Cursor: Building an AI-Powered Code Editor
The Genesis: From "Wow, Copilot is Amazing!" to "We Can Do Better"
- Cursor's founders, with backgrounds in competitive programming and companies like Stripe, were fascinated by Scaling Laws and LLMs.
- GitHub Copilot was a game-changer, but they felt its progress stalled.
- GPT-4's arrival sparked the idea to create a better coding tool, initially for themselves.
- The team transitioned from Vim to VS Code due to Copilot's allure, leading to Cursor being built on VS Code.
Product Iteration Philosophy: Experiment Constantly, But Only Ship What You Love
- Early on, Cursor experimented with various LLM-assisted coding approaches.
- Many ideas were scrapped because the team didn't find them useful in their own daily coding.
- They prioritize "dogfooding," ensuring features are genuinely helpful before release.
Tech Deep Dive: Long Context, Model Distillation, and the Secret of "Fun"
- Context Window is Key: Large context windows (100k+ tokens) enable code understanding and editing across entire projects.
- Model Distillation: The "Apply" button's seamlessness is achieved through model distillation. A large model is trained, user data is collected, and a smaller, faster model is distilled from it. This process is repeated for continuous improvement.
- "Fun" Matters: Low latency and smooth interactions create a sense of flow, boosting coding willingness and efficiency. This is why they prioritize models like Claude Sonnet and optimize for responsiveness. They focus on elements like:
- Minimizing distractions from the model.
- Remembering recently opened files.
- Creating "wow" moments with features like tab-completion for refactoring.
Infrastructure and Model Choices: DIY Approach
- Custom Indexing Infrastructure: They built an indexing system to help AI quickly understand large codebases, handling billions of files daily, utilizing S3 and vector databases.
- In-house Inference Service: Cursor runs its own inference service for code embeddings and tab completion models.
- GPU Resource Management: They face the challenge of efficiently allocating GPU resources for different project sizes and user needs.
- Strategic Model Selection: They have been using DeepSeek models for a long time, valuing their strong foundation, code-specific training, and manageable inference costs.
Future Vision: Gradual Evolution, Unlimited Potential
- More Powerful Agents: Future agents will handle complex tasks like migrating to rustls in gRPC implementations, understanding project structure, and automating cross-file modifications.
- Incremental Change: They foresee a gradual adoption of AI-powered coding, similar to Copilot's integration, where developers adapt naturally to new, more efficient methods.
- Architects for All: AI empowers developers to tackle complex projects, large refactors, and experiments more easily