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Claude Code Tips & Tricks: Working with Lovable

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When AI Tools Collide: A Real-World Experience #

While building my latest project, Palette Kit (a React/TypeScript app for managing colored pencil gradients), I discovered something interesting: using Claude Code and Lovable together is incredibly powerful, but it requires a specific approach to avoid some nasty pitfalls.

Honestly, I nearly broke my entire deployment pipeline learning this the hard way. But here’s what I learned – and how you can make these tools work beautifully together without the pain I went through.

The Perfect Storm: What Went Wrong #

Picture this: I’m happily developing with VSCode and Claude Code locally, adding database schema changes through Supabase. Everything’s working perfectly in development. Then I try to deploy through Lovable and… complete failure.

Let’s face it, we’ve all been there – that moment when your perfectly working local setup suddenly becomes a house of cards in production.

The culprit? A “Type Safety Gap” that created a cascade of problems:

  1. Database migration worked (Claude Code handled this perfectly)
  2. Local development ran fine (types were correct in my working environment)
  3. Deployment failed with cryptic TypeScript errors (Lovable couldn’t resolve the type mismatches)

The root cause was stale type definitions. My generated Supabase types didn’t reflect my schema changes, creating a disconnect between what my code expected (updated_at columns I’d added) and what TypeScript knew about (the old schema).

The “Helpful” Fix That Made Everything Worse #

Here’s where things got really interesting. Lovable, being helpful, attempted to fix the TypeScript errors by creating a module override file that completely replaced my Supabase types. Unfortunately, this created an avalanche of new problems:

  • Relationship types became arrays instead of objects
  • 50+ new TypeScript errors cascaded through the codebase
  • The build system couldn’t resolve the conflicting type definitions

I went from a simple missing column issue to a completely broken build system.

The Solution: Surgical Precision #

Instead of letting Lovable override everything with a sledgehammer approach, the fix was surgical. I used Claude Code to make precise updates to the type definitions:

// In src/integrations/supabase/types.ts - just add the missing columns
brands: {
  Row: {
    // ... existing fields
    updated_at: string  // Add this
  }
  Insert: {
    // ... existing fields  
    updated_at?: string // And this
  }
  Update: {
    // ... existing fields
    updated_at?: string // And this
  }
}

Rather than replacing entire modules, I updated only what needed changing. Problem solved.

Building a Bulletproof Verification System #

This experience taught me that when you’re working with multiple AI tools, you need robust verification at integration points. I added a build verification system to catch issues before they reach deployment:

{
  "scripts": {
    "typecheck": "tsc --noEmit",
    "verify-build": "npm run lint && npm run typecheck && npm run test:run && npm run build",
    "ci": "npm run verify-build"
  }
}

Now, before any commit, I run:

  1. Type checking to catch TypeScript errors
  2. Tests to ensure functionality works
  3. Build verification to confirm production builds succeed

Understanding Each Tool’s Sweet Spot #

After going through this pain, I’ve learned where each tool truly excels. It’s actually quite complementary when you understand their strengths:

Lovable’s Strengths #

  • ✅ Rapid UI development and design iteration
  • ✅ Great for exploring ideas and getting quick feedback
  • ✅ Handles deployment infrastructure seamlessly
  • ❌ Struggles with complex TypeScript scenarios
  • ❌ Can introduce “helpful” fixes that create more problems

Claude Code’s Strengths #

  • ✅ Deep TypeScript understanding and precision
  • ✅ Handles complex database migrations reliably
  • ✅ Excellent for debugging build and type issues
  • ✅ Maintains type safety across large codebases
  • ❌ No deployment capabilities (you’ll need other tools)
  • ❌ Requires more manual oversight and verification

The Hybrid Workflow That Actually Works #

Based on this experience, I’ve developed a three-phase approach that leverages each tool’s strengths:

Phase 1: Rapid Development (Lovable) #

  • Build core UI components quickly
  • Establish basic functionality and user flows
  • Get initial deployment pipeline working
  • Focus on design and user experience

Phase 2: Complex Features (Claude Code) #

  • Database schema changes and migrations
  • Advanced TypeScript patterns and type safety
  • Comprehensive testing and error handling
  • Performance optimizations and refactoring

Phase 3: Integration (Both Tools) #

  • Use build verification to catch integration issues
  • Let Lovable handle the deployment process
  • Use Claude Code to debug any problems that arise
  • Maintain type safety throughout

Database Schema Changes: A Special Case #

Database changes deserve special mention because this is where I got burned the most. Here’s what I learned the hard way:

❌ Don’t Let Lovable “Fix” Type Issues #

It often creates module overrides that break more than they fix. The tool means well, but its fixes can be too broad.

✅ Do Use Claude Code for Schema Work #

  • Create proper database migrations
  • Update type definitions surgically and precisely
  • Add comprehensive tests for new functionality
  • Verify builds locally before pushing

✅ Do Establish Type Safety Guards #

# Always run before deployment
npm run typecheck && npm run test:run && npm run build

Real Impact: The Numbers #

After implementing this hybrid approach and build verification system:

  • TypeScript errors: 50+ → 0
  • Test coverage: 298 tests, all passing consistently
  • Build time: ~1.8s (fast and reliable)
  • Deployment success: ❌ → ✅ (no more failed deployments)

Key Takeaways for Multi-Tool Development #

1. Type Safety is Non-Negotiable #

Neither tool should compromise TypeScript safety. If one does, use the other to fix it properly.

2. Build Verification Saves Hours #

Catching issues locally prevents deployment failures and lengthy debugging sessions.

3. Know Each Tool’s Sweet Spot #

  • Lovable: UI-heavy work, rapid prototyping, deployment
  • Claude Code: Complex logic, database work, type debugging

4. Surgical Fixes Beat Sledgehammers #

When types are wrong, update them precisely rather than overriding entire modules.

5. Test the Integration Points #

The places where both tools touch your code (types, builds, deployments) need extra verification and attention.

Making It Work in Practice #

Using Claude Code and Lovable together effectively requires:

  • Clear boundaries for each tool’s responsibilities
  • Robust verification to catch integration issues early
  • Type safety discipline to prevent cascading errors
  • Deep understanding of each tool’s strengths and limitations

When you get this balance right, you truly get the best of both worlds: Lovable’s rapid development capabilities combined with Claude Code’s technical depth and reliability.

The key is recognizing that these tools are complementary, not competitive. Use them in sequence and you’ll build better applications faster than with either tool alone.

This post is based on real experience building Palette Kit, a React/TypeScript application for managing colored pencil gradients. The project uses Supabase for the backend, has 298 passing tests, and successfully deploys via Lovable after implementing these practices.