Very true. If things are too reliable, systems can come to depend on them always being so so reliable... So it can actually pay off to inject transient issues deliberately.

https://netflix.github.io/chaosmonkey/ and similar can help.

That said ... most instability is introduced with normal changes, so every engineer can be a chaos monkey. ;-)

You know, one of the things I miss from when I wrote most code manually (now it's a minority of code) is that I would have a high rate of random error.

This was extremely tedious, and annoying, of course, but things would break in all sorts of educational ways.

And once in a while, the computer would do something truly magical, something I didn't even know computers can do.

LLMs still make mistakes, of course, but they make different kinds of errors than I do.

I've been trying to replicate the effect with software that reproduces my own type of errors, randomly omitting tokens from the program. (A strange type of fuzzing the program space.)

Some don't compile, some crash at runtime, most don't do anything interesting, but I know for a fact the process is worthwhile.

What would be really interesting would be to create a cheap verification process for this at scale, i.e. a "wall of TVs" each containing the output of a program, so that the "worthwhileness" of each one can be evaluated at a glance.