Minimal agent design, extension patterns
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JLR has set up six "centres of wellbeing" for employees in the UK. Some of the focus is preventative, including exercise classes and NHS health checks. But the initiative also helps staff who need support with physiotherapy, counselling and occupational health.
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.