I help AI coding agents work on codebases too big for them

I build open-source tools that give coding agents the context and the checks they need on large and legacy systems: a code knowledge graph, architecture and API-contract rules a commit can fail on, and diagrams and dashboards generated from plain files an agent can write. Forty years of engineering, now aimed at the gap between adopting AI and getting it to work on real code.

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  • symgraph — a knowledge-graph MCP server that gives agents a real map of a codebase, and says when it isn’t sure
  • tropism — architecture rules an agent can actually check: import cycles and layering across ten languages, with no build and no network
  • brake — fails the commit when an API change would break a consumer, across OpenAPI, protobuf and GraphQL
  • forge — describe, render, and lint architecture from a single file, with mmd and puml rendering Mermaid and PlantUML in pure Rust
  • teleplot — Kibana dashboards without Kibana: one read-only binary over Elasticsearch, with an MCP endpoint so agents can write the dashboards
  • increment — read a change as two aligned files in the terminal, and walk a branch commit by commit
  • bx — run local MCP servers as native binaries, without a Node or Python runtime

What changed in September and October → · All projects on GitHub →

Building resilient, scalable, and maintainable systems. From event-driven patterns to domain-driven design, I write about the architectural choices that shape successful products.

Rooted in software craftsmanship, TDD, and clean code. I argue for technical health over technical debt, and for continuous improvement in the craft.

How coding agents get real work done on real codebases: the checks that keep them honest inside the edit loop, what an MCP server is for once an agent can write the file itself, and what code review becomes when agents write most of the code.

Looking for where to start? These are the pieces that have held up best — the tools change, the problems don’t:

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