Hi! I am Han Lee.
I build and operate machine learning systems, with expertise on GenAI, agentic systems, LLM agents, search engines, recommendation engines, and large language models. I am the guy to call for fixing spaghetti codes, processes, and orgs.
I write about machine learning engineering, evaluation, compound AI systems, and the tech industry — drawing on years of experience shipping ML at scale and investing in the sector.
A practical guide to designing and implementing AI evaluation systems, grounded in the research papers that shaped the field.
Early Access — 50% OffRecent Posts
See all →A Taxonomy of RL Environments for LLM Agents
A structured guide to RL environments for LLM agents. RL environments are the training grounds that shape what agents can learn. This guide covers the five core components (task distribution, harness, verifier, state management, config), the architectural question of where the model lives relative to the environment, verifier design principles, and a practical decision framework for building your own environments.
It's-a Me, Agentic AI
An intuitive guide to agentic AI model development and agent frameworks using Super Mario as an extended analogy. Small Mario is a base model, the Super Mushroom is the model harness, power-ups are agent skills, and learning to beat levels is reinforcement learning. If you can understand Mario, you can understand how agentic AI systems are built.
The Evaluation Design Lifecycle: From Business Need to Valid Metrics
A systematic process for translating stakeholder needs into valid, actionable AI evaluation metrics — the evaluation design lifecycle.
Databricks' Strategic Playbook: Reynold Xin on Growth, AI, and the Future of Data Infrastructure
Reynold Xin, Apache Spark's top contributor and Databricks executive, shares candid insights on Databricks' 60% YoY growth strategy, contrarian GTM investments, $1B+ AI revenue, OpenAI partnership dynamics, and the future of AI-native databases. Learn how Databricks' DNA-focused M&A strategy, multi-product expansion, and strategic cloud partnerships position them to challenge Oracle's $100B OLTP market while maintaining software's 80-90% margins in the AI era.
Claude Agent Skills: A First Principles Deep Dive
Technical deep dive into Claude Agent Skills' prompt-based meta-tool architecture. Learn how context injection design, two-message patterns, LLM-based routing, and runtime context modification enable sophisticated AI agent behaviors. Complete implementation guide covering SKILL.md structure, execution lifecycle, permission scoping, and design patterns for building LLM tool systems. Essential for AI engineers, prompt engineers, and technical architects building agentic applications.