MacMind – A transformer neural network in HyperCard on a 1989 Macintosh
I trained a transformer in HyperCard. 1,216 parameters. 1989 Macintosh. And yes, it took a while. MacMind is a complete transformer neural network, embeddings, positional encoding, self-attention, backpropagation, and gradient descent, implemented entirely in HyperTalk, the scripting language Apple shipped with HyperCard in 1987. Every line of code is readable inside HyperCard's script editor. Option-click any button and read the actual math. The task: learn the bit-reversal permutation, the opening step of the Fast Fourier Transform. The model has no formula to follow. It discovers the…
MacMind – A transformer neural network in HyperCard on a 1989 Macintosh launched on April 16, 2026 with 159 votes, #198 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
MacMind is a complete transformer neural network built in HyperTalk, the scripting language for Apple's 1987 HyperCard software, running on a 1989 Macintosh. It includes embeddings, positional encoding, self-attention, backpropagation, and gradient descent—all readable and editable directly in HyperCard's script editor. The 1,216-parameter model learns to recognize bit-reversal permutations, a step in the Fast Fourier Transform, discovering patterns purely through attention and repeated training cycles without explicit formulas.
written from the facts on this page · September 2026
From the sources
Single-layer transformer in HyperTalk for the classic Macintosh - SeanFDZ/macmind
A transformer neural network implemented entirely in HyperTalk, trained on a Macintosh SE/30. MacMind is a 1,216-parameter single-layer single-head transformer that learns the bit-reversal permutation, the opening step of the Fast Fourier Transform, from random examples. Every line of the neural network is written in HyperTalk, a scripting language from 1987 designed for making interactive card stacks, not matrix math. It has token embeddings, positional encoding, self-attention with scaled dot-product scores, cross-entropy loss, full backpropagation, and stochastic gradient descent. No compiled code. No external libraries. No black boxes. The same fundamental process that trained MacMind,…from github.com
In the maker’s words, at launch
I trained a transformer in HyperCard. 1,216 parameters. 1989 Macintosh. And yes, it took a while. MacMind is a complete transformer neural network, embeddings, positional encoding, self-attention, backpropagation, and gradient descent, implemented entirely in HyperTalk, the scripting language Apple shipped with…
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