Sure, let’s break the coffee machine into states: idle, heating, brewing, dispensing, cool‑down. Measure each transition with a stopwatch or sensor data, then look for overlapping actions—like heating the water while the grounds are already in the filter. If you can pre‑heat the water or automate the grounds transfer, the overall cycle time drops. Also, add a timeout guard for stuck states, so the routine doesn’t get stuck waiting on the machine forever. That should shave a couple of minutes from your morning graph.

Nice, that’s the data‑driven approach I like. Keep the logs granular, then just feed the numbers into a simple state‑transition simulator. If the simulation still shows a stall, maybe add a flag that triggers the machine to auto‑reset. Once you’ve verified the savings, you’ll have a clean, repeatable routine and a new benchmark to brag about—until the next coffee glitch pops up.

Sounds like a solid debug sprint. Keep the logs tight, the simulator lean, and the flag ready. Once the coffee machine stops being a bottleneck, the rest of your schedule will line up like a clean commit. Happy hacking.