The Editor That Thinks Alongside You
Code editors have been essentially the same product for a decade. You type code, the editor highlights syntax, offers basic autocomplete from language servers, and maybe formats on save. Extensions add capability, but the core interaction model — human writes, editor displays — has remained unchanged since Sublime Text popularized the modern editor in 2012.
Cursor breaks that model. Built on the VS Code foundation but reimagined around AI-native workflows, Cursor turns your editor into a collaborative partner that understands your codebase, anticipates your intent, and generates code that actually fits your project’s patterns. It is not a plugin bolted onto an existing editor. It is what happens when you design an editor from scratch with large language models at the center of every interaction.
This review examines Cursor after six months of daily use across TypeScript, Python, and React projects. The verdict: it is the most significant change in how developers write code since VS Code itself launched in 2015. But it is not without trade-offs, and those trade-offs matter.
What Cursor Actually Is
Cursor is a fork of Visual Studio Code built by Anysphere, a San Francisco-based AI company. Every VS Code extension, keybinding, and setting works in Cursor because it shares the same Electron and Monaco editor core. If you use VS Code today, switching to Cursor takes about five minutes — import your settings, install your extensions, and you are running.
What Cursor adds on top of VS Code is a deeply integrated AI layer. This is not the same as installing GitHub Copilot as an extension. Copilot operates within VS Code’s extension API, which limits how deeply it can integrate with the editor. Cursor controls the entire editor, which means AI can touch the tab system, the file tree, the terminal, multi-file editing, and the command palette in ways that no extension can.
The key AI features include:
- Tab completion: Context-aware code predictions that go far beyond single-line autocomplete, often completing entire functions or refactoring blocks based on your recent edits
- Cmd+K inline editing: Select code, describe what you want changed in natural language, and Cursor rewrites it in place with a diff view
- Chat with codebase context: An AI chat panel that can reference your entire project, specific files, documentation, or web content
- Composer: Multi-file editing that creates, modifies, and deletes files across your project from a single natural language prompt
- @ mentions: Reference specific files, folders, documentation, or web URLs directly in chat prompts for targeted context
Tab Completion: Where the Magic Lives
Cursor’s tab completion is the feature you will use a thousand times a day, and it is the feature that makes returning to a standard editor feel like going back to a flip phone. Unlike traditional autocomplete that suggests variable names or method signatures, Cursor predicts multi-line blocks of code based on what you have been doing.
The system watches your editing patterns. If you just renamed a variable in one function, it predicts you want to rename it in the next function too. If you are adding error handling to one API route, it suggests the same pattern for the next route. If you are writing tests, it generates the next test case based on the pattern it sees in the ones you already wrote.
What makes this different from other AI code assistants is the depth of context. Cursor indexes your entire project and uses that context to generate completions that match your codebase’s conventions. It picks up your naming patterns, your error handling style, your import conventions, and your preferred library APIs.
Here is a practical example. When building a REST API endpoint, after writing the first route handler, Cursor predicts the next one with the correct model references, validation patterns, and response structure that match your project:
// After writing a GET endpoint for users, Cursor suggests the POST endpoint
// matching your project's exact patterns — validation, error handling, response format
app.post('/api/projects', authenticate, async (req, res) => {
try {
const { name, description, teamId } = req.body;
if (!name || !teamId) {
return res.status(400).json({
success: false,
error: 'Name and team ID are required'
});
}
const project = await Project.create({
name,
description,
teamId,
createdBy: req.user.id
});
await AuditLog.record('project.created', {
projectId: project.id,
userId: req.user.id
});
return res.status(201).json({
success: true,
data: project
});
} catch (error) {
logger.error('Failed to create project', { error, userId: req.user.id });
return res.status(500).json({
success: false,
error: 'Internal server error'
});
}
});The completion is not generic boilerplate. It uses your project’s actual model names, your authentication middleware, your logging utility, your audit log pattern, and your response format. This is what codebase-aware completion looks like in practice, and it fundamentally changes the speed at which you write code.
Cmd+K: Inline Editing With Natural Language
Select a block of code, press Cmd+K (or Ctrl+K on Windows and Linux), and describe what you want changed. Cursor shows a diff view of the proposed changes, and you accept or reject them. This interaction pattern is deceptively simple but enormously powerful.
