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Anthropic is claiming a significant milestone in the history of mathematics and artificial intelligence. The company used its Claude model to produce a fully computer-verified version of Fermat's Last Theorem, rendering the famous proof into thirteen million lines of checkable Lean code. The process took eleven days — which Anthropic says compressed what would otherwise be years of human effort.
That story sits alongside a quieter but meaningful development in the open-source infrastructure space. A team building AI compute primitives has made their control plane fully public, offering an optimized stack for GPU inference and sandboxed environments. It supports multiple container runtimes, includes a custom image format, and is positioned as a genuine alternative to managed platforms like Modal — the kind of tooling that serious AI teams quietly depend on.
And for anyone deploying machine learning models in the real world, a piece from Towards Data Science cuts through a common frustration. The argument is straightforward: a model that only runs on your laptop is not actually done. Walking through a FastAPI churn prediction endpoint, the author lays out everything that breaks between a working notebook and a live, callable service — a gap that remains surprisingly wide.
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