[nemo1618]

Further evidence that there is some kind of weird parallel universe thing going on with LLMs. "Figuring out esoteric errors" is one of the things I would cite as a particular strength of agents. I am repeatedly amazed at their ability to root-cause weird behavior on my systems. Here is one example: https://xcancel.com/lukechampine/status/2047032091053859138

[lanstin]

I had an experience where we were getting some unusual behavior in a new release of a data store (for various reasons main hadn’t been released in quite a while). We gave Claude code the weird symptom details and the git repo and the tags for the good version and the bad version and it found the subtle bug. This was six months ago.

Now unlike the SRE case, we were the dev team and understood exactly what the logging meant as far as a problem goes, so our prompt started with the correct 0.1% of the system to look at. SRE typically has to start by finding that 0.1% slice from rather more generic metrics. And their interventions have higher risk than a controlled rollout of new code with a specific fix.

[apitman]

This is my experience as well. The two areas I get the most leverage out of AI are root cause analysis and vibe coding green field prototypes.