I don't know ... as of now I am literally instructing it to solve the chained expression computation problem which incurs a lot of temporary variables, of which some can be elided by the compiler and some cannot. Think linear algebra expressions which yield a lot of intermediate computations for which you don't want to create a temporary. This is production code and not an easy problem.

And yet it happily told me what I exactly wanted it to tell me - rewrite the goddamn thing using the (C++) expression templates. And voila, it took "it" 10 minutes to spit out the high-quality code that works.

My biggest gripe for now with Gemini is that Antigravity seems to be written by the model and I am experiencing more hiccups than I would like to, sometimes it's just stuck.

Can't argue with that, I'll move my Bayesian's a little in your direction. With that said, are most other models able to do this? Also, did it write the solution itself or use a library like Eigen?

I have noticed that LLM's seem surprisingly good at translating from one (programming) language to another... I wonder if transforming a generic mathematical expression into an expression template is a similar sort of problem to them? No idea honestly.

People's objections are not the quality of code or analysis that Gemini produces. It's that it's inept at doing things like editing pieces of files or running various tools.

As an ex-Googler part of me wonders if this has to do with the very ... bespoke ... nature of the developer tooling inside Google. Though it would be crazy for them to be training on that.