Why Leave Github Copilot?

Comparing the capabilities of Cursor and Github Copilot, more and more people are switching from Github Copilot to Cursor. So why is Cursor becoming so popular? Many authors recommending Cursor haven't clearly explained what makes Cursor superior to Github Copilot. Essentially, Cursor's main advantages are reflected in two aspects.

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More and more people are switching from Github Copilot to Cursor, so why is Cursor becoming so popular? For AI-assisted coding, what capabilities are most important? Many authors recommending Cursor haven't clearly explained what makes Cursor superior to Github Copilot.

Essentially, Cursor's main advantages are reflected in two aspects:

Code Modification Capability
Context Reference Capability

Code Modification Capability: Cursor's Core Advantage

Imagine you're writing an article. "Insertion" is like adding new content at the end of the article, while "modification" is adjusting and improving parts that are already written. The same applies to programming:

"Inserting code" is like adding new functionality at the end of a program
"Modifying code" is optimizing or correcting existing code

These two operations create vastly different coding experiences. With modification capability, it's like having a programming assistant on standby, ready to help you quickly adjust and refine code, not just add new content at the end.

This core advantage not only makes Cursor more powerful in functionality but also makes the entire coding experience smoother and more efficient.

Github Copilot's Limitations

Github Copilot primarily performs code insertion operations based on context. While this is helpful, its functionality is limited to appending new code.

In Github Copilot's official example:

You need to input a function header in a JavaScript file:

function calculateDaysBetweenDates(begin, end) {

Then GitHub Copilot will automatically suggest the remainder of the code. This operation only appends a piece of code without modifying the existing code.

Cursor's Comprehensive Editing Capabilities

In comparison, Cursor can not only insert new code but also directly modify existing code.

This capability is demonstrated in several aspects:

Multi-line Editing: Cursor can suggest modifications for multiple lines of code based on the current code context. All you need to do is press the Tab key to let Cursor make the modifications.

This smooth experience really makes you feel like someone is coding alongside you.
Inline Editing: By using the Ctrl/Cmd K shortcut, you can select the code block you want to edit and then enter modification instructions in the prompt bar. Cursor will intelligently modify the selected code based on your instructions.

If you find Cursor's modified code meets your expectations, simply click Accept. This interaction method is why many find Cursor very user-friendly. (Mainly because Github Copilot doesn't support modifications, making it unable to provide this experience)
Intelligent Prediction: Cursor can intelligently predict your next code intention and provide corresponding suggestions.

In this example, when you change the variable name from 'updates' to 'updatesToServer', Cursor predicts that the 'updates' variable below should also be updated to 'updatesToServer'.

So after you modify code in one place, Cursor will automatically suggest that other places in the code need to be synchronized. At this point, you just need to keep pressing Tab, which feels amazing.
Composer Feature: Although still in Beta, Cursor's Composer feature already demonstrates the ability to simultaneously edit and generate multiple files, which is particularly useful in complex projects.

This comprehensive editing capability makes Cursor's user experience far superior to Github Copilot, giving developers a true sense of "taking off."

Context Reference Capability: More Intuitive, More Powerful

In AI-assisted coding, accurately understanding and utilizing context information is crucial. Cursor also excels in this aspect, providing more intuitive and powerful context reference capabilities.

Cursor's @ Symbol Reference

In Cursor's AI input box (such as Cmd K, Cmd L, or Terminal Cmd K), you only need to type the @ symbol to bring up a suggestion list showing referenceable context information. This list automatically filters based on your input, showing only the most relevant suggestions.

The available context options are clear and straightforward; users can immediately understand what kind of context information each option represents. These options basically cover all possible context information needed in daily development.

Among them, @Codebase provides global code search capability. Cursor processes your project code for indexing in advance and stores the relevant index information locally (while Copilot relies on Github's API for remote searching).

Github Copilot's Complex Reference Method

In comparison, Github Copilot provides two context reference methods: Chat participants and Chat variables, using @ and # symbols respectively. This design not only increases usage complexity but also lacks intuitive and clear naming.

Compared to Cursor, the range of context choices GitHub Copilot can provide is also relatively limited, unable to achieve the comprehensive coverage that Cursor offers.

Chat participants:

Chat variables:

It's worth noting that Github Copilot only caught up with multi-file context introduction functionality early this year. From Github's changelog, it's clear they still have much to learn and reference from Cursor in this aspect.

Conclusion

Through its powerful code modification capabilities and intuitive context reference functionality, Cursor provides developers with a more efficient and intelligent AI coding assistant than Github Copilot. If you're looking for a tool that can truly improve coding efficiency and quality, try Cursor. It might give you an unprecedented sense of coding "taking off"!

Have you used Cursor or Github Copilot? Feel free to share your experiences and thoughts!