I agree that having a reference compiler available is a huge caveat though. Even if we completely put training data leakage aside, they're developing against a programmatic checker for a spec that's already had millions of man hours put into it. This is an optimal scenario for agentic coding, but the vast majority of problems that people will want to tackle with agentic coding are not going to look like that.
We have seen at least 3 of these projects - the JustHTML one, the FastRender and this one. All started from beefy tests and specs. They show reimplementation without manual intervention kind of works.
JustHTML is a success in large part because it's a problem that can be solved with 4 digit LOC. The whole codebase can sit in an LLM's context at once. Do LLMs scale beyond that?
I would classify both FastRender and Opus C compiler as interesting failures. They are interesting because they got a non-negligible fraction of the way to feature complete. They are failures because they ended with no clear path for moving the needle forward to 80% feature complete, let alone 100%.
From the original article:
> The resulting compiler has nearly reached the limits of Opus’s abilities. I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.
From the experiments we've seen so far it seems that a large enough agentic code base will inevitably collapse under its own weight.
Never was.
I'd argue that no one would really care given it's GCC.
But if you worked for GiantSodaCo on their secret recipe under NDA, then create a new soda company 15 years later that tastes suspiciously similar to GiantSodaCo, you'd probably have legal issues. It would be hard to argue that you weren't using proprietary knowledge in that case.
It's not a clean-room implementation because of this:
> The fix was to use GCC as an online known-good compiler oracle to compare against