Humans fail in infinitely more complicated ways than LLMs. They can have a difficult personality, a medical issue, family stress, hangover, sleep deprivation or they can just wake on the wrong side of the bed. On any given day, you never know if you will get an expert in domain X or a sleep-deprived version of the same that accidentally drops a database.
Indeed, if you remember before AI took the world by storm, HN used to be chock-full of articles about how the hiring process is broken for both employers and candidates, where you can never tell if what you see is what you get.
When I run a local LLM I get none of that. I hit the intelligence walls or buggy behaviour, but it doesn't matter if it's 8am or 8pm, the model behaves exactly the same. If something doesn't work as I wished, I can retry as many times as I wanted without the model getting angry at me.
On the other hand, an American company can sell your chats to adtech/insurance/your government in ways that can harm you quite directly. Something worth considering.
UB supersedes volatile, once the compiler hits UB then all bets are off. Compilers can and do optimize out UB branches, which is almost never what you want... yet here we are.
In my experience, curiosity and intelligence are very strongly correlated. There is a real gap between people with the curiosity and ability to explore and learn, and people without. This is often handwaved as "motivation" but it's more than just that.
In fact, the gap is so large that it can be really hard for a person on one side of it to understand how people on the other side think.
As a counterpoint, I found GPT 4.5 by far the most interesting model from OpenAI in terms of depth and width of knowledge, ability to make connections and inferences and apply those in novel ways.
It didn't bench well against the other benchmaxxed models, and it was too expensive to run, but it was a glimpse of the future where more capable hardware will lead to appreciably smarter models.
