Transformer Debugger combines automated explainability and sparse autoencoding techniques, allowing for rapid exploration before writing code and enabling intervention in the forward pass to observe its impact on specific behaviors. It identifies and explains the activation reasons of specific components (neurons, attention heads, autoencoder latent representations) within the model, showcasing automatically generated explanations for why these components are strongly activated, and tracks connections between components to help discover circuits.