While the overall goal is to build arbitrarily large, complex features and projects that are too much for ChatGPT or IDE-based tools, another aspect that I've put a lot of focus on is how to handle mistakes and corrections when the model starts going off the rails. Changes are accumulated in a protected sandbox separate from your project files, a diff review TUI is included that allows for bad changes to be rejected, all actions are version-controlled so you can easily go backwards and try a different approach, and branches are also included for trying out multiple approaches.
I think nailing this developer-AI feedback loop is the key to getting authentic productivity gains. We shouldn't just ask how well a coding tool can pass benchmarks, but what the failure case looks like when things go wrong.