Cursor IDE Review: A New AI-Powered Code Editor

Explore our in-depth Cursor IDE review, focusing on its AI features, user experience, and how it stacks up against other code editors for developers.

Cursor IDE Review: An AI-Native Development Experience

In the rapidly evolving landscape of developer tools, AI has moved beyond mere autocomplete and into the core workflow. Cursor, an AI-native IDE built on the familiar VS Code foundation, aims to be at the forefront of this shift. It promises to deeply integrate large language models (LLMs) into every aspect of coding, from generating new features to debugging complex issues. But does it deliver on this promise, or is it just another wrapper around existing AI tools? Let’s dive in.

Overview: What is Cursor?

Cursor is an integrated development environment designed from the ground up to leverage AI. It’s essentially a heavily modified fork of Microsoft’s popular VS Code, which means developers already familiar with VS Code will find its interface immediately recognizable. The core idea behind Cursor is to bake AI capabilities directly into the editing experience, allowing developers to interact with LLMs for code generation, explanation, debugging, and refactoring without leaving their editor.

Unlike simply installing an AI extension in VS Code, Cursor integrates AI at a deeper level, aiming for more context-aware responses and a smoother workflow. It positions itself as an “AI-first IDE,” suggesting that AI isn’t an add-on, but a fundamental part of the development process within its environment.

Key Features

Cursor’s strength lies in its comprehensive suite of AI-powered features, all accessible directly within the editor.

AI Chat and Context Awareness

At the heart of Cursor is its integrated AI chat interface. You can open a chat panel and ask questions directly related to your code. What sets it apart from a generic chatbot is its ability to understand context.

This deep contextual understanding is crucial for generating useful code and explanations, minimizing the need to copy-paste code snippets into a separate browser tab.

Code Generation and Modification

Cursor offers several ways to generate or modify code using AI:

Diff View for AI Changes

A critical feature for responsible AI usage is the ability to review changes. Cursor presents all AI-generated modifications as a standard diff, similar to what you’d see in a version control system. This allows you to meticulously examine what the AI has changed, accept parts of it, or discard it entirely. This transparency is crucial for maintaining code quality and understanding the AI’s suggestions.

Open-source Model Support and Customization

Cursor understands that not all developers want to rely solely on proprietary cloud-based LLMs. It offers:

VS Code Compatibility

Since Cursor is built on VS Code, it inherits many of its benefits:

Real-world Usage

Let’s look at how Cursor might fit into a typical developer’s workflow.

Scenario 1: Debugging a Runtime Error

Imagine you’re working on a Python Flask application, and after a recent change, you hit a KeyError at runtime.

  1. You run your tests or application, and see a traceback in your terminal:

    Traceback (most recent call last):
  File "/app/routes.py", line 25, in get_user_data
    user_id = request.json['user_id']
KeyError: 'user_id'

  2. Instead of manually scanning routes.py and trying to reproduce the exact request, you copy the entire traceback.

  3. In Cursor, you open the AI chat, type @error followed by pasting the traceback.

  4. Cursor analyzes the traceback, identifies routes.py and the line number. It might then suggest: “It looks like the request.json payload is missing the ‘user_id’ key. You should add a check for its presence before accessing it, or ensure the client sends it.”

  5. It could then offer a code modification using Ctrl/Cmd+K on the relevant line in routes.py:

    # Original
# user_id = request.json['user_id']

# Cursor's suggestion
user_id = request.json.get('user_id')
if user_id is None:
    return jsonify({"error": "user_id is required"}), 400

    You review the diff, accept the change, and move on.

Scenario 2: Implementing a New Feature with Boilerplate

You need to add a new API endpoint to an existing Node.js Express application that handles user authentication.

  1. You navigate to your auth.ts file.

  2. You use Ctrl/Cmd+L and type: “Generate an Express middleware function checkAuth that verifies a JWT token from the Authorization header. It should extract the token, verify it using jsonwebtoken (assume JWT_SECRET is in process.env), and if valid, attach the decoded user payload to req.user before calling next(). If invalid or missing, it should send a 401 Unauthorized response.”

  3. Cursor generates a function, potentially including necessary imports:

    import { Request, Response, NextFunction } from 'express';
import jwt from 'jsonwebtoken';

interface AuthenticatedRequest extends Request {
    user?: any; // Define a more specific type for user payload
}

export const checkAuth = (req: AuthenticatedRequest, res: Response, next: NextFunction) => {
    const authHeader = req.headers.authorization;

    if (!authHeader || !authHeader.startsWith('Bearer ')) {
        return res.status(401).json({ message: 'No token provided' });
    }

    const token = authHeader.split(' ')[1];

    try {
        const decoded = jwt.verify(token, process.env.JWT_SECRET as string); // Add type assertion or check
        req.user = decoded;
        next();
    } catch (error) {
        return res.status(401).json({ message: 'Invalid token' });
    }
};

  4. You review the generated code, make minor adjustments (e.g., adding process.env.JWT_SECRET type assertion), and integrate it into your routes.

