As others have said, you didn't always build the maze. Or you built a lovely intuitive path and then were hit with an unexpected new requirement that forced you to add twisty little passages. Or, like me, you're not a perfect being and had to compromise based on some complication you didn't expect.
You don’t choose what your forebears have written, though.
And the more you put in procrastination-encouraging half-solutions, the worse your code base gets.
Years ago I worked on a project that had a n tier architecture and facade pattern for the frontend
It was good architecture for the lead developer who set it up but bad for the new team who need to update the tech
So comments and docs are both valuable
Nowadays with AI the calculus has changed once more
Unless I tell them not to, LLMs lean on slapping verbose comments of the worst kind - describing the code instead of the reasons for putting it there.
I ask them to write comments in ASD-STE100 Simplified Technical English, but all I really get from that is tersness.
Also the other day I stumbled upon a huge pile of documentation and I'm still trying to figure out if it's human or machine written. I stopped reading it half way through as I figured that perhaps it wasn't written for humans to read.
Edit: changed the LLMs pronoun to it.
Your english is really good, except for that little mistake.
I wonder if that's actually useful for an LLM though. It's additional context that should steer the LLM not to change the code to do something else.
Well, my LLMs are "smarter" than yours. They'll describe why the code is there. They'll even try to keep these comments in sync with code as it makes changes.
This includes describing the "why" behind the change even on code affected only accidentally, e.g. by reformat or reindent. And, if it wrote some code and then later learned half of it is wrong, it'll remove the offending parts and leave comments telling what used to be there, and why it isn't anymore.
Same for commit/PR messages.
May or may not be related to a recent tendency in Opus/Fable models I noticed, to eagerly turn user feedback into rules, self-correct by adding more rules, and then when some rule fails, correct it by adding a counter-steering rule - accumulating rules until eventually getting lost in them.