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Comment by unshavedyak | Hacker News Reader

Is there a name for the reverse? I'm interested in having a local LLM monitor an incoming, stateful data stream. Imagine chats. It should have the capability of tracking the current day, active participants, active topics, etc - and then use that stateful world view to associate metadata with incoming streams during indexing.

Then after all is indexed you can pursue RAG on a richer set of metadata. Though i've got no idea what that stateful world view is.

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This is an interesting idea but I'm having trouble understanding what you're to achieve. Do you mean the LLM would simply continuously update it's context window with incoming data feeds realtime, and you use it as an interface? That's pretty akin to summarization task, yes? Or are you augmenting the streams with "metadata" you mentioned?

Yea, the state i mentioned i think would be managed by several entities. Ie time, current date, etc - all could be automated without involvement of the LLM of course. However as conversations come in, the LLM would also modify the state with context clues from the conversation.

Then, when future messages come in from alternate streams (say, browser history), they could (maybe, hah) be made more rich. More likely though i would expect it to be the opposite scenario, browser informs chat, etc.

I say this because in many cases i imagine my chat conversations in my household have a severe lack of context. We often jump to vocal communication, and then paste links, etc. In a perfect world i think i'd even take home camera audio transcripts and do the same.

Ie i don't want to _just_ index a browser log as "interested in Rust. Some library about BTree", etcetc - but additional sources of data could try to store what it is i am actively doing, and associate that to the browser log.

All of this of course is nothing i'd ever want to leave the house. My hope though is that it would lean into what LLMs do well. Without the expectation of actual LLM intelligence.

So perhaps you're suggesting we sort of "boil-down" an information source into a sort of base representation of meaning and intent, something similar to vector store, and relate the many inputs together in this space using the LLM as glue like one does manually creating links in a web of Zettelkasten for research. I think this is something that the field is rapidly moving towards in personal information management.

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