TRANSFORMER NEURAL NETWORK RUNS ON 1989 MAC

MacMind includes all core transformer components: embeddings, positional encoding, self-attention mechanisms, backpropagation, and gradient descent. The entire implementation uses HyperTalk, the scripting language that shipped with HyperCard, with every line of code readable and inspectable within the application's script editor. The project trains the network to learn bit-reversal permutation—a specific computational task. While execution is slow on 1989-era hardware, the implementation demonstrates that modern machine learning architectures are theoretically implementable even with severely limited computational resources. The effort showcases both the flexibility of HyperCard's scripting capabilities and highlights how far neural network optimization has advanced. The readable code format makes the mathematical operations transparent, offering educational value alongside the technical achievement.