I am not surprised but this one sticks out...

> 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.

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Garlef 2 hours ago | parent | next [-]

> Scattering the implementation in various files all over the source tree

If you treat the source tree seriously, you can communicate a lot with how it is structured

	▲	_pdp_ 2 hours ago \| parent [-]
		Well you can communicate organisation structure but not logic or intent. The directory is a tree and the Code is a graph. 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.

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BurningPenguin 3 hours ago | parent | prev | next [-]

Kinda surprising to me, since i had some trouble with Cursor & Co. once the file went over ~800 lines. It repeatedly failed to edit it, until i split it up into multiple logical components. As it should have been from the beginning...

Though, it was some time ago, so things might have improved?

	▲	tnelsond4 2 minutes ago \| parent \| next [-]
		Yeah, that was my experience with Grok, whenever I gave it a file with over 400 lines it would just fail to comprehend it or be too lazy to write too much at a time. Splitting stuff up into separate files helped.
	▲	_pdp_ 2 hours ago \| parent \| prev [-]
		VSCode basically any model can edit the 20K file without any issues. The coding harness does not read the entire file at once though. It reads chunks of it so the size does not really matter. What matters is how close are the things the agent needs to make the edit.

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doix an hour ago | parent | prev [-]

> Scattering the implementation in various files all over the source tree does not help much building the mental model.

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.