J
jvkoch
Guest
I built this website to host a data-driven model of my coffee sample roaster.
I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!
The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.
The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.
My next steps are to add other roasters and the ability to practice control/reference tracking.
Comments URL: https://news.ycombinator.com/item?id=45493128
Points: 52
# Comments: 18
I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!
The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.
The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.
My next steps are to add other roasters and the ability to practice control/reference tracking.
Comments URL: https://news.ycombinator.com/item?id=45493128
Points: 52
# Comments: 18