I'm an ambitious fullstack developer with a passion for building intelligent, data-driven applications that solve real-world problems. My portfolio showcases a strong foundation in both frontend and backend development, with particular expertise in Node.js, React, and AI integration. I thrive on challenging projects that push the boundaries of modern web development—from building custom HTTP servers from scratch to implementing machine learning systems and real-time data streaming. What sets me apart is my ability to bridge the gap between cutting-edge AI technologies and practical web applications. Whether it's implementing semantic search with vector embeddings, integrating LLMs for personalized recommendations, or developing computer vision systems, I bring a unique combination of AI literacy and solid software engineering fundamentals. I'm driven by curiosity, constantly learning new technologies, and committed to writing clean, maintainable code that scales.

React & Modern JavaScript: Proficient in React 19 with hooks, component-based architecture, and state management. Built multiple production-ready SPAs with responsive design and optimized user experiences Modern Build Tools: Experienced with Vite, Astro, and modern bundling/optimization techniques for fast, performant web applications TypeScript: Strong typing and type-safe development practices for scalable codebases UI/UX Implementation: Responsive design, progressive state management, and interactive user interfaces

Node.js Fundamentals: Deep understanding of core Node.js concepts including custom HTTP servers, stream-based processing, event-driven architecture, and async/await patterns API Development: Built RESTful APIs with proper error handling, input sanitization, and security best practices Real-time Communication: Implemented Server-Sent Events (SSE) for live data streaming and real-time updates File System Operations: Advanced fs/promises usage, automated document generation with PDFKit

Data Modeling: Transaction management, secure data storage, and efficient query patterns File-based Systems: JSON data handling, async file operations, and data persistence strategies

LLM Integration: Experience with Google Generative AI and RAG (Retrieval-Augmented Generation) architectures Vector Embeddings: Semantic search implementation using embedding-based similarity matching Computer Vision: Trained custom YOLOv5 models achieving 90%+ precision for object detection Reinforcement Learning: Built training pipelines with PPO algorithms and 1M+ training steps

API Integration: Third-party service integration and data pipeline construction Performance Optimization: Stream processing, efficient algorithms, and resource management Security: Input validation, sanitization, and secure application design Modern Web Standards: Progressive enhancement, accessibility, and cross-browser compatibility

Real-time gold investment tracking application built with pure Node.js HTTP module featuring Server-Sent Events for live price streaming, automated PDF report generation with PDFKit, and secure transaction management. Implemented custom HTTP server without frameworks, demonstrating advanced Node.js fundamentals including stream-based request parsing, async file operations with fs/promises, input sanitization, and event-driven architecture for professional investment tracking and analysis.

A semantic search-based movie recommendation engine that uses vector embeddings and Retrieval-Augmented Generation (RAG).

Personalized travel recommendation application built with React 19 and Vite featuring an interactive quiz system, Google Generative AI integration, and intelligent destination matching. Implemented modern web development practices with component-based architecture, progressive state management, and responsive design optimized for discovering perfect Brazilian travel destinations.

Object detection system using YOLOv5 for rugby analysis. Developed a model with 90%+ precision that can identify key game elements including players, referees, scrums, rucks, and more in real-time (8.8ms per image).

Machine learning system for Super Auto Pets using reinforcement learning (PPO) and computer vision. Developed a coaching system that provides strategic recommendations based on game state analysis.

Official team website built with Astro framework and TypeScript featuring responsive design, team roster management, match schedules, and media galleries. Implemented modern web development practices with component-based architecture and optimized performance.

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• 📧 augusto.mk.lee@gmail.com