Built a Professional Label Designer 100% with AI - Here's What I Learned

[lams001]

Hello everyone!

I’d like to share a project: LabelMake Pro, a professional label design platform that is 100% built with AI assistance (Cursor + Claude Sonnet). Not a single line of code was manually written - everything was generated through AI conversations and iterations.

AI Development Journey:

• Started with Claude Sonnet 3.7 (early development and core features)

• Upgraded to Claude Sonnet 4.0 (major feature additions and optimizations)

• Currently using Claude Sonnet 4.5 (latest improvements and refinements)

• All development done through Cursor IDE with Claude Sonnet

What It Does

LabelMake Pro is a full-featured web-based label design system:

• Visual Drag-and-Drop Editor - Built with Konva.js, supports all standard design operations (drag, resize, rotate, align)

• 20+ Barcode Formats - EAN-13, Code128, QR Code, DataMatrix, GS1, and more

• Batch Processing - Automatically generate hundreds of labels from CSV/Excel files

• Multiple Export Formats - PDF, PNG, SVG, ZPL (for thermal printers)

• Compliance Features - FDA/EU label templates and validation

• Template System - Save and reuse designs, share templates

• User Management - Multi-tier subscription system (Guest/Free/Pro)

• Internationalization - Multi-language support with AI-powered translation

Live Demo: https://www.labelmakepro.com

The AI Development Story

Starting Point

I had zero code. Just a clear vision: build a professional label design tool that could compete with existing solutions.

The Process

1. Requirements → AI: I described what I needed (drag-and-drop editor, barcode generation, batch processing, etc.)

2. AI Generated Code: Cursor + Claude Sonnet generated the entire codebase - frontend (Vue 3 + TypeScript), backend (Node.js + Express), database (PostgreSQL)

3. Iteration Loop: Test → Find bugs → Describe to AI → AI fixes → Repeat

4. Architecture Decisions: AI helped with complex decisions (coordinate systems, rendering optimization, state management)

5. Version Evolution: Progressed through Claude Sonnet 3.7 → 4.0 → 4.5, with each version bringing improved capabilities

AI Version Progression

• Claude Sonnet 3.7: Foundation and core architecture, initial features

• Claude Sonnet 4.0: Major feature additions, performance optimizations, complex integrations

• Claude Sonnet 4.5: Latest refinements, edge case handling, production-ready polish

What Was Built

• Frontend: 653 files (351 TypeScript + 285 Vue components)

• Backend: 192 files (all TypeScript)

• Total Lines: 400,000+ lines of code

• Development Time: ~6 months of AI-assisted development

• Current Version: v2.42.0

Technical Highlights

Frontend Stack

• Vue 3.5 + TypeScript (strict mode, no any types)

• Konva.js for canvas rendering

• Element Plus for UI components

• Pinia for state management

• Full type safety throughout

Backend Stack

• Node.js + Express + TypeScript

• PostgreSQL + Prisma ORM

• Redis for caching

• JWT authentication

• Bull queue for background jobs

Key Technical Challenges Solved

1. Coordinate System Consistency

• Problem: Konva uses center coordinates for circles/polygons, but storage uses top-left coordinates

• Solution: AI helped design a conversion system that handles both seamlessly

2. Canvas Rendering Performance

• Problem: Smoothly rendering hundreds of elements

• Solution: Layer-based rendering, selective updates, caching strategies

3. Batch Processing

• Problem: Efficiently generating 1000+ labels from CSV data

• Solution: Template variables, data binding, optimized export pipeline

4. Type Safety

• Challenge: Maintaining strict TypeScript types across 400K+ lines

• Solution: AI consistently enforced type safety, caught errors early

What Worked Well

AI was excellent at:

• Quickly generating boilerplate code

• Implementing standard patterns (CRUD APIs, authentication, etc.)

• Fixing bugs when I described the symptoms

• Refactoring code for better organization

• Enforcing type safety

Complex features that AI handled well:

• Canvas-based drag-and-drop system

• Barcode/QR code generation integration

• Multi-format export (PDF, PNG, SVG, ZPL)

• User permission system

• Internationalization system

What Was Challenging

Areas that required more iteration:

• Complex coordinate transformations (needed multiple iterations)

• Performance optimization (required profiling and targeted fixes)

• Edge cases in batch processing (needed real-world testing)

• UI/UX refinements (required user feedback)

Key Learnings

1. AI is incredibly powerful for structured development - When you have clear requirements, AI can generate production-ready code

2. Iteration is key - The first version often needs refinement. AI excels at iterating based on feedback

3. Type safety matters - Strict TypeScript caught many issues early. AI was great at maintaining type consistency

4. Architecture decisions still need human judgment - AI can implement, but you need to guide the overall structure

5. Testing with real users is essential - AI can’t predict all edge cases. Real usage revealed issues that needed fixing

Current Status

Fully Functional: The system is live and working
Production Ready: v2.42.0 with all core features implemented
Real Users: Already serving actual customers
Continuous Improvement: Still iterating based on feedback

What’s Next

• More templates and design elements

• Mobile app version

• API for third-party integrations

• Advanced AI features (auto-layout suggestions, design optimization)

Technical Deep Dive

If anyone’s interested in the technical details:

• Architecture: Vue 3 frontend, Node.js backend, PostgreSQL database

• Rendering Engine: Custom Konva-based system with layer management

• Export System: Multi-format exporter with DPI control

• Batch Processing: Template variable system with CSV/Excel import

I’m happy to share more about:

• How I structured conversations with Claude Sonnet across different versions

• Differences in AI capabilities between Claude Sonnet 3.7, 4.0, and 4.5

• Architecture decisions and trade-offs

• Performance optimizations

• How version upgrades improved development efficiency

• Lessons learned from the process

Final Thoughts

Building a full SaaS product 100% with AI assistance was an incredible experience. It’s not magic - it still requires:

• Clear requirements

• Iterative refinement

• Testing and debugging

• User feedback

But AI dramatically accelerated the process. What would have taken years of solo development was completed in months.

I’d love to hear your thoughts, questions, or feedback!