AI: Where in the Loop Should Humans Go?
honeycomb.io
honeycomb.io
We've got an OSS autonomous agent at Bosun called Kwaak that works in parallel to the developer (it spawns workspaces in docker containers), basically pulling the human all the way outside the loop. Right now it'll get many basic things right but sometimes it's flaky and it basically makes you carefully read its merge requests. I wonder if at some point the agents become so good that they only rarely make mistakes, making the reviewing of their merge requests ever more tedious as their mistakes become harder to spot and farther apart.
> By comparison, people in a system start from a broad situation and narrow definitions down and add constraints to make problem-solving tractable.
It's been awhile but back when I was studying this most people were specific-to-general thinkers, i.e. the opposite of this. That people start with specific cases/examples and generalize as they learn more of them, narrow-to-broad. Has the research on this changed?