Summary: Cursor adds advanced PyTorch training features

Why It Matters: This change shows Cursor is making its AI coding tools better for complex machine learning tasks. This could attract more developers who work with AI models.

What Changed:

• The code example for PyTorch MNIST experiments was greatly expanded.
• The new code includes features like mixed precision training, validation splitting, and configuration management.
• The code now uses tqdm, yaml, pathlib, and json libraries.
• The MLP model now includes dropout layers and a more complex architecture.
• The train_model function was updated to use a configuration file, gradient clipping, and save training history and checkpoints.
• The description of the PyTorch MNIST Experiments was updated to highlight new features like AMP, train/val split, cosine schedule, gradient clipping, checkpoints, YAML config, saved history, confusion matrix, classification report, and a CLI runner.
• The "Trusted by over half of the Fortune 500" section now includes "Where is the model picker UI implemented? * Searching" in the codebase understanding example.
• The copyright section now links "SOC 2 Certified" to a security page.