Every significant feature in Cursor now involves a custom model, which was a surprising and counterintuitive development for us, and Cursor's autocomplete experience predicts the next actions across multiple files and locations within a codebase, requiring models to be fast and specialized in understanding code diffs.

The progression of AI models has allowed Cursor to continuously improve its features, enabling more effective coding and problem-solving.

The goal with Cursor is to replace coding with a better method, allowing users to simply describe what they want and have it built.

The ensemble of models approach allows Cursor to leverage the strengths of various models, optimizing for both cost and performance in code generation.

To succeed with Cursor, users should develop a taste for the model's capabilities, understanding its strengths and limitations to effectively utilize its features.

We didn't expect to develop our own models, but now every significant feature in Cursor involves a custom model in some way.

New users of Cursor should break tasks into smaller parts to better leverage the model's capabilities, rather than attempting to complete large tasks in one go.

The use of Cursor's Agent feature is particularly beneficial for laying out new code bases and addressing specific coding challenges.

Cursor's team utilizes its own product to build and improve Cursor, creating a self-improving feedback loop that enhances feature development.