Coding with a Co-pilot: How the Cursor Team Builds Their AI-First Editor by Using It

In the rapidly evolving landscape of software development, AI is no longer a futuristic concept but an increasingly integral co-pilot. Cursor, an AI-first code editor built as a fork of VS Code, is at the forefront of this shift. What makes Cursor's development particularly fascinating is not just the advanced AI features it offers, but the fact that its own engineering team extensively uses Cursor to build and refine the very editor itself. This deep immersion creates a powerful, real-time feedback loop, shaping a tool that truly understands and anticipates developer needs.

Cursor: More Than Just an Editor, An AI Collaborator

Cursor aims to transform the coding experience by embedding AI deeply into the developer's workflow. Its core philosophy revolves around making software development faster, smarter, and more intuitive. Key features that enable this include:

Codebase-Aware AI Chat: Developers can "talk" to their entire codebase, asking questions, understanding complex logic, and getting contextual help without leaving the editor. Features like referencing files and symbols with "@" notation, using images as context, and even pulling in web search results (@Web) make this an incredibly versatile tool.
Intelligent Autocompletion & Multi-Line Edits (Tab): Moving beyond single-line suggestions, Cursor's AI can predict and generate multiple lines of code, adapting to recent changes and existing patterns.
Inline AI Editing & Generation (Ctrl+K): Developers can select code and instruct the AI to refactor, debug, or add functionality using natural language, or generate new code blocks from scratch.
Agent Mode: A more autonomous AI capability designed to tackle tasks end-to-end, from understanding the context within a codebase to running terminal commands and iteratively fixing errors.
Customizable AI Guidance (.cursorrules): Teams can create .cursorrules files to define project-specific coding standards, preferred frameworks, and other conventions, ensuring the AI's suggestions align with their established practices.

This suite of AI-driven tools is designed to feel native to the development process, turning the editor into an active partner in creation.

Building Cursor, With Cursor: The Ultimate Internal Proving Ground

The most compelling aspect of Cursor's development story is the explicit and intense internal use of the product. Michael Truell, co-founder and CEO of Anysphere (the company behind Cursor), has highlighted that "Cursor's rapid success was fueled by intense internal use (dogfooding) and iterative development, ensuring the product met real user needs." (Reddit, discussing a Latent Space podcast episode).

This means the engineers building Cursor are:

Their Own First Users: Every new AI feature, every UI tweak, every performance optimization is immediately put to the test in the most demanding scenario: building a complex, AI-powered code editor.
Constantly Validating AI Efficacy: When a Cursor developer uses the AI chat to understand a legacy module of Cursor's own codebase, or employs Ctrl+K to refactor a complex function within the editor, they are directly experiencing the AI's strengths and weaknesses.
Driving Practical Improvements: If an AI suggestion is slightly off, if the context-awareness isn't quite right, or if a new AI-driven workflow feels cumbersome, the internal team feels that friction immediately. This accelerates the process of identifying, prioritizing, and implementing improvements.
Refining the Human-AI Interaction: The vision of "vibe coding"—where developers guide the AI with higher-level intent—is something the Cursor team is living daily. Their experience shapes how AI prompts are best formulated, how AI suggestions are presented, and how developers can maintain control while benefiting from AI assistance.

The Benefits of This AI-Powered Symbiosis

This unique development practice yields significant advantages that directly benefit Cursor users:

Rapid, Relevant Iteration: Because the core users (Cursor's own developers) are also the creators, the feedback loop is incredibly tight. Issues are identified and addressed quickly, and new features are often born out of the team's direct needs while building the editor. User reports often praise the fast pace of feature rollouts and model updates, a likely consequence of this internal agility.
Deep Understanding of Developer Pain Points: The Cursor team isn't just guessing what developers need from an AI code editor; they are experiencing those needs firsthand. This leads to features that are genuinely useful and well-integrated into common coding workflows.
Enhanced AI Reliability and Context-Awareness: Building a tool that can understand and operate on its own codebase is a significant technical challenge. By tackling this "meta-problem," Cursor pushes the boundaries of what AI can do in terms of code comprehension and generation for complex projects.
Practical Best Practices: Features like .cursorrules likely emerged from the team's own efforts to ensure the AI consistently followed their internal coding standards. The best practices for interacting with the AI are discovered and refined through their daily work.

Navigating the Meta-Challenges

Of course, using an AI-first tool to build that same tool isn't without its unique complexities:

Debugging the Debugger: When the AI offers a suboptimal suggestion for Cursor's own code, is it an issue with the prompt, the underlying LLM, Cursor's context retrieval, or the code itself? This adds layers to the debugging process.
Avoiding AI-Induced Echo Chambers: The team must be mindful that their advanced understanding of how to prompt and guide their own AI doesn't create features that are powerful but unintuitive for new users less familiar with the nuances of AI interaction.
Maintaining Developer Agency: As AI becomes more capable, ensuring that the developer remains in control and that the AI acts as an assistant rather than an opaque decision-maker is a critical design philosophy that the team must constantly reinforce, even for themselves. User feedback (like the AIM Research article discussing AI "paternalism") highlights this as an ongoing industry-wide conversation.

Despite these potential complexities, the overwhelming benefit is a product that evolves with an almost unparalleled intimacy with its core purpose: to make developers more productive and innovative with the help of AI.

Conclusion: The Future of Coding, Built by Its First Adopters

Cursor's approach of its own team being the primary, most intensive user of its AI-first code editor is a powerful testament to their confidence in the AI-assisted future of software development. By "eating their own AI cooking," they are not just testing features; they are living the developer experience they aim to create for others. This symbiotic relationship between the builders and their tool ensures that Cursor is continuously sharpened against the real-world challenges of software engineering, pushing the boundaries of what's possible when human ingenuity and artificial intelligence code together.