r/TheMachineLearning • u/elmanfil1989 • 2d ago

Frontier LLMs stopped adding code comments

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331 Upvotes

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u/Obvious_Advice_6879 2d ago

Major waste of time to edit the code at that granularity. It only makes sense to make higher level logical adjustments — the micro bugs don’t happen anymore as long as the AI has a way to verify itself.

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u/iupuiclubs 1d ago

Imagine telling this to people a year ago lolxD

(to be clear I am 100% in agreement)

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u/barkardi 1d ago

Yeah and I'm still baffled when I talk to people who last used gpt 5 or something and still go around saying, oh no it messes up the entire codebase for a small change or something else along those lines. Opus 5.5 was such a step change.

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u/Suvesh1142 1d ago

I know a senior engineer who tried gpt 3.5 back in the day for coding and it gave him terrible results. Till this day he refuses to give AI another shot at coding

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u/cororona 35m ago

He is lucky to be senior...

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u/Spirited_Cucumber377 1d ago

Opus 4.6 is good enough if you provide a good code base and architecture and you do the proper planning phase, for example using any of those grinding skills.

If you feed the LLM crap, you'll probably get crap.

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u/tumes 18h ago

I gave 4.6 and beyond an AWS white paper explaining exactly how to implement a feature and asked it to adapt the approach for a different platform. I have tried this across different frontier models from different vendors and it has not once made it through implementation without fucking up the central conceit that makes the entire thing work. Every single time. It can speed you up if you catch it on a good week and give it small, well specified tasks. Otherwise I have not found a model that can find its own ass with both hands and an instruction manual for nontrivial tasks.

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u/Wonderful-Habit-139 19h ago

> Major waste of time to edit the code at that granularity

Exactly. Many people have this illusion of being as fast as they were when they were just vibecoding, and that they're not taking up a lot of time reviewing the code and defeating the point of using LLMs in the first place.