wyrd

Validate design hypotheses against a codebase by testing a hypothesis file to track outcomes and update confidence through closed-loop testing. This process uses rejection history and learning signals to drive continuous improvement.

Closed-loop learning from codebase rejection history is the process of tracking outcomes and updating confidence. It calibrates predictions by extracting patterns from recent rejections to improve future design hypotheses.

Calibrate confidence levels from codebase rejection history by running a calibration command that adjusts predictions. This updates your current confidence state based on past rejection outcomes and learning signals.

Yes, you can extract patterns from recent codebase rejections to improve future hypotheses. The learning feature analyzes rejection history to identify patterns, updating confidence and storing results in integrated grimoires.

Yes, you need a markdown hypothesis file format to test against your codebase. You validate your design hypotheses by passing the specific file path to the test command for closed-loop tracking.

Your design hypothesis confidence state needs continuous calibration to account for new rejection history and learning signals. Adjusting predictions ensures your confidence levels accurately reflect current codebase realities.