Leveraging AI to Supercharge Development
Production agentic systems, multi-region consumer infrastructure, and spec-driven AI-native development — built from first principles and shipped as real products.
Three-layer agentic framework: single-agent loop, multi-agent orchestrator, modular tool SDK.
Multi-region typing platform: voice narration, 7 langs × 9 regions, anycast routing.
AI content factory: spec-driven, RAG, Report Cards, model routing, self-correction.
How I think about LLMs, RAG, agents, and production AI — first principles to shipped systems.
Full production RAG for real verticals — HR, customer transactions, healthcare. Reference architectures end-to-end.
LangChain, CrewAI, AutoGen, LlamaIndex, Pydantic AI — honest comparison with code, pros/cons, and when to pick each.
25 years across Java, Python, TypeScript, SQL, NoSQL, Kafka, Spring Boot, React, Next.js — and why stack breadth is a multiplier in the AI era.
The production deep dives above represent my core AI-native work. The tabs below cover adjacent areas I actively explore: Onyx RAG prototyping, MCP server patterns, Claude Code excellence techniques, and polyglot AI strategy for multi-model orchestration.
Retrieval-Augmented Generation Systems
Model Context Protocol Implementation
Advanced AI-Powered Development
Multi-Model AI Orchestration
As AI capabilities evolve at breakneck speed, staying ahead means mastering not just individual models, but the art of orchestrating multiple AI systems to create applications that are intelligent, scalable, and cost-effective.