Hey Product Hunt! 👋 I built this because I was spending 3–4 hours every week scanning arXiv and Twitter for important AI papers — and still missing things. The core idea: instead of manually curating or using keyword filters, run every paper through an LLM-powered analysis pipeline that scores innovation across 4 dimensions. Then surface the top papers AND the "hidden gems" — papers that score high on innovation but haven't been noticed yet (low citations, no Twitter buzz). Each paper also gets practical use cases — not just "what this paper says" but "how you could apply this." The whole pipeline costs about $0.30 per run (~$0.004 per paper analyzed). Stack is Python + FastAPI + PostgreSQL + GitHub Actions. I'd love feedback on: - Is the innovation scoring actually useful? - What would make you switch from your current paper-reading workflow? - What topics/sources am I missing? Archive: https://ramitsharma94.github.io/... Subscribe: https://ramitsharma94.github.io/...

PaperMine

The AI papers that matter — scored, summarized, delivered

The AI papers that matter — scored, summarized, delivered