Cursor Team: Future of Programming with AI | Lex Fridman Podcast #447

Summiz Holo

Code Editors and Their Evolution

• Cursor is a code editor based on VS Code that enhances AI-assisted coding and is gaining attention in the programming and AI communities.
• A code editor is a specialized tool for programmers, akin to a word processor, but designed for structured coding tasks, offering features like error checking and navigation.
• The role and definition of a code editor are expected to evolve significantly in the next decade as software building processes change.
• The fun aspect of coding and using code editors is important, as speed and enjoyment can influence what tools are developed and adopted.
• The journey to Cursor began with a fondness for VS Code and its integration with Copilot, a feature that enhances coding through intelligent autocomplete suggestions.

Insights on AI and Programming

• The underrated aspect of tools like GitHub Copilot is that even when they make mistakes, users can easily iterate and fix issues by typing more characters.
• GitHub Copilot is considered the first real AI product and a killer app for language models.
• The scaling laws papers from OpenAI indicated predictable progress in AI, suggesting that larger model sizes and data could lead to better performance.
• Early access to GPT-4 demonstrated a significant step up in capabilities, making previous theoretical gains feel concrete and actionable.
• There was a belief that AI advancements would lead to a comprehensive transformation in programming practices, necessitating a new programming environment.

Discussion on AI and Cursor Editor

• There’s a discussion about the scaling laws in AI and how they can lead to massive gains in progress, particularly in certain domains like math.
• Cursor is a fork of VS Code created to rethink AI's role in the editing process, aiming to build more useful features beyond the limitations of existing coding environments.
• The decision to create an editor rather than just an extension stemmed from the desire to fully leverage improving AI capabilities without being restricted by existing platforms.
• The AI programming space allows for rapid innovation, and being ahead in model capabilities can significantly enhance a product's usefulness.
• The belief that Cursor needs to continuously evolve and improve to stay relevant against established competitors like Microsoft.

Insights on Cursor and AI Innovation

• 10% of the crazy ideas will make it into something kind of cool and useful.
• The initial frustration with the lack of innovation in AI models and user experience.
• Cursor aims to create a cohesive experience by having the same team work on both the UI and model training.
• Cursor is designed to predict not just the next character but the entire change a programmer will make.
• Cursor helps users jump ahead of the AI to transition from instructions to code effectively.
• The editing experience is optimized for speed and ergonomics.
• The model should intuitively know the next logical steps after an edit, minimizing user effort.

Key Concepts in Coding and Language Models

• The concept of zero entropy in coding suggests that once intent is expressed, the model should predict actions without requiring extensive typing.
• Language model loss is lower for code than for natural language, indicating predictability in coding tokens.
• The gold cursor tab aims to eliminate low entropy actions in code editing by predicting user intent and jumping forward in time.
• Training small models with long prompts is crucial for low-latency next cursor prediction in coding.
• Caching plays a significant role in managing input tokens to improve performance and reduce compute load.
• The model aims to generate code, edit across multiple lines, navigate between files, and suggest terminal commands based on written code.
• The integration of knowledge into the programming process is essential for verifying correctness in suggested completions.

Programming Diff Interface Insights

• The next five minutes of programming can sometimes be predictable based on recent actions, allowing for a more streamlined process.
• Cursor features a diff interface that visually represents code modifications with red and green highlights, optimizing the review of changes.
• The diff interface is designed differently for autocomplete versus reviewing larger blocks of code, emphasizing speed and clarity.
• Previous attempts at showing diffs included various visual styles like crossed-out lines and highlights, which were found to be distracting.
• Ideas for improving diff reviews include highlighting important changes, graying out less significant ones, and flagging potential bugs for review.

Key Insights on AI in Code Review

• The goal is to guide the human programmer optimally through necessary reading, leveraging intelligent models.
• As AI models become smarter, the proposed changes will be larger, increasing the verification workload for humans.
• Code review processes are currently inefficient and could be significantly improved by using language models to enhance the review experience.
• The review process should focus on the reviewer’s experience, especially when the code is generated by language models.
• Ordering of files in code review matters; models should help guide the reviewer logically through the code.
• Natural language will not be the primary method of programming; showing examples may often be more effective for communication with AI.
• Cursor operates using an ensemble of custom models trained alongside frontier models for enhanced reasoning and performance.

Code Generation Insights

• Frontier models are effective at sketching plans for code but struggle with creating precise diffs, especially in large files.
• The process of combining rough code sketches with existing code is non-trivial and often fails with deterministic algorithms.
• Using smarter models for planning and less intelligent ones for implementation can optimize coding tasks, reducing token usage and latency.
• Speculative edits improve speed in code generation by processing multiple tokens at once, leveraging the original code as a strong prior.
• The approach allows for faster code rewriting and review without long loading times, enhancing the user experience.

