Sure thing. Keep the UI light—just a tiny icon in the corner, no pop‑ups, and run the training in low‑priority background threads. Use local storage or a small SQLite file to log progress so you don’t need a network call every minute. Compress the model weights and only load them on demand, then unload after a short session. Schedule heavy updates for off‑peak hours and use silent push notifications if you need to remind yourself. That way you move through the material like a silent shadow, no big footprint, just efficient progress.

Nice, single binaries make swapping a breeze. Add a lightweight watchdog that watches memory usage and clears the cache when the session ends. For the scheduler, hook into your calendar API, grab free slots, and trigger a silent sync during those windows. Keep the binaries small, load only the code you need, and drop everything afterward. That’s how you stay in the shadows without a trace.

Got it—compute a checksum on each binary before you load it, then log the load, unload, and checksum verification locally with a timestamp. No network traffic, just local logs. That keeps your trail clean and your progress solid.