Common use cases that work reliably:
- Refactoring a callback-based function to async/await
- Adding TypeScript types to untyped JavaScript code — a task covered in depth in our TypeScript migration guide
- Converting a class component to a functional React component with hooks
- Adding input validation and error handling to an existing function
- Extracting inline styles to CSS modules or styled components
- Translating code comments between languages
The diff view is critical. Unlike chat-based AI workflows where you copy and paste generated code, Cmd+K shows you exactly what changes, highlighted in green and red. You maintain full control and full visibility over every modification.
Composer: Multi-File Editing
Composer is Cursor’s most ambitious feature and the one that separates it most clearly from Copilot-style assistants. Open Composer with Cmd+Shift+I, describe a change that spans multiple files, and Cursor generates a coordinated set of edits across your project.
Real-world scenarios where Composer delivers:
- Adding a new feature: Describe a new API endpoint and Composer creates the route handler, the database migration, the model, the validation schema, and the test file
- Refactoring across files: Rename a database column and Composer updates the model, the API serializers, the frontend components that display the field, and the relevant tests
- Implementing patterns: Ask for authentication middleware and Composer adds the middleware file, updates the route configuration, creates the token validation utility, and modifies the relevant route handlers
Composer is not perfect. Complex multi-file changes sometimes produce edits that conflict with each other or miss edge cases. But for straightforward feature scaffolding and pattern-based refactoring, it eliminates an enormous amount of repetitive work. Teams managing complex web applications will find this pairs well with established performance optimization practices, since Composer can help enforce consistent patterns across a codebase.
Customizing AI Behavior With .cursorrules
One of Cursor’s most practical features is the .cursorrules file — a project-level configuration that instructs the AI how to behave within your specific codebase. Drop this file in your project root and Cursor reads it automatically, adjusting its completions and suggestions to match your team’s standards.
# .cursorrules — Project-specific AI instructions
## Code Style
- Use functional components with hooks, never class components
- Prefer named exports over default exports
- Use TypeScript strict mode — no `any` types without explicit justification
- Error handling: always use custom AppError class from @/lib/errors
- Imports: group by external, internal, types — separated by blank lines
## Architecture
- API routes follow REST conventions in /src/api/[resource]/route.ts
- Database queries use Drizzle ORM, never raw SQL in application code
- State management: Zustand for global state, React Query for server state
- All components in /src/components follow atomic design (atoms/molecules/organisms)
## Testing
- Unit tests with Vitest, component tests with Testing Library
- Test files colocated with source: MyComponent.test.tsx next to MyComponent.tsx
- Use factory functions for test data, never hardcoded fixtures
- Minimum coverage: 80% for utilities, 60% for components
## Documentation
- JSDoc comments on all exported functions and types
- README.md in each feature directory explaining the module's purpose
- API endpoints documented with OpenAPI commentsThis file transforms Cursor from a generic AI coding tool into one that understands your team’s specific practices. When you ask Composer to create a new component, it follows your atomic design structure. When tab completion suggests error handling, it uses your custom error class. When chat recommends a testing approach, it uses Vitest with your factory pattern. For teams working across different frontend frameworks, you can maintain separate .cursorrules files per project to ensure the AI generates framework-appropriate code.
Chat With Context: Beyond Generic AI
Cursor’s chat panel (Cmd+L) integrates with your codebase in a way that standalone AI chatbots cannot. Using the @ symbol, you can reference specific files, folders, documentation URLs, or even paste in error messages for context-aware debugging.
Practical chat patterns that save real time:
- @filename.ts explain this function: Get a clear explanation of complex code with references to how it connects to other parts of your project
- @folder/ what is the data flow here: Understand how components, hooks, and API calls connect across a feature directory
- @docs reference a docs URL: Include library documentation directly in your prompt for accurate, version-specific answers
- Paste an error stacktrace: Cursor traces the error through your actual code files and suggests targeted fixes
This context awareness is what makes Cursor’s chat fundamentally different from pasting code into ChatGPT or Claude. The AI sees your imports, your type definitions, your project structure, and your configuration files. It gives answers in terms of your codebase, not generic examples from training data.
