Klingon Meets TinyML
Qapla'! If you don't speak Klingon, that's roughly equivalent to saying "success" — an appropriate greeting for a project that has accomplished something few would have thought possible. Rather than simply running a pre-trained language model on an ESP32-S3 microcontroller, this open source project teaches the chip to train its own transformer from scratch. And, because ordinary languages apparently weren't nerdy enough, it learns Klingon along the way.
Most microcontroller-based AI projects focus specifically on inference. A model is trained on a powerful GPU, compressed, copied onto an embedded device, and used to generate predictions. But this project doesn’t offload the training process to a more powerful machine. The ESP32-S3 starts with no knowledge whatsoever, initializes its weights randomly, tokenizes its training data, performs forward passes, computes cross-entropy loss, runs backpropagation, updates weights using stochastic gradient descent with momentum, and periodically saves checkpoints to flash memory.
MajQa' (📷: Carloscodix)
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