GradNet

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Inspiration

Students everywhere graduate with big dreams — but no clear path forward. Finding the right alumni mentor can be random, slow, and intimidating. We wanted to fix that. GradNet was inspired by the idea of making mentorship discovery as easy and powerful as a Google search. Every student deserves guidance from someone who walked their dream path — and now they can find that guide with just one search.

What it does

GradNet allows students from any college to: Instantly search alumni by major, skill, and career path. View matching alumni profiles pulled from Linkd's People Search API. It turns mentorship discovery from a stressful guessing game into a smooth, empowering experience.

How we built it

Frontend: Built with React.js and TypeScript for a strong, scalable UI. Styled using Tailwind CSS for a clean, mobile-friendly design. Backend: Built with Flask (Python) to handle API requests securely and process connections to the Linkd People Search API. Communication: Built on RESTful API principles to connect the frontend and backend smoothly and efficiently. Search System: Students can type their major and skill, and GradNet fetches and displays the perfect alumni mentors.

Challenges we ran into

Data Handling at Scale: We needed to design a search that feels instant, even while pulling from a massive alumni database. Connecting Frontend and Backend: Making sure the TypeScript frontend and Flask backend communicated securely and handled API errors gracefully. UX Design: We had to think carefully about making the experience clean and simple, ensuring students wouldn’t feel overwhelmed by too much information. Managing Scope: We had bigger ideas (like live chat, direct scheduling), but we focused on building the best possible MVP within the hackathon timeframe.

Accomplishments that we're proud of

Building a full-stack, scalable product within 36 hours. Creating a clean, simple UX that anyone can understand and enjoy. Seamlessly integrating an external People Search API into a real-world, useful product. Crafting a solution that isn’t just for UCLA — but can scale to any college or university.

What we learned

How to build dynamic queries using Natural Language Processing (NLP) through Groq AI. How to build modular, scalable apps using React, TypeScript, Flask, and REST APIs. How to structure and optimize external API calls for large datasets. How to design for simplicity: when you handle big data, clarity is everything. How important strong team communication is when coordinating frontend and backend development under a tight deadline.

What's next for GradNet

Add Direct Messaging: Allow students to directly message and schedule mentorship sessions inside GradNet. AI-Powered Smart Matchmaking: Use AI to suggest the best mentor matches based on user career goals, not just skills or major. Mobile App Launch: Build a mobile version of GradNet to make mentorship discovery even more accessible.