This argument gets repeated frequently, but to me it seems to be missing final, actionable conclusion.

If one "doesn't know Kubernetes", what exactly are they supposed to do now, having LLM at hand, in a professional setting? They still "can't" asses the quality of the output, after all. They can't just ask the model, as they can't know if the answer is not misleading.

Assuming we are not expecting people to operate with implicit delegation of responsibility to the LLM (something that is ultimately not possible anyway - taking blame is a privilege human will keep for a foreseeable future), I guess the argument in the form as above collapses to "it's easier to learn new things now"?

But this does not eliminate (or reduce) a need for specialization of knowledge on the employee side, and there is only so much you can specialize in.

The bottleneck maybe shifted right somewhat (from time/effort of the learning stage to the cognition and the memory limits of an individual), but the output on the other side of the funnel (of learn->understand->operate->take-responsibility-for) didn't necessary widen that much, one could argue.

So maybe a hybrid approach would make more sense? Something like /.well-known/skills/README.md exposed and owned by the providers?

That is assuming that the whole idea of "skills" makes sense in practice.

One example would be when you have a naive implementation of some algorithm and you want to introduce a second one, optimized but with much more complex implementation. Then this naive one will act as a model for comparisons.

Another case that comes to mind is when you have rather simple properties to test (like: does it finish without a crash, within a given time?, does not cross some boundaries on the output?), and want to easily run over a sensible set of varying inputs.

Well... you are sending your data to a remote location that is not yours.

All the rest of the effect will depend on the specifics of your network. Observing the impact on localhost shows scale of the effect that does not come from the network (more or less) and puts a lower bound on its size one can expect in more realistic conditions.

Dilbert comes down to the caves where trolls (accountants) reside and gets a tour. The guide points to a troll sitting behind a desk, and mumbling in a stupor: "nine, nine, nine...".

Guide: And this is our random numbers generator.

Dilbert: Are you sure those are random?

Guide: That's the problem with randomness - you can never be sure.

Edit: Found it here: https://www.americanscientist.org/article/the-quest-for-rand....

And thank you, Scott - many laughs thanks to you.

Was just about to react to someone being wrong on the internet and say that this is not true. Instead, TIL that this is, in fact, the case. Since 2024Q4.

Thanks for invalidating my stale cache.

It's useful for initializing state that could not have been initialized before booting in the target environment. Canonical example, I guess, being ssh server and client keys management, but the list of modules it implements is long.