Bounded, observable, reliable — how we design AI systems that behave predictably in production.
Machine learning systems increasingly rely on data generated by devices operating in the real world: cameras capturing environments, sensors streaming telemetry, microphones collecting audio, and applications logging user interactions. These signals make excellent training data because they reflect the actual conditions models need to perform under. The moment a training
Telemetry is supposed to be your safest window into production reality. But in device plus 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,
When you first validate a bursty image pipeline on LittleFS, it usually looks great: average write times stay low, and short tests feel done. Then you run a 6-to-12-hour soak, flash creeps toward full, and suddenly P95/P99 write latency starts spiking. Frames arrive in bursts, your producer thread blocks
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
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 doesn&