It's possible, but we're at the moment when most of us can ask Fable to implement a custom compiler to a custom target for our favorite language, and even use it as a part of custom solution. Why do I need someone else's implementation? Where's the magic in this project? What's the secret sauce?
>Where's the magic in this project? What's the secret sauce?
Someone else paying for the tokens.
Also someone seeing it through (should that come). Obviously we're not "at the moment when most of us can ask Fable to implement a custom compiler to a custom target for our favorite language, and even use it as a part of custom solution", without thousands to spare and lots of time to shape the solution.
Even if it does cost thousands (does it? I genuinely have no idea how to scope such a thing) that might be a good price if a custom compiler to your custom target is something you really want. People have paid far more for far less.
If you're a hobbyist trying to compile python to your weird little arduino based thing, then that's a lot of money and you would want to use somebody else's solution, no doubt.
But if you're an aerospace company trying to compile for a flight control computer (and I guess you really want to use python for some reason), spending thousands of dollars on tokens to make and maintain a custom compiler could represent serious savings.
The big picture impact of AI that I see/anticipate the most is SAAS dying out because AI coding makes this kind of enablement and support software easier to make in-house, and this feels like an example of that, but maybe I'm seeing what I expect to see.
The “status” section of the project’s readme explicitly says that it is not passing the full test suite, and that the AOT compiler passes fewer tests than the JIT one.
It also explicitly says that they’re still working on building out the standard library.
I’m maybe not as pessimistic as leobuskin, but they are absolutely right that this is not the first time someone has tried to build an alternative Python implementation, and that all previous ones have failed because they weren’t able to get close enough to 100% parity to be acceptable to most users. Python is an unusually quirky language. I kind of wonder if “written in Rust” adds an extra headwind here because there’s nothing even remotely memory-safe about Python’s extension mechanism. I don’t know enough to know, but I have read about the death of a few of these projects in the past and a common theme of the post-mortem seems to be, “It went so smoothly at the start that we were caught off guard how much of a brick wall the last 5% was going to be.”
> What is explicitly not done yet — this is the active roadmap, in order:
> CPython test suite (cpython-full): the standing grind; failures are clustered and burned down per wave.
Awesome. Not for this repo specifically; more about the trend. More people are realizing that we have such powerful tools at our disposal and will want to do something awesome, worth while with them. Of course, many will fall off after a week, then more after a month, but some will survive. Knowledge will be spread and some will be winners through adoption. Grit can lead to knowledge, and can lead to awesome stuff.
I am a fan of AI assistance, but “ratchet” is pretty much a Claude giveaway. The kids, now in their twenties because the reference is dated, might make a joke here.
I hate to be that guy, but... one week old project, clear signs of vibing. I will be shocked if the remaining work listed (cpython test suite) proceeds in any reasonable timeline.
This is a pretty hard problem to just solve in a week.
EDIT: and man, these kind of comments LLM created comments are really starting to grind my gears as my job slowly turns into reviewing LLM PRs:
> Known gaps at the language level are burned down through the ratcheted floors above — the committed floor files, not this README, are the authoritative compatibility baseline.
"Very experienced" might mean different things to you. The oldest repo on their GH is from 2017. As for highly skilled: Could you point closer to which parts of their portfolio we are supposed to be awestruck by?
>when it's vibed it works, until it doesn't and then it's really hard to make it work again
Is it?
People have solved AI bugs with AI. If some vibe project eventually hits some bug and stops working, what exactly stops using AI to fix it? Is the idea that bugs will go beyond the limits of AI capability?
If you meant to say that when an AI vibe coded project beyond some complexity it's difficult for a human coder to manually go through all the code they didn't write, understand it, and find the issue, sure.
The problem is the _way_ AI will solve an AI bug. I've seen the loop countless times. There's a creeping complexity and brittleness that creeps in over time as more and more complexity is left purely to the LLM agent. It will become unsustainable without a human understanding and making course corrections at some point.
Given the stdlib modules listed as "explicitly not done yet", I'm going to say: it doesn't yet, in any meaningful sense. The question then becomes: how confident do we feel that it will work in the near future?
I was trying to say "not confident at all" but hedged a bit too much.
