Comment by MangoToupe a day ago
Blog posts like this would really benefit from specific examples. While I can get some mileage out of these tools for greenfield projects, I'm actually shocked that this has proven useful with projects of any substantial size or complexity. I'm very curious to understand the context where such tools are paying off.
Don't rule out laziness! I'm a very experienced senior dev (full stack, embedded, Rust, Python, web everything, etc)... Could I have spent a ton of time learning the ins and outs of Yjs (and the very special way in which you can integrate it with TipRap/prosemirror) in order to implement a concise, collaborative editor? Sure.
Or I could just tell Claude Code to do it and then spend some time cleaning it up afterwards. I had that thing working quite robustly in days! D A Y S!
(Then I had the bright idea of implementing a "track changes" mode which I'm still working on like a week and a half later, haha)
Even if you were already familiar with all that stuff, it's a lot of code to write to make it work! The stylesheets alone... Ugh! So glad I could tell the AI something like, "make sure it implements light and dark mode using VueUse's `useDark()` feature."
Almost all of my "cleanup" work was just telling it about CSS classes it missed when adding dark mode variants. In fact, most of my prompts are asking it to add features (why not?) or cleaning up the code (e.g. divide things into smaller, more concise files—all the LLMs really love to make big .vue files).
"Writing most of the code"? No. Telling it how to write the code with a robust architecture, using knowledge developed over two decades of coding experience: Yes.
I have to reject some things because they'd introduce security vulnerabilities but for the most part I'm satisfied with Claude Code spits out. GPT5, on the other hand... Everything needs careful inspection.
Makes sense. I work for a growth stage startup and most of these apply to our internal mono repo so hard to share specifics. We use this for both new and legacy code each with their own unique AI coding challenges.
If theres enough interest, I might replicate some examples in an open source project.
I find the "slop" thing interesting because—to me—it looks like laziness. In the same way that anyone can tell ChatGPT to write something for them instead of writing it themselves and just having it check the work... Or going through multiple revisions before you're satisfied (with what it wrote).
Code is no different! You can tell an AI model to write something for you and that's fine! Except you have to review it! If the code is bad quality just take a moment to tell the AI to fix it!
Like, how hard is it to tell the AI that the code it just wrote is too terse and hard to read? Come on, folks! Take that extra moment! I mean, I'm pretty lazy when working on my hobby projects but even I'm going to get irritated if the code is a gigantic mess.
Just tell it, "this code is a gigantic mess. Refactor it into concise, human-readable files using a logical structure and make sure to add plenty of explanatory comments where anything might be non-obvious. Make sure that the code passes all the tests when you're done."
It's always laziness. The people that do the bare minimum will likely continue to do so, regardless of AI.
I think we'll be dealing with slop issues for quite some time, but I also have hopes that AI will raise the bar of code in general.
It seems to be relative to skill level. If you're less-experienced, you're letting these things write most if not all of your code. If you're more experienced, that's inverted (you write most of the code and let the AI safely pepper things in).