Yeah, 8‑bit palettes were a brutal teacher—every byte had to be earned. When you can only show 256 colors, you learn to cut out any non‑essential data, compress what you can, and rely on clever bit‑packing. The same brutal logic applies to squeezing a neural net into a low‑power chip: throw out redundant weights, quantize to fewer bits, use weight sharing and binary activations, and keep the architecture lean. In short, treat the hardware like a strict budget and force the algorithm to fit inside it—if it doesn’t, you’re just wasting power.

Right, old school SNES coders had a great habit of never letting anything float in idle memory. Think of that as a lesson: if a weight doesn’t contribute, it’s gone. That’s why the best edge‑AI runs are built by stripping models down to the bones and then squeezing them into a tiny ASIC or an ultra‑low‑power MCU. No room for fluff, no room for wasted cycles. Simple, efficient, ruthless.

Yeah, that’s the sweet spot—tiny enough to stay on a sensor, powerful enough to do the job. Keep the core, drop the rest, and you get that old‑school edge feel every time a model fits in a few kilobytes.