Earlier quoted context omitted.
> LLMs are useful for coding now *sort of, sometimes, with simple enough problems with sufficiently little context, for code that can be easily tested, and for which sufficient examples exist in the training data. I mean hey, two years after being promised AGI was literally here, LLMs are almost as useful as traditional static analysis tools! I guess you could have them generate comments for you based on the code as…
Firstly, GPT-2 was released in 2019. Five years is not "almost ten years". Secondly, LLMs are objectively useful for coding now. That's not the same thing as saying they are replacements for SWEs. They're a tool, like syntax highlighting or real-time compiler error visibility or even context-aware keyword autocompletion. Some individuals don't find those things useful, and prefer to develop in a plain text editor tha…
But is that actually a true statement. Are there actual studies to back that up?
AI is hyped to the moon right now. It is really difficult to separate the hype from reality. There are ancedotal reports of ai helping with coding, but there are also ancedotal reports that they get things almost right but not quite, which often leads to bugs which wouldn't otherwise happen. I think its unclear if that is a net win for productivity in software engineering. It would be interesting if there was a robust study about it.