Gemini, TensorFlow & Our Own Weather Station — What Comes Next
We're putting together something that, as far as we can tell, no prediction-market trader has done before: owning the data pipeline end to end. Most bots in this space stop at public weather APIs. StormBot is moving towards a stack where the data starts at our own physical sensors and ends at on-chain settlement.
StormBot Ground Station — Austin, Texas
Construction is underway on a dedicated weather-measurement facility outside Austin, Texas. This isn't a backyard setup. The site is being kitted out with professional-grade meteorological instruments — high-precision thermometers, barometric sensors, anemometers, rain gauges and humidity probes — each calibrated to WMO standards.
Why bother? Because Polymarket weather books resolve against Weather Underground station data, and those stations have known biases — urban heat-island effects, awkward sensor placement, slow calibration drift. Operating our own station and continuously cross-referencing it against the WU readings lets us model the precise gap between forecast models and the data that actually settles each market. Nobody else has that signal.
Target operational date: Q3 2026
Google Gemini Alongside Claude
Gemini is being wired in as a second reasoning model that runs in parallel with Claude. Each model analyses the same candidates independently, and the engine cross-references both opinions before any order routes. Agreement is treated as elevated confidence; disagreement flags the trade for review. Two frontier models converging on the same call is a stronger signal than any single model on its own.
A Custom TensorFlow Network
A bespoke TensorFlow model is being trained on five-plus years of historical forecasts paired with the Polymarket resolution outcomes for the matching cities and dates. Its job is to learn the patterns the physics-based models systematically miss — the consistent gap between WU stations and official forecasts in certain cities, the regimes that produce larger-than-expected temperature swings — and to surface them in real time as a third, independent probability estimate that sits beside the ensemble core and the reasoning models.