Featured · Web/AI
FlashFind
Full-Stack / ML Developer
Hyperlocal AI marketplace: users submit natural-language "Flash Requests" and the system parses intent with an LLM then Smart-Pings nearby students ranked by a Random Forest to fulfill needs in minutes.
Inspiration
Built to solve urgent student needs (forgotten charger, last-minute textbook) by turning campus inventory into an on-demand network — faster than fragmented marketplaces and better for community reuse.
What it does
Students submit a short natural-language Flash Request; an LLM parses intent and item, and a lightweight ML classifier ranks and Smart-Pings nearby students most likely to help. The flow is optimized for speed, low noise, and privacy.
How we built it
Two-step AI pipeline: Google Gemini for language-to-JSON parsing, then a scikit-learn Random Forest (saved as a .joblib) for fast logical matching. Backend is FastAPI (Python) with MongoDB, frontend is React + TypeScript.
Challenges & wins
Key challenges were data alignment with training vs live data and solving cold-start via a synthetic-data factory. We're proud of integrating the Two-Step AI and training a custom Random Forest from synthetic examples.
What's next
Scale to other communities and emergency scenarios where rapid resource distribution matters.