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.

Cursor's features range from simple tab completion for familiar code to more complex agent functionalities for editing multiple files, adapting to user expertise.

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

Cursor was among the first coding companies to effectively leverage advanced language models, closing the gap in coding capabilities with various features.

AI tools will make it easier to generate low-level code, improving autocomplete capabilities and potentially producing high-quality code.

Even with large context windows in models, specialized tools are necessary for lookups to ensure accurate context management and memory selection.

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

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

Training retrieval models and integrating various sources of context can enhance the model's understanding of large codebases, especially by leveraging recent changes made by developers.