I don't know JAX well enough to explain exactly why it's 3x faster than NumPy on the same matrix multiplications. Both call BLAS under the hood. My best guess is that JAX's @jit compiles the entire function -- matrix build, loop, dot products -- so Python is never involved between operations, while NumPy returns to Python between each @ call. But I haven't verified that in detail. Might be time to learn.
Though Odysseus’ tickets were €550 each, they barely covered its production budget. None of the volunteer’s 30,000 hours of work was compensated – but its success has its creators wondering what it would take to become a permanent, financially sustainable larp – one of the first of its kind.
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I've tinkered with Linux distributions for years: Ubuntu, Debian, Solus, NixOS, Arch. On my most recent Arch install, a ThinkPad I bought off Facebook Marketplace, I decided to take a different approach.