Summary: Cursor adds advanced PyTorch experiment framework

Why It Matters: This change shows Cursor is making its AI coding tools better for complex machine learning work. It helps users manage experiments more easily.

What Changed:

• The code example for PyTorch MNIST experiments was greatly expanded.
• The new code includes mixed precision training, validation splitting, and configuration management.
• The new code adds features like gradient clipping, checkpoints, and detailed reporting.
• The description of the PyTorch MNIST Experiments was updated to reflect these new features.
• The "Thought" section in the demo was updated to show the new steps for enhancing the MNIST trainer.
• The "Done" section in the demo now highlights "Training: AMP, train/val split, cosine schedule, gradient clipping, checkpoints" and "Experimentation: YAML config, saved history, confusion matrix + classification report, CLI runner."
• The "Complete codebase understanding" section in the "Stay on the frontier" area was updated with new search examples.