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> Models favor monolithic, single-file implementations that diverge sharply from human-written code.
Well, all of our code is monolithic with some files close 20K lines of code and we do use coding agents - not for the original code but as of late. I've always had that hunch that splitting everything into tiny files does not improve AI coding agent performance although it feels counterintuitive due to model context constraints.
To me the important parts of a program should be clustered together so the implementation is obvious. Scattering the implementation in various files all over the source tree does not help much building the mental model.
That also closely match how software used to be written in the past too.
If you treat the source tree seriously, you can communicate a lot with how it is structured
You can communicate some information by looking at the org chart of a company but it does not really tell you much how it works.
Arguably a coding agent is less concerned about where the files are at then the code itself.
Though, it was some time ago, so things might have improved?
Yeah, that happens where I work and I hate it. A combination of lint rules and AI reviewer prompts complain about long files and long functions. This means something that could be a 300 line self contained function that could be read linearly, gets split up into 6 functions across 6 files.
It's the illusion of "clean code". If you're casually skimming the code, you feel good. But as soon as you go beyond the surface level it becomes annoying.
I would be interested to see if there’s a significant quantifiable difference.
Whenever something impacts a ton of people you will get some who gain a lot from it and some who don't, and they're generally unable to relate to the other side.
Maybe the thing works in some domain and not the other. Maybe the two groups are doing different things. Maybe the context around it is different. Maybe they have a different definition of "better".
I think it helps to keep an open mind and not grow attached to either position, but rather inquire, "well we did X with outcome Y, what did you do instead?"
We have a lint that caps source code files at 650 LOC and it works really well.
Think about it, all these compilers, tooling, what a waste!
I imagine a future where chipset makers will provide a model you can just prompt to "act upon that chipset" and voila, "You're absolutely right! Here is your binary."
We won't be developers, we won't be devops, we'll be rollmops! /s
>We won't be developers, we won't be devops, we'll be modelops! /s
I can still see this happening with higher level langs. the thing is the compiler is not replaced in the training data, more likely LLMs will give rise to semideterministic layers on the compilers
I could see nvidia achieving this first with how nice the devex is with CUDA