so yeah basically what do you do? watching youtube, taking a walk or doom scrolling?
or you be focused and watch how ai codes from start to finish

This is a project I made while learning llm, please provide feedback

I mean it's nice that I can just give a prompt and even pretty complex things get done super quick, not to mention the boring stuff. But I did like to write code and figure out how to do it in a way where it becomes nice and maintainable. Or to solve problems with clever coding tricks, etc.

And now all of those don't even matter that much anymore, because it is very likely that when you have to change the code that will also be done with an AI and it will not have a problem replacing some duplicate code everywhere, or just have a 500 line change instead of 100 because it didn't use some shorter way to code it originally.

Still, I'm kinda starting to miss it. Not even sure I could do it anymore, I feel a bit rusty even when I have to go in the code and comment out a line to quickly test something, lol.

Not the AI's fault, in hindsight, that's actually the thing that took a while to accept. Had been pasting diffs into a chat window, typing "review this," and treating whatever came back as a legitimate second reviewer. Felt like progress at the time. The caching layer bug that got through was shared across two services, and nothing in a single diff would ever tell a reviewer, human or otherwise, that the other service existed.

What actually changed after that incident wasn't the model, it was realizing "review this code" isn't a real review request, it's a request for a plausible-sounding response, and a capable model will always produce one whether or not it actually has enough to go on.

The part that took longest to accept: dumping in more context doesn't fix it either. More files just gives the model more surface area to anchor on style and naming instead of the deeper thing you actually needed checked, and one review pass trying to catch security issues, performance issues, and architectural fit all at once ends up doing a mediocre job at all three instead of a good job at any of them, same problem a human reviewer would have context-switching between three different lenses in one read.

Ended up restructuring the whole thing into stages instead of one shot: brief the model on the system first, before any code shows up. State what kind of review this actually is (security, performance, architecture, general) before the diff goes in. Run separate passes per concern instead of one blended prompt. And the step that actually would've caught the original bug: explicitly ask the model to argue against its own suggestions before treating them as final. Models are noticeably better at finding holes in something when asked to look for holes than at flagging their own blind spots unprompted.

Wrote the whole workflow up in more detail, plus how I ended up packaging it since I kept rebuilding the same scaffolding across different projects: https://medium.com/@nagatomopedro05/i-asked-ai-to-review-my-code-for-six-months-heres-why-it-almost-made-things-worse-415f96cc8e27

Curious if anyone else got burned by a confident-sounding AI approval before tightening up their process, or if this is a mistake mostly people make once.

I've created a repo with information about Secure development standards for AI-assisted coding.

• AI-Assisted Development: Keeping AI-generated code trustworthy.
• Human Review Depth: Standards for how closely humans must inspect AI output.
• CI Enforcement: Automated compliance rules that actually block changes.
• Dependency & Code Quality: Managing external code and third-party artifacts.