Inspiration
Hackathons are all about collaboration, but finding the right teammate is often one of the biggest challenges. Many participants either join random teams or struggle to find people whose skills complement their own. I wanted to make team formation smarter by using AI to recommend compatible teammates based on skills, interests, and project preferences. MatchMinds aims to help builders spend less time searching for teammates and more time building great projects.
What it does
MatchMinds is an AI-powered teammate matching platform for hackathons. Users create a profile describing their technical skills, interests, experience level, and the type of teammate they're looking for. The AI analyzes all available profiles and recommends the best match, along with:
A compatibility score Reasons why the pair would work well together A potential project idea A suggested team name Combined strengths of the team
This helps participants quickly discover teammates with complementary skills instead of relying on random matching.
How I built it
Frontend: HTML, CSS, JavaScript Backend: Flask (Python) AI: OpenAI API Environment Management: Python dotenv Storage: In-memory profile management for the MVP
Users submit their profiles through a clean web interface. The backend stores participant information and passes it to the OpenAI API, which evaluates compatibility and generates meaningful recommendations. The results are then displayed in an easy-to-understand dashboard with compatibility insights and project suggestions.
Challenges I ran into
Designing prompts that produced consistent and useful teammate recommendations. Managing profiles efficiently without using a database while keeping the experience smooth. Handling situations where very few participant profiles were available. Building a simple yet intuitive interface within the limited hackathon timeframe. Refining the AI output so that recommendations felt practical rather than generic.
Accomplishments that I'm proud of
Built a complete end-to-end AI-powered teammate matching application during the hackathon. Successfully integrated the OpenAI API into a real-world workflow. Generated personalized compatibility explanations instead of simple score-based matching. Added creative features such as AI-generated project ideas and team names to enhance the experience. Delivered a functional MVP that demonstrates how AI can improve collaboration in hackathons.
What I learned
Throughout the project, we learned how prompt engineering significantly affects the quality of AI-generated recommendations. I also gained hands-on experience integrating large language models into a Flask application, structuring user data for AI analysis, and designing a workflow that balances automation with meaningful human collaboration. Most importantly, I learned how quickly an MVP can be built when AI is used as a development accelerator.
What's next for MatchMinds
Our vision is to turn MatchMinds into a full-fledged hackathon networking platform. Future plans include:
User authentication and persistent profiles Database-backed profile storage Real-time teammate recommendations Filters based on location, hackathon, tech stack, and availability Team chat and collaboration features Integration with hackathon platforms such as Devpost AI-powered team balancing for larger groups Learning from successful teams to improve future matching accuracy
Ultimately, I want MatchMinds to become the go-to platform for helping innovators find the right teammates and build stronger projects together.
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