[mmsc]

> an LLM can ingest unstructured data and turn it into a feed.

An LLM can try to do that, yes. But LLMs are lossy compression. RSS feeds are accurate, predictable, and follow a pre-defined structure. Using LLMs to ingest data which can easily be turned into an parseable data structure seems strange: use the LLM to do the "next part" of the formula (comprehension, decision making, etc)

There is also LLMs.txt https://llmstxt.org/ eg https://joshua.hu/llms.txt / https://joshua.hu/llms-full.txt

[hombre_fatal]

I mean that your RSS feed can basically be "Go to https://techcrunch.com/latest/ and use each non-video item as a feed item" or "Go to x.com/some_user and make each tweet a feed item", and the LLM can do a perfect extraction of links from html response blobs.

The only thing you have to do is ensure it can reliably get the response html. Maybe MCP browser + proxy or mirror to seem more human.

I built this for myself. The idea is that each feed is a url + title + a prompt to tell the LLM how to extract the links you want so that it generalizes over all websites.

And each feed item is a canonicalized url + title + a local copy of the content at that url which is an improvement over RSS since so many RSS feeds don't even contain the content.