# Papers with Code as a Gemini Notebook source

> ML papers matched to their implementations and benchmarks — import the paper page and the README together.


_Markdown version of https://gemini-notebook-hub.online/sources/papers-with-code — for AI and LLM crawlers. Content is identical to the web page._

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- Site: [paperswithcode.com](https://paperswithcode.com/)
- Category: Code & ML research
- Access: free

ML papers matched to their implementations and benchmarks — import the paper page and the README together.

It links a paper to its open-source implementation and to leaderboards showing how it compares against other methods on the same benchmark, so you can check a paper's claims against running code rather than the abstract alone. That combination is useful for an ML notebook comparing several approaches to one task. Its coverage leans toward recent, widely reproduced papers, so it's a weak source for older or less popular methods.

## Import into Gemini Notebook

1. Copy the link: https://paperswithcode.com/
2. In your notebook, click Add source → Website.
3. Paste the link and confirm — several links can be pasted at once, one per line.

**Tip:** Documentation pages import better than repositories: paste a project's docs URL or README page, not the repo root.

## More code & ml research sources

- [Hugging Face Papers](https://gemini-notebook-hub.online/sources/hugging-face-papers.md) — A daily-curated feed of AI research, each paper with a discussion page that adds context.
- [GitHub](https://gemini-notebook-hub.online/sources/github.md) — READMEs, docs folders and wikis are strong sources for how a tool actually works — paste the rendered page URL.
- [MDN Web Docs](https://gemini-notebook-hub.online/sources/mdn-web-docs.md) — The reference for web platform APIs — reliable ground truth for a web-dev notebook.
- [Stack Exchange](https://gemini-notebook-hub.online/sources/stack-exchange.md) — Accepted answers on the technical sites make good targeted sources for narrow questions.