Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)

I don't understand the point of automating note taking. It never worked for me to copy paste text into my notes and now you can 100x that?

The whole point of taking notes for me is to read a source critically, fit it in my mental model, and then document that. Then sometimes I look it up for the details. But for me the shaping of the mental model is what counts

The space of self building artefacts is interesting and is booming now because recent LLM versions are becoming good at it fast (in particular if they are of the "coding" kind).

I've also experimented recently with such a project [0] with minimal dependencies and with some emphasis on staying local and in control of the agent.

It's building and organising its own sqlite database to fulfil a long running task given in a prompt while having access to a local wikipedia copy for source data.

A very minimal set of harness and tools to experiment with agent drift.

Adding image processing tool in this framework is also easy (by encoding them as base64 (details can be vibecoded by local LLMs) and passing them to llama.cpp ).

It's a useful versatile tool to have.

For example, I used to have some scripts which processed invoices and receipts in some folders, extracting amount date and vendor from them using amazon textract, then I have a ui to manually check the numbers and put the result in some csv for the accountant every year. Now I can replace the amazon textract requests by a llama.cpp model call with the appropriate prompt while still my existing invoices tools, but now with a prompt I can do a lot more creative accounting.

I have also experimented with some vibecoded variation of this code to drive a physical robot from a sequence of camera images and while it does move and reach the target in the simple cases (even though the LLM I use was never explicitly train to drive a robot), it is too slow (10s to choose the next action) for practical use. (The current no deep-learning controller I use for this robot does the vision processing loop at 20hz).

[0]https://github.com/GistNoesis/Shoggoth.db/

Put AI in your product name, make billion dollars. Put Karpathy in your blog article, get hired by Anthropic as Principal engineer. Milk money as long as fad last. No one is thinking about customer needs, everyone is trying to wash hands in the wave as it last.

Karpathy's original post for context:

https://x.com/karpathy/status/2039805659525644595

https://xcancel.com/karpathy/status/2039805659525644595

LLM models and the agents that use them are probabilistic, not deterministic. They accomplish something a percentage of the time, never every time.

That means the longer an agent runs on a task, the more likely it will fail the task. Running agents like this will always fail and burn a ton of token cash in the process.

One thing that LLM agents are good at is writing their own instructions. The trick is to limit the time and thinking steps in a thinking model then evaluate, update, and run again. A good metaphor is that agents trip. Don't let them run long enough to trip. It is better to let them run twice for 5 minutes than once for 10 minutes.

Give it a few weeks and self-referencing agents are going to be at the top of everybody's twitter feed.

How do you anticipate teams deploying this? I’m wary of GitHub for sensitive business documents, and wonder what an easy secure agent friendly deployment looks like. Cloudflare or GCP are maybe good candidates

The BM25-first routing bet is interesting. You mention 85% recall@20 on 500 artifacts, but the heuristic classifier routing "short lookups to BM25 and narrative queries to cited-answer" raises a practical question: what does the classifier key on to decide a query is narrative vs short? Token count? Syntactic structure? The reason I ask is that in agent-generated queries, the boundary is often blurry - an agent doing a dependency lookup might issue a surprisingly long, well-formed sentence. If the classifier routes those to the more expensive cited-answer loop it could negate the latency advantage of BM25 being first.

Any particular reason for BM25? Why not just a table of contents or index structure (json, md, whatever) that is updated automatically and fed in context at query time? I know bag of words is great for speed but even at 1000s of documents, the index can be quite cheap and will maximise precision

I love that so many people are building with markdown !

But also would like to understand how markdown helps in durability - if I understand correctly markdown has a edge over other formats for LLMs.

Also I too am building something similar on markdown which versions with git but for a completely different use case : https://voiden.md/

Couldn't you instruct your LLM to make the starting dir configurable?

Don’t know if Karpathy even wrote this version. Where are the citations?

I was looking for something similar to try out. Cool!

love the bm25-first call over vector dbs. most teams jump to vectors before measuring anything

need to try out asap. love the „the office“ vibe

Cool idea. But is anyone actually building real stuff like this with any kind of high quality?

Every time I hear someone say "I have a team of agents", what I hear is "I'm shipping heaps of AI slop".

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why not an Obsidian vault with a plugin?