Highlights
- Computes a shared central meeting point and recommends nearby bars for groups
- Combines mobile UX with location services to reduce venue-selection friction
- Delivered as an open-source project with separate backend, frontend, and landing repos
- Shipped an end-to-end functional prototype under hackathon time constraints
Technologies
Java 
Android 
Python 
FastAPI 
Docker 
Git Open-source mobile app that helps groups choose the best bar by computing a fair midpoint from everyone’s location.
Context
StarBars was created to solve a common group-planning problem: choosing a good place to meet when everyone starts from different locations. The goal was to make this decision faster and fairer by computing a practical midpoint and surfacing options nearby.
What I built
I helped build an open-source mobile application where users can:
- Add group locations
- Compute a shared central area
- Discover nearby bars in that radius
- Review place details before deciding where to meet
The product focused on turning a frequent social friction point into a quick, map-assisted flow.
Technical approach
The solution combined an Android mobile app (Java) with a Python backend API stack. We integrated geospatial and place-search services to calculate midpoint-based suggestions and return candidate venues. The project was organized as open source with dedicated repositories for backend, frontend, and landing page, using Git-based collaboration throughout the build process.
Outcome
StarBars delivered a working end-to-end prototype during HackUPC 2022, validating the core product idea and proving we could ship a usable geolocation-heavy experience under tight timelines. It also strengthened our ability to coordinate across distributed teamwork and unfamiliar APIs while keeping the user experience simple.
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