-
-
the sign in page after that you can see the discover page and statistics page
-
this is the landing page where we can see we can test the TrustAnalyzer
-
this is the statistics page where you can see the posted opportunities trusts are they legit/safe or not
-
this is the result of the link we shared with it
-
this is the discover page here you can find some of the ScholarSync picked opportunites
Inspiration
Students constantly rely on third-party links for internships, scholarships, and job opportunities. Unfortunately, many of these links are misleading, outdated, or outright scams. We noticed that while platforms aggregate opportunities, none focus on trust and credibility. ScholarSync was inspired by the simple question: “How can a student know whether an opportunity link is actually worth their time and safe to trust?”
What it does
ScholarSync is a trust-aware opportunity discovery platform for students. It allows users to: Paste any job, internship, or scholarship link Instantly receive a Trust Score (0–100) View a transparent trust breakdown (base trust, domain reputation, content heuristics, historical signals) Get actionable recommendations and risk flags Visually understand trust progression through charts and graphs The goal is to help students apply with confidence, not guesswork.
How we built it
Frontend: Next.js (App Router), TypeScript, Tailwind CSS Backend: FastAPI (Python) Trust Engine: Rule-based scoring system Domain reputation signals Content heuristics (risk keywords, legitimacy cues) Historical adjustments (simulated, extensible) Data Flow: REST APIs connecting frontend and backend Visualization: Trust trend charts and trust factor breakdown charts Architecture: Modular design, allowing future ML integration without refactoring Everything was designed to be explainable, auditable, and extensible.
Challenges we ran into
Designing a trust system without large datasets Balancing accuracy vs transparency Handling edge cases where well-known brands can still be misused Debugging API-frontend integration under tight time constraints Ensuring charts and analysis updated dynamically without breaking UI Each challenge pushed us to build a more robust and scalable solution.
Accomplishments that we're proud of
Built a complete trust analysis engine from scratch Achieved real-time trust scoring with explainable logic Added recommendations and risk flags for better decision-making Designed a clean, professional UI suitable for real users Created a foundation that can scale to ML-based trust ranking Most importantly, ScholarSync solves a real problem faced by students daily.
What we learned
Trust is not just a score — it’s about explainability Even rule-based systems can be powerful when designed well UI clarity is as important as backend logic Building with future ML integration in mind saves time later Hackathons reward clarity of impact, not just complexity
What's next for ScholarSync
Integrating machine learning models for adaptive trust scoring Adding crowdsourced feedback and reporting Expanding historical signals with real user data Browser extension for on-the-fly trust analysis Partnering with universities and career platforms ScholarSync aims to become the standard trust layer for student opportunities.
Built With
- fastapi
- jwt
- nextjs
- postgresql
- python
- rest-api
- tailwindcss
- typescript
