That said, double dispatch as in the visitor pattern is often too hard to analyse for optimisation and I think humans frequently get a bit lost with it as well. Fortunately pattern matching is doing away with it. I think it's one of these gang of four patterns that has a lot of people scratching their heads and wondering if the open/closed principal is that worth sticking to if this is the outcome.

In one of the articles on this topic they state: > To further show that trace accuracy is only loosely connected to solution accuracy, we then train models on noisy, corrupted traces which have no relation to the specific problem each is paired with, and find that not only does performance remain largely consistent with models trained on correct data, but in some cases can improve upon it and generalize more robustly on out-of-distribution tasks

which actually maps somewhat to regularisation techniques in image processing where you might add noise to an image or drop data to make the model more robust to changes.

The other fiction people have derived a problem from is Game of Thrones. Its brand of "might is right" political realism in the context of warring kingdoms was narratively interesting but a poor choice for the voting public to start relating to.

I get this is tongue in cheek, but it's so wrong that it needs pointing out. Even if that is a thought in their head, they clearly have to have others in order to produce the text.

But in general, checking that a human wrote it is economic as much as anything else: attention is currency limited by time and energy, AI effort is abundant and therefore low value, human effort is less abundant and therefore more valued. Pyrite can look pretty but it's not what people want.

But then there are definitely cases where LLMs are slower and less reliable than someone using tooling by hand, especially like deterministic refactoring operations can be done close to instantly if you already know what needs to be done and how to do it.