Trying until you find something that doesn’t fail is the model used throughout evolution & engineering, think of bridges collapsed, airplanes falling from the s

Trying until you find something that doesn’t fail is the model used throughout evolution & engineering, think of bridges collapsed, airplanes falling from the sky or exploded steam locomotives. In engineering you learn from these mistakes and try never making them again. Do we want to go through this evolution every time we solve a software issue? Just because we can with an unlimited number of cheap tokens? I think not, I’d rather use the knowledge build up that also knows about the edge cases forgotten to test. Or better, use multiple models that evaluate each other, as Entropic describes it in their recent report https://news.ycombinator.com/item?id=49316271 https://news.ycombinator.com/item?id=49316271 > We expect that agents coordinating in the wild will act in higher variance ways than we see here, because they’ll have different backgrounds and therefore different contexts. They also, presumably, won’t all be Claudes.