They seem fine with the output of the current hodge-podge of the original algorithm results plus massaging by many downstream ML pipelines that run one after the other without context of how each stop might affect the next.

More reasons it's an odd comparison.

Edit: Yep, appears I have it wrong. Thanks for the pointers. The non-fiction tag missed my eye.

It reads like an indictment of the government employee personally, rather than the rules and constraints that employee is forced to use.

Probably fair to comment on the interaction, whether the person was rude, and so on. But blaming them for not accepting email is kind of silly. They are not empowered to do that kind of thing.

Well, and doing them programmatically and automatically without any AI is also possible, if not trivial...and has been for some time.

Then things like the jit, by default, doing run time profiling and adaptation.

Thousands of little weights driven by obscure attributes of the site that you're not really going to figure out by thrashing and changing stuff.

I understand Anthropic is not public, but I assume there's an IPO coming.

They are much worse at code/patterns/apis that were locally created, including things created by the same LLM that's trying to fix a problem.

I think LLMs are also creating a decline in the amount of good troubleshooting information being published on the internet. So less future content to scrape.

Troubleshooting and fixing the big mess that nobody fully understands when it eventually falls over?