Introduction

> The role of AI in programming is transformative; it’s not just about coding but enhancing human creativity. "Cursor is revolutionizing the way we interact with code, making it more intuitive and empowering developers to focus on solving complex problems."

> Collaboration between humans and AI is the future, opening up new possibilities in design and engineering. "The blend of AI-assisted tools with programming doesn't replace us; it augments our capabilities and redefines what we can achieve together."

Code editor basics

> Code editors are like souped-up word processors for programmers, providing visual differentiation of code tokens and tools for navigation and error checking. The future of code editors is likely to evolve as software development changes.

> Making code editors fun and fast is crucial for user engagement and productivity. Being fast is fun, and speed is a key factor that draws people to building things on computers due to the rapid integration process compared to other disciplines.

GitHub Copilot

> My journey with Cursor stemmed from a deep-rooted love for coding tools, starting as pure Vim users. The turning point came when I experienced Copilot in VS Code; it was a revelation. "The experience of Copilot with VS Code was more than good enough to convince me to switch." That intimacy of feeling understood by our tools was transformative, igniting our passion to create something even better.

> The emergence of scaling laws from OpenAI around 2020 sparked a fascinating dialogue among us about the future of knowledge work. It painted a picture of consistent improvement in AI capabilities, with "bigger might be better for model size and data size." This prompted us to envision how programming would evolve, leading us to the realization that we needed to design a new environment for it.

> The thrill of tinkering with early versions of GPT-4 was a game-changer. It solidified our belief that "this wasn’t just a point solution thing" but an entirely new era in programming. Our vision shifted towards building a programming environment that harnessed these advances, guided by the excitement of the progress we were witnessing.

Cursor

> Cursor Team shared two main insights in their interview:

> Firstly, they emphasized the need to innovate beyond existing limitations in AI-assisted programming tools like VS Code. By forking VS Code, Cursor Team aimed to unleash the full potential of AI in the editing process without being restricted by pre-existing constraints.

> Secondly, Cursor Team highlighted the competitive edge of Cursor over tools like Copilot, stressing the importance of continuous innovation and staying ahead in AI capabilities. They emphasized the significance of rapidly implementing new features and pushing the boundaries of what is achievable in programming tools through ongoing research and experimentation.

Cursor Tab

> First off, there's this powerful vision we've been working towards: "we wanted the model to be able to edit code for us." The goal is to create a seamless experience where, after making an edit, the model should know exactly where to navigate next—like pressing Tab to jump instantly down the line. "Once you express your intent, the model should just sort of read your mind," making coding feel almost effortless.

> Furthermore, we've discovered that coding has inherent predictability. "There are a lot of tokens in code that are super predictable." This led us to design with efficiency in mind, using techniques like caching and sparse models. We aim for a system that can handle the long input sequences while providing immediate, high-quality outputs that enhance productivity—eliminating those low-entropy actions and letting coders focus more on creative problem-solving.

Code diff

> "Our focus on optimizing different diff interfaces is driven by the need to enhance user experience, especially during activities like autocomplete. The goal is to make it really fast to read and understand diffs, guiding the user's attention effectively."

> "In the space of UX design engineering, we are exploring ways to intelligently guide programmers through code reviews, highlighting important pieces while minimizing distractions. By leveraging intelligent models, we aim to streamline the verification process and make it more efficient."

> "While natural language programming may have a role, it may not completely replace traditional coding methods. The ability to communicate with AI through various means, such as showing examples or directly interacting, will likely coexist with natural language interfaces, catering to different needs and preferences."

ML details

> The technology behind Cursor is truly a game changer. We utilize a unique ensemble of custom models, specifically tailored to perform better than the generic frontier models, especially in complex tasks like code diffs. It's fascinating how we've trained our models not just to sketch out rough changes, but to accurately apply those changes, countering the notion that combining these steps is trivial - "it's not a deterministic algorithm,” and a misunderstanding of this leads to bad user experiences.

> Speed is crucial, and we tackle this through speculative edits, an innovative approach where we process chunks of original code in parallel to enhance efficiency. This method allows us to verify and generate code rapidly, enabling users to start reviewing content even before the process is fully completed. It’s a blend of cutting-edge technology and practical usability - "it just streams down a lot faster" and keeps the workflow seamless.

GPT vs Claude

> - Different models in the GPT family excel in various categories of programming tasks. Sonnet stands out for reasoning ability, while maintaining a consistent level of performance across different coding challenges.

> - Real-world programming involves nuanced, unstructured tasks that go beyond standard benchmarks. Public benchmark data can be misleading, requiring a balance between quantitative evaluation and qualitative human feedback for assessing model performance accurately.

