Cursor AI Coding in Practice: Why I Switched from Copilot

In mid-2025, I switched my daily driver from GitHub Copilot + VS Code to Cursor. After three months of daily use, I want to lay out what I actually found — not a sponsored post, a real comparison.

Core Difference: Tab Completion vs Conversational Editing

GitHub Copilot is fundamentally an enhanced autocomplete: it predicts what you’ll write next, and you hit Tab to accept or ignore. The flow is smooth and barely interrupts your thinking.

Cursor’s core difference is that it understands your intent, not just the next token.

Concrete example. Say you want to convert a synchronous function to async/await:

# original code
def fetch_user(user_id):
    response = requests.get(f"/api/users/{user_id}")
    return response.json()

Copilot’s approach: You manually change def to async def, then Copilot suggests await line by line, but you confirm each one and decide yourself whether to swap out requests.

Cursor’s approach: Select the whole function → Cmd+K → type “convert to async/await using httpx instead of requests, add type annotations”.

import httpx

async def fetch_user(user_id: str) -> dict:
    async with httpx.AsyncClient() as client:
        response = await client.get(f"/api/users/{user_id}")
        response.raise_for_status()
        return response.json()

Done in one shot, with type annotations and error handling included. Copilot needs multiple rounds to reach the same result.

This gap barely shows on simple tasks but opens wide when you’re doing something like “refactor this entire module.”

Cursor’s Three Killer Features

Composer / Agent Mode

This is the core reason I stayed. Composer lets you describe a task in natural language, Cursor plans out changes across multiple files, shows you the plan for confirmation, then executes.

For example: “Add pagination to the user list API — backend uses page and limit params, frontend component updates in sync, update API doc comments too.”

Cursor lays out the plan:

Will modify the following files:
  src/api/users.py          — add page/limit query params
  src/models/user.py        — add count query method
  src/schemas/user.py       — add paginated response schema
  frontend/components/UserList.tsx   — add pagination UI
  frontend/hooks/useUsers.ts         — update API call logic

Confirm and it executes everything at once. This is dramatically faster for cross-file feature development.

Agent Mode extends this further: it can automatically run terminal commands (like running tests, reading error output) and continue adjusting code based on the results. Writing a feature and then letting Agent run tests and fix failures on its own has become a regular part of my workflow.

Codebase Context (@Codebase)

Cursor indexes your entire repository. In Chat you can ask directly:

• “Where is AuthMiddleware registered?”
• “Which files handle database connection management?”
• “What calls into UserService?”

More practically, the @ reference syntax:

@file src/routes/users.py       # pull a specific file into context
@folder src/models/             # pull an entire directory
@web https://fastapi.tiangolo.com/tutorial/sql-databases/  # pull external docs

When joining an unfamiliar codebase, this feature significantly cuts onboarding time. When I took over a large Go service, I spent half an hour having Cursor map out the module dependencies and data flows — more effective than reading documentation for a week.

Rules for AI (.cursorrules)

This is Cursor’s project-level AI config file. Define your project’s coding rules once, and all subsequent AI responses will follow them automatically:

# .cursorrules example

## Code Style
- Always use TypeScript strict mode; no any types
- No var; use const/let only
- Functional components only, no class components

## Error Handling
- All async functions must have try/catch or return a Result type
- API errors use AppError type; never throw raw Error

## Project Conventions
- Comments in English; variable names in camelCase
- New features require corresponding unit tests
- Never directly modify node_modules

Rules apply automatically to all Chat and Composer responses — no need to repeat yourself each time. For teams this is especially valuable: everyone who opens the project gets an AI that already knows the project’s conventions. The community has assembled a large collection of examples at awesome-cursorrules.

Real Comparison: Three Scenarios

Scenario 1: Writing New Features

With Copilot: You build the function skeleton, Copilot completes as you type. When your direction is clear, this flows well. But if you’re exploring an unfamiliar API for the first time, it has limited ability to help because it doesn’t know your full intent.

With Cursor: Describe the feature in Composer — intent, context, and constraints together. Cursor generates a complete implementation. Your job becomes review and adjustment, not line-by-line input. For features in familiar domains, a 3-5x speed improvement is realistic.

Scenario 2: Refactoring Legacy Code

This is where the gap is most obvious.

With Copilot: It can’t see global context. Suggestions are often reasonable within the current file but may break calling conventions in other files. I’ve had Copilot suggestions that were correct locally but broke three other files’ interfaces — only caught when tests ran.

