#447 – Cursor Team: Future of Programming with AI
Lex Fridman Podcast
Deep Dive
Why did the Cursor team decide to fork VS Code instead of writing an extension?
The decision to fork VS Code was driven by the need for more control over the programming environment as AI capabilities improve. Extensions have limitations that would lock in certain features and hinder innovation as models get better.
What are the key features that make Cursor stand out compared to GitHub Copilot?
Cursor offers faster and more intelligent features like next-action prediction and code editing across multiple lines. It also integrates AI more deeply into the editing process, making the experience more organic and efficient.
How does Cursor's tab feature work, and what is its purpose?
The tab feature in Cursor allows the model to predict the next edit or action the programmer will take, making coding faster by eliminating low-entropy actions. It aims to make the editing process feel more like having a fast colleague looking over your shoulder.
What technical challenges did the Cursor team face in making the tab feature work efficiently?
The challenges included training small, efficient models, handling long contexts, and ensuring fast performance. Techniques like speculative decoding and using sparse models were employed to improve speed and accuracy.
How does Cursor handle diffs for code changes across multiple files?
Cursor optimizes diff interfaces for different types of code changes, such as auto-complete suggestions versus larger code blocks. It aims to make the review process easier by highlighting important parts of the diff and graying out less critical changes.
Why is Cursor focusing on local models less compared to cloud-based models?
Cursor's focus on cloud-based models is due to the limitations of local models, especially on less powerful machines and the need for larger models that can't fit on a single node. Cloud-based models also allow for more sophisticated features and faster iteration.
What role does synthetic data play in improving AI models for programming?
Synthetic data helps in training models for tasks where real data is scarce or expensive. It can be used to generate training data for bug detection, improve model accuracy, and create verified data for specific tasks like solving math problems or passing coding tests.
How does Cursor envision the future of programming with AI?
Cursor sees the future of programming as a hybrid where humans remain in control, making critical decisions while AI handles the repetitive and predictive tasks. This will allow for faster iteration and more creative control over the codebase.
- Code editors are advanced word processors for programmers.
- They offer visual differentiation, navigation, and error checking.
- The role of code editors will evolve with AI-assisted coding.
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