The million dollar (perhaps literally) question is – could @kentonv have written this library quicker by himself without any AI help?
I estimate it would have taken a few weeks, maybe months to write by hand.
That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec.
In my attempts to make changes to the Workers Runtime itself using AI, I've generally not felt like it saved much time. Though, people who don't know the codebase as well as I do have reported it helped them a lot.
I have found AI incredibly useful when I jump into other people's complex codebases, that I'm not familiar with. I now feel like I'm comfortable doing that, since AI can help me find my way around very quickly, whereas previously I generally shied away from jumping in and would instead try to get someone on the team to make whatever change I needed.
I don't think this is a fair assessment give the summary of the commit history https://pastebin.com/bG0j2ube shows your work started on 2025-02-27 and started trailing off at 2025-03-20 as others joined in. Minor changes continue to present.
> That said, this is a pretty ideal use case: implementing a well-known standard on a well-known platform with a clear API spec.
Still, this allowed you to complete in a month what may have taken two. That's a remarkable feat considering the time and value of someone of your caliber.
Your analysis is far too superficial to extract anything meaningful. I know for a fact that I have small projects that took me only a couple of days to get done which have a commit history ranging a few months. Also, software is never done. There's always room to refactor, and LLMs turn that into trivial problems. Lastly, is your project still under development if your commits are README updates, linter runs, and renaming variables?
There is a reason why commit history is not used to track productivity.