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PaperMine
The AI papers that matter — scored, summarized, delivered
Every week, 4,600+ AI papers are published. AI Research Newsletter uses a 7-stage pipeline to score each on 4 innovation dimensions and deliver the 15 that matter most — every Sunday. **Key features**: Multi-source discovery (5 sources, deduplicated) Innovation scoring (novelty, impact, breadth, technical surprise) Hidden gems (high innovation + low citations) Practical use cases per paper Trend detection vs. historical baselines Full-text analysis, not just abstracts
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/...
About PaperMine on Product Hunt
“The AI papers that matter — scored, summarized, delivered”
PaperMine was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #231 on the daily leaderboard. Every week, 4,600+ AI papers are published. AI Research Newsletter uses a 7-stage pipeline to score each on 4 innovation dimensions and deliver the 15 that matter most — every Sunday. **Key features**: Multi-source discovery (5 sources, deduplicated) Innovation scoring (novelty, impact, breadth, technical surprise) Hidden gems (high innovation + low citations) Practical use cases per paper Trend detection vs. historical baselines Full-text analysis, not just abstracts
On the analytics side, PaperMine competes within Newsletters, Artificial Intelligence, GitHub and Tech — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how PaperMine performed against the three products that launched closest to it on the same day.
Who hunted PaperMine?
PaperMine was hunted by Ramit Sharma. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of PaperMine including community comment highlights and product details, visit the product overview.
Ramit Sharma