Tangental, but I worry that LLMs will cause a great stagnation in programming language evolution, and possibly a bunch of tech.

I've tried using a few new languages and the LLMs would all swap the code for syntactically similar languages, even after telling them to read the doc pages.

Whether that's for better or worse I don't know, but it does feel like new languages are genuinely solving hard problems as their raison d'etre.

>Personally my theory is that Gemini benefits from being able to train on Googles massive internal code base and because Rust has been very low on uptake internally at Google, especially since they have some really nice C++ tooling, Gemini is comparatively bad at Rust.

Were they to train it on their C++ codebase, it would not be effective on account of the fact that they don't use boost or cmake or any major stuff that C++ in the wider world use. It would also suggest that the user make use of all kinds of non-available C++ libraries. So no, they are not training on their own C++ corpus nor would it be particularly useful.

> Personally my theory is that Gemini benefits from being able to train on Googles massive internal code base

But does Google actually train its models on its internal codebase? Considering that there’s always the risk of the models leaking proprietary information and security architecture details, I hardly believe they would run that risk.

That's interesting. I've tried Gemini 2.5 Pro from time to time because of the rave reviews I've seen, on C# + Unity code, and I've always been disappointed (compared to ChatGPT o3 and o4-high-mini and even Grok). This would support that theory.

Interesting, Gemini must be a monster when it comes to Go code then. I gotta try it for that