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

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

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

The future of Cursor will require a different approach, focusing on hiring passionate people and encouraging bottom-up experimentation.

Being its own best customer allows Cursor to quickly iterate on features, discarding those that don't work without needing external user feedback.

Spend ample time using LLMs to define the scope and architecture of your project before transitioning to coding tools like Cursor.

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