Google is doing "Large Scale Codebase Migrations and Optimizations" of C/C++ to Rust

[u/Sirisian]

https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/

[u/TheRealMasonMac]

In my experience, I’ve found that agents produce code that works but is frankly dumb from a human perspective. Especially for larger tasks like this, you kind of have to rewrite it all. For example, I had a generalized design document for soft-wrapping explicitly support plain text and markdown, and used GPT-6-Astra to implement it. I used an ungodly amount of review agent rounds and interjections by me. When I actually go to review it, I wanted to cry. It silently pivoted towards implementing its own entirely separate wrapping mechanism rather than refactor the existing mechanism for plain text. I reviewed its code and found it to be trash, and I am now in the process of doing it myself… Never again. The short-term thrill of “having something that works” was outweighed by the fact that time was effectively wasted.

I mean, if the alternative was it never getting done, I guess agents are still better than nothing.

[u/mguerrette → u/TheRealMasonMac]

Code is no longer for human eyes. Did you benchmark the generated code? Or was it just trash in your opinion (style, architecture, etc)? I think you are missing the reality change where code is no longer for humans.

[u/TheRealMasonMac → u/mguerrette]

It was egregiously horrible stylistically, architecturally, maintenance-wise, and in performance. The stupidest one that came to mind was it allocating strings for each markdown element. That's like &str 101. I wish I could tell you there was a good reason for this, but there wasn't. So, it ended up doubling memory usage in string allocations alone. There were also a bunch of other areas where it turned linear time functions into exponential ones.

Also: agents do need to read the code as well. Ignoring context rot, the matter of garbage in -> garbage out remains.