I help AI coding agents work on codebases too big for them
I build open-source tools — code knowledge graphs, code intelligence, and architecture tooling — that give coding agents the context they need on large and legacy systems. Forty years of engineering, now aimed at the gap between adopting AI and getting it to work on real code.
What I’m building
- symgraph — a knowledge-graph MCP server that gives agents a real map of a codebase
- tropism — architecture rules an agent can actually check: import cycles and layering across ten languages, with no build and no network
- forge — describe, render, and lint architecture from a single file
- bx — run local MCP servers as native binaries, without a Node or Python runtime
Software Architecture
Building resilient, scalable, and maintainable systems. From event-driven patterns to domain-driven design, I write about the architectural choices that shape successful products.
Featured Content
Looking for where to start? These are the pieces that have held up best — the tools change, the problems don’t:
- Technical Health over Technical Debt: Why internal quality is a health question rather than a ledger you service.
- Defending the Domain Model: Models decay as the business moves. Keeping yours honest at the boundaries.
- Team Distribution Models and Conway’s Law: If team shape and system shape must match, decide which one you’re choosing.
- Zero Down-time Relational Databases: Backward and forward compatible schema change. Written in 2013, still how it’s done.
- Code Review After the Pull Request: What review becomes once agents write most of the code.
Latest Insights
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