The Lab

Home Assistant at the center, ESP32 sensors around the house, and the fun part: wireless M-Bus heat meters and floor-heating thermostats decoded straight from 868 MHz radio with an SDR — custom rtl_433 decoders publishing into MQTT with auto-discovery. If a device emits radio waves, it can probably become a sensor entity.

A UniFi-based home network where the security logs don’t just scroll by: IPS events ship via syslog into Elasticsearch through hand-built Grok pipelines, queryable and dashboarded. Lab-scale infrastructure run with production-scale habits.

A clinker-built mahogany motor yacht from 1967 — twin Mercedes diesels, continuously restored to preserve her original appearance — quietly fitted with modern electronics: SignalK and NMEA 2000 instrumentation, and telemetry flowing into InfluxDB and Grafana. Her fuel system spawned two projects of its own: Sightglass and Virtual fuel consumption, below.

The boat’s fuel tanks are older than the idea of a fuel sensor — but they have a sight glass, and a sight glass can be looked at. So a small camera watches it, a vision model reads the level off the image, and the result publishes as an ordinary MQTT sensor. One safeguard worth stealing: a reading is never shown if the camera and the tank sensors disagree — out-of-sync data is worse than no data.

Twin 1960s diesels, no flow sensors, and no desire to cut into fuel lines. Instead: a model estimates burn from engine RPM using a cubic propeller-law curve, then calibrates itself against reality every time the tank level visibly drops — solving for the scale and idle-baseline that make the model match the measured fuel actually consumed. Estimated data is always labeled estimated; it never pretends to be a measurement.