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I'm excited to share my latest project - an AI-powered web scraper that transforms how we approach data extraction from websites. Built with production-ready architecture and modern engineering practices. 🔧 Technical Stack & Implementation: Frontend Architecture: • Vite + React 18 + TypeScript for type-safe, performant UI • Tailwind CSS for responsive, modern design system • Comprehensive test suite with Vitest & React Testing Library • Real-time form validation with error handling Backend Architecture: • FastAPI with async/await for high-concurrency processing • Pydantic for robust data validation and serialization • Clean architecture with service layer separation • Comprehensive error handling and structured logging AI Integration: • Multi-provider LLM support (OpenAI/OpenRouter APIs) • Intelligent content processing with structured JSON responses • Configurable model selection and temperature controls • Fallback mechanisms for service availability Output Generation: • Multi-format export engine (Word, PDF, Excel, Text) • Async file generation with optimized memory usage • Dynamic filename generation with collision handling DevOps & Quality: • Environment-based configuration management • Git security with comprehensive .gitignore patterns • CORS-enabled for secure cross-origin requests • Production-ready with proper secret management Key Engineering Highlights: ✅ Full end-to-end testing with integration test suite ✅ Type-safe codebase with comprehensive error boundaries ✅ Modular service architecture for easy extensibility ✅ Professional documentation with developer/user guides ✅ Async processing for optimal performance under load What makes this special: Instead of basic web scraping, this uses natural language prompts to intelligently extract structured data. Users simply describe what they want ("Extract all product names and prices"), and the AI handles the complex parsing logic. Open Source & Contributions Welcome! 🌟 I'm inviting the developer community to contribute! Whether you're interested in: • Frontend UX/UI improvements • Backend performance optimizations • Additional LLM provider integrations • New output format support • Documentation enhancements Repository: https://github.com/D-artisan/dartisan-ai-webscraper This project demonstrates modern full-stack development practices with AI integration - perfect for portfolios and real-world usage. Drop a comment if you'd like to contribute or have questions about the implementation!