Code Debugging

When your experiment code breaks, this walks through a structured debugging process: categorize the error (syntax vs runtime vs logic), analyze the root cause from tracebacks, apply targeted fixes with retry logic, then reflect on what went wrong to prevent it next time. It's built around a state machine that escalates from minimal edits to full rewrites after repeated failures. The workflow handles common ML debugging scenarios like device mismatches, shape errors, and the dreaded 0% accuracy bug. Honestly, the real value is in forcing you to slow down and categorize instead of randomly tweaking code until something works.