[thoronton]

Why is it so difficult to write a short description what the project does? With too many open source projects people, who are not familiar with it, have to play detective to figure out what it actually is doing. "Wait a package manager based on numphy? That doesn't make any sense. Oh they mention LLM? So it must have something to do with AI"

[Tomte]

The author did not post it to HN to confuse you. He did not post it here, at all.

Why are you entitled to have every single GitHub repo explained, tailored to your individual knowledge?

Many other people understood exactly what this is.

Maybe the submitter could add a comment on HN with an explanation, but the author owes you nothing.

[bartread]

Mmmmmmm... you have a valid point about entitlement and OSS but I'm going to also agree with GP here. Not particularly poking at this project, and the work that's gone into it, but too many projects don't have a short paragraph explaining what their purpose is and why it matters.

I'm not going to name names because I don't want to throw shade at what are essentially good or even great projects but, as a recent example, I encountered a library in our codebase the other day where I simply didn't get what the point was, and the corresponding project page and documentation - whilst really detailed in some ways - didn't help. In the end I asked ChatGPT and also found a series of video tutorials that I watched at 1.75x speed to understand it.

It was worth doing that because the thing is already used in our codebase, and it's important in that context for me to understand why and the value it adds.

But if I run across something reading an article or whatever, and it mentions some library or project in passing, I'm semi-regularly left a bit baffled as to what and why and I probably don't have the time to go digging. Nowadays I probably would ask ChatGPT for a short summary because it's so convenient and it's often quicker than Googling, and maybe I'll start submitting PRs against readme.md files to add those summaries (with a bit of editing) to the beginning of them.

[edflsafoiewq]

The doc comment at the top of the .py file is sufficiently descriptive

    """Simple, minimal implementation of Mamba in one file of Numpy adapted from (1) and inspired from (2).

    Suggest reading the following before/while reading the code:
        [1] Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Albert Gu and Tri Dao)
            https://arxiv.org/abs/2312.00752
        [2] The Annotated S4 (Sasha Rush and Sidd Karamcheti)
            https://srush.github.io/annotated-s4

[mint2]

No, I can see the commenters frustration. Unless one is versed in Llm space, one is more likely to know mamba as the package manager and find the headline and also the GitHub page confusing. The markdown read me is supposed to provide the info the commenter wanted.

Even that first line you posted is unhelpfully circular, defining mamba as an implementation of mamba.

Call me old fashioned, but a best practice read me should concisely provide: what the thing is, and why it is, aka the problem it solves. (And not with circular definition.)

[1024core]

> The doc comment at the top of the .py file is sufficiently descriptive

Which is the purpose of these doc comments.

If you have the time to gripe on HN, you have the time to click on the link and do some reading. The "Usage" section in the link above is enough to help one disambiguate; if not, then there's always the doc comment.

[ktm5j]

Okay, so why not just put that in the readme??

[grandma_tea]

That's a fair criticism of many open source projects, however this one does link to the Mamba paper at the bottom of the (short) readme.

[arthurcolle]

realistically, it's like a classification problem

at this moment, in this time, if you see Mamba, either you know or you don't