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

Our goal with Cursor is to invent a new type of programming that focuses on specifying intent for how software should work, moving towards a world after code.

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

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.

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 initial development of Cursor involved a year of experimentation before they found the right product fit.

As AI models become more powerful, the competition will favor those who create the best user experiences, allowing new entrants like Cursor to challenge established players like GitHub Copilot.

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 early days of Cursor were driven by youthful ambition and a desire to build something impactful in AI, stemming from our experiences with programming and AI projects during our time at MIT.