I don't know - we are discussing techniques - like having information in files, or in a semantic database, or in a relational database - as if there was one way that could dominate all information access. But finding the right information is not one task - if the needed information is a summary of expenses from a period of time then the best source of it will be a relational database, if it is who is the head of the HR department in a particular company - then it could probably be easy found on the company intranet pages (which are kind of graph database). It does not really matter much if the searcher is a human or LLM - there are some differences in the speed, the one time useful context length and the fact that LLMs are amnesiac - but these are just parameters, the task for humans is immensely complicated and there is no one architecture and there will not be one for LLMs.

I also vibed a brainstorming note with my knowledge base system. The initial prompt: """when I read "We replaced RAG with a virtual filesystem for our AI documentation assistant (mintlify.com)" title on HackerNews - the discussion is about RAG, filesystems, databases, graphs - but maybe there is something more fundamental in how we structure the systems so that the LLM can find the information needed to answer a question. Maybe there is nothing new - people had elaborate systems in libraries even before computers - but maybe there is something. Semantic search sounds useful - but knowing which page to return might be nearly as difficult as answering the question itself - and what about questions that require synthesis from many pages? Then we have distillation - an table of content is a kind of distillation targeting the task of search. """ Then I added a few more comments and the llm linked the note with the other pages in my kb. I am documenting that - because there were many voices against posting LLM generated content and that a prompt will be enough. IMHO the prompt is not enough - because the thought was also grounded in the whole theory I gathered in the KB. And that is also kind of on topic here. Anyway - here is the vibed note: https://zby.github.io/commonplace/notes/charting-the-knowled...

The real thing I think people are rediscovering with file system based search is that there’s a type of semantic search that’s not embedding based retrieval. One that looks more like how a librarian organizes files into shelves based on the domain.

We’re rediscovering forms of in search we’ve known about for decades. And it turns out they’re more interpretable to agents.

https://softwaredoug.com/blog/2026/01/08/semantic-search-wit...

This mirrors something we ran into building an AI pipeline for audio content. The problem with traditional RAG is that chunking destroys the structure that actually matters — you end up retrieving fragments that are semantically similar but contextually useless.

The filesystem metaphor works because it preserves heirarchy. Documents have sections, sections have relationships, and those relationships carry meaning that gets lost when you flatten everything into embeddings.

Curious how this handles versioning though. Docs change constantly and stale context fed to an LLM is arguably worse than no context at all.

And you did not teach it to access chroma directly, because there is no adapter? Or because it is so much better at using FS tooling?

But in the end, I would expect, that you could add a skill / instructions on how to use chromadb directly

To be honest, I have no idea what chromadb is or how it works. But building an overlay FS seems like quite lot of work.

I am really enjoying this renaissance in CLI world applications. There's so much possible.

I'm working on a related challenge which is mounting a virtual filesystem with FUSE that mirrors my Mac's actual filesystem (over a subtree like ~/source), so I can constrain the agents within that filesystem, and block destructive changes outside their repo.

I have it so every repo has its own long-lived agent. They do get excited and start changing other repos, which messes up memory.

I didn't want to create a system user per repo because that's obnoxious, so I created a single claude system user, and I am using the virtual file system to manage permissions. My gmail repo's agent can for instance change the gmail repo and the google_auth repo, but it can't change the rag repo.

Edit: I'm publishing it here. It's still under development. https://github.com/sunir/bashguard

I am not familiar with the tech stack they use, but from an outsider point of view, I was sort of expecting some kind of fuse solution. Could someone explain why they went through a fake shell? There has to be a reason.

This is definitely the way. There are good use cases for real sandboxes (if your agent is executing arbitrary code, you better it do so in an air-gapped environment).

But the idea of spinning up a whole VM to use unix IO primitives is way overkill. Makes way more sense to let the agent spit our unix-like tool calls and then use whatever your prod stack uses to do IO.

I dont understand the additional complexity of mocking bash when they could just provide grep, ls, find, etc tools to the LLM

I think this is a great approach for a startup like Mintlify. I do have skepticism around how practical this would be in some of the “messier” organisations where RAG stands to add the most value. From personal experience, getting RAG to work well in places where the structure of the organisation and the information contained therein is far from hierarchical or partition-able is a very hard task.

But SQLite is notoriously 35% faster than the filesystem [0], so why not use that?

[0] https://news.ycombinator.com/item?id=14550060

I don't get it - everybody in this thread is talking about the death of vector DBs and files being all you need. The article clearly states that this is a layer on top of their existing Chroma db.

This puts a lot of LLM in front of the information discovery. That would require far more sophisticated prompting and guardrails. I'd be curious to see how people architect an LLM->document approach with tool calling, rather than RAG->reranker->LLM. I'm also curious what the response times are like since it's more variable.

RAG should no have have been represented as a context tool but rather just vector querying ad an variation of search/query - and that's it.

We were bitten by our own nomenclature.

Just a small variation in chosen acronym ... may have wrought a different outcome.

Different ways to find context are welcome, we have a long way to go!

> even a minimal setup (1 vCPU, 2 GiB RAM, 5-minute session lifetime) would put us north of $70,000 a year based on Daytona's per-second sandbox pricing ($0.0504/h per vCPU, $0.0162/h per GiB RAM)

$70k?

how about if we round off one zero? Give us $7000.

That number still seems to be very high.

Let's say I want a free, local or free-tier-llm, simple solution to search information mostly from my emails and a little bit from text, doc and pdf files. Are there any tool I should try to have ollamma or gemini able to reply with my own knowledge base?

I think generally we are going from vector based search, to agentic tool use, and hierarchy based systems like skills.

If grep and ls do the trick, then sure you don't need RAG/embeddings. But you also don't need an LLM: a full text search in a database will be a lot more performant, faster and use less resources.

Why not a simple full text search in Postgres ?

> "The agent doesn't need a real filesystem; it just needs the illusion of one. Our documentation was already indexed, chunked, and stored in a Chroma database to power our search, so we built ChromaFs: a virtual filesystem that intercepts UNIX commands and translates them into queries against that same database. Session creation dropped from ~46 seconds to ~100 milliseconds, and since ChromaFs reuses infrastructure we already pay for, the marginal per-conversation compute cost is zero."

Not to be "that guy" [0], but (especially for users who aren't already in ChromaDB) -- how would this be different for us from using a RAM disk?

> "ChromaFs is built on just-bash ... a TypeScript reimplementation of bash that supports grep, cat, ls, find, and cd. just-bash exposes a pluggable IFileSystem interface, so it handles all the parsing, piping, and flag logic while ChromaFs translates every underlying filesystem call into a Chroma query."

It sounds like the expected use-case is that agents would interact with the data via standard CLI tools (grep, cat, ls, find, etc), and there is nothing Chroma-specific in the final implementation (? Do I have that right?).

The author compares the speeds against the Chroma implementation vs. a physical HDD, but I wonder how the benchmark would compare against a Ramdisk with the same information / queries?

I'm very willing to believe that Chroma would still be faster / better for X/Y/Z reason, but I would be interested in seeing it compared, since for many people who already have their data in a hierarchical tree view, I bet there could be some massive speedups by mounting the memory directories in RAM instead of HDD.

[0] - https://news.ycombinator.com/item?id=9224

So you did GraphRAG but your graph is a filesystem tree.

I love the multipronged attack on RAG

RIP RAG: lasted one year at a skillset that recruiters would list on job descriptions, collectively shut down by industry professionals

Is this related to that thing where somehow the entire damn world forgot about the power of boolean (and other precise) searching?

haha, sweet. One of the cooler things I've read lately

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