@glyph @benjamineskola Part snark, part honest question:
Does it really matter what the code looks like or how it’s organized when machines are the ones reading and writing the code?
Discussion
@glyph @benjamineskola I think a third thing (and the way I use them) is that code reviews are for teaching and steering. Especially on a legacy code base that’s being slowly modernized, I’ll leave tips and hints on ways to improve the code instead of following the old anti-patterns around it. Most of these are non-blocking comments, but a lot of the time, the other devs take the recommendations to heart, make the changes, and then I see them following similar practices on future PRs.
@ramsey @benjamineskola yep, a critical perspective as well, but difficult to fit into a short piece on this. also dovetails with genAI problems too, because due to the current state of most tools I am assuming your code goes in the context window and not the training data. but once we have models that are trained or fine-tuned on your org’s codebase, that is a very specific and more immediate risk of calcification and repetition of historical mistakes
@glyph @benjamineskola Part snark, part honest question:
Does it really matter what the code looks like or how it’s organized when machines are the ones reading and writing the code?
@ramsey @glyph @benjamineskola
if we had AGI it wouldn't matter, but we don't, and LLMs are a dead end towards that goal.
A lot of pro-LLM arguments hinge on what I tend to call "weaponized science fiction", a dangerous trap to fall into.
Sorry for the jump scare in the back half, but, this is my attempt to (A) write about The Problem without comprehensively making everything *about* The Problem all the time, and, (B) hack off a bit of useful philosophizing and writing from my Enormous Never-Going-To-Be-Finished Omnibus Post