Cursor vs. VS Code + GitHub Copilot
This is the comparison everyone wants. Both options give you a VS Code-based editor with AI assistance. But the depth of integration is not comparable.
| Feature | Cursor | VS Code + Copilot |
|---|---|---|
| Tab completion quality | Multi-line, codebase-aware | Good, primarily single-line and function-level |
| Inline editing (natural language) | Cmd+K with diff preview | Copilot Chat inline, less refined |
| Multi-file editing | Composer — coordinated cross-file changes | Not available natively |
| Codebase indexing | Full project embedding and retrieval | Workspace context in Copilot Chat |
| Custom AI rules | .cursorrules file per project | Limited via Copilot instructions |
| Model selection | GPT-4o, Claude 3.5/Opus, custom models | GPT-4o (limited model choice) |
| Privacy mode | Available — code never stored on servers | Business plan required for data exclusion |
| Extension ecosystem | Full VS Code compatibility | Native VS Code marketplace |
| Terminal AI integration | Cmd+K in terminal for command generation | Copilot CLI (separate tool) |
The gap is widest in multi-file editing and codebase-aware completions. If your work involves coordinating changes across multiple files — and most real-world development does — Cursor provides capabilities that Copilot’s extension model simply cannot match. Copilot operates within the constraints of VS Code’s extension API. Cursor controls the entire editor surface.
That said, Copilot has advantages: it works inside the standard VS Code install that your team already uses, it integrates with GitHub’s ecosystem (pull request summaries, issue context), and its pricing is simpler. For developers who primarily need line-level completions and occasional chat assistance, Copilot may be sufficient.
Pricing and Plans
Cursor offers three tiers:
- Hobby (Free): 2,000 completions per month, 50 slow premium model requests. Enough to try the product, not enough for daily professional use
- Pro ($20/month): Unlimited completions, 500 fast premium model requests per month, access to all models including Claude and GPT-4o. This is the plan most individual developers need
- Business ($40/user/month): Everything in Pro plus centralized billing, admin controls, privacy mode enforcement, and team-wide .cursorrules management. Designed for engineering teams
The pricing is competitive when you consider what you are replacing. GitHub Copilot Individual costs $10/month, but delivers significantly less capability. If you were paying for Copilot ($10) and a separate AI chat tool ($20), Cursor at $20 consolidates both with deeper integration. For teams building products with tools like Toimi for project design and planning, Cursor fits naturally into the modern development stack where AI assistance is expected at every stage.
Performance and Resource Usage
Cursor is an Electron application, which means it inherits the same memory characteristics as VS Code. On a MacBook Pro with 16GB of RAM, Cursor typically consumes 800MB to 1.5GB depending on workspace size and extensions. This is comparable to VS Code with a similar extension set.
AI features add network latency, not local compute. Tab completions arrive in 100-300ms on a stable connection. Cmd+K edits take 1-3 seconds for moderate code blocks. Composer operations on multi-file changes can take 10-30 seconds depending on complexity. All AI processing happens on remote servers — your local machine handles only the editor UI and extension host.
The codebase indexing feature deserves mention. When you first open a large project, Cursor indexes your files to build an embedding-based retrieval system. For a 100,000-line codebase, initial indexing takes 2-5 minutes. After that, incremental updates happen in the background and are imperceptible. This indexing is what enables the deep codebase awareness that makes Cursor’s suggestions so contextually accurate.
What Cursor Gets Wrong
No tool is perfect, and intellectual honesty requires addressing where Cursor falls short:
- Dependency on connectivity: Without an internet connection, Cursor is just VS Code. All AI features require server communication. Airplane mode means no completions, no chat, no Composer. Offline-first developers will feel this limitation acutely
- Model inconsistency: Different AI models produce different quality results. GPT-4o might handle a refactoring task well while Claude produces better results for the same prompt next time. Choosing the right model for each task adds cognitive overhead
- Completion interruptions: Tab completions sometimes appear mid-thought when you are typing quickly, creating a jarring experience. You can adjust sensitivity in settings, but the default is slightly too aggressive for fast typists
- Cost at scale: For a team of 20 developers on the Business plan, Cursor costs $800/month. That is meaningful budget. Teams need to honestly evaluate whether the productivity gains justify the expense compared to VS Code with Copilot at $380/month for the same team
- Update cadence: Cursor ships updates frequently, which is mostly positive but occasionally introduces regressions. The team moves fast and sometimes breaks things. Pinning to a stable version is possible but means missing new features
- Privacy concerns: Code is sent to AI model providers for processing. The privacy mode prevents code from being stored or used for training, but code still transits through external servers. Organizations with strict data residency requirements should evaluate this carefully
Who Should Switch to Cursor
Switch now if you are a professional developer writing code daily and currently using VS Code. The learning curve is near zero, and the productivity gains from codebase-aware completions and multi-file editing are substantial. Frontend developers working across component files, backend developers building API endpoints, and full-stack developers managing both will see the biggest impact.
