A developer has built Signet, an autonomous wildfire monitoring system in Go that processes satellite and weather data to track fire activity without human intervention. The system ingests data from NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, and other sources, then uses Gemini to orchestrate 23 tools for analysis. Currently monitoring across the continental US, Signet continuously triages detections and makes time-bounded predictions that get scored against later data.

The technical architecture separates raw data ingestion from LLM-driven analysis, with structured tables storing detections, observations, and official cross-checks. Where clean rules break down - such as deciding which weak signals warrant investigation - the system uses multimodal reasoning to correlate satellite imagery with geographic and environmental data. It evaluates fire behavior, nearby exposure, and likely persistence using data from USGS, LANDFIRE, Census, and OpenStreetMap.

While already opening incidents from raw satellite detections and matching some to official NIFC reporting, the system still struggles with false positives, detection latency, and incident matching. The creator seeks feedback on whether autonomous monitoring provides value beyond manual methods or simply adds noise to the process.