Prompt engineering

> The importance of prompt design cannot be understated; "it depends on which model you're using" because each one responds differently. We strive to maximize success by carefully choosing what to include in a prompt, considering the available context while avoiding overwhelm and confusion for the model.

> We closely relate prompt structuring to web design, drawing inspiration from approaches like React. The essence is in "declaratively deciding what you want," allowing the system to intelligently render the data, making the process not only flexible but also easier to debug.

> Encouraging users to express themselves is vital; there's a balance between being "lazy" and being articulate. While it's tempting to allow free form input, nudging programmers toward clearer intent can lead to better results, as "not enough intent is conveyed" can leave the model uncertain about how to proceed.

AI agents

> Agents are cool and resemble humans, indicating progress towards AGI. They are not fully useful yet but show promise for specific tasks like bug fixing.

> Agents won't take over all of programming as value lies in iterating and adapting, where instant feedback is crucial for developers to improve efficiently.

> Techniques like KV caching and efficient attention schemes like multiquery attention help make the system fast by optimizing memory and reducing computational load.

> Optimizations translate to a smoother user experience with larger caches, faster token generation, and the ability to handle bigger prompts without sacrificing performance.

Running code in background

> The concept of a "Shadow workspace" is all about enhancing user experience by running computations in the background, allowing AI to predict user needs not just in the immediate future, but over a longer horizon: “the idea is if you can actually spend computation in the background then you can help the user maybe like at a slightly longer time Horizon.”

> Integrating language server protocols into Cursor enables the AI to learn and adapt without disrupting the user's workflow, giving the models access to vital feedback while remaining invisible: “you want to show that same information to the models... in a way that doesn't affect the user because you wanted to do it in background.”

Debugging

> - The Model's ability to automate tasks and run code locally versus in a remote sandbox environment poses interesting challenges, especially in reproducing the user's environment effectively.

> - When it comes to Agents for coding, focusing on bug finding at various levels and addressing logical implementation issues stands out as a key priority. The struggle lies in the models' ability to generalize well beyond pre-training data and adapt to specific coding tasks such as bug detection and fixes.

Dangerous code

> The importance of thorough documentation in coding can’t be overstated; “for every single line of code inside the function, you have to comment,” especially when you consider that a single misstep could lead to catastrophic failures. This practice not only aids human understanding but enhances AI bug detection, reinforcing the idea that “you might not notice a danger until it’s too late.”

> I’m genuinely excited about the future of coding with AI. The vision that “the model will suggest a spec, and a smart reasoning model computes a proof” makes me hopeful for a world where bug verification and prevention becomes seamless. The quest to formally verify entire codebases down to hardware is ambitious but totally feasible.

> Integrating user incentives, like monetary rewards for effective bug detection, could revolutionize how we interact with coding tools. “I would pay a large amount of money for a bug,” especially if it enhances my coding experience and acknowledges the work done. While controversial, this could be a game-changer in motivating quality contributions in the coding community.

Branching file systems

> One key reflection is about the potential for integrating code running in the terminal with suggestions on how to improve it: "There's a question of whether it happens in the foreground too or if it happens in the background... we suspect something like this could make a lot of sense."

> Another highlight is the discussion on branching in databases and file systems: "I feel like everything needs branching... it would be really interesting if you could Branch a file system, right?... maybe the AI agents will test against some branch... it's sort of going to be a requirement for the database to support branching."

Scaling challenges

> Scaling has been a wild ride, especially as we tackle the technical challenges that come with each new level of usage. It’s fascinating to see how “it’s very hard to predict where systems will break when you scale them,” and we’re constantly learning through real-world applications.

> A key part of our architecture involves using hash-based reconciliation to ensure the local codebase aligns with the server. We’re able to avoid massive network overhead by focusing only on discrepancies, allowing for a “kind of hierarchical reconciliation” that keeps everything efficient.

> The switching point in user experience is huge; we're seeing that our system's ability to query a codebase means developers can quickly find the information they need, even in complex environments. It’s like magic when “very often it finds the right place that you were thinking of” without the hassle of traditional searches.

> Privacy is a vital concern for us; we’re exploring homomorphic encryption as a potential solution to keep user data secure while leveraging powerful models in the cloud. Balancing “the risks of centralization with the benefits of easy access” is a challenge we’re committed to navigating thoughtfully.

Context

> My key insight is that there are trade-offs with including automatic context in models. The more context you add, the slower and more expensive the models become, impacting performance and accuracy. It's crucial to find a balance by setting a high bar for the relevance of the context included.