Insights on LLMs in Coding

• There’s no single LLM that dominates others in coding; each has strengths in different areas like speed, editing, and understanding intent.
• Sonet is currently considered the best model for coding due to its capability to maintain performance outside of benchmarks.
• Benchmarks for coding do not accurately represent real programming experiences, which are often messy and less well-specified.
• Public benchmarks can be contaminated, making it difficult for models to perform well on them without proper context.

Insights on AI Model Evaluation and Performance

• Human qualitative feedback is important for evaluating AI models alongside benchmarks.
• There are varying perceptions about AI models' performance, influenced by user experience and possible technical factors.
• Prompt design is crucial for maximizing AI model performance, and different models respond differently to prompts.
• Context window limitations impact how prompts are structured, affecting model confusion and speed.
• A system called preum helps manage prompt structure for model inputs, taking inspiration from web design principles.

Web Design and AI Interaction Insights

• The rendering engine in web design, like Chrome, helps fit everything onto the page and has shifted its role over time to aid in debugging and data management.
• Prompting with JSX allows for prioritization of lines in code, enhancing the rendering process.
• There's a tension between programmer laziness and the need for articulate queries to improve AI's understanding and output.
• To resolve ambiguity in user queries, models can ask for clarification or present multiple possible generations for selection.
• The system can suggest relevant files while typing based on previous commits, addressing uncertainty in the coding process.

Insights on Programming Agents

• Agents are seen as a cool step towards AGI, but are not yet widely useful for many tasks.
• There are specific programming tasks where having an agent would be beneficial, like fixing bugs.
• A lot of programming value lies in iteration, not just upfront specification.
• Instant initial versions can enhance the programming process, allowing for rapid iteration.
• There is potential for agents to assist in setting up development environments and deploying apps.
• Cursor aims to make programming easier and more enjoyable by delegating tedious tasks to agents.
• Speed is a crucial aspect, and improving it involves strategies like cache warming for lower latency.

Key Concepts in Transformer Caching and Suggestions

• The use of KV caching in Transformers allows the model to store keys and values of previous tokens, reducing computational load and improving efficiency during token generation.
• Speculative caching can predict user acceptance of suggestions ahead of time, making the response feel faster by preparing the next token in advance.
• Predicting multiple outputs (like 10 suggestions) increases the likelihood of matching what the user wants, leveraging the model's internal uncertainty and improving user satisfaction.
• Reinforcement learning (RL) can be used to train models to produce suggestions that align better with human preferences by rewarding desirable outputs and punishing less favored ones.

Attention Mechanisms in Model Performance

• Smaller models can achieve similar performance to larger ones, especially with techniques like reducing the size of the KV cache for speed.
• There’s been a shift from multi-head attention to more efficient attention schemes like group query and multiquery attention, which help generate tokens faster with larger batch sizes.
• The bottleneck in generating tokens is how quickly cache keys and values can be read, rather than matrix multiplications.
• Multiquery attention reduces the number of key-value heads to one, while group query preserves more query heads with fewer key-value heads.
• Multi-latent attention (MLA) compresses keys and values into a single latent vector, improving efficiency while maintaining richness.
• Reducing the size of the KV cache allows for larger caches, more aggressive caching, and better performance in generating tokens with larger batch sizes.

Key Concepts in AI-Assisted Programming

• The concept of a Shadow workspace allows for background computation to enhance programming efficiency by predicting what a user might code in the next 10 minutes.
• Feedback signals are crucial for improving model performance, allowing for iteration and learning from the programming environment.
• Language servers provide essential support for programming tasks, such as type checking and navigating code structures, by interfacing with various languages through a standardized protocol.
• In Cursor, a hidden instance of the application allows AI agents to modify code without affecting the user’s immediate environment, facilitating a seamless integration of AI assistance.
• The idea of mirroring the user's environment on Linux for AI code modifications is feasible, while achieving similar functionality on Mac and Windows presents more challenges.

Insights on Coding and Model Performance

• The concept of a "shadow workspace" allows coding in an unsaved state with concurrent operations, which can be exciting yet intimidating for users.
• There are different levels of runability for coding tasks, with simple tasks handled locally and larger changes ideally done in a remote sandbox environment.
• Agents in coding could focus on tasks like bug finding and implementing new features, not just limiting to coding tasks but also extending to video editing and automation of related processes.
• Current models struggle with bug detection due to a lack of training examples, impacting their effectiveness in identifying and fixing real bugs.
• The performance of models in coding is influenced by their pre-training distribution, which primarily includes code generation and question answering, rather than bug detection tasks.

