on device

GGUF is the format behind most local LLM runs, and its quant names look cryptic. The signal is decoding what Q4_K_M means and why mixed-precision k-quants beat naive uniform quantization.

On-device AI trades raw capability for privacy, offline use, and latency, under brutal memory and battery limits. The signal is the small-quantized-model stack plus a hybrid router that escalates the hard queries to the cloud without leaking private context.

Recognizing walking, driving, or a fall from a sensor stream is windowed time-series classification, usually on a battery-bound device. The signal is windowing, temporal smoothing, and the on-device constraints most candidates skip. Here is the design.

On-device AI is a real product surface (privacy, offline, latency), and it forces hard constraints. The signal is the quantization plus format plus runtime stack and the tradeoffs you accept under tight memory and battery budgets.