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Features of Cursor IDE compared to VS Code and other coding environments

5 min read

From: hu-po

Cursor IDE is a development environment described as a VS Code mimic or extension, deeply integrating ChatGPT functionality for coding tasks [00:01:28]. It’s developed by a startup and is intended to streamline the development process by bringing AI capabilities directly into the IDE [00:02:57].

Core Features and AI Integration

Cursor’s primary appeal lies in its advanced AI integration in coding environments, powered by OpenAI’s API (specifically GPT-4) [00:03:08]. This differs from traditional workflows where developers might switch between a code editor and a browser tab for AI assistance, eliminating the need for constant copy-pasting [00:02:40].

Key AI-driven functionalities include:

  • Integrated Chat Panel (Command Shift L): Allows users to ask questions directly to the AI [00:02:29]. This chat can analyze the entire codebase for context [00:03:32], although it initially struggled with deeply nested directories [00:17:43].
  • Edit and Write Code (Control/Command K): Users can highlight code and provide instructions to the AI for modifications, such as adding documentation (docstrings) [00:18:43] or refactoring [00:59:00].
  • @ Symbols for Context: Typing @ in the chat or edit prompt brings up a dropdown of files and code symbols, allowing the AI to generate code with specific dependencies or answer questions about particular files [00:39:34] [00:23:34].
  • Documentation Integration: Users can add custom documentation (e.g., by providing a URL to an API reference) [00:46:04]. The AI can then use this context to answer questions or generate code, as demonstrated with the Dynamixel SDK [00:46:29]. This was highlighted as a particularly “cool feature” [01:32:16].
  • Auto Debug: A “blue Auto debug button” appears after a terminal error, allowing the AI to analyze the error message and suggest fixes [00:27:59]. While it can identify bugs and suggest solutions, it often requires manual implementation of those fixes [00:29:13].
  • Fix Lints: Allows quick fixes for linting errors, such as changing wildcard imports to explicit imports or updating variable naming conventions (e.g., global variables to ALL_CAPS) [00:56:41] [00:57:23].

Comparison to Other Coding Environments

VS Code

Cursor is built to mimic VS Code, even appearing as a .appimage file that opens what seems like a VS Code instance with Cursor’s features built in [00:01:41].

  • Similarities: Shares the foundational UI and many familiar shortcuts and functionalities.
  • Differences:
    • AI Integration: Cursor’s key differentiator is its deeper, more integrated AI capabilities compared to standard VS Code [00:02:32].
    • Real-time Linting: Unlike VS Code, Cursor does not always provide immediate visual feedback (e.g., red highlights) for syntax errors or undefined variables [00:29:34].
    • Manual File Operations: The AI in Cursor cannot directly manipulate files (e.g., move or delete them) [00:08:03], even though the underlying VS Code UI allows manual drag-and-drop file movement [01:18:45].
    • Python Interpreter Selection: Setting up the correct Python interpreter can be less intuitive than in vanilla VS Code [00:32:42].

GitHub Copilot

AI-driven coding tools like Cursor and Copilot aim to enhance productivity.

  • Copilot’s Tab-Autocomplete: The speaker notes that GitHub Copilot excels at “tab-autocomplete,” quickly suggesting code snippets as you type, which can feel faster than Cursor’s more explicit “edit” commands [00:37:01]. Copilot can also suggest full test cases [01:02:08].
  • Copilot X / Voice: The speaker was unaware of GitHub Next’s “Copilot Voice” (a voice-activated AI coding assistant) and “Code Brushes” (tools to apply transformations to code) [01:25:41], which indicate Microsoft’s rapid development in this area.
  • Enabling Copilot in Cursor: It is possible to enable GitHub Copilot within Cursor, but it requires signing in to GitHub [01:05:24].

Open Interpreter

Open Interpreter is an AI tool that can execute code on the local machine, including running shell commands (like moving files) [00:10:04].

  • Capability Gap: Cursor IDE lacks the direct file manipulation capabilities that Open Interpreter provides [00:08:03]. The speaker highlights that Open Interpreter’s core functionality (running shell commands via subprocess.Popen) is not overly complex and could potentially be integrated by developers themselves if they prefer to avoid external dependencies [01:22:03].

Use Cases and Performance

During the demo, Cursor was used for:

Criticisms and Limitations

  • Performance: AI operations, particularly code editing, were noted to be “really slow,” sometimes feeling slower than manual typing [00:20:00]. This waiting period was compared to the compile times in C or mobile development [01:10:44].
  • Cost and Restrictions: Cursor’s subscription model is $20/month, and it imposes a limit on GPT-4 requests (500 per month), using its own OpenAI API key rather than allowing users to supply their own (though some suggest this is configurable) [00:21:27].
  • Clunkiness: The overall user experience for complex tasks can feel “clunky” [00:22:27], requiring several manual steps even after AI suggestions [00:37:37].

Conclusion and Future Outlook

While Cursor IDE offers a marginally better experience than current VS Code setups due to its deeper AI integration [01:17:14], its long-term viability is questioned [01:31:13]. Microsoft and GitHub’s internal teams (like GitHub Next) are actively developing similar and potentially more advanced AI coding tools and features (e.g., Copilot Voice, Code Brushes) [01:26:54]. This raises concerns about Cursor’s ability to compete and maintain its market position without a significant differentiator or an acquisition by a larger tech company [01:06:50]. The ability to integrate external documentation was highlighted as its most impressive feature [01:32:16].

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