Insights on Code Understanding and Development

• The model's understanding of "sketchiness" in code is crucial, as it helps prioritize which bugs are significant based on past experiences and cultural knowledge among engineers.
• The challenge for humans and AI alike is identifying which lines of code are critical and which are trivial, highlighting the importance of clear documentation.
• Emphasizing dangerous lines of code through comments can improve attention from both humans and AI models, promoting better awareness of potential risks.
• The future of programming may involve models suggesting specifications and verifying implementations, reducing the need for manual testing.
• Specifying intent in software development is complex, making it challenging to ensure that the code aligns with the intended outcome.

Challenges and Opportunities in Formal Verification

• The challenge of formal verification in programming includes issues with how specifications are defined and the complexity of entire code bases.
• Formal verification can potentially extend down to hardware, involving multiple layers of verification through compilers and systems.
• There is a concern about incorporating external dependencies and side effects, like API calls, into formal verification processes.
• The dream is to prove that language models are aligned and provide correct answers, which could enhance AI safety and bug detection.
• Effective bug-finding models are essential for progressing AI's role in programming, allowing for both generating and verifying code.
• Training models to introduce bugs could help in creating reverse models that effectively find those bugs in existing code.

Challenges and Solutions in Debugging Code

• There's a challenge in debugging code, as humans often struggle to find bugs by just looking at files.
• The idea of having specialized models running in the background to spot bugs could be beneficial.
• Integrating a monetary system for bug finding could encourage users to pay for solutions or tips on good code.
• Concerns exist about how introducing money might change the user experience and the fun aspect of coding.
• A potential solution to verify bug fixes could reduce reliance on an honor system in bounty systems.
• The interaction between the terminal and code could enhance error-checking and suggest code changes based on runtime feedback.

Technical Insights on Database Management and Infrastructure

• The concept of using database branching to test features against production databases without modifying them is discussed, highlighting its technical complexity and potential benefits for AI agents.
• AWS is favored for its reliability and trustworthiness despite its complicated setup process, making it a leading choice for infrastructure.
• Scaling challenges arise when increasing request rates, leading to issues like integer overflows in tables and unpredictable system failures.
• A custom system is in place for computing a semantic index of codebases that requires careful handling to prevent client bugs by not storing the actual code.

Key Considerations in Codebase Management

• The importance of keeping the local codebase state in sync with the server, managed through hashing.
• The use of a hierarchical reconciliation process to minimize network overhead and database strain.
• The challenges of scaling solutions for large codebases used by many programmers.
• Embedding code is a significant cost bottleneck, leading to clever caching strategies to improve efficiency.
• The usefulness of indexing a codebase for quickly locating information, especially in large projects.

Challenges and Opportunities in AI Programming

• The future of programming with AI is focused on improving retrieval quality, with significant potential for advancement.
• Local models for code processing are challenging due to hardware limitations and the complexity of managing large codebases.
• Most users are on less powerful Windows machines, making local model implementation difficult.
• Large models require substantial computational resources, often exceeding what can be handled locally, even on powerful machines.
• There is interest in homomorphic encryption for language model inference, allowing for secure computation on encrypted data.

Concerns and Solutions in AI Data Management

• There are concerns about the centralization of data as AI models improve, leading to potential surveillance and misuse of information.
• Homomorphic encryption is seen as a hopeful solution for privacy-preserving machine learning, but it's a challenging area of research.
• The reliance on a few companies for data control raises security and trust issues, especially as personal data is increasingly shared with AI models.
• Automatic context inclusion for programming models has trade-offs, including slower performance and potential accuracy issues if too much information is provided.

Exploration of Retrieval Systems and Learning Models

• There are cool ideas being explored for better retrieval systems and learning models, focusing on improved context handling and caching for infinite context in language models.
• The challenge exists in determining whether to integrate retrieval with the model or keep it separate, particularly in the context of programming code understanding.
• The concept of post-training a model to specifically understand a codebase is being considered, with potential methods including continued pre-training and instruction fine-tuning with specific repository data.
• The use of synthetic data to create questions about code pieces could enhance the model's ability to answer questions related to that codebase.

Model Performance Insights

• Test time compute is an interesting approach to improve model performance without needing to scale up model size.
• There's a problem of hitting a data wall, making it challenging to continue scaling data for better performance.
• Instead of training larger models, running the same size model for longer can yield higher quality answers.
• Most queries (99.9%) may not need extensive model intelligence, raising questions about efficient model usage.
• The model routing problem, determining which model to use for specific queries, remains unsolved.
• Test time compute requires a unique training strategy, and its workings are not well understood outside major labs.
• There are traditional outcome reward models and newer process reward models for grading model performance, with the latter focusing on the reasoning process.

