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 manage experiments more easily.

What Changed:

• The "In Progress" count changed from 4 to 3 in one demo section.
• The "PyTorch MNIST Experiments" demo now shows a "Ready for Review 3" status instead of "Generating".
• The PyTorch MNIST Experiments demo now includes a detailed description of the completed work: "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 snippet for train_model.py was greatly expanded to include:
  • New imports like random_split, transforms, tqdm, yaml, Path, json.
  • Functions to load configuration (load_config) and a more advanced get_dataloaders function with data augmentation and validation splitting.
  • The MLP class now takes a config and includes dropout layers and a more complex architecture.
  • The train_model function was rewritten to use a configuration file, support mixed precision (AMP), learning rate scheduling, gradient clipping, and save training history and checkpoints.
  • The "Complete codebase understanding" section now includes "Where is the model picker UI implemented? * Searching * Reading".