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.
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.
Otherwise, you would have known.
Unless you don't have experience and you believe the whole "You are right! it _is_ a and not b" bs...
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.”?
https://github.com/mii-nipah/voxcpm-rs
--- 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.
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.