Experiment Code

This generates and refines ML experiment code for research papers, handling the full cycle from initial implementation to debugging and plotting. It enforces a specific structure with separate run directories, JSON logging, and mandatory visualization outputs. The iterative improvement loop is interesting: it reads results, reflects on what worked, makes targeted edits, and keeps the best variant. Works with PyTorch or scikit-learn, explicitly avoids placeholder code, and includes safeguards like verifying your accuracy isn't accidentally zero. Best for when you're implementing experiments and need something more structured than ad-hoc scripting but don't want to set up a full MLOps pipeline.