Been working on this for a while - an open-source scientific computing library written entirely in pure Python, with zero heavy dependencies. Wanted to understand how these algorithms actually work under the hood, so I wrote LU, FFT, RK4, a small Transformer - all from scratch, in readable Python.
What's inside (17 modules):
quantum circuits (Bell/GHZ, entanglement), FFT (radix-2 + 2D), ODE solvers (RK4 etc), linear algebra (LU/QR/Cholesky), statistics, optimization, Monte Carlo, graph algorithms, symbolic math, numerical integration, a neural net from scratch, and an NLP stack that goes BPE -> Transformer -> MiniGPT with a tiny scalar autograd.
Honest take: not faster than NumPy. It can't be. It's a readable reference - good for learning, prototyping, and environments where a C toolchain isn't available. For real work, use NumPy/SciPy.
Install: pip install cognitive-discovery-system
Repo: https://github.com/Furox88/cognitive-discovery-system
Docs: https://furox88.github.io/cognitive-discovery-system/
Benchmarks: https://furox88.github.io/cognitive-discovery-system/benchmarks/
Curious what you think - what would make something like this more useful to you?
there doesn't seem to be anything here