Hi everyone, We rely so heavily on tools like NumPy, SciPy, and PyTorch that most of us treat them...
Released pure Python v0.6.0 scientific platform without NumPy: O(N³) LU algebra, quantum sim, neural networks, O(N log N) FFT; 350+ tests at 95% coverage. Shows algorithmic optimization is practical in vanilla Python for debugging AI/ML internals and understanding framework abstractions.
Hi everyone,
We rely so heavily on tools like NumPy, SciPy, and PyTorch that most of us treat them like black magic. I wanted to break that illusion. My goal was to
see if I could build a professional-grade computational science platform using nothing but the Python Standard Library.
It turned into an obsessive deep dive into algorithmic optimization, memory management, and raw math.
I've just released v0.6.0, and here is what it currently handles natively in Pure Python:
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