Cursor - VS Code with AI that doesn't suck

Cursor is VS Code with AI that can actually read your entire project, not just the file you're currently editing. This sounds obvious but GitHub Copilot can't do this shit, which is why its suggestions are often useless.

The Reality of Using It Daily

I've been using Cursor for 6 months in production. Here's what actually happens:

The autocomplete is legitimately good. Unlike Copilot which suggests the same boilerplate garbage, Cursor knows what you're building and suggests code that actually fits. Tab completion usually works in under 100ms, but the AI chat can take 5-10 seconds for complex requests.

The agents feature is where it gets interesting and/or scary. You can literally tell it "implement user authentication with JWT" and it will:

1. Install the right packages
2. Set up the database models
3. Create the API endpoints
4. Write the middleware
5. Add error handling

I've watched it implement entire features correctly. I've also watched it completely fuck up and suggest implementing authentication by storing passwords in localStorage. The safeguards are decent - it asks before running terminal commands - but you still need to review everything.

Codebase Understanding That Actually Works

This is the killer feature. Unlike every other AI coding tool, Cursor builds an index of your entire project and can reason about it. Ask it "where is user validation handled?" and it will find the exact file and function, even in a 100k+ line codebase.

This works through embedding models that understand code semantically. It's not just grep with extra steps - it actually knows that validateUserCredentials() and checkUserAuth() are related concepts.

The indexing takes forever on first startup for large projects (5+ minutes for a Rails monolith), but after that it's fast. Works great on small to medium codebases. Large repos (50k+ files) can be slow to index but still functional.

Technical Reality Check

Privacy Mode exists but half the cool features don't work offline. Background Agents need cloud processing, so if you enable Privacy Mode you're basically using expensive VS Code.

Performance is solid. The editor itself is just VS Code with extra stuff bolted on, so it's as stable as VS Code. The AI features occasionally shit the bed when their servers are having issues, but 95% uptime in my experience.

Language Support covers everything VS Code does. The AI is notably better with TypeScript/React than with more obscure languages, probably because of training data distribution.

Enterprise Features exist if you want to pay $40/user/month. SSO works fine, admin controls are basic but functional. The usage analytics show how much your team is spending on AI tokens, which is depressing.

Version and Update Reality

As of August 2025, they're on version 0.40.x despite what their marketing says. Updates come frequently (weekly) and occasionally break things. I've had Tab completions stop working after updates, fixed by restarting the app.

The changelog is actually readable and honest about what they're fixing, which is refreshing compared to most software. You can also find real user discussions about performance issues and setup workflows in the community forums.

Agents: Either Amazing or Terrifying

The agents system is legitimately impressive when it works. I've had it implement entire REST APIs, fix database migration issues, and even debug webpack config problems. Here's a real example from last week:

Me: "The build is failing because of some TypeScript error with generics"

Agent:

1. Ran npm run build
2. Read the error output
3. Found the problematic generic constraint in utils/api.ts:47
4. Fixed the type definition
5. Ran build again to verify

Total time: 3 minutes. Would have taken me 20 minutes to track down that specific generic fuckup.

But they also occasionally go off the rails. I once asked an agent to "add logging to the auth service" and it proceeded to install Winston, configure log rotation, set up CloudWatch integration, and rewrite half the authentication logic. Sometimes they're too thorough.

The safeguards are decent - agents ask before running potentially destructive commands. But they still require babysitting. Never let an agent work unsupervised on anything important. You can find examples of agents going wrong in the community forums.

Tab Completion That Doesn't Suck

Unlike GitHub Copilot's single-line suggestions, Cursor's Tab completion can predict entire functions. It's particularly good with TypeScript interfaces and React components.

Example: Start typing interface User and it correctly predicts:

interface User {
  id: string;
  email: string;
  createdAt: Date;
  // ... other fields based on your existing User usages
}

The latency is usually under 100ms, which feels instant. Sometimes it takes 2-3 seconds if their servers are busy, but that's rare.

Gotcha: Tab completion breaks occasionally after updates. The fix is always restarting the app, but it's annoying when you're in flow state.

Chat with @-References That Work

The chat interface lets you reference specific parts of your codebase with @ symbols. This actually works unlike most AI tools:

• @filename.py - includes the entire file in context
• @functionName - finds and includes just that function
• @docs - pulls in documentation for libraries you're using
• @web - searches the web for current info

Real usage: "Look at @auth/login.ts and @types/user.ts - why is the login failing for admin users?"

It will actually read both files and give you a useful answer about type mismatches or logic errors. This is miles better than copying/pasting code into ChatGPT.

Natural Language Refactoring

Highlight code and describe what you want: "make this function async" or "add error handling with try/catch". It works surprisingly well for common refactoring patterns.

I use this constantly for:

• Converting callback hell to async/await
• Adding TypeScript types to JavaScript functions
• Wrapping API calls with error boundaries
• Converting class components to hooks

Warning: It sometimes gets creative with error handling. Always review the changes because it might add generic catch blocks that swallow important errors. You can customize this behavior with .cursorrules files and project-specific rules.

The Enterprise Reality

At $40/user/month, the enterprise features are expensive but functional:

• SSO integration with SAML/OIDC works fine
• Usage analytics show how much your team spends on AI tokens (prepare to be depressed)
• Privacy controls let you enforce local processing only
• Admin dashboard for managing users and billing

The team collaboration features are basic. Shared codebase indexes work but there's no real-time collaboration like in Figma. It's more "everyone uses the same smart VS Code" than "collaborative coding environment."

Performance Reality Check

Indexing Performance: First-time indexing of a large codebase takes forever. My company's Rails monolith (200k+ lines) took 8 minutes to index initially. After that, incremental updates are fast.

Memory Usage: Cursor uses significantly more RAM than VS Code (typically 2-4x). On my MacBook Pro with 16GB, it occasionally gets sluggish with multiple large projects open. There are performance optimization guides available.

Network Dependency: Many features break when offline or with spotty internet. The local processing option helps but disables the best features.

Stability: It's basically VS Code under the hood, so crashes are rare. The AI features occasionally timeout or give weird responses when their servers have issues, but the editor itself stays stable.

Current version (0.40.x as of August 2025) is quite stable. Earlier versions had more frequent crashes and indexing bugs. Check the troubleshooting guide for common issues.