Asking an LLM tp change programming language of an implementation is completely different from asking it to code from spec. It's orders of magnitude simpler in practice. I converted some 60kloc of Java to C++ and it works. There were some issues where the Java implementation used runtime reflection because that needs creative workarounds and not all of the C++ translations worked on the first try. And that was my first serious attempt at a task with an LLM. I could likely do better now. An important task simplification here is that a well designed codebase can be converted in small pieces and then joined back together. So the total amount of code converted becomes an irrelevant metric.

Yes, the task is very different, but also it will be months to a year until we know the results of the bun experiment.

People who independently tried to use it reported that it is very much not workable:

- "CCC compiled every single C source file in the Linux 6.9 kernel without a single compiler error (0 errors, 96 warnings). This is genuinely impressive for a compiler built entirely by an AI. However, the build failed at the linker stage with ~40,784 undefined reference errors."(https://github.com/harshavmb/compare-claude-compiler)

- Overall it’s an interesting experiment, and shows the current bleeding edge of Claude’s Opus 4.6 model. However the resulting product is also a clear example of the throwaway nature of projects generated almost entirely by AI code agents with little human oversight. The prototype is really impressive, but there is no real path forward for it to be further developed. It can build the Linux kernel [for RISC-V], which is impressive. It can also build other things… if you are lucky, but you really cannot rely on it to work. (https://voxelmanip.se/2026/02/06/trying-out-claudes-c-compil...)

Anthropic themselves said that the codebase was effectively bricked and that their agents could not salvage it.