With Cursor: @Codebase to analyze global dependencies → Composer to plan changes → modify all affected files at once. This is what “an AI that actually understands your project” looks like in practice. I recently migrated an auth system from JWT to session-based across 8 files; Cursor handled it in one pass, 100% test pass rate.

Scenario 3: Debugging

With Copilot: Limited help during debugging because it has no context about your error. You end up copying relevant code into Copilot Chat and manually explaining the situation.

With Cursor: Paste the error directly into Chat:

RuntimeError: CUDA out of memory. Tried to allocate 2.34 GiB
  File "train.py", line 47, in train_step
    outputs = model(batch['input_ids'], attention_mask=batch['attention_mask'])

Cursor analyzes the stack trace, cross-references @file train.py, pinpoints the issue (wrong batch size, missing gradient accumulation config, or uncleaned cache), and gives specific fixes — not generic advice about memory management.

Drawbacks and Caveats

No perfect tool exists; Cursor has real limitations:

Cost: Cursor Pro is $20/month, double GitHub Copilot’s $10/month. Whether that’s worth it depends on your work. Mostly writing simple CRUD? Probably not. Building complex systems with heavy code navigation? Yes.

Data Privacy: Code gets sent to Cursor’s servers for processing. Cursor offers Privacy Mode (enable in settings; code isn’t stored), but it’s still transmitted. For sensitive codebases, this needs consideration. Enterprise users can self-host or connect directly to Anthropic/OpenAI via API keys to avoid the data routing issue.

Learning Curve: Getting the most out of Composer Mode requires learning to write good prompts. Too vague produces code that needs heavy revision; too detailed and you might as well write it yourself. Finding the right granularity takes practice — most people find their groove around two weeks in.

Overkill for Simple Tasks: For repetitive boilerplate (like a batch of similar getters/setters), Copilot’s Tab completion is faster than opening Composer. Cursor’s value is in complex tasks; for simple ones it can feel heavyweight.

My Conclusion

After three months of daily use, here’s my verdict:

Cursor is better for:

• Cross-file feature development and refactoring (core strength)
• Onboarding to unfamiliar codebases and building global understanding
• Complex debugging where root causes span multiple files
• Large-scale code changes: architecture shifts, API migrations

Copilot is better for:

• Continuous code completion while typing without interruption
• Simple repetitive code generation quickly
• Teams that already have GitHub Enterprise licenses (near-zero marginal cost)

My actual workflow now: Cursor as my only IDE. Cursor’s built-in Tab completion is good enough that I’ve stopped using Copilot as a supplement entirely.

If you primarily do backend development, tooling, or spend significant time reading and modifying existing codebases, I’d recommend trying Cursor for a month. The free tier gives you basic functionality; the $20/month Pro unlocks Claude and GPT-4o models plus unlimited Composer usage. See Cursor Docs for full details.

Migration Guide: Switching from Copilot to Cursor

If you decide to try Cursor, migration friction is nearly zero:

Config migration:

• VS Code themes, fonts, and keybindings import directly (Cursor is a VS Code fork)
• Most VS Code extensions work in Cursor out of the box
• Settings in .vscode/ directories are recognized automatically

Recommended ramp-up sequence:

1. Start with Cmd+K for small inline edits — get a feel for AI editing within a selection
2. Ask Chat questions about your current project and explore @Codebase indexing
3. In week two, try Composer on a small, self-contained feature

What to avoid: jumping straight into using Composer for large refactors. Composer’s effectiveness depends heavily on prompt quality. Build intuition on smaller tasks first — you’ll learn what level of specificity gets the best results.

One last thing: regardless of tooling, the most important skill remains your own understanding and judgment of the code. AI-generated code still needs your review — you can’t accept it blindly. Cursor makes the process faster; it doesn’t replace engineering judgment.

Common Questions

Which AI model does Cursor use? Cursor Pro supports Claude Sonnet 4.5, GPT-4o, and cursor-small (Cursor’s own model). You can set a default in settings and switch per-conversation as needed.

Does Cursor leak my code? By default, Cursor sends code to its servers and may use it to improve its models. If privacy is a concern, enable “Privacy Mode” in settings — Cursor commits not to store or train on your code. For enterprise compliance scenarios, use your own API keys to connect directly to Anthropic or OpenAI, so code doesn’t route through Cursor’s servers at all.

Can Cursor and GitHub Copilot coexist? Yes, but Cursor includes its own Tab completion, which conflicts with Copilot. Most users who switch to Cursor disable the Copilot extension. You won’t miss it.