This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
As a college student, I've spent way too much time searching for hackathons, internships, and other opportunities across platforms like Unstop, LinkedIn, and various event websites.
The frustrating part wasn't finding opportunities. It was keeping track of them.
One opportunity would be on one platform, another somewhere else, and by the time I remembered to check again, registration might already be closed. I wanted to participate in more hackathons, explore internships, and discover opportunities relevant to my interests, but keeping track of everything became a task of its own.
So, I started building Opportunity Radar.
Opportunity Radar is an AI-assisted platform designed to bring opportunities into one place and help students discover which ones are relevant to them.
Here's what the current version can do:
- Discover opportunities: Browse a structured collection of opportunities, including hackathons and other student-focused listings.
- Match opportunities to your profile: Get relevance scores based on your interests and eligibility information.
- Extract information using AI: Turn unstructured opportunity information into structured data, with a fallback path when local AI inference isn't available.
- Add opportunities: Save additional listings through the interface.
- Work with real application links: Keep the source and application information connected to each listing.
The project currently has a working React frontend and FastAPI backend, with opportunity listing, matching, extraction, and API health checks implemented.
The bigger vision is to make opportunity discovery less fragmented, so students can spend less time hunting for registrations and more time actually applying, building, and participating.
Who would pay for it?
Students would be the primary users, but the potential paying customers are institutions and communities that want to help students find relevant opportunities.
College placement cells, student clubs, hackathon communities, and career development teams could use a platform like this to curate opportunities, distribute relevant listings, and help students discover options they might otherwise miss.
I'm still exploring the business model. For now, the priority is building something useful, validating the core experience, and expanding its coverage responsibly.
Demo
**Live demo:https://youtu.be/Byri8N50mLo
Code
**GitHub repository: https://github.com/Prachivats-21/Opportunity-radar
The project is being developed as an open-source application, with a frontend, backend API, opportunity data, matching logic, AI-assisted extraction, and tests.
How I Built It
I built Opportunity Radar using a combination of web development, backend APIs, deterministic matching logic, and open-weight AI.
Current tech stack:
- Frontend: React, TypeScript, and Vite
- Backend: Python and FastAPI
- AI inference: Ollama with a locally accessible model, including the Gemma model family explored during development
- Data validation: Pydantic
- Opportunity matching: Profile-based matching logic with relevance scoring
- Testing: Automated backend tests and frontend build checks
One of the interesting parts was figuring out where AI actually helps and where it doesn't need to be involved.
Opportunity listings often contain unstructured information, including descriptions, eligibility requirements, and deadlines. AI can help extract that information into a structured format. Once the information is structured, deterministic logic can handle matching and validation more predictably.
This hybrid approach makes the system easier to test and reason about than relying on a model to make every decision.
I'm also working toward a modular source-integration architecture, so additional opportunity providers can be connected without rebuilding the entire application around each one.
The current version is a foundation, not a finished all-in-one aggregator. Broader platform integrations, automated updates, and more comprehensive deadline tracking are part of the direction I'm working toward.
Why Does Open Innovation Matter?
Opportunity Radar is built around a problem that affects students across different colleges, backgrounds, and technical skill levels.
Open innovation matters here because opportunity discovery shouldn't depend on access to an expensive proprietary AI service or a closed ecosystem.
Using open-weight models and local inference gives me greater control over experimentation, model selection, and the way information is processed. It also lets me explore a hybrid architecture where AI handles unstructured information while transparent application logic handles matching.
Open source makes the project easier for other developers to inspect, test, extend, and contribute to. Someone could improve the matching algorithm, add a new authorized data source, improve extraction accuracy, or build a feature for a particular student community.
I'd also like the source integrations to remain responsible. Not every platform provides a public API, and not every website permits automated collection. Building openly means making those limitations visible and designing integrations around permitted access rather than treating every website as data to scrape.
For me, open innovation isn't just about using an open model. It's about making the solution itself something others can understand and build upon.
Prize Categories
Select only the partner categories that match the tools and requirements actually used in the submitted project.
- Open-source AI / open-weight model category, if applicable to the challenge's official criteria.
- Any other applicable partner category, after checking its specific eligibility requirements.
What's Next?
The next steps for Opportunity Radar are to expand the opportunity database, add authorized source integrations, improve deadline tracking, strengthen profile-based recommendations, and validate the product with actual students.
The long-term goal is simple: students shouldn't have to be professional opportunity hunters just to find opportunities worth pursuing.
This is version one, and there's plenty left to build. But it starts with a problem I've experienced myself, and I'm excited to keep working on it.
Thanks for checking out Opportunity Radar! 🚀
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