Transitioning from traditional full-stack web development to AI engineering—leveraging production software architecture to build intelligent systems that think, reason, and autonomously solve complex problems.

Actively maintaining and contributing to community-driven AI initiatives:

• OpenMAIC ⭐ 33,365
  Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click

• diagram-design ⭐ 33,586
  38 editorial diagram types for Claude Code, Codex, and Pi. Self-contained HTML + SVG. No shadows. No Mermaid slop.

• docling ⭐ 66,149
  Get your documents ready for gen AI

• awesome-llm-apps ⭐ 136,609
  100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.

• knowledge-catalog ⭐ 9,112
  Google Cloud Knowledge Catalog tools and samples.

Exploring emerging patterns in agentic AI, workflow automation, and multimodal systems:

• ai-cookbook — Practical, code-first AI engineering recipes covering LLM APIs, RAG pipelines, document extraction, and conversational AI
• pgvectorscale-rag-solution — An implementation of pgvector scale to build powerful RAG solutions
• ResearchSwarm — A swarm of specialized AI agents that collectively researches complex topics and produces structured, cited synthesis reports
• Open-Knowledge-Format-OKF — A curated knowledge base documenting Google Cloud's open specification for portable metadata and context
• AI-Skin-Specialist — Multimodal healthcare assistant combining voice, image, and video analysis with conversational AI guidance
• django-celery-redis-async-tasks — Async task queue patterns with Django, Celery & Redis — foundational architecture for AI agent frameworks
• agentic-ai-with-pydantic-ai — Type-safe, production-grade LLM agents with structured outputs, dependency injection, and tool use
• Job-Hunter-Agent — AI agent that searches LinkedIn, Naukri, Wellfound and Cutshort in parallel with scored job matches

I enjoy building in public, sharing practical AI patterns, and contributing to tools that make intelligent systems more useful and accessible.

• Building reusable AI architecture patterns and prototypes
• Exploring practical LLM integrations for production-ready systems
• Sharing learnings around agents, RAG, evaluation, and multimodal experiences

• 🎯 Building production-grade AI systems that blend reliability with innovation
• 🔒 Type-safe LLM applications that scale
• 🤖 Scalable multi-agent architectures solving real problems
• 📦 Full-stack development with modern frameworks
• 🌟 Open-source contributions that matter