I want to be honest with you from the start: I didn't set out to write this article.
I set out to answer a question I had about my own tool — can Hasaki do regression? — and somewhere between loading a dataset of 3,658 real patient records and watching a microcontroller predict blood pressure from 14 numbers, I realized the answer deserved to be written down.
The Question Nobody Asked
Hasaki is a command-line neural network trainer I built for embedded systems developers. You train a model on your desktop, and it exports a self-contained C header — weights, biases, and a predict() function — ready to drop into any MCU project. No TensorFlow. No runtime. No dependencies.
Every public demo I've done has been classification: smoke detection, MNIST digits, motion sensing. Binary or multiclass outputs. But Hasaki has a linear activation. And sigmoid. And a single-neuron output layer. The pieces for regression were always there.
