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Mojo: Chris Lattner's Next Big Swing at Revolutionizing Machine Learning Programming

2025-09-05

Chris Lattner, creator of LLVM and Swift, discusses his new language, Mojo, with Ron. Mojo aims to make harnessing the full power of modern GPUs productive and fun. The design focuses on making a language easy to use while providing the control needed for state-of-the-art kernel writing. A key concept is requiring programmers to understand hardware details, but making that manageable and shareable through type-safe metaprogramming. The goal is to support specialization for both the computation and the hardware platform. Lattner argues this is necessary to prevent single vendors from dominating the AI computing ecosystem.

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Jane Street Quant: From Math Competitions to AI-Driven Trading

2025-03-16

In Young Cho, a quantitative trader at Jane Street, shares her unconventional career path from pre-med to quantitative trading. She recounts her experiences interning and working at Jane Street, including using programming languages like OCaml and VBA for trading and development, and humorous anecdotes about interacting with brokers. The episode delves into Jane Street's trading research, from simple linear models to complex deep neural networks, and how they leverage machine learning in low-data, high-noise environments subject to frequent regime changes. In Young Cho details the four stages of her research process: exploration, data collection, modeling, and productionization, and discusses the tension between flexible research tools and robust production systems. Finally, she offers a glimpse into the future directions of Jane Street's machine learning research, including expanding into more asset classes and data modalities, and leveraging AI to enhance trader efficiency.