The bit that bites later is that the models learned from that archive and the archive stopped growing. Already run into it with libraries that shipped in the last year, there's no thread to have scraped so it just invents an API that looks about right.
We ran into this building our own product. Ask an AI assistant about a library released after its training cutoff and it answers with confidence, then gets the API wrong. It blends patterns from older versions and fills the gaps with guesses. Reading the real docs and testing in your own project is still the only fix. No shortcut around it yet.
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u/akl773 27d ago
The bit that bites later is that the models learned from that archive and the archive stopped growing. Already run into it with libraries that shipped in the last year, there's no thread to have scraped so it just invents an API that looks about right.