I pretty well span the devops spectrum from building/maintaining services to running/integrating/monitoring them in prod. LLMs are definitely better at the dev side than the ops side, no doubt about that. And when it comes to firewalld and many other sysadmin tools I agree it can often be faster to just hand type than to have the LLM do it. Even just writing Dockerfiles it's often faster to do it by hand than the LLM because the LLM will screw it up 6 to 12 times before getting it right, and usually "getting it right" is because I told it something like, "dude you can't mount, you need to copy." It's especially insanely stupid when it comes to rootless podman.

But that said, there are still plenty of ops-y situations where AI can be very helpful. Even just "here's a 125k lines of prod logs. Can you tell me what is going wrong?" has saved me lots of time in the past, especially for apps that I'm not super familiar with. It's (sometimes) pretty good at finding the needle in the haystack. The most common workflow I have now is to point an agent at it and while it's griding on it I'll do some hand greps and things. I've gotten to the bottom of some really tricky things much faster because of it. Sometimes it points me in the wrong direction (for example, one time it noticed that we were being rate-limited by the Cloudflare API, and instead of adding a single flag to the library calls it wrote it's own very convoluted queue system. But it was still helpful because at least it pinpointed the problem).

The other "small pieces of software" I find it very helpful for are bash functions or small scripts to do things. The handwritten solution is usually quick, but rarely as resilient/informative as it could be because writing a bunch of error handling can 5x or 10x the handwritten time. I will usually write the quick version, then point AI at it and have it add arg passing/handling, error handling, and usage info/documentation. It's been great for that.