Inspiration
Hiring developers at my company is slow and noisy. Resume screening takes a lot of time and still doesn’t reliably reflect real coding ability. I wanted to explore whether hiring decisions could be based on actual proof of work instead of resumes and keywords.
What it does
AI-Native Job Board replaces resume screening with artifact-based evaluation. Recruiters create task-focused job descriptions, and candidates apply by submitting GitHub commits or repositories. AI evaluates the code against the job requirements and produces a relevance score and explanation, helping recruiters shortlist candidates faster and more objectively.
How I built it
I built the MVP using Next.js, with AI-assisted job description rewriting and code evaluation powered by Gemini 3. The system fetches GitHub artifacts, analyzes code structure and patterns, compares them against task-level job descriptions, and surfaces ranked results in a recruiter-facing leaderboard.
Challenges I ran into
The biggest challenge was keeping the scope realistic while still demonstrating an AI-native system. Fetching and analyzing code artifacts reliably and making AI outputs explainable rather than opaque were also key challenges.
Accomplishments that I’m proud of
I’m proud of shipping a complete end-to-end flow that solves a real hiring problem I face personally. The system demonstrates a clear shift from resume-based screening to proof-of-work-based evaluation within a tight time constraint.
What I learned
I learned that hiring isn’t a matching problem—it’s a learning problem. Real signals come from work artifacts, not credentials, and AI systems are far more valuable when they assist human decision-making rather than replacing it.
What’s next for AI-Native Job Board
Next, I want to expand beyond developer roles by supporting different artifact types, improve scoring using feedback from real hiring outcomes, and evolve the system into a continuous learning platform for hiring.
Built With
- gemini
- githubapi
- langchain
- nextjs
- supabase
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