Show HN: Open-source RSS reader with LLM-based tags and scoring to filter noise

For a long time, I'd been looking for a news reader that could filter out noise and surface only stories that really interest me. I couldn’t find one, so I built it myself. That's the way, right? :-D

In my case (over 1000 news/day), it filters out over 80% of news (those that are irrelevant for me), saving me a corresponding amount of time.

Here’s how it works:

- For each news entry, the reader automatically assigns multiple tags.

- You can create scoring rules like `books + sci-fi -> +5 score`, `politics + new-york -> -10 score`.

- News entries are sorted by score, putting the most relevant content on top.

The reader is open-source, self-hosted, and can work without proprietary LLM APIs. You can replace the APIs entry points with your self-hosted OpenAI-compatible API, or fully disable them, or implement custom non-LLM tag processors.

I'd love your feedback, questions, or suggestions!

Follow development here:

- GitHub: https://github.com/Tiendil/feeds.fun

- Reddit: https://www.reddit.com/r/feedsfun/

- Discord: https://discord.gg/C5RVusHQXy

- Blog: https://blog.feeds.fun/