This was one of the first blog posts I've written in decades. I'm definitely open to improving, though. Unfortunately, the codebase I've been working on over the past few months is private, but I'll see if I can share some real examples from it.
I wrote it myself and used AI to help polish the grammar, as well as improve some of the copy and tone. I also used it to review some of the ideas and claims I made. I can confidently say the content itself was written by a human, while the polishing was done with AI.
This is a great point that I hadn't considered, and it's definitely valuable. I've used APM systems that track errors by version, Sentry being one of them, and I've found it very useful when those errors include context about which commit introduced the issue. It makes it much easier for both me and the LLM I'm using to identify and understand the root cause.
> Are you really going to review these small changes?
Yes! Now that the code is well structured, it's much easier for me to review. Of course, I'm not too nitpicky. I mainly focus on architectural changes that may have drifted from the initial spec or anything unexpected. I use those moments to understand what led to the implementation and what could be improved.
Those reviews usually teach me something, or I identify a gap that can be addressed by adding more context for the agent, usually in AGENTS.md.
I also spend some time checking whether the tests actually cover the scenarios I care about. When I come across a test and don't understand why it's there, it's usually a good signal that I should pay closer attention to that area.
Ultimately, what's most important to me is feeling confident when deploying changes, especially larger ones, to production.
After trying a few different ways of coding with AI, I think I found a nice middle ground where you can still be fast at building and stay able to review the AI-generated code. Curious how you folks have been handling this.
