It's worth noting that the context here is a system that was translated by an LLM which is the ideal use case for it. 90% of the thinking had already been done by developers of the existing system.
That said, I don't know if it matters at this point.... On top of Brandolini's law (mentioned elsewhere), what's more likely to happen is that developers' intellect, made available in open source, is just going to be harvested as context for different projects (I'm not taking about training data, I'm taking about devs literally cloning repos into an agent to harvest intellect).
For example, in ruby some kind of stringly typed reflection utility might Dry out code. In Rust, that might mean writing a trait over some subset of types and a macro helper, but would require careful dosing work.
Letting the LLM decide is the approach in question and the author was pointing out how spectacularly it failed to translate a complete, human designed system.
You do still have to think, but you have to think about how to reorganize code in a way that allows AI to succeed.
This is not a great way to showcase the capabilities of this technology.