◆ Writing
Notes on building.
Honest writing on AI systems engineering. Not thought leadership. Not tutorials. Observations from building production systems: what worked, what failed, and why.
What to look for when hiring an AI systems architect
Most companies hire AI architects wrong. They look for model expertise when they should be looking for systems thinking. Here is what actually separates good from great in this role.
The RAG chunking problem that costs most teams months
Most RAG implementations fail for the same reason: naive chunking that destroys document structure. Here is what to do instead, and how to measure whether it worked.
How to measure LLM quality in production (not just at benchmark time)
Benchmark scores tell you what a model can do on a test day. Production metrics tell you what it does to your users every day. These are different measurements of different things.
Agentic development is a discipline, not a tool
Claude Code and similar tools are widely available. The discipline required to use them at production speed is not. Here is what it actually takes.
Building a Vanta competitor in 21 days with Claude Code
A precise account of how TraceLayer was built in three weeks using Claude Code as the primary development environment. Not a tutorial. A report from the field.
Multi-agent systems are easier than you think. They are rarely the right answer.
The technical implementation of multi-agent LLM systems is well-understood. The decision of when to use them is not.
Claude Code as IDE — the 2026 workflow
How I actually use Claude Code day-to-day as my primary development environment. Tools, workflows, habits, and where it breaks.