For greenfield work, we are using mostly Rust, although small utilities built with Go or Python are also acceptable provided they have a comprehensive set of unit tests, an integration test suite, well-defined specifications and acceptance criteria, and thorough documentation. Once you have that, it's much easier for the AI to work on it. (Our main motivation for doing this is that it takes far fewer tokens now to get stuff done since we have all that, to the point I can leave Qwen 3.6 running in 24/7 loops accomplishing things.)
replyIf your build is bloated and slow, it would seem prudent to go and fix that, possibly converting a codebase from some other language or build system to something that can be built and tested very quickly. Slow builds and slow unit tests are a choice.
I'll contend that any Rust build involving Cargo is bloated and slow. `rustc` is impressively slow on a translation unit basis, and Cargo is basically a build recursion bingo card. Throw in about 900 micro point releases in flight at any given time?
replyIf you're not running an elite `bazel` or `buck2` RBE with `nativelink`? You're not even playing.
I make sure projects are split into much smaller units so we don’t need some gargantuan bazel based build.
replyHaving a bloated, barely-maintainable codebase and a build that takes hours and needs 100GB of storage is not a badge of honour.
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