> It also can't do rust really well

I have not had this experience at all. It often doesn't get it right on the first pass, yes, but the advantage with Rust vibecoding is that if you give it a rule to "Always run cargo check before you think you're finished" then it will go back and fix whatever it missed on the first pass. What I find particularly valuable is that the compiler forces it to handle all cases like match arms or errors. I find that it often misses edge cases when writing typescript, and I believe that the relative leniency of the typescript compiler is why.

In a similar vein, it is quite good at writing macros (or at least, quite good given how difficult this otherwise is). You often have to cajole it into not hardcoding features into the macro, but since macros resolve at compile time they're quite well-suited for an LLM workflow as most potential bugs will be apparent before the user needs to test. I also think that the biggest hurdle of writing macros to humans is the cryptic compiler errors, but I can imagine that since LLMs have a lot of information about compilers and syntax parsing in their training corpus, they have an easier time with this than the median programmer. I'm sure an actual compiler engineer would be far better than the LLM, but I am not that guy (nor can I afford one) so I'm quite happy to use LLMs here.

For context, I am purely a webdev. I can't speak for how well LLMs fare at anything other than writing SQL, hooking up to REST APIs, React frontend, and macros. With the exception of macros, these are all problems that have been solved a million times thus are more boilerplate than novelty, so I think it is entirely plausible that they're very poor for different domains of programming despite my experiences with them.

i've also been using opus 4.5 with lots of heavy rust development. i don't "vibe code", but lead it with a relatively firm hand- and it produces pretty good results in surprisingly complicated tasks.

for example, one of our public repos works with rust compiler artifacts and cache restoration (https://github.com/attunehq/hurry); if you look at the history you can see it do some pretty surprisingly complex (and well made, for an LLM) changes. its code isn't necessarily what i would always write, or the best way to solve the problem, but it's usually perfectly serviceable if you give it enough context and guidance.