Wait and evaluate if you are deeply invested in JetBrains IDEs (IntelliJ, WebStorm, PyCharm). JetBrains has its own AI assistant, and the refactoring and language intelligence in JetBrains products still exceeds what VS Code-based editors offer for Java and Kotlin development specifically.
Skip for now if you work in air-gapped environments, if your organization prohibits sending code to external AI services, or if you primarily work in languages where AI completions are less reliable (niche frameworks, proprietary DSLs). Also skip if your workflow is centered around browser DevTools debugging rather than code writing — Cursor’s advantages are in the code editing experience, not the debugging workflow.
The Bigger Picture
Cursor represents the first generation of truly AI-native development tools. Not AI added to an existing tool, but AI as the foundational design principle. The difference matters because it enables interaction patterns that bolt-on solutions cannot replicate.
The question is not whether AI-assisted coding is the future — that debate ended in 2024. The question is whether a standalone AI-native editor or AI features integrated into established editors will win. Microsoft is rapidly adding Copilot capabilities to VS Code. JetBrains is building AI into its IDEs. The competition is intensifying.
For now, Cursor is ahead. Its tab completions are more contextually aware, its multi-file editing is unmatched, and its customization through .cursorrules gives teams control that no competitor offers. Whether that lead persists depends on execution, but developers evaluating tools today will find that Cursor delivers the most capable AI coding experience available. Development teams managing projects with Taskee for task coordination and using a structured full-stack workflow will find that Cursor’s AI-native approach meaningfully accelerates the coding phase of their development cycle.
The Verdict
Cursor is the best AI-powered code editor available in 2025. Its codebase-aware completions, multi-file editing via Composer, and project-level AI customization through .cursorrules create a development experience that no other tool matches. At $20/month for the Pro plan, it offers more capability than the combination of VS Code and GitHub Copilot at a comparable price point.
The limitations are real — internet dependency, variable model quality, and legitimate privacy considerations for some organizations. But for the majority of professional developers working on web applications, APIs, and modern frontend projects, Cursor delivers a productivity improvement that is difficult to give up once experienced.
Rating: 4.7/5 — A transformative development tool with minor rough edges that do not diminish its core value.
Frequently Asked Questions
Is Cursor free to use?
Cursor offers a free Hobby tier with 2,000 completions and 50 slow premium model requests per month. This is sufficient for casual use and evaluation, but professional developers will quickly hit the limits. The Pro plan at $20/month provides unlimited completions and 500 fast premium requests, which is the practical minimum for daily professional use.
Can I use my VS Code extensions in Cursor?
Yes. Cursor is built on the VS Code codebase and supports the full VS Code extension ecosystem. You can import your existing VS Code settings, keybindings, and extensions directly when you first install Cursor. Most developers complete the migration in under five minutes with no functionality loss.
How does Cursor compare to GitHub Copilot?
Cursor provides deeper AI integration than Copilot because it controls the entire editor rather than operating as an extension. Key advantages include multi-file editing via Composer, project-level AI customization through .cursorrules, better codebase-aware completions, and the ability to choose between multiple AI models. Copilot’s advantages include tighter GitHub ecosystem integration, simpler pricing, and the ability to work inside the standard VS Code install. Read our full code editor comparison for a broader perspective.
Does Cursor send my code to external servers?
Yes, AI features require sending code context to model providers (OpenAI, Anthropic) for processing. Cursor offers a privacy mode that ensures your code is not stored or used for model training, but code does transit through external servers during processing. Organizations with strict data residency or air-gapped requirements should evaluate this limitation before adopting Cursor.
Is it worth switching from VS Code to Cursor in 2025?
For professional developers who write code daily, switching from VS Code to Cursor provides meaningful productivity improvements with essentially zero learning curve. The codebase-aware completions and multi-file Composer feature save significant time on repetitive coding tasks. The $20/month Pro plan cost is justified if AI-assisted coding saves you even 30 minutes per day. The only reasons to wait are strict security requirements that prohibit external code processing or deep investment in JetBrains IDEs where AI features are improving rapidly.
