Cursor is an AI-powered code editor built by a team of researchers and engineers aiming to create "the engineer of the future" - a human-AI hybrid programmer that is significantly more effective than traditional approaches. The team (Aman Sanger, Arvid Lunnemark, Michael Truell, and Sualeh Asif) discusses their vision, technical challenges, and thoughts on the future of programming in an AI-assisted world - with Lex Fridman.
The best programmers are the ones that really love, absolutely love programming... when they're sad, they say 'I just really need to code.
Here are the big ideas from the 2.5 hrs of the conversation
As long as humans are actually the ones designing the software... we think you'll really want the human in the driver's seat dictating these decisions
Key Takeaways:
The Philosophy Behind Cursor Cursor's core philosophy is keeping the human programmer in control while leveraging AI to dramatically boost productivity. The team believes the future of programming involves humans making high-level decisions and injecting intent, with AI handling lower-level implementation details. This approach aims to preserve the creative and strategic aspects of programming that humans excel at.
The team envisions programming evolving to become higher-bandwidth communication between humans and computers, moving beyond just typing code to more directly expressing intent. They're exploring ideas like editing code at different levels of abstraction, from pseudocode to implementation details.
On competition with Github Copilot
Market Dynamics and Innovation The team believes the AI-assisted programming space is fundamentally different from past software markets. They argue that the ceiling for improvement is extremely high, with products likely to become dramatically more useful over the next 3-4 years. This creates opportunities for startups and new entrants to compete effectively against established players by building superior products.
"I think this space is a little bit different from past software spaces over the 2010s... the ceiling here is really really really incredibly high and so I think that the best product in 3 to 4 years will just be so much more useful than the best product today."
Advancing AI Code Generation
Cursor utilizes custom-trained models alongside large language models to power features like code completion and editing. They've developed techniques like "speculative edits" to predict likely code changes and pre-cache them, enabling near-instantaneous suggestions.
The team has created a system called "preum" for dynamically formatting prompts sent to language models, inspired by React's declarative approach to UI. This allows fine-grained control over what context is included when querying AI models.
Cursor employs retrieval techniques to find relevant code from across a project when generating suggestions. They've developed efficient ways to index and search codebases without storing actual code content on their servers, addressing privacy concerns.
Technical Challenges in Building an AI-Powered Editor
One major challenge is applying AI-generated changes to existing code. The team has developed custom models for this "apply" step, as even advanced language models struggle with tasks like accurate line number handling in large files.
Optimizing for low latency is crucial for a responsive editing experience. Cursor uses techniques like KV caching, speculative decoding, and custom attention mechanisms (e.g., multi-query attention) to reduce inference time and memory usage.
The team is exploring ideas like "shadow workspaces" where AI agents can experiment with code changes in the background without affecting the main codebase. This could enable more complex AI assistance while preserving user control.
The Future of Programming
The Cursor team believes AI will make programming more accessible and enjoyable by handling tedious aspects like boilerplate code. However, they emphasize that human creativity, taste, and high-level decision-making will remain crucial.
They predict a shift towards faster iteration cycles, where programmers can quickly try out ideas and refine them with AI assistance, rather than needing to plan everything meticulously upfront.
While some fear AI might make traditional programming skills obsolete, the team argues that deep understanding and passion for programming will remain valuable. They suggest the nature of programming skills may evolve, with increased emphasis on system design and creative problem-solving.
Scaling AI and Research Directions
The team discusses the challenges of scaling up AI models, including the need for massive compute resources and engineering talent to optimize training and inference.
They highlight the potential of techniques like knowledge distillation and synthetic data generation to push the boundaries of model capabilities. The team also explores the promise and challenges of techniques like reinforcement learning from AI feedback (RLAIF) for improving models.
Cursor is investigating ways to automatically detect when more powerful models (like GPT-4 with tool use) are needed for complex tasks, while using faster, specialized models for routine operations.
TODO for Engineers:
- Experiment with AI-assisted coding tools: Allocate time to explore and integrate AI-powered development tools like Cursor into your workflow. Start with simple tasks and gradually expand usage as you become comfortable with the technology.
- Focus on high-level system design skills: As AI takes on more implementation details, invest in improving your ability to architect complex systems and make strategic technical decisions. This may involve studying advanced design patterns, distributed systems, or domain-specific architectures relevant to your field.
- Cultivate adaptability and rapid learning: With the pace of change in AI and software development accelerating, commit to regular learning and skill updates. Set aside dedicated time each week to explore new technologies, programming paradigms, or AI capabilities that could impact your work.
