About the project
Built for workers, before the rush
Uberedge began with a computational physicist seeing a recurring problem in the food delivery business and rideshare: workers must react to platform demand signals after the busiest areas have already formed. As platforms reduce pay, workers need better information sooner.
Inspiration
Why workers deserve demand information before a platform reacts.
OPEN PAGE →What it does
Events, forecasts, schedules, and positioning recommendations in one workflow.
OPEN PAGE →How we built it
The Python, Flask, mapping, and Google Cloud foundation.
OPEN PAGE →Challenges we ran into
Turning scattered event signals into clear, trustworthy forecasts.
OPEN PAGE →Accomplishments that we're proud of
A multi-city map, timetable, and worker-focused recommendation flow.
OPEN PAGE →What we learned
Demand comes from more than major sports and concerts.
OPEN PAGE →What's next for Uberedge
Verified live events, traffic-aware routes, and calibrated forecasts.
OPEN PAGE →