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.

Building models for Cursor involves training them to autocomplete changes in a codebase, focusing on speed and accuracy in predicting both deletions and additions.

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

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

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

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

The model's vision of how people would program finally became effective, leading to competition with Cursor.

By combining the intelligence of the 3.5 Sonnet model with custom retrieval models, Cursor can now perform multi-file edits, which has driven mass adoption of the platform.