HackStack

Project Info

MovieMatch

Devpost

Inspiration

Our team's shared love for movies inspired us to create MovieMatch. We often found it challenging to decide on a movie to watch together, so we wanted to build an app that could help us and others in similar situations.

What it does

MovieMatch allows users to create groups with friends, curate a list of favorite movies, and receive personalized movie recommendations based on the group's preferences.

How we built it

We built MovieMatch using React Native for the frontend to ensure cross-platform compatibility. For the backend, MovieMatch is powered by Convex, providing scalability and efficiency. We also integrated third-party APIs like IMDb using Postman for movie data and recommendations. Postman Workspace Link: link

Challenges we ran into

As it was our first time working with React Native, creating a full-stack, and integrating APIs, we faced a heap of challenges. Managing these learning curves while ensuring the app's functionality and performance was a significant challenge. Using services like Postman and Convex, tech that is built for ease-of-use and rapid development, helped us immensely.

Accomplishments we're proud of

We're proud of successfully implementing real-time syncing of group preferences and generating accurate movie recommendations. We also managed to create a user-friendly interface that made it easy for users to create groups and add movies to their lists. Getting past that first step of getting the stack to compile and subsequently building upon it was extremely rewarding.

What we learned

Through this project, we learned a lot about integrating third-party APIs, managing real-time data updates, and optimizing performance in a mobile app. We also improved our teamwork and communication skills, which were crucial for collaborating effectively in a fast-paced hackathon environment.

What's next

In the future, we plan to add more features to MovieMatch, such as user profiles, ratings, and reviews. We also want to enhance the recommendation algorithm to provide even more personalized suggestions based on individual user preferences. Additionally, we aim to improve the overall user experience by adding more interactive elements and refining the design.

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • JavaScriptIn code
  • ReactIn code
  • TypeScriptIn code

3 of 3 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

41 KB

Source files

20

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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