My Takeaway of Cursor and GitHub Copilot
The world of coding is evolving at so quickly with AI-powered tools. Recently, I had the opportunity to put two prominent AI coding assistants to the test: GitHub Copilot (specifically, the VS Code integration) and Cursor.
Let's be honest, "GitHub Copilot in VS Code" is a bit of a mouthful, akin to "Chevy Silverado 1500 AT4 Duramax Diesel carbon fiber edition." For simplicity's sake, throughout this post, "GitHub Copilot" will refer to its integration within VS Code.
Both tools leverage artificial intelligence to assist developers. For anyone familiar with VS Code, the transition to Cursor is remarkably smooth, as it's built on the same familiar IDE. This common ground made my initial experience with Cursor feel like a natural extension, rather than a steep learning curve.
My experience began at the enterprise level with GitHub Copilot, then I explored Cursor for personal projects. This dual experience offered unique insights into their strengths and limitations.
One immediate difference was LLM flexibility. With GitHub Copilot, I could choose from models like Gemini, GPTs, and Claudes. I gravitated towards GPTs due to their superior memory for conversation context. Cursor, however, doesn't offer this level of LLM customization.
My first time using Cursor was an experience. It's designed to be more assertive, often suggesting modifications across multiple subroutines or even proposing entirely new files and functions. While you can accept or deny these changes, the scale of suggestions can be overwhelming, sometimes making you feel like you're evaluating more than coding.
However, once past that initial surprise, **Cursor truly shines in accelerating coding**. It proactively opens, edits, and creates files, leaving you with a simple accept/deny decision. This aggressive approach often results in a faster development cycle after the initial bumps. It's worth noting that **GitHub Copilot has recently introduced its own agentic capabilities**, aiming to bridge this gap in proactive, multi-file modifications. In terms of raw "intelligence," I haven't found one definitively smarter; their core capabilities feel comparable.
A crucial distinction is their suitability for enterprise environments. Cursor is currently not ideal for corporate use due to its policy of using your code for training its engine, raising significant privacy and intellectual property concerns. GitHub Copilot, with its enterprise offerings, addresses these concerns more robustly.
While powerful, these tools aren't silver bullets. My experience deploying a project to Render highlighted this: Cursor helped with boilerplate but struggled with a simple database issue. This led me to a crucial rule: If you don't get a satisfactory answer after three attempts, stop trying with the AI. It's often more efficient to use your own skills or seek human help.
Both tools also suffer from their pre-trained LLMs. When working with the latest APIs, where access methods might have recently changed, they often fall short. Without real-time web search capabilities, they can't provide accurate code. This is where tools like Perplexity shine, offering live web searches for cutting-edge AI coding tasks. However, for projects not dealing with brand-new APIs, both Cursor and GitHub Copilot remain highly effective.
One feature that truly won me over in Cursor was its ability to generate Git commit messages. It excels at capturing changes, eliminating the mental overhead of crafting concise and informative messages. This significantly speeds up version control, producing neat and descriptive commits.
Finally, all AI code assistants currently fall short when debugging large-scale codebases or integrating complex new features. In these scenarios, a fundamental understanding of the codebase's architecture is essential to guide the AI. While AI can assist with specifics, grasping the "big picture" remains a distinctly human challenge, requiring experience and practice. But, I think this capability can be added to these tools within the next few years.
Summary: Cursor vs. GitHub Copilot
| Feature/Aspect | GitHub Copilot | Cursor |
|---|---|---|
| Backend LLM Choice | Customizable (e.g., GPTs, Geminis, Claudes) | Not customizable |
| Initial Experience | More conservative suggestions, less overwhelming | Can be overwhelming with extensive, multi-file suggestions |
| Coding Speed & Agentic Capabilities | Good, now with agentic features for proactive changes | Potentially faster due to proactive multi-file modifications |
| Enterprise Use | Generally suitable (with enterprise plans) | Not recommended due to code being used for training |
| Latest API Support | Limited (pre-trained, no web search) | Limited (pre-trained, no web search) (Consider Perplexity for real-time info) |
| Git Commit Messages | Standard | Excellent, intelligent commit message generation |
| "Intelligence" | No significant difference in perceived "smartness" | No significant difference in perceived "smartness" |
| Overarching Challenge | Struggles with "big picture" of large codebases | Struggles with "big picture" of large codebases |
