# [derive(Clone)] Is Broken

> "Of course. This is an excellent example that demonstrates a fundamental and powerful concept in Rust: the distinction between cloning a smart pointer and cloning the data it points to. [...]"

Then I post the compiler's output:

> "Ah, an excellent follow-up! You are absolutely right to post the compiler error. My apologies—my initial explanation described how one might expect it to work logically, but I neglected a crucial and subtle detail [...]"

Aren't you also getting very tired of this behavior?

I keep thinking the LLM contribution to humanity is/will be a net negative in the long run.

It’s times like this when I wonder if we’re even using the same tools. Maybe it’s because I only even try to actively use them when I expect failure and am curious how it will be (occasionally it just decides to interpose itself on a normal search result, and I’m including those cases in my results) but my success rate with DuckDuckGo Assist (GPT-4o) is… maybe 10% of the time success but the first few search results gave the answer anyway, 30% obviously stupidly wrong answer (and some of the time the first couple of results actually had the answer, but it messed it up), 60% plausible but wrong answer. I have literally never had something I would consider an insightful answer to the sorts of things I might search the web for. Not once. I just find it ludicrously bad, for something so popular. Yet somehow lots of people sing their praises and clearly have a better result than me, and that sometimes baffles, sometimes alarms me. Baffles—they must be using it completely differently from me. Alarms—or are they just failing to notice errors?

(I also sometimes enjoy running things like llama3.2 locally, but that’s just playing with it, and it’s small enough that I don’t expect it to be any good at these sorts of things. For some sorts of tasks like exploring word connections when I just can’t quite remember a word, or some more mechanical things, they can be handy. But for search-style questions, using models like GPT-4o, how do I get such consistently useless or pernicious results from them!?)

There's a difference in question difficulty distribution between me asking "how do I do X in FFmpeg" because I'm too lazy to check the docs and don't use FFmpeg frequently enough to memorize, compared to someone asking because they have already checked the docs and/or use FFmpeg frequently but couldn't figure out how to do specifically X (say cropping videos to an odd width/height, which many formats just don't support), for instance. Former probably makes up majority of my LLM usage, but have still occasionally been suprirsed on the latter where I've come up empty checking docs/traditional search but an LLM pulls out something correct.