October 6, 2024 • 2hr 37min
#447 – Cursor Team: Future of Programming with AI
Lex Fridman Podcast
Key Takeaways
- Cursor is a code editor built on VS Code that adds powerful AI-assisted coding features using large language models
- The Cursor team believes AI will fundamentally change programming, but humans will remain "in the driver's seat" for the foreseeable future
- Key features of Cursor include:
- Cursor Tab - AI-powered autocomplete and code generation
- Apply - AI-assisted code editing across multiple files
- Chat - Natural language interaction with AI about your codebase
- The team uses custom ML models and techniques like speculative decoding to make Cursor fast and responsive
- They believe the ceiling for AI-assisted programming tools is very high, with rapid innovation expected in coming years
- Programming skills will remain valuable but may shift more towards high-level design and creative problem-solving
Introduction
Lex Fridman interviews the founding team of Cursor - Michael Truell, Sualeh Asif, Arvid Lunnemark, and Aman Sanger. Cursor is a popular AI-assisted code editor built on VS Code that aims to dramatically boost programmer productivity. The team discusses the origins of Cursor, how it works under the hood, their vision for the future of programming, and their thoughts on the broader impacts of AI on software development.
Topics Discussed
Origins of Cursor (14:00)
- The team was inspired by OpenAI's scaling laws papers showing predictable progress in language models
- Early access to GPT-4 in late 2022 showed them the massive potential of applying these models to programming
- They decided to fork VS Code to have full control over the editor experience rather than just building a plugin
"We set off to build that sort of larger vision around that." - Michael Truell on deciding to create Cursor
Key Features of Cursor (18:53)
- Cursor Tab - AI-powered autocomplete that can predict and generate entire code changes
- Apply - AI-assisted editing across multiple files
- Chat - Natural language interaction with AI about your codebase
The team focuses on making these features fast, ergonomic, and deeply integrated into the coding workflow.
Technical Details of Cursor Tab (25:20)
- Uses small, specialized models trained on the task of predicting edits
- Employs techniques like sparse (MoE) models and speculative decoding
- Extensive caching to minimize latency
- Can predict edits across multiple files and even suggest terminal commands
"The goal of Cursor Tab is let's eliminate all the low entropy actions you take inside of the editor. When the intent is effectively determined, let's just jump you forward in time, skip you forward." - Sualeh Asif
Challenges of Code Diffs and Review (31:35)
- Designing intuitive diff interfaces for both small and large code changes
- Exploring ways to highlight important parts of diffs and guide human review
- Potential to improve on traditional code review processes using AI
Machine Learning Details (39:46)
- Cursor uses an ensemble of custom models alongside large foundation models
- Specialized models like Cursor Tab outperform general models on specific tasks
- Apply model handles the tricky task of actually implementing code changes
- Various optimizations like multi-query attention to improve speed
Comparing Language Models for Coding (45:20)
- Team currently considers Anthropic's Claude model best overall for coding tasks
- OpenAI's GPT-4 excels at reasoning but doesn't always understand coder intent as well
- Public benchmarks don't fully capture real-world coding scenarios
Prompt Engineering and Context Management (51:54)
- Cursor uses a system called "Preempt" to dynamically manage prompt context
- Inspired by React's declarative approach to layout
- Allows fine-grained control over what context is included for different scenarios
AI Agents and Background Processing (59:20)
- Team is exploring running AI agents in the background to assist with coding tasks
- Potential use cases include bug fixing, code migration, and preparing for upcoming work
- Challenges around safely allowing AI to modify files and run code
"I would love to have an agent that just goes off, does it. And then a day later I come back and I review the thing." - Arvid Lunnemark on potential for background AI agents
Optimizing for Speed (1:02:48)
- Extensive use of caching, including preemptively warming caches
- KV (key-value) cache optimizations to reduce memory usage and improve latency
- Exploring techniques like multi-query and multi-latent attention
Infrastructure and Scaling Challenges (1:37:47)
- Team primarily uses AWS for infrastructure
- Dealing with scaling issues as user base grows rapidly
- Complex system for efficiently indexing and embedding user codebases
"It's very hard to predict where systems will break when you scale them. You can't really try to predict in advance, but there's always something weird that's going to happen when you add this extra zero end." - Sualeh Asif
The Role of Context in AI Coding Assistants (1:51:58)
- Balancing automatic context inclusion with performance and model confusion
- Exploring techniques to improve context retrieval and relevance
- Potential for models to build deeper understanding of specific codebases
Thoughts on OpenAI's GPT-4 with "Constitution" (1:57:05)
- Team is still exploring how to best integrate more advanced models like GPT-4 with "Constitution"
- Challenges around latency and user experience with non-streaming models
- Belief that test-time compute approaches will continue to improve rapidly
Synthetic Data for AI Training (2:08:27)
- Three main categories of synthetic data:
- Distillation - Using larger models to train smaller, specialized models
- Exploiting asymmetry - e.g. generating synthetic bugs to train bug-finding models
- Generate-and-verify - Creating data that can be automatically verified
- Synthetic data likely to play a major role in improving AI coding capabilities
The Future of Programming (2:25:32)
- Team believes humans will remain "in the driver's seat" for the foreseeable future
- AI will dramatically boost productivity but won't replace creative problem-solving
- Programming may shift towards higher-level design and rapid iteration
- Skills like taste, creativity, and system design will become more important
"I think we're really excited about a future where the programmer's in the driver's seat for a long time, and you've heard us talk about this a little bit, but one that emphasizes speed and agency for the programmer and control." - Michael Truell
Conclusion
The Cursor team presents a compelling vision for the future of programming that embraces AI assistance while keeping human creativity and decision-making at the center. They believe AI will dramatically boost programmer productivity and make coding more accessible, but also that the most successful developers will be those who can effectively leverage AI tools while bringing their own creativity and problem-solving skills to bear. The team's focus on speed, ergonomics, and deep integration of AI into the coding workflow sets Cursor apart in the rapidly evolving landscape of AI-assisted development tools.