> Another important point is the ongoing exploration of training models to understand specific code bases. This involves a variety of approaches, from continued pre-training with code data to post-training with fine-tuning on code-related questions. The goal is to enhance models' ability to answer questions accurately and efficiently about a given code repository.

OpenAI o1

> Test time compute is a fascinating evolution in programming; it allows us to maximize performance without constantly needing to scale models. "Instead of training a model that costs so much and runs so infrequently, we can have a model that handles 99.9% of queries and just run it longer for the rare cases that need maximum intelligence."

> The integration of process reward models is a curious area of exploration. It’s about "grading the Chain of Thought," which can enhance how we generate and evaluate outputs. If we can evaluate every step, we can potentially find more effective paths to the right answer.

> The competitive landscape is evolving rapidly, with a "ceiling that is incredibly high." Continuous innovation is essential; the best products will only get better, and this creates ample opportunity for startups like Cursor to develop unique solutions that stand out not just through the latest models but through thoughtful user experience and custom functionalities.

Synthetic data

> One key insight shared was the taxonomy of synthetic data, which includes distillation to train smaller task-specific models, using synthetic data for bug detection by introducing and detecting bugs, and producing texts with language models that can be easily verified for language quality like Shakespeare-level language. It shows the potential of synthetic data in training models effectively in various domains like language and code.

> Another important point made was about the challenges of verification in synthetic data generation, highlighting the need for reliable verification methods like tests or formal systems for ensuring correctness. It emphasized that having a trustworthy verifier is crucial for the success of synthetic data applications, especially for complex or open-ended tasks, where achieving perfect verification remains a significant obstacle.

RLHF vs RLAIF

> I've been reflecting on how the balance between human feedback and model performance can really shape the effectiveness of our systems. Using human labels to train a reward model is great when you can gather a lot of feedback, but it’s not the only path forward. In our experience, "a mix of reinforcement learning from human feedback (RHf) and reinforcement learning from verification (Rif)" can yield impressive results with far fewer examples. Just “50 to 100 examples” can be sufficient to fine-tune and align the model's prior understanding with our specific goals.

> I also find it fascinating how verification can be simpler than generation. It’s intriguing to think that while we leverage language models to sift through their own outputs for improvements, we can attain a recursive enhancement. Although it won't directly mirror our expectations, embracing this blend of methods allows us to refine our processes and ultimately push the boundaries of what our models can achieve.

Fields Medal for AI

> It's intriguing how ranking can sometimes be easier than generation, especially in the context of complexity theory and the P versus NP problem. There's a certain allure to the idea that certain problems may be easier to verify than to prove initially. The notion of who would receive credit for proving P equals NP or P not equal to NP is a captivating and open question, potentially leading to a Fields Medal-like recognition for AI.

> The path to solving complex mathematical problems, such as those like the Birch and Swinnerton-Dyer conjecture or the Riemann hypothesis, can appear quite murky. Unlike tackling International Mathematical Olympiad (IMO) problems that had a clearer path and tactics, these deep mathematical conjectures present a different challenge altogether. Despite the potential allure of AGI, receiving a Fields Medal before AGI seems like a more probable scenario, possibly even before 2030.

Scaling laws

> Scaling laws are more intricate than just the compute and parameter size; context length and optimization strategies for inference budgets are pivotal. "The dimensions to these curves are more than what we originally used." It’s essential to understand the balance between different parameters for effective model training.

> There’s merit in the philosophy that “bigger is certainly better” when it comes to raw performance, but we should also be exploring knowledge distillation. It’s about finding ways to get the most capable, efficient models, even if it means overtraining a smaller model to gain valuable insights.

> The real limitation in AI development isn’t just capital but also the engineering talent and innovative ideas that fuel research. "Even with all the compute in the world, we're ultimately limited by really good engineering." Easing the process for brilliant engineers could massively accelerate advancements in AI technology.

The future of programming

> Programming is evolving towards prioritizing speed and agency for the programmer, emphasizing the importance of human control over automated solutions. We believe that retaining control over decisions and detailed specifications is crucial in engineering, driving innovation and high-quality software development.

> The current era presents an exciting time for software development, with a focus on reducing boilerplate work, increasing speed, and enhancing individual control. The evolving landscape of programming is steering towards magnifying creative ideas and tastes, making programming more enjoyable and less rigid in terms of traditional approaches.

> The future of programming holds promises of faster iterations and increased productivity through a balance of AI assistance and human creativity. Our goal is to create a hybrid engineer who effortlessly navigates codebases, makes high-impact decisions efficiently, and optimizes the programming process, ultimately aiming to enhance the experience for developers worldwide.