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 →Data Aggregation Is Not a Moat
Data aggregation used to be valuable because collection, access, parsing, transformation, storage, analysis, and maintenance were expensive. Browser agents and foundation models compress that operating cost, which means many data moats built on non-proprietary public data are weaker than they look.
Hidden Technical Debt of AI Systems: Agent Harness
The agent is the model plus the harness. The runtime is where the harness lives. As models get better, the structure we put around them turns from scaffolding into ceiling. Sutton's Bitter Lesson is now arriving on the harness, and the asymmetry between training-time and production-time harnesses is where most of the next debt will accumulate.
Don't Outsource Your Understanding
Outsourcing the work to AI is fine. Outsourcing the understanding is where it breaks. A 150-year-old law firm learned the difference in front of a federal judge.
Hidden Technical Debt of AI Systems: Agent Runtime
Everyone talks about the model, the prompts, the evaluations. Almost nobody talks about the runtime the agent actually runs in. The runtime is the agent. Sculley's hidden technical debt argument applies twice over to agent systems, and the dev/prod parity gap on runtime infrastructure is the next bill nobody is budgeting for.
"Determinism" is the Biggest Cope in AI Adoption
We've never had determinism in software. We just had the illusion of it. Turing's Halting Problem and Rice's theorem proved that verifying software correctness was never a guarantee anyone could offer. What AI systems shift is not reliability — it's the evaluation surface.