I see this as a case of the "quick to get to a POC that falls apart after sustained development for the same reasons it didn't work pre-Fable" problem.
pickle files are usually the limiter here. I would be surprised if it can handle pickle files since it relies so much on runtime LUTs of the objects and arbitrary object definitions. This usually doesn't work in other use cases such as swig or cython either IIRC.
For NumPy/Pytorch, the C API is much bigger issue than pickle. I have not looked at the architecture of this, but given it uses its own IR + replaces ref counting w/ a GC, I am assuming it does not have C API compatibility.
Can those AI slop projects have a reserved tag on HackerNews? So many in the past few weeks I wouldn't have clicked and wasted my time on if I knew it was just some vibe-coded garbage.
I see the same thing, and believe that ironically AI is going to bring about the return of good search engines as we’re currently drowning in slop and need a real way to filter it.
1. It's not Python by any means, it's a subset with its own runtime, its own quirks and nuances;
2. It will be impossible to maintain parity with CPython without AI assistance;
3. It will die the same way as dozens of similar (even non-AI projects) died before, and reasons will be the same: (1) and (2).
Arguably, passion for a project is without price.
Someone else paying for the tokens.
Also someone seeing it through (should that come). Obviously we're not "at the moment when most of us can ask Fable to implement a custom compiler to a custom target for our favorite language, and even use it as a part of custom solution", without thousands to spare and lots of time to shape the solution.
If you're a hobbyist trying to compile python to your weird little arduino based thing, then that's a lot of money and you would want to use somebody else's solution, no doubt.
But if you're an aerospace company trying to compile for a flight control computer (and I guess you really want to use python for some reason), spending thousands of dollars on tokens to make and maintain a custom compiler could represent serious savings.
The big picture impact of AI that I see/anticipate the most is SAAS dying out because AI coding makes this kind of enablement and support software easier to make in-house, and this feels like an example of that, but maybe I'm seeing what I expect to see.
A subset of python is python. Half a tomato is still tomato
>2. It will be impossible to maintain parity with CPython without AI assistance
What does that even mean? If you would have said that it's impossible to update to python 3.15 of further, I'd get it.
The funny thing about this is not that the first sentence is wrong, which it is. It’s the failed reductio ad absurdum.
It runs and passes the full cpython testsuite, just 5x faster.
With AI it's 100x easier to maintain than by hand.
It reminds my on pperl. same approach using crane lift. Looks good
It also explicitly says that they’re still working on building out the standard library.
I’m maybe not as pessimistic as leobuskin, but they are absolutely right that this is not the first time someone has tried to build an alternative Python implementation, and that all previous ones have failed because they weren’t able to get close enough to 100% parity to be acceptable to most users. Python is an unusually quirky language. I kind of wonder if “written in Rust” adds an extra headwind here because there’s nothing even remotely memory-safe about Python’s extension mechanism. I don’t know enough to know, but I have read about the death of a few of these projects in the past and a common theme of the post-mortem seems to be, “It went so smoothly at the start that we were caught off guard how much of a brick wall the last 5% was going to be.”
> What is explicitly not done yet — this is the active roadmap, in order: > CPython test suite (cpython-full): the standing grind; failures are clustered and burned down per wave.
Is a pretty oof sentence for a project with one contributor and no users. Just reeks of llm barf with no oversight.
This is a pretty hard problem to just solve in a week.
EDIT: and man, these kind of comments LLM created comments are really starting to grind my gears as my job slowly turns into reviewing LLM PRs:
> Known gaps at the language level are burned down through the ratcheted floors above — the committed floor files, not this README, are the authoritative compatibility baseline.
https://github.com/can1357/selene
it doesn't matter as long as it works.
Is it?
People have solved AI bugs with AI. If some vibe project eventually hits some bug and stops working, what exactly stops using AI to fix it? Is the idea that bugs will go beyond the limits of AI capability?
If you meant to say that when an AI vibe coded project beyond some complexity it's difficult for a human coder to manually go through all the code they didn't write, understand it, and find the issue, sure.
Same thing people claim every time a new model is released, yet never seems to be true.
I see this as a case of the "quick to get to a POC that falls apart after sustained development for the same reasons it didn't work pre-Fable" problem.