The premise of Atomic, the knowledge base project I'm currently working on, is that there is still significant value in vectors, even in an agentic context. https://github.com/kenforthewin/atomic

I've started to think about maybe a fine-tuned model is needed, specifically for "journal data retrieval" or something like that, is anyone aware of any existing models for things like this? I'd do it myself, but since I'm unwilling to send larger parts of my data to 3rd parties, I'm struggling collecting actual data I could use for fine-tuning myself, ending up in a bit of a catch 22.

For some clients projects I've experimented with the same idea too, with less restrictions, and I guess one valuable experience is that letting LLMs write docs and add them to a "knowledge repository" tends to up with a mess, best success we've had is limiting the LLMs jobs to organizing and moving things around, but never actually add their own written text, seems to slowly degrade their quality as their context fills up with their own text, compared to when they only rely on human-written notes.

Isn’t the biggest benefit of graph databases the indexing and additional query constructs they support, like shortest path finding and whatnot?

Clicked the link expecting to see some tool or method that actually allows graph-like queries and traversals on files in a file system, all I found was some rant about someone on the internet being wrong.

Waste of time.

It can only handle 3 way multiple cross references by using 2 folders and a file now (meta) and it's very verbose on the disk (needs type=small otherwise inodes run out before disk space)... but it's incredibly fast and practially unstoppable in read uptime!

Also the simplicity in using text and the file system sort of guarantees longevity and stability even if most people like the monolithic garbled mess that is relational databases binary table formats...

I've got scraps of paper and legal pads and post-it notes and just throw them away after they've been sitting around for a while and I forget what they are about.

1. Why does AI need that folder structure? Why not a flat list of files and let the AI agent explore with BM25 / grep, etc.

2. pre-compute compression vs compute at query time.

Kaparthy (and you) are recommending pre-compressing and sorting based on hard coded human abstraction opinions that may match how the data might be queried into human-friendly buckets and language.

Why not just let the AI calculate this at run time? Many of these use cases have very few files and for a low traffic knowledge store, it probably costs less tokens if you only tokenize the files you need.

Anyway, why care how the data is stored? You need a catalog. You need an index. You need automation. Helps keeping order and helps with inevitable changes and flips and pivots and whims and trends and moods and backups and restoration and snapshots and history and versioning and moon travels and collaboration and compatibility and long summer evening walks and portability.

Maybe it is why mind maps never spoke to me. I felt that a tree structure (or even - planar graphs) were not enough to cover any sufficiently complex topic.

Deep inside a project dir, feels like some the ease of LLMs is just not having to cd into the correct directory, but you shouldn't need an LLM to do that. I'm gonna try setting up some aliases like "auto cd to wherever foo/main.py is" and see how that goes.

In somewhat of an inversion, I've been getting the initial naming done by an LLM (well, I was, until CoPilot imposed file upload limits and the new VPN blocked access to it) --- for want of that, I just name each scan by Invoice ID, then use a .bat file made by concatenating columns in a spreadsheet to rename them to the initial state ready for entry.

Folders give you hierarchical categories.

You still want tags for horizontal grouping. And links and references for precise edges.

But that gives you a really nice foundation that should get you pretty damn far.

I also now am telling the llm to add a summary as the first section of the file is longer.

Maybe someday someone will expose these in the form of a graph database API (just for fun).

My “knowledge” is spread out on various SaaS (Google, slack, linear, notion, etc). I don’t see how I can centralize my “knowledge” without a lot of manual labour.

Now just need to find a good way to maintain the order...