> - It's terrible if you let it write too much. The biggest problem I've had is not that it doesn't write correctly, it's that it think it knows how and then pr

> - It's terrible if you let it write too much. The biggest problem I've had is not that it doesn't write correctly, it's that it think it knows how and then produce good looking code at a glance but with wrong logic. So the same problem ML has in every endeavor where we have a good metric of "correctness" that's distinct from plausibility, like OCR or natural language translation: very good at spitting out stuff that superficially resembles training data, and whether that happens to be right is totally accidental. Surprisingly good odds if you're working on something boring within the "bounds" of the model, sure, but also pretty likely to think that "now on sale" is a zillion times more likely to be announced on an advert than "a decision has been made to release this product (at an unspecified future date)."