Scenario 3: Understanding a Legacy Codebase

You’ve inherited a large, undocumented Java Spring Boot application. You encounter a complex service method and need to understand its purpose and side effects.

  1. You open the Java file containing the method.
  2. You select the entire method.
  3. You open the AI chat and type: “Explain this @selection in simple terms. What does it do, what are its inputs, and what are its potential side effects?”
  4. Cursor provides a summary, breaking down the logic, explaining parameters, and highlighting any database interactions or external API calls it detects. This saves significant time compared to manually tracing execution paths.

These scenarios highlight how Cursor aims to integrate AI as a constant assistant rather than just a separate tool, streamlining common development tasks.

Pricing

Cursor offers multiple pricing tiers for individual developers and teams, with AI usage increasingly based on model usage and inference credits rather than fixed request counts.

Additionally, Cursor allows you to use your own API keys for models like OpenAI’s GPT-4. This means you pay the model provider directly for usage, potentially reducing Cursor’s subscription cost if you’re already paying for API access or if you exceed Cursor’s bundled limits. Using local models (e.g., via Ollama) completely bypasses these costs, offering a powerful option for privacy and cost control, though performance will depend on your local hardware.

Pros

Cons

Who is it for?

Verdict

Cursor represents a compelling vision for the future of IDEs. By deeply integrating AI into the development loop, it genuinely streamlines many common tasks, from generating code to explaining complex logic. It’s more than just VS Code with an AI extension; it’s an IDE where AI is a first-class citizen, deeply aware of your project’s context.

While the AI’s output isn’t always perfect and requires human oversight, the speed and convenience it offers are undeniable. The diff view for changes is a critical design choice, empowering developers to maintain control and quality. The flexibility to use various LLMs, including local ones, is a significant advantage, addressing both performance and privacy concerns.

For developers who are curious about leveraging AI directly in their workflow, or those already using AI tools in a fragmented manner, Cursor is absolutely worth trying. Start with the free tier to assess its value for your specific use cases. It’s not a replacement for fundamental programming skills, but it is a powerful co-pilot that can significantly enhance productivity and potentially change how you approach coding tasks. It’s a strong contender in the emerging category of AI-native developer tools.

FAQ

Is Cursor just VS Code with AI?

While Cursor is built on the VS Code codebase, it’s more than just VS Code with an AI extension. Its AI capabilities are deeply integrated into the editor’s core functionalities, allowing for more context-aware interactions and a smoother workflow (e.g., project-wide context, specific @ commands, and integrated diff views for AI-generated code). It’s a distinct product designed with an “AI-first” philosophy.

Can I use my existing VS Code extensions with Cursor?

Generally, yes. Because Cursor is built on top of VS Code, it supports many existing VS Code extensions, including common developer tools such as linters, formatters, debuggers, Git integrations, and themes. Extensions can typically be installed through the Visual Studio Marketplace within Cursor. However, some extensions may not function fully due to Cursor-specific integrations, unsupported APIs, or differences from upstream VS Code behavior.

What about data privacy when using Cursor?

Cursor provides several privacy-related configuration options, but the exact data flow depends on which AI features and model providers you use.

  1. Cursor-hosted models:
    When using Cursor-managed AI features and hosted models, portions of your code and prompts may be transmitted to Cursor’s infrastructure and, depending on the model configuration, to third-party model providers for processing.

  2. Bring Your Own API Keys (BYOK):
    Cursor supports using personal API keys for providers such as OpenAI or Anthropic. In these cases, requests are generally processed under your agreement with the selected provider rather than through Cursor-managed model quotas. However, some Cursor features may still involve Cursor infrastructure for request orchestration or product functionality.

  3. Local LLM Integration:
    Cursor can connect to locally hosted models such as Ollama or LM Studio through OpenAI-compatible endpoints. However, current implementations may still require exposing the local model through a reachable HTTPS endpoint, and some request routing can still involve Cursor services. As a result, this should not automatically be interpreted as a fully offline or zero-data-transfer workflow.

Cursor states in its privacy and security documentation that customer code is not used to train models by default without explicit opt-in or permission, particularly for business and enterprise-oriented privacy modes.

How good is the AI in Cursor?

The quality of the AI’s output in Cursor is highly dependent on several factors:

Expect it to be a powerful assistant, but not an infallible oracle. Human review of AI-generated code is always essential.

Is Cursor worth the price?

Whether Cursor is worth the price depends on your individual usage and the value it brings to your workflow.