Any examples of AI improving a project's code

A developer reported that Anthropic's Claude Code significantly accelerated a four-week refactor merging several repositories, cutting the time by 2-3x and improving test coverage and code quality. The developer cautioned that AI tools are poor architects and that developers remain responsible for committed code, predicting that careless 'vibe-coders' will face production failures, while experienced developers can be up to 50% more productive.

Not open-source but recently at work I did a huge refactor that combined several separate but related repositories we had which duplicated quite a bit of functionality, and even some code. Claude code was a huge help in extending the test suite, doing the mechanical changes my typing spend is abysmal compared to an LLM , and identifying and diagnosing bugs. Merged into the main branch after 4 weeks, and it’s a huge relief to me. The result is a much cleaner, better-tested codebase that I still know back-to-front. I could have done all of this without AI tools, but it would have taken much longer 2 - 3x - a no-go in small startup trying to find PMF. While my experience was positive, I think a lot of the discourse about AI lacks nuance. You have to know where its strengths and weaknesses are, and use the right tool for the job. It’s fantastic for large mechanical refactors and I think it can be really useful if you take a pair-programming approach even if it’s just acting as a reviewer , but it’s a shitty architect and you need to make sure you aren’t being lazy and offloading key design decisions and mission-critical work to it. As a developer, you are responsible for what gets committed and deployed. I think a lot of the exuberant vibe-coders moving fast now are going to be in for a shitty time in the coming months when slop they pumped out without a careful review starts breaking in prod. That said, I think experienced devs who use it appropriately can produce the same or better quality output in less time maybe 0 - 50% more productive, depending on what the actual work is . The thread is specifically about quality, but there are other use-cases where quality is less important: one off scripts, internal tools, etc. where an LLM can basically one-shot it without much supervision. That’s cool too.