This is an intimidating amount of code! 12,303 lines of C and 244,740 lines of Python, which looks to be a ton of monkeypatching plus huge amounts of test code.
Only one commit added all of that, just two hours ago.
The published numbers are impressive, but its hard to evaluate how much trust can be put in a project of this complexity at this early stage.
I've restored the git history. This show hn probably would have gone much better if I knew you could add a text section to the link. I had written up a whole post to explain the project and why the code exists. Only for that to get like 3 upvotes and disappear into the void. Then I posted this and it got more attention but had no context since I never planned it.
The project has a few goals:
(1) see how far I can push AI -- this project is extremely complex -- an n:m work-stealing stackful runtime for Python with multiple I/O backends. If I can reasonably get such a project stable fully vibe coded. Then that sells the model to me at least.
(2) scale Python's networking as there is a huge gap between Python and Go. Most people don't think of Python as their first choice for networking. But I love python, i think its extremely elegant. So what if we could add a runtime extension that improved python networking.
That's the work so far at least. It was about 2 - 3 months of testing and the commits now show the history.
This is a seriously impressive project. I see your pitch is M:N work-stealing across real cores on free-threaded 3.13t/3.14t which i think is only possible because nogil now exists. which makes gevent seem lackluster in comparison
Does your code have a significant dependency on the version of Python? How easy will it be for you to maintain your code to support Python 3.15, 3.16, etc.? Is it too dependent on the implementation of Python 3.13 and 3.14 or its low level aspects? What is all the Python code doing?
Very cool. Just a few days ago, I had noted a proposed spec for a biologically-inspired high-level concurrency model where no user-facing channels/send/recv are required. The user secrets proteins, defines their receptors, and the system handles the rest. Unlike you, I don't have an implementation, but now with GPT 5.6 released it's within reach. The actionable spec prompt is at https://gist.github.com/impredicative/a0dd4ac68cd05e9d3855dc...
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[ 3.8 ms ] story [ 42.2 ms ] threadOnly one commit added all of that, just two hours ago.
The published numbers are impressive, but its hard to evaluate how much trust can be put in a project of this complexity at this early stage.
The project has a few goals:
(1) see how far I can push AI -- this project is extremely complex -- an n:m work-stealing stackful runtime for Python with multiple I/O backends. If I can reasonably get such a project stable fully vibe coded. Then that sells the model to me at least.
(2) scale Python's networking as there is a huge gap between Python and Go. Most people don't think of Python as their first choice for networking. But I love python, i think its extremely elegant. So what if we could add a runtime extension that improved python networking.
That's the work so far at least. It was about 2 - 3 months of testing and the commits now show the history.