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 tasks. It helps users do more advanced work with AI.

What Changed:

• The "In Progress" count changed from 4 to 3.
• The "Ready for Review" count changed from 2 to 3.
• The "PyTorch MNIST Experiments" section was updated with a detailed description of new features: "Done, configurable MNIST experiment framework with AMP and reports. * Training: AMP, train/val split, cosine schedule, gradient clipping, checkpoints * Experimentation: YAML config, saved history, confusion matrix + classification report, CLI runner".
• The code example for train_model.py was significantly expanded to include:
  • New imports like random_split, tqdm, yaml, Path, json.
  • A load_config function for YAML configuration.
  • An updated get_dataloaders function with data normalization, augmentation, and train/validation splitting.
  • An updated MLP class constructor to accept a config and include dropout layers.
  • A rewritten train_model function to use a config, implement mixed precision (AMP), gradient clipping, learning rate scheduling, and save training history and checkpoints.
• The "Access the best models" section changed "Grepped" to "Grepping".