Logs, traces, and metrics — how we keep visibility into a fleet of connected devices.
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 an AI feature behaves well, it feels effortless: the user asks a question, the model replies, and everything just works. But under that smooth surface, LLM calls are some of the most complex operations in your stack, huge prompts, variable latency, opaque provider behavior, and costs that can drift
Real-time telemetry is the heartbeat of connected products. When the stream looks smooth, teams ship with confidence, when it stutters, trust erodes fast. The silent killers aren't exotic algorithms, they're everyday mistakes: rendering on every message, letting arrays grow forever, and forgetting to tear down listeners