# Project export: Spotter

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## Project metadata

- Hackathon: Cal Hacks 11.0
- Tagline: Tired of searching for a study spot during exams? With Spotter, check ahead to see if library spots are open. No more wandering—just studying! Find your spot faster and focus more with Spotter!
- Devpost: https://devpost.com/software/spotter-6q0uts
- GitHub: not linked
- Demo: http://ww.usespotter.com/
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

Finding a study spot during exam season or a table during a hackathon is frustrating and time-consuming. We wanted to solve this problem by creating an app that allows users to check for available spots ahead of time, so they can focus more on working and less on searching.

### What it does

Spotter helps students find available study spots in libraries and common areas. Users can view real-time spot availability, set favorite study locations, and receive notifications when spots open up during their preferred study times.

### How we built it

We built Spotter using React Native for the mobile app, Expo for ease of development, and integrated NFC readers for spot tracking. On the backend, we used Supabase for real-time data storage and notifications. The interface provides a clean map view of available study spots.

### Challenges we ran into

One challenge was ensuring real-time updates for spot availability, especially during peak times. Integrating NFC readers with reliable spot detection also posed a technical challenge, as did predicting user preferences with limited initial data.

### Accomplishments we're proud of

We’re proud of creating a smooth user interface that offers real-time spot updates and allows users to set preferences for notifications. Our NFC integration and initial machine learning-based recommendations are also big achievements in terms of functionality and future scalability.

### What we learned

We learned a lot about integrating NFC technology with mobile apps and optimizing for real-time updates. User experience design was another key area of growth, as we focused on making the app intuitive and functional, especially during high-traffic periods like exam season.

### What's next

Next, we plan to expand Spotter by incorporating machine learning for smarter spot recommendations based on user habits. We’ll also work on integrating more universities and public spaces, adding features like heatmaps, and improving real-time spot detection through additional sensors or user feedback.

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Supabase (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.