MLP built from scratch in pure NumPy with GUI showing training internals

A developer released an educational tool implementing a small MLP with manual backprop in pure NumPy, reaching about 98.5% on MNIST. During training it shows per-layer gradient norms, inactive neuron percentages, weight distributions, layer-by-layer t-SNE, and supports neuron ablation and pruning experiments.

• Framework-free prototype learner Jayce trains 1.6x-4x faster than backprop2026-09-22