# Stack Exchange as a Gemini Notebook source

> Accepted answers on the technical sites make good targeted sources for narrow questions.


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

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

Accepted answers on the technical sites make good targeted sources for narrow questions.

An accepted, highly-voted answer on a well-moderated site like Stack Overflow has usually been checked by more than one person who ran the fix themselves, which makes it a decent source for a debugging-focused notebook chasing one specific error. It works best for narrow, answerable questions rather than open-ended design ones. Answers age, especially for fast-moving frameworks, so check the question's date before trusting a years-old fix.

## Import into Gemini Notebook

1. Copy the link: https://stackexchange.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

- [Papers with Code](https://gemini-notebook-hub.online/sources/papers-with-code.md) — ML papers matched to their implementations and benchmarks — import the paper page and the README together.
- [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.