IMHO there is little point of these conversion projects. It screams of "look at me, see what I made" and when the attention goes down a little nothing was ever pushed to the repo ever again.<p>Perhaps I am out of touch, but a project with author/s that have passion for every line, function and purpose, feels more real and worth my trust to spend time using it.
I'd go even further: 'look at me, see what I paid for'.<p>This isn't much different than the 'builder brained' coworker who is obsessed with creating technical debt, not owning it. Throwing shit at the wall and seeing what sticks, passing it off as sage wisdom.<p>It'd be interesting to see the math behind offsetting the GPU crunching with more power efficient linting. Assuming every person or CI job switched <i>(and the model stays offline)</i>, how many years are we looking at?
Can someone with a deeper understanding of these sort of processes say something about the intricaies of building such a transformation process? Seems like constucting the architecture and feedback loops that guide the LLM to achieve a specific goal (byte wise bevhaviour replication) seems rather non trivial and as its on field of research? How common is it to achieve what the authors are publishing here as been having achieved? How elaborate are the achieved goals (i assume that what is specified here is rather precise and assume its true - in good faith for the sake of my question and as it is stated for the sake of being potentually reviewed/checked against)? How advanced are the metrics/behavioral constraints that guided the process? Whats the state of the art of this sort of ehm<i>..reversereplication(?)archaeomorphic kyberneering(?), archaehylomorphic programming(?). Seems like an interesting approach and maybe thats also partially because I haven't seen such an approach - regarding the specificity and methology of defining the desired endresult.<p></i>[some speculative neologismification based on my (limited) understanding of ancient greek ethymology《to illustrate my aesthesis of that process. For the notion of hylomorphism see gilbert simondon ("machine") philosophy]<p>Adding: polite request to overlook potential orthographic deficiencies of text
In this case, it’s maybe more “I can access that luxurious model you all pleb are banned from using”
Eh, I'm not that interested in participating in the marketing. I don't believe Fable/Mythos-lite/whatever is needed to translate. Or, as you call it, luxurious.<p>Fair point, though. Agreed in principle.
I don't think you are. My first reaction was: "cool, now maintain it"
Using less electricity or time for the same result seems a pretty good point.
Most software is not a single finished artifact though, it's a community, a process, knowledge, documentation, and mindshare. This has none of those, so by default it'll die as a project immediately.<p>To gain any of those is a much bigger problem: is the code structured well enough to get contributors over? Do the contributors know Rust? What about all the open bug reports? What about the edge cases that aren't triggered by the benchmarked projects, how do you even find them?
...what's the point?<p>pylint keeps being developed, maintained as usual, etc. and the LLM conversion pipeline (little more than "rewrite the diff in rust, make no mistakes" in a loop) runs in the background. why do you care about it? do you care about maintainability of the <i>output</i> of your C compiler?
> 100x faster for byte-for-byte identical output<p>> little point<p>...yeah.
[dead]
While I agree with your point in general, rewriting a big widely used project in a stricter language is always a good thing. It improves the dev-ex of people contributing to these projects and more importantly helps people seperate logic into silos. Python is inherently limited in which kinds of abstraction it can express.
> rewriting a big widely used project in a stricter language is always a good thing<p>Always might be a too strong word. Rust is, by design, a language with low development velocity.<p>So you risk:
1. ossification of the current architecture and deferment of important features; or
2. reliance on AI coding to recover velocity.<p>Maybe for some 2 does not look like a risk, but I think it's too early to call. We have yet to see the effects of extensively using these tools on large scale projects, for years and decades.
In an open source tool, there is no value without community of contributors.<p>The value of the discussed project is exactly <i>zero</i> right now <i>in the best-case scenario</i>.<p>It's more likely to be negative: because there has been no contact with reality (no users have used it in production), the risk is higher than using the existing one.<p>IOW,<p>1. Only <i>after</i> some brave souls use this in production, will the value of this project rise to zero.<p>2. Only <i>after</i> a community (could even just be a single person) demonstrates commitment to this project will it have a non-zero positive value.<p>Since it was done primarily by someone who was never part of the original community, and they have yet to demonstrate a commitment to maintenance, there is no value to this project.<p>> While I agree with your point in general, rewriting a big widely used project in a stricter language is always a good thing.<p>Assuming everything else stays the same, sure. But everything else <i>is not</i> the same - there is no community, no commitment to maintenance, high risk and, worst of all, no human involvement. This project has <i>negative</i> value now due to the risk.<p>> It improves the dev-ex of people contributing to these projects<p>What contributors? There are none, and there are unlikely to be any for the majority of the new repos created like this.<p>Improving the devex of zero contributors improves exactly nothing.<p>> Python is inherently limited in which kinds of abstraction it can express.<p>Sure, but successful projects require committed humans. This has none.
It is the easiest use-case for agents because deterministic tool for testing/verification exists and code of the source project is available. Give it enough tokens and test data and it will produce a clone tool pretty much autonomously in several iterations.<p>It is still a niche use-case.
> A Rust reimplementation of pylint that produces byte-for-byte identical output — 15–2300× faster (median ~85×).<p>> prylint is not "inspired by" pylint. [...] Where pylint has bugs, prylint reproduces them. Where pylint crashes, prylint reports the same crash message.<p>This looks very strange to me. There's no paper or explanation as to why the output should be identical to the real Pylint. Looking at GitHub, all the commits are by Claude, and otherwise, adamraudonis doesn't seem to have any connection to anyone else.<p>I don't want to accuse anyone of anything unjustly, but this post seems more like a kind of malware SEO. Is this project legit?
> There's no paper or explanation as to why the output should be identical to the real Pylint.<p>To be a drop-in replacement?
> There's no paper or explanation as to why the output should be identical to the real Pylint.<p>Because that was the prompt they used. Seems par for the course with vibe coded projects.
That caught me off guard too – is that the intention, or the actual verifiable outcome?<p>Potentially if there are failing tests of known bugs in pylint then Fable could have tried to reproduce those bugs in prylint, but that doesn't necessarily mean identical behaviour – at best only identical test-time behaviour.<p>Seems the vibe coder likely wanted it to "produce byte-for-byte identical output", but realistically there's no way to actually guarantee that as the description suggests.<p>It's one thing to burn tokens on a project like this and share it to see if there's any interest, but quite another to make exaggerated claims about its portability.
Because verifying such claims will require roughly as much work as doing the thing manually; that or the community adopting his project, and testing the claims against their code bases (who doesn't want to run random LLM-generated code on their own codebase to verify the OP claims?)<p>The OP claims align with billions (trillions?) of invested money at the moment. There is a very strong current that want to amplify this narrative.
Do you generally pylint an entire codebase at once though? Why not just the file you are actually working on? Based on the homeassistant results (10.3 hours to lint 17.5k files), it only takes about 2 seconds to pylint a file, which doesn't really feel like enough of a wait to need an entirely new linter
Why do this? Ruff already exists.
I’m interested in python bytecode to rust code compiler, that will save a lot of time and energy.
So basically `ruff check --select=PL` but worse in every single way, maintained by a system that's not even available anymore? It even uses Ruff's code under the surface.
*look inside*
> ruff
>Where pylint crashes, prylint reports the same crash message.<p>As always, three lines in you realize that the doc you're reading hasn't been written (maybe not even read) by a human.<p>So so tired of this breach of trust.
... and Mythos just found 10k of zero-days. Dept of trading issued an order to restrict foreigners access to PyLint. /s
Its fascinating to see these code conversions. I reckon its easier for LLMs because the existing code acts like a precise spec and double up as prompts which the user does not have to provide.