Show HN: Signet – Autonomous wildfire tracking from satellite and weather data

I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking. All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats. Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment. It also records time-bounded predictions and scores them against later data, so the system