Progressive disclosure, same reason you don't get assaulted with all the information a website has to offer at once, or given a sql console and told to figure it out, and instead see a portion of the information in a way that is supposed to naturally lead you to finding the next and next bits of information you're looking for.
> use cases
This is essentially just where you're moving the hierarchy/compression, but at least for me these are not very disjoint and separable. I think what I actually want are adaptable LoRa that loosely correspond to these use cases but where a dense discriminator or other system is able to adapt and stay in sync with these too. Also, tool-calling + sql/vector embeddings so that you can actually get good filesystem search without it feeling like work, and let the model filter out the junk.
> let the AI calculate this at run time?
You still do want to let it do agentic RAG but I think more tools are better. We're using sqlite-vec, generating multimodal and single-mode embeddings, and trying to make everything typed into a walkable graph of entity types, because that makes it much easier to efficiently walk/retrieve the "semantic space" in a way that generalizes. A small local model needs at least enough structure to know these are the X ways available to look for something and they are organized in Y ways, oriented towards Z and A things.
Especially on-device, telling them to "just figure it out" is like dropping a toddler or autonomous vehicle into a dark room and telling them to build you a search engine lol. They need some help and also quite literally to be taught what a search engine means for these purposes. Also, if you just let them explore or write things without any kind of grounding in what you need/any kind of positive signals, they're just going to be making a mess on your computer.
Two reasons I think:
Coding agents simulate similar things to what they have been trained on. Familiarity matters.
And they tend to do much better the more obvious and clear a task is. The more they have to use tools or "thinking", the less reliable they get.
It doesn't. The human creating the files needs it, to make it easier to traverse in future as the file count grows. At 52k files, that's a horrendous list to scroll through to find the thing you're looking for. Meanwhile, an AI can just `find . -type f -exec whatever {} \;` and be able to process it however it needs. Human doesn't need to change the way they work to appease the magic rock in the box under the desk.
why? The human would just talk to the AI agent. Why would they need to scroll through that many files?
I made a similar system with 232k files (1 file might be a slack message, gitlab comment, etc). it does a decent job at answering questions with only keyword search, but I think i can have better results with RAG+BM25.
Just because AI exists doesn't mean we can neglect basic design principles.
If we throw everything out the window, why don't we just name every file as a hash of its content? Why bother with ASCII names at all?
Fundamentally, it's the human that needs to maintain the system and fix it when it breaks, and that becomes significantly easier if it's designed in a way a human would interact with it. Take the AI away, and you still have a perfectly reasonable data store that a human can continue using.