Latest Interviews
Showing 1–3 of 3 transcripts.
Clear all filters- Y Combinator45 min
How This 25-Year-Old Built A $675M Legal AI Startup (With No Legal Experience)
Gustaf Alströmer, Max Junestrand, Mark Mandelmann, Martin Splitt
Founded in 2024 by Max Giunestrand, Legora is an AI-powered workspace for legal professionals that recently secured an $80 million Series B funding round led by Iconic and General Catalyst. The company differentiates itself through a "software + service" hybrid model and a data-centric architecture that ensures GDPR compliance by hosting all European client data within the EU. With a flat organizational structure that scaled from ten to 100 employees in less than a year, Legora is aggressively expanding its operations to hubs in New York, London, and Stockholm to support law firms in navigating the AI transition.
- Jane Street1h 6m
The Uncertain Art of Accelerating ML Models with Sylvain Gugger
Sylvain Gugger, Ron Minsky, Jeremy Howard, Mark Mandelmann, Mark Mirchandani, Francesc Campoy, Gabriel Sanchez
Former fast.ai co-author Jeremy Howard discusses his transition from mathematics education to optimizing machine learning infrastructure at Jane Street, highlighting breakthroughs in learning rate schedules and image resizing that previously secured top benchmark placements. He details the development of the Hugging Face Accelerate library, a lightweight tool designed to abstract complex hardware parallelism and eliminate boilerplate code for training across diverse GPUs and TPUs. The discussion further explores the architectural constraints of financial data, the dominance of PyTorch's iterative execution model, and Jane Street's rigorous approach to reproducibility and custom model development for high-frequency trading.
- Y Combinator51 min
Joan Lasenby on Applications of Geometric Algebra in Engineering
Joan Lasenby, Craig Cannon, Mark Mandelmann, Carrie Nordlund
A joint project between the Engineering and Architecture departments at Cambridge utilizes Geometric Algebra to enable drones to reconstruct the built environment through line-based computer vision, overcoming the limitations of traditional point-cloud methods. By leveraging this mathematical framework to unify scalars, vectors, and higher-dimensional primitives, the researchers facilitate coordinate-free motion extraction and sensor fusion for complex robotic tasks. While the approach prioritizes developer productivity and geometric clarity over raw computational speed, it faces adoption barriers due to its steep learning curve despite surging interest from younger engineers.