This is the thing. I do use LLMs (mostly Anthropic).
It just does not generate good useable code. I have to review every single change to a higher degree than I would my own code because it likes to slip in hidden nasties. I have to rewrite at least 50% of what it generates.
That being said, I know devs who swear that they don’t even write code anymore. Like this rust port. I can’t even fathom blindly merging something his massive.
"rewrite 50% of what it generates"
See, I'll not claim they write good code. But have you considered maybe your standards are a little bit too high for the tool? I made like 15 tools already using AI for my use, most of them I barely needed to touch in the code. The code is not great, no, but it's not useless and that's what matter for me. You try and iteratively ask for the AI to do things.
If you want to ensure a higher degree of quality you can ask for tests and use techniques such as mutation testing to increase coverage, etc.
If you expect the same level of quality as you would write by hand, then you probably is better off... not using those tools.
I mean if I was rewriting 50% of the generations I get I would not be using them at all.
I think we're still seeing pretty wild variance in how effective LLMs can be for code, depending on who is driving it. I've seen some folks getting themselves into messes pretty regularly with LLMs. But, ever since Opus 4.5, it's been pretty obviously better to work with it than without it, remarkably better in some use cases. Porting an application with source available and a huge existing test suite is pretty much the ideal use case for an LLM. It has everything it needs to succeed. I can't imagine why anyone would embark on a porting effort without an LLM at this point.
Or, perhaps, you are the one who is mistaken and other people ARE having success with large and complex programs?
I am not saying you ARE wrong, but I don’t know how you could be so certain that no one else is having success with complex, AI written, code.
There are well known, established, and respected engineers creating AI projects right now. For example, antirez, the creator of Redis, created the DS4 project. When you see these sorts of projects, do you never think, “Maybe I might be wrong about this.”?
---
Just to be clear, I'm not saying they don't make mistakes. In fact I constantly scream into the void with the sheer amount of absolute stupidity of those models, however I would never say, using them for what I use, that they can only be used for simple and small use cases.
And yet we have stories[0] of companies judging merit on tokens used.
Rather than using these tokens to do rewrites that have the potential to massively improve the day to day, they're just burnt for the sake of burning them.
It's individual initiative, and company culture that are at play as much as budget.
> It's individual initiative, and company culture that are at play as much as budget.
I agree, but parent comment was insinuating that gp could just use an llm to verify their hypothesis, which is what I was attempting to point out in my comment. The tool isn't out of reach, but not everyone has employer sponsored LLM plans.
It just does not generate good useable code. I have to review every single change to a higher degree than I would my own code because it likes to slip in hidden nasties. I have to rewrite at least 50% of what it generates.
That being said, I know devs who swear that they don’t even write code anymore. Like this rust port. I can’t even fathom blindly merging something his massive.