AI
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 provide invaluable training data because they reflect the true conditions under which models must perform. However, the moment
Software
Retrieval-Augmented Generation has become the default architecture for practical AI systems because it separates reasoning from knowledge freshness. Models no longer need to contain every fact at training time; instead, they can retrieve relevant information from search indices, document stores, internal knowledge bases, and user-generated corpora at runtime. That architectural
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’re building an on-device image capture pipeline, privacy can’t be something you “handle later.” If a full-frame or identifiable capture ever gets persisted or shipped—even briefly—you’ve already created a privacy risk and a testing headache. A stronger pattern is to make privacy a data-format
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
A production RAG system feels “fast” when it’s consistent: answers arrive quickly, follow-ups don’t suddenly slow down, and costs don’t jump with traffic. The biggest lever to achieve that isn’t usually the model—it’s retrieval. When retrieval is engineered like a disciplined subsystem (with budgets,
Software
Stable BLE streaming can feel like magic when it works: sensors stay responsive, data arrives smoothly, and the app feels “live” without burning the battery or stuttering the UI. The good news is that this isn’t luck—it’s engineering. When you treat BLE as an end-to-end streaming pipeline
Software
A user-visible history blends signals that behave nothing alike: taps and toggles from the app, device-originated events, backend jobs that complete later, human edits, and (increasingly) AI insights that evolve as models improve. These inputs arrive asynchronously, often out of order, sometimes duplicated, and occasionally retracted. If you treat the
Software
User-facing AI rarely gets a “perfect” moment. Users act now, while context arrives later: uploads finish after the screen has moved on, background sync dribbles in, retrieval indexes lag behind writes, and models improve between releases. If you treat an AI insight as a single final answer, you end up
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