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Matthew Hou
Matthew Hou

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Claude Code vs Cursor vs GitHub Copilot: What Nobody Tells You After Using All Three

#programming #webdev #ai #productivity

Last month I refactored a 15,000-line TypeScript monolith into a modular architecture. I used all three major AI coding tools — not because I was testing them, but because each one was better at a different phase of the work.

That experience taught me more about these tools than any review article ever could. Here's what I learned.

The Moment I Understood Copilot

I was adding error handling to 30+ API endpoints. Same pattern every time: wrap the handler, catch specific errors, return the right status code. Boring, repetitive, critical.

Copilot nailed this. After I wrote the first two handlers, it predicted the rest with 95% accuracy. I just tabbed through them.

// I wrote this once:
app.get('/users/:id', async (req, res) => {
  try {
    const user = await userService.findById(req.params.id);
    if (!user) return res.status(404).json({ error: 'User not found' });
    res.json(user);
  } catch (err) {
    if (err instanceof ValidationError) return res.status(400).json({ error: err.message });
    logger.error('Failed to fetch user', { err, userId: req.params.id });
    res.status(500).json({ error: 'Internal server error' });
  }
});

// Copilot predicted the next 28 handlers correctly.
// Tab. Tab. Tab. Done in 20 minutes instead of 2 hours.
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That's Copilot's superpower: it learns YOUR patterns within your session and extends them perfectly. It's not creative. It's consistent. And for 70% of daily coding work, consistency beats creativity.

Where Copilot failed me: I asked it to redesign the database schema. It suggested the most common Stack Overflow answer — a normalized relational schema. Fine for a tutorial. Wrong for my read-heavy, denormalized use case. It couldn't reason about tradeoffs.

The Moment I Understood Cursor

I needed to add WebSocket support across the entire app. New connection manager, event handlers, client-side hooks, server middleware, tests — probably 12 files.

I opened Cursor's Composer, typed:

"Add WebSocket support for real-time notifications. Server: connection manager with heartbeat, event handlers for user.updated and order.completed. Client: React hook useWebSocket with auto-reconnect. Tests for connection lifecycle."

It generated all 12 files. The connection manager had heartbeat logic. The React hook handled reconnection with exponential backoff. The tests covered connect/disconnect/reconnect scenarios.

Was it perfect? No. The heartbeat interval was too aggressive (5s instead of 30s) and it missed a race condition in the reconnect logic. But it got me from zero to 85% in 10 minutes. I spent another 30 minutes polishing.

That's Cursor's superpower: it thinks in features, not files. You describe what you want at a high level, it implements across your entire project.

Where Cursor failed me: Token limits. I was deep into a complex refactoring session, providing context about why certain design decisions existed. Mid-conversation, I hit the limit. The next message had no memory of what we'd discussed. I had to start over, re-explain everything, and the momentum was gone. On a $20/month plan, this happens at least twice a week.

The Moment I Understood Claude Code

The monolith had a mysterious bug: every Tuesday at 3 AM, the job queue would deadlock. Logs showed nothing obvious. The bug had existed for months.

I pointed Claude Code at the repo and said: "The job queue deadlocks every Tuesday around 3 AM. Find the root cause."

It spent 2 minutes reading the codebase. Then it found it:

The weekly analytics cron job (runs Tuesday 3 AM) acquires a lock on 
the `reports` table, then tries to update `job_status`. Meanwhile, 
the normal job processor holds a lock on `job_status` and tries to 
read `reports` for the completion notification.

Classic lock ordering deadlock. The fix: make the analytics job 
acquire locks in the same order as the job processor 
(job_status → reports), or use a separate database connection 
for the notification query.
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It traced through 6 files, understood the timing relationship between two seemingly unrelated systems, and identified a lock ordering issue. No human had figured this out in months.

That's Claude Code's superpower: it reads and reasons about your entire codebase. Not just pattern-matching — actually understanding the relationships between components, the timing of operations, the implications of design decisions.

Where Claude Code failed me: I asked it to quickly rename a variable across 5 files. It gave me a thoughtful explanation of naming conventions, suggested three alternative names with pros and cons, and asked clarifying questions about the domain model. I just wanted find-and-replace. Sometimes you need a junior dev, not an architect.

The Real Framework

After using all three, I stopped thinking about which is "best" and started thinking about which is right:

Copilot when I know exactly what to write and want to go fast.

  • Repetitive patterns, boilerplate, extending existing code
  • The coding equivalent of autocomplete on steroids
  • Cost: $10/month — the best value in AI coding

Cursor when I know what I want but not how to build it.

  • New features, multi-file implementations, rapid prototyping
  • The coding equivalent of a pair programming session
  • Cost: $20/month — worth it for the Composer workflow

Claude Code when I don't even know what's wrong.

  • Debugging complex issues, understanding legacy code, architecture decisions
  • The coding equivalent of hiring a consultant
  • Cost: API pricing or Max plan — expensive but irreplaceable for hard problems

The Uncomfortable Truth

The developers I know who ship the fastest aren't loyal to one tool. Their typical day:

  • 9 AM: Copilot — knock out tickets, fix small bugs, write tests
  • 2 PM: Cursor — build the afternoon's feature in Composer mode
  • 5 PM: Claude Code — review the day's architecture decisions, investigate that weird test failure

The skill that matters most in 2026 isn't knowing how to code. It's knowing how to communicate intent to AI — clearly, precisely, with the right context. That skill works across every tool.

The best developers aren't writing more code. They're writing better prompts.

I'd love to hear your experience. Which tool surprised you? Which one let you down? Drop a comment — the most useful comparisons come from real production work, not weekend demos.

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