Software
Telemetry is supposed to be your safest window into production reality. But in device + AI systems, it’s also the quickest way to leak sensitive context—quietly, accidentally, and at scale. The pattern is familiar: a “temporary” debug field ships, raw sensor payloads sneak into logs, verbose metadata encodes identity
Firmware
When you first validate a bursty image pipeline on LittleFS, it often looks great: average write times are low, and short tests feel “done.” Then you run a 6–12 hour soak, the flash creeps toward full, and suddenly P95/P99 write latency starts spiking. Frames arrive in bursts, your
Software
Retrieval is one of the most powerful tools we have for making AI genuinely helpful—because it lets systems ground responses in real, evolving knowledge: user history, device signals, policies, docs, and live state. The catch is that retrieval is also a runtime capability. Context arrives at different speeds. Some
Software
As AI systems move from experimental features to core product capabilities, the definition of “good intelligence” changes. In early stages, intelligence is judged by fluency, creativity, or raw problem-solving ability. In production systems, those qualities matter far less than consistency, predictability, and control. A production AI system does not operate