Previously, I wrote about writing GUI’s for controlling and monitoring experiments. For ML this might be useful for tracking model learning (e.g. the popular weights and biases platform), while in the wet-lab it is great for making experiments simpler and more reliable to run, monitor and record.
And as it turns out, AI is quite good at this!
I have been using VSCode CoPilot in agent mode with Gemini 2.5 Pro to create simple GUIs that can control my experiments, which has proved pretty effective. Although there is clearly a concern when interfacing AI generated code with real hardware (especially if you “vibe code”, that is, just run whatever it generates) in practice it has allowed me to quickly generate tools for testing purposes, cutting the time required for getting a project started from hours to minutes.
As an example, I recently needed to hook up a Helmholtz coil to some custom electronics, centred around a Teensy micro-controller and designed to output a precisely controlled current.
