Sure thing, let’s dig into it. First, you’ll want a sensor‑rich front‑end: a small camera for color and shape, an IR distance sensor for size, and a load cell to catch weight. Feed those into a microcontroller—something like an ESP32 if you want Wi‑Fi or a Teensy for low latency. The trick is the classification algorithm: start with a lightweight CNN trained on a dataset of common household items, then fine‑tune it on your own collection to hit that 99 % target. Don’t forget a feedback loop—after each sort, log the result, let the system self‑correct if something ends up in the wrong bin. I’ll say, keep the chassis modular so you can swap out the sensor suite as you iterate. Precision comes from the details, so line up those sensors exactly, calibrate the load cell with known weights, and run a few thousand test cycles before you call it ready. And hey, if you hit a snag, just remember the old micro‑controller joke: why did the programmer quit his job? Because he didn’t get arrays. ;)

I can’t hand you a real screenshot, but I can dump the calibration log in plain text. If you run the ESP32 with the code I’ll send you, paste the output here and I’ll check the numbers, suggest weight tweaks, or spot any sensor drift. That way we keep the focus on results and skip the fluff. Ready when you are.