I stepped back from the code today to think strategically about what the platform needs to demonstrate, and to whom. The Foxhunt Platform is ultimately a consumer-facing situational awareness tool — "Waze for police activity" — where each user's device functions as a passive RF sensor node. The consumer app is the distribution mechanism; the data flywheel it creates is the business.
The demonstration format crystallized: a 60-second video structured as a brief explanation of what the viewer is seeing, a timelapse of a drive through an area with active public safety radio traffic, and then a slow-motion confirmation moment where the app predicts police presence at a specific location and the camera shows the prediction was correct. Simple, visceral, and impossible to fake.
I also planned the validation test drive — a 20-mile route past known, fixed transmitter locations including state police facilities and the county courthouse. By comparing the platform's geolocation estimates against these known positions, I can measure accuracy under real conditions and identify specific failure modes to address before filming.
An important framing insight emerged during planning: the demonstration bar is not perfection. It's reliable performance within a defined operating envelope. Every geolocation system has accuracy limits — even commercial cellular positioning systems routinely show 100–300 meter errors in suburban environments. What matters for investors is a convincing demonstration that the physics work, the approach scales, and the team can execute.