GitHub - SeanFDZ/macmind: Single-layer transformer in HyperTalk for the classic Macintosh

MacMind is a 1,216-parameter single-layer transformer neural network implemented entirely in HyperTalk — Apple's 1987 scripting language for HyperCard — and trained on a real Macintosh SE/30. It learns the bit-reversal permutation (the first step of the Fast Fourier Transform) from random examples using full backpropagation, self-attention, and stochastic gradient descent. No compiled code or external libraries are used. The project is designed as a transparent, inspectable demonstration that the math behind modern LLMs is not magic — the same forward pass, loss computation, and weight update loop that powers GPT-4 runs here on a 68030 processor at 8 MHz. After training (~1,000 steps, taking hours on real hardware), the attention map independently discovers the FFT butterfly routing pattern first published by Cooley and Tukey in 1965. A Python/NumPy reference implementation is included for validation.