BuddyFinder

A user-friendly website that allows you to find new friends in your local area

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Inspiration

In a world where mobility is the norm, newcomers often face the daunting task of integrating into unfamiliar environments. The challenge of forming meaningful connections can be overwhelming, and thus we decided to develop BuddyFinder - a platform designed to bridge the gap between newcomers and local communities, fostering friendships based on shared interests and hobbies.

What it does

BuddyFinder is a web-based platform that allows users to create personalized profiles, highlighting their interests and hobbies. Users can view these profiles one at a time, choosing to either connect via a friend request or move to the next suggestion.

How we built it

BuddyFinder was built using Spring Boot for the backend, ensuring robust data management and security for user profiles, whereas the frontend was crafted using Next.js and Tailwind CSS with the help of an AI tool called TempoLabs, providing a responsive and intuitive user interface.

Challenges we ran into

One major challenge we ran into was properly connecting the frontend and the backend of the web application. We kept running into errors while attempting to properly fetch the data from the backend so it could be displayed into the webpage.

Accomplishments that we're proud of

We are particularly proud of developing a user-friendly platform that genuinely addresses the needs of newcomers. Moreover, we are proud of being able to implement all the planned functionalities on the backend, including user authentication and the retrieval of potential friends' profile data.

What we learned

This project was a tremendous learning curve in terms of technical skill development and teamwork. We honed our skills in front-end and back-end development. The Next.js framework was a new addition to the skillset of the front-end team. Additionally, we learned the importance of user-centered design and iterative testing in creating a successful application.

What's next for BuddyFinder

Looking ahead, we plan to firstly resolve the integration issue and make BuddyFinder a fully functional full-stack web application. We also aim to implement ML-driven recommendations to allow users to find friends with similar interests and hobbie, allowing for a more personalized experience.

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