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Over the last 10 years we've seen Machine Learning consume everything, from the tech industry to the Nobel Prize, and yes, even the ML acronym. This rise in ML has also come along with an unprecedented buildout of infra, with Llama 3 now reaching 4e25 floating point operations, or 40 yottaflops, or 40 trillion trillion floating point operations. To build these ML models, you need ML systems, like PyTorch. In this talk, Horace will (attempt to) answer: - How have ML systems evolved over time to meet the training needs of ML models? How does building ML systems differ from regular systems? - How do we get the most out of a single GPU? What's the point of compilers if we're just training a single model? - And what is the right way to think about scaling to 10s of thousands of GPUs?

Features of the Notafs filesystem for MirageOS which, designed to be used on satellites with SpaceOS, lets users use Irmin as a filesystem for MirageOS.

Accessing the power of static typing for server-side form validation and error reporting

Announcing a new filesystem for MirageOS projects, designed to be used on satellites with SpaceOS, that also lets developers use Irmin with MirageOS!

David Sancho Moreno's FUN OCaml 2024 talk recording! Overview by David: server-reason-react implements react-dom/server and some of React's internals in OCaml. Its purpose is to natively render HTML markup from the server for a Reason React application. This pushes the idea for universal code (sharing code between the browser and native) and this talk is the story of all of this, and what are the solutions we applied at Ahrefs. server-reason-react: https://github.com/ml-in-barcelona/server-reason-react Connect with us Website: https://fun-ocaml.com/ Twitter: https://x.com/FunOCaml Bluesky: https://bsky.app/profile/fun-ocaml.com #ocaml