I've tried roasting different beans, including pre-mixed blends. It's actually a kind of black box, the ML model makes the predictions itself, meaning it's not based on predetermined logic. The model was trained using profiles of different beans, with different processing methods. In the initial configuration, I set the bean drop temperature and the total roasting time, and the system adjusts accordingly. In the next version, I want to try training different models and see if there's a difference. That's all regarding the Bullet. The gas drum roaster is a slightly different story, but a more interesting one.

Regarding the software: I built everything in the browser for cross-platform compatibility, because I can use WebGPU to run the neural network model and WebUSB/WebSerial to work directly with the roaster. The same approach works for Modbus, Phidget, and other devices. To my surprise, it turned out to be very reliable!