I feel like I'm taking crazy pills sometimes. You have a file with a set of snippets and you prefer to ask the AI to hopefully run them instead of just running it yourself?
I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.
In the other cases I see what the computer outputs, LEARN, and then the functionality of finding what I need just isn't useful next time. Next time I just type the command.
I don't get it.
For example, I have a pretty good grasp of regular expressions because I'm an old Perl programmer, but I find processing json using `jq` utterly baffling. LLMs are great at coming up with useful examples, and sometimes they'll even get it perfect the first time. I've learned more about properly using `jq` with the help of LLMs than I ever did on my own. Same goes for `ffmpeg`.
LLMs are not a substitute for learning. When used properly, they're an enhancement to learning.
Likewise, never mind the idiot CEOs of failing companies looking forward to laying off half their workforce and replacing them with AI. When properly used, AI is a tool to help people become more productive, not replace human understanding.
Because now the capabilities of the model grow over time. And I can ask questions that involve a handful of those snippets. When we get to something new that requires some doing, it becomes another snippet.
I can offload everything I used to know about an API and never have to think about it again.
But when you give it tools for retrieving all client+server logs combined (for a web application), it can use it and just get what it needs as simply as possible.
Or it'll start finding a function by digging around code files with grep, if you provide a tool that just lists all functions, their parameters and locations, it'll find the exact spot in one go.