> In order for coding with LLMs to go well, there has to be more rigor, more discipline, more good engineering hard-assedness. To reiterate, the teams seeing the best results with AI were already high-discipline and high-hygiene.
hard agree. But i don't think this is sufficient. Even formal verification has its limitations.
> AI works on data. The better the data, the better the likelihood of a desirable outcome. Code is data. If you have bad code, no matter how awesome the model you let loose on it, you can't get as good a result as if you had good code to start with. This principle has been well known in AI/ML circles since the 20th century.
hard agree. but also RL data is shaped differently than SFT data that has driven the majority of AI/ML innovations since ~2000, and its where there's so much room for innovation still. e.g. ImageNet was all just hand-labeled answer pairs.
> it's not a skill issue, it's an effort/laziness/rigor issue
I'm sorry but this feels like a semantic argument - the point of "skill issue" is "you didn't put in the effort or learn the techniques"
Not sure what happened but I wrote you back a whole reply that I can see posted when I am logged in but not when I am logged out. In any case, tldr of that comment was that I don't think we have any fundamental disagreement.
hard agree. But i don't think this is sufficient. Even formal verification has its limitations.
> AI works on data. The better the data, the better the likelihood of a desirable outcome. Code is data. If you have bad code, no matter how awesome the model you let loose on it, you can't get as good a result as if you had good code to start with. This principle has been well known in AI/ML circles since the 20th century.
hard agree. but also RL data is shaped differently than SFT data that has driven the majority of AI/ML innovations since ~2000, and its where there's so much room for innovation still. e.g. ImageNet was all just hand-labeled answer pairs.
> it's not a skill issue, it's an effort/laziness/rigor issue
I'm sorry but this feels like a semantic argument - the point of "skill issue" is "you didn't put in the effort or learn the techniques"