The SimpleMind class is a powerful yet straightforward implementation of a neural network in JAX. It supports various activation functions, optimizers, and regularization techniques, making it versatile for different machine learning tasks. With parallel backpropagation and detailed logging, it provides an efficient and transparent framework for neural network training.

Professor Codephreak asked automindX to audit itself; its ten-point blueprint describes what mindX (the tree grown from automindX gitmind seed) already is. The mirror is honest: one gap (coverage 70 vs 80) is named, not hidden. Improvement is a circuit, not a line.

MindXAgent is the metagent of mindX’s core — the agent that runs the autonomous self-improvement loop.