[elros]

Whenever I need some sort of quick data pipeline to modify some sort of file into another format, or do some batch transformation, or transform some sort of interface description into another syntax, or things like that, that would normally require me to craft a grep, awk, tr, etc pipeline, I can normally simply paste a sample of the data and with a human language description get what I need. If it’s not working well I can break up the steps in smaller steps.

In my experience, it seems the people who have bad results have been trying to get the AI to do the reasoning. I feel like if I do the reasoning, I can offload menial tasks to the AI, and little annoying things that would take one or two hours start to take a few minutes.

That very quickly adds up to some real savings.

[theshrike79]

AI is a force multiplier for experienced people.

The ones who know what they want to do, how it should be done, but can't really be arsed to read the man pages or API docs of all the tools required.

These people can craft a prompt (prompt engineering :P) for the LLM that gets good results pretty much directly.

LLMs are garbage in garbage out. Sometimes the statistical average is enough, sometimes you need to give it more details to use the available tools correctly.

Like the fact that `fd` has the `-exec` and `--exec-batch` parameters, there's no need to use xargs or pipes with it.