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Nice idea. Our new CTO brought in a tool he made for analyzing cyclomatic complexity and it’s been useful since we’re a heavily AI-forward shop.
BTW, you can avoid your comments being flagged and killed by writing them yourself! I know it’s tempting to offshore it to AI (especially after you’ve vibe-coded a whole project) but some genuine human communication goes a long way.
I know cyclomatic complexity has been heavily debated for a long time, but I do think it’s valuable. It’s really good at highlighting common annoyances like overly clever code, nested ternaries, dense functions with too many branches…
The only thing is that these issues seem like human code problems and IME LLMs don’t really write code like this anymore. It’s almost the opposite in python, actually, where Claude leans on writing lots of 2-3 liner private utils which is a separate kind of complexity and organization problem.
I still find it useful specifically for React where it’s frustratingly normalized to write many branches in your JSX though.
The overuse of “gate” in this post title makes me think the entire thing was vibe coded even the marketing.
It is called Impact"Gate" https://github.com/marketplace/actions/impact-gate
Personally I’m ok with vibe coded projects - certainly feels like the future of things, and I think the line between vibe coded and “professionally” coded is increasingly blurring - but I completely agree on the marketing/communications piece. Ideally your communication about a project conveys real expertise and ownership, signaling that you really understand the problem you’re trying to solve and the tradeoffs you made in your approach to do so. I am very hesitant to use a project where it feels like the eng couldn’t pass a pop quiz about how it works + why.
How the change impact formula works: https://blog.officefloor.net/2026/08/measuring-blast-radius-...
Seems like the posts on this site are largely LLM-generated though? Correct me if I’m wrong.
[delayed]
A sloppy project to find slop in projects. It sure works well
Interesting idea even for non-AI code.
Yes, I've run it against a bunch of open source projects with long histories (before AI) to see if it predicts bugs. Seems file size is still a better predictor. However, for the projects where good coding was strictly adhered to and others that were not, it showed the differences appropriately. So I've found it useful in general for Software erosion.
Maybe I missed it, but it look like this has just a single metric. Maybe instead of making a new project, you could try to get this metric added to a existing tool like https://dekobon.github.io/big-code-analysis/index.html which already has dozens of metrics.
Yes, I'm doing my own research on AI augmented pipelines https://blog.officefloor.net . I actually found most code quality tools look for bugs and complexity, but nothing much about cohesive erosion. The nice thing about this metric, is that it determine the files where the erosion is occurring. I turned it into a GitHub action to make it easier to access to get wider feedback on the metric. The GitHub action triggers on your merge request and tells you the files where erosion is occurring to refactor. This stops erosion before it gets too expensive to change (big refactors or rewrite). Yes, happy to work with others to get the metric into other tools.
That project in itself looks very interesting. How are people using it, any examples of how people get this into an actual report / CI test / benchmark / whatever ?