Typst is cool. I probably would have used it myself if it were mature enough by the time I started with this project.

I would also point out that users of roffume can consider roff an “implementation detail” and ignore it entirely. The main benefit of roffume is the creation of “per-application, self-contained folders” containing all inputs (markdown), outputs (PDFs), and commands.

I guess the argument is: If you want human-readable archives, you can then pick txtar. But mongotar basically adds some of the tar features which were missing from txtar

Hey, absolute props for replying to my code by making a counter-code of your own!

I don’t have the time to look into this right now, but wanted to say that this is the best I could hope of posting my random projects here: Having someone else tell you what they would do instead.

And for context, this was a weekend experiment. I wanted to try building “agentic stuff,” and exploring the not-so-promising intersection of information theory, LLMs, and code quality.

Will edit later after I read yours.

So how does it connect to any LLM without using their API?

Well, the typical use would be by local Agent subscription. But in theory any LLM could work, so long as it’s made to follow the filesystem protocol that faaah expects.

There’s a video in the repo with an example of how it works, plus a diagram.

Do you think rather than AI was used for most of the code? I don’t think it was.

If what you’re saying is that “vibe-coded” applies when “some AI was used”, and that it’s a bad thing, that’s a valid opinion that I might agree with in some contexts (like an ethical one).

It’s not, but some AI was used for the code. I think vibe coding it would have produced a very different (and larger) code base. What makes you think it’s vibe coded?

Yes. The idea is that you can have any tool on one side (which expects an OpenAI-like LLM API, which is a bit of a de facto standard), and any LLM on the other side (which can be an agent subscription).

I used this, for example, to test GraphRAG locally (which is designed to make a lot of LLM API calls) by piping it to an agent subscription I already had (and not needing to buy expensive API tokens for the low volume I needed).

Thanks!

Yeah, I don’t like agents either, so I’ll take it. This just solved a real problem I had, so I went ahead and shared it

Well, the main reason why I post my projects is for people to tell me what alternatives I’m missing, so thanks for that.

I’m actually not that familiar with Kilo, but I believe it’s an agent/IDE-extension, and that you can use different providers for it, including coding agents (I think this is what you were referring to.)

So basically my use case is more niche. I just had an external tool (Graphrag) that only worked with an LLM API, and I had just an agent subscription, so i built an agnostic protocol via the filesystem to make them work together.

It’s agnostic because you can have any tool on one side (which expects an OpenAI-like LLM API), and any LLM on the other side (which can be an agent subscription).

Nothing in Kilo would have allowed me to get Graphrag working, I think.

What in particular? Maybe I can tell you.

My initial use case was testing graphrag locally, which might become relevant at work later on, so I generalized the thing I did to get it working through an agent subscription. Actually that usage is public too: https://github.com/sebastiancarlos/graphrag-faaah