This is going to happen, but it's too expensive for your LLM to do the scanning, and instead someone needs to build and maintain the index while allowing other people to subscribe to concepts. The problem is no one has sorted out the embedding space this all lives in.

I'd argue LLMs are getting cheaper, so it will get more feasible for LLMs to soon act on our behalf, bringing only what we're interested in.

Projects like OpenClaw and Hermes already show that this can work whether the source is RSS or simply a website the agent visits.

Even Google now envisions this, since they recently announced "information agents" (https://blog.google/products-and-platforms/products/search/s...) that will keep working in the background. They surely have an index they can use, but I wonder whether that's necessary? AI agents like Claude Code suggest it's possible to use simple keyword searches, without maintaining vector indexes - https://www.tigerdata.com/blog/why-cursor-is-about-to-ditch-...

It could be that soon we're gonna get a fully personalized briefing on the topics that we're interested in, or maybe a new kind of feed, replacing social media.

I'm actually working on the briefing idea myself: https://briefin.com

keywords are a start but not enough imo - consider a concept subscription such as "any of my political representatives making statements about firearm control"

That's why I reached for Apples own local LLM to fool with similar ideas like this: https://pageforth.com. Apple is better than I expected at this. Right now it filters through things like hacker news articles and whatever else you point it at to summarize and find things that match your interests. Apple's LLM reminds me of Claude like 3 years ago. It's weak for sure. But useful for small dose kind of problems.