Coordination,” aka the Gao-Rexford conditions Unison file synchronizer Barefoot Networks

    1時間35分

  2. 3月17日

    Why Testing is Hard and How to Fix it with Will Wilson

    Will Wilson is the founder and CEO of Antithesis, which is trying to change how people test software. The idea is that you run your application inside a special hypervisor environment that intelligently (and deterministically) explores the program’s state space, allowing you to pinpoint and replay the events leading to crashes, bugs, and violations of invariants. In this episode, he and Ron take a broad view of testing, considering not just “the unreasonable effectiveness of example-based tests” but also property-based testing, fuzzing, chaos testing, type systems, and formal methods. How do you blend these techniques to find the subtle, show-stopper bugs that will otherwise wake you up at 3am? As Will has discovered, making testing less painful is actually a tour of some of computer science’s most vexing and interesting problems. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: Antithesis, Will’s company FoundationDB’s deterministic simulation framework QuickCheck — the original Haskell property-based testing library, by Koen Claessen and John Hughes Hypothesis — property-based testing for Python, created by David MacIver QuviQ — John Hughes’ company commercializing QuickCheck, including automotive testing work Netflix Chaos Monkey Goodhart’s law — “When a measure becomes a target, it ceases to be a good measure” CAP theorem — the impossibility result for distributed systems that FoundationDB claims to have in some sense violated. Paxos — the consensus algorithm FoundationDB reimplemented from scratch Large cardinals, an area Will studied before abandoning mathematics Lyapunov exponent — measure of chaotic divergence Chesterton’s fence The Story of the Flash Fill Feature in Excel Building a C compiler with a team of parallel Claudes Barak Richman, “How Community Institutions Create Economic Advantage: Jewish Diamond Merchants in New York”

    1時間48分

  3. 2025/09/03

    Why ML Needs a New Programming Language with Chris Lattner

    Chris Lattner is the creator of LLVM and led the development of the Swift language at Apple. With Mojo, he’s taking another big swing: How do you make the process of getting the full power out of modern GPUs productive and fun? In this episode, Ron and Chris discuss how to design a language that’s easy to use while still providing the level of control required to write state of the art kernels. A key idea is to ask programmers to fully reckon with the details of the hardware, but making that work manageable and shareable via a form of type-safe metaprogramming. The aim is to support both specialization to the computation in question as well as to the hardware platform. “Somebody has to do this work,” Chris says, “if we ever want to get to an ecosystem where one vendor doesn’t control everything.” You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: Democratizing AI compute (an 11-part series)Modular AIMojoMLIRSwift

    1時間13分

  4. 2025/07/25

    The Thermodynamics of Trading with Daniel Pontecorvo

    Daniel Pontecorvo runs the “physical engineering” team at Jane Street. This group blends architecture, mechanical engineering, electrical engineering, and construction management to build functional physical spaces. In this episode, Ron and Dan go deep on the challenge of heat exchange in a datacenter, especially in the face of increasingly dense power demands—and the analogous problem of keeping traders cool at their desks. Along the way they discuss the way ML is changing the physical constraints of computing; the benefits of having physical engineering expertise in-house; the importance of monitoring; and whether you really need Apollo-style CO2 scrubbers to ensure your office gets fresh air. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers)Some research on CO2’s effects on human performance, which motivated us to look into CO2 ScrubbersThe Open Compute ProjectRail-Optimized and Rail-only network topologies.Immersion cooling, where you submerge a machine in a dielectric fluid!

    59分

  5. 2025/05/28

    Building Tools for Traders with Ian Henry

    Ian Henry started his career at Warby Parker and Trello, building consumer apps for millions of users. Now he writes high-performance tools for a small set of experts on Jane Street’s options desk. In this episode, Ron and Ian explore what it’s like writing code at a company that has been “on its own parallel universe software adventure for the last twenty years.” Along the way, they go on a tour of Ian’s whimsical and sophisticated side projects—like Bauble, a playground for rendering trippy 3D shapes using signed distance functions—that have gone on to inform his work: writing typesafe frontend code for users who measure time in microseconds and prefer their UIs to be “six pixels high.” You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: Bauble studioJanet for Mortals, by Ian HenryWhat if writing tests was a joyful experience?

    1時間20分

  6. 2025/03/12

    Finding Signal in the Noise with In Young Cho

    In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street. Now she helps lead the research group’s efforts in machine learning. In this episode, In Young and Ron touch on the porous boundaries between trading, research, and software engineering, which require different sensibilities but are often blended in a single person. They discuss the tension between flexible research tools and robust production systems; the challenges of ML in a low-data, high-noise environment subject to frequent regime changes; and the shift from simple linear models to deep neural networks. You can find the transcript for this episode on our website.

    1時間

  7. 2024/10/14

    The Uncertain Art of Accelerating ML Models with Sylvain Gugger

    Sylvain Gugger is a former math teacher who fell into machine learning via a MOOC and became an expert in the low-level performance details of neural networks. He’s now on the ML infrastructure team at Jane Street, where he helps traders speed up their models. In this episode, Sylvain and Ron go deep on learning rate schedules; the subtle performance bugs PyTorch lets you write; how to keep a hungry GPU well-fed; and lots more, including the foremost importance of reproducibility in training runs. They also discuss some of the unique challenges of doing ML in the world of trading, like the unusual size and shape of market data and the need to do inference at shockingly low latencies. You can find the transcript for this episode  on our website. Some links to topics that came up in the discussion: “Practical Deep Learning for Coders,” a FastAI MOOC by Jeremy Howard, and the book, of which Sylvain is a co-author.The Stanford DAWNBench competition that Sylvain participated in.HuggingFace, and the Accelerate library that Sylvain wrote there.Some of the languages/systems for expression ML models that were discussed: PyTorch, TensorFlow, Jax, Mojo, and TritonCUDA graphs and streamsHogwild concurrency

    1時間6分

  8. 2024/10/07

    Solving Puzzles in Production with Liora Friedberg

    Liora Friedberg is a Production Engineer at Jane Street with a background in economics and computer science. In this episode, Liora and Ron discuss how production engineering blends high-stakes puzzle solving with thoughtful software engineering, as the people doing support build tools to make that support less necessary. They also discuss how Jane Street uses both tabletop simulation and hands-on exercises to train Production Engineers; what skills effective Production Engineers have in common; and how to create a culture where people aren’t blamed for making costly mistakes. You can find the transcript for this episode  on our website. Some links to topics that came up in the discussion: More about production engineering at Jane Street, including how to apply.Notes on Site reliability engineering in the wider world.Alarm fatigue and desensitization.Jane Street’s 1950’s era serialization-format of choice,Some games that Streeters have used for training people to respond to incidents.

    54分
すべて見る（29）

予告編

番組について

Listen in on Jane Street’s Ron Minsky as he has conversations with engineers who are working on everything from clock synchronization to reliable multicast, build systems to reconfigurable hardware. Get a peek at how Jane Street approaches problems, and how those ideas relate to tech more broadly. You can find transcripts along with related links on our website at signalsandthreads.com.

情報

  • クリエイター
    Jane Street
  • 配信期間
    20年 - 26年
  • エピソード
    29
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Jane Street
  • 番組のWebサイト
    Signals and Threads

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