I'm just building some data points.
The important things to get right are the same as they were before though. Work from well refined stories that are not too broad in scope. Ensure you have enough good acceptance criteria that will help prove that the code works as intended.
(I get a bit of exposure to that from my open source projects but it's much easier to close or ignore those.)
Yeah so unverified PR's is super annoying, but people using LLMs to answer things incorrectly they could look at themselves is a big issue too.
I often hear this get dismissed along the lines of "oh you didn't context engineer hard enough" - but the default state of the model is to very confidently state a thing to be true when it hasn't searched correctly.
That is a trait of a very junior engineer - one who, if they never learned to fix this behaviour would be fired.
It seems objectively _worse_ than what we had before - trained engineers who gained wisdom over a long time horizon and had a reputation they'd lose if they kept incorrectly stating things.