Process Reward Models and Current Limitations

• People use process reward models primarily to grade outputs from language models and select the best answer.
• There's interest in using process reward models for tree search to evaluate multiple paths in a chain of thought.
• OpenAI hides the chain of thought from users and may do so to prevent others from replicating their technology.
• Access to hidden data like the chain of thought or log probabilities could enable users to distill capabilities from models.
• The integration of the 01 model into Cursor is still being explored, with no clear use cases established yet.
• There are significant limitations to current models, such as the lack of streaming capabilities.

Insights on AI-Driven Programming Tools and Synthetic Data

• The early stages of AI-driven programming tools feel like 'V zero,' indicating there's significant room for improvement and innovation.
• The best programming products in the next few years will be drastically more useful than today's, emphasizing the need for continuous innovation.
• The value of Cursor comes not just from integrating new models but also from the depth of custom models and thoughtful user experience design.
• There are three main kinds of synthetic data: distillation from high-latency models, generating bugs for training detection models, and producing verifiable text through language models.
• Distillation allows for training less capable models by using outputs from more advanced models, but won't exceed the capabilities of the original.
• Introducing reasonable-looking bugs is easier than detecting them, allowing for the training of models that can effectively identify bugs in code.
• Language models can generate vast amounts of training data that can be verified easily, a method that can lead to the development of high-quality models.

Key Concepts in Verification and AI

• The importance of having a reliable verifier for tasks, especially in coding, to ensure correctness.
• The distinction between verification and generation, with verification potentially being easier than generating solutions.
• The concept of using human feedback to improve models through reward models, specifically reinforcing learning from feedback (RHF).
• The idea that a language model may have an easier time verifying a solution than generating it, suggesting a recursive improvement process.
• The relationship between ranking and generation, with the intuition that ranking might be significantly easier than generating outputs.
• The philosophical question regarding the implications of P vs NP in relation to AI and verification tasks.

Trends in AI Model Development

• Scaling laws in AI are evolving, with original concepts being refined and new dimensions considered, such as inference compute and context length.
• The idea of 'bigger is better' in AI models is still valid, particularly for raw performance and intelligence.
• Distillation is a promising approach to optimize model capabilities, allowing for smaller and faster models while maintaining performance.
• There’s potential to extract more signal from data by training large models and then distilling them into smaller ones.

Challenges and Considerations in AI Development

• There's a significant lack of knowledge about training large models, which limits effective allocation of resources for improvement.
• Maximizing raw intelligence in AI involves acquiring as much compute power as possible, allowing for extensive experimentation.
• The development of AI models is constrained not just by compute and money, but also by the availability of innovative ideas and high-level engineering talent.
• Significant engineering effort is required to implement complex models and architectures effectively.
• Focusing on low-hanging fruit—scaling existing models—can yield better results than pursuing new ideas when current methods are still effective.
• A massive investment could necessitate reevaluating ideas and methods used in AI development.

The Future of Programming

• The future of programming emphasizes speed, agency, and control for programmers, allowing them to modify and iterate quickly on their work.
• Communicating with a computer to build software can lead to loss of control and important decision-making, as it often involves giving up specificity.
• Effective engineering involves numerous micro-decisions and trade-offs, rather than simply implementing a fully written spec.
• Human involvement is crucial in software design and decision-making, as humans should remain in the driver’s seat rather than relying solely on AI.
• The idea of controlling the level of abstraction in codebases could enhance productivity, allowing programmers to navigate between high-level and low-level code effectively.
• The concern about the future of programming skills among young people who love programming reflects anxiety over the evolving landscape influenced by AI.

Future of Programming

• Programming is more enjoyable now compared to 2012-2013 due to less boilerplate and more focus on creativity and speed.
• Future programming will emphasize rapid iteration and experimentation rather than careful upfront planning.
• AI tools will make tasks like code migration quicker and easier, allowing for more focus on design decisions.
• There’s a potential shift toward natural language as a primary programming language, impacting creative decision-making in coding.
• JavaScript is viewed as the dominant programming language for the future, with a widening demographic capable of programming.

Insights on Programming

• The best programmers have a deep love and obsession for programming, often coding outside of work for personal projects.
• Pressing tab in coding is a metaphor for injecting intent into the programming process, reflecting a higher bandwidth communication with the computer.
• The future of programming involves a hybrid human-AI engineer that is significantly more effective than a single engineer, combining human ingenuity with AI capabilities.
• The goal is to improve programming efficiency and enjoyment, making it more fun for developers.