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Apr 8, 2026 · 7 min

Visualizing context: how to see the system before you build it

Every hard engineering decision I have seen go wrong had the same root cause: the person making the call could not see the whole system. Context maps, failure propagation graphs, and temporal visualizations are how you fix that.

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AI against underengineering

The real story of agentic engineering is not 10x velocity - it is finally being able to afford the boring, correct, invisible work that turns brittle systems into real ones.

Apr 7

Why Ruby is quietly the best language for AI

Everyone assumes you build AI systems in Python. After a year of building agentic products in Ruby, I think that assumption is wrong — and here's why.

Apr 6

Your team is afraid of AI. Here is how to fix that.

The most common failure mode in AI adoption is not a tooling problem. It is a feelings problem. How to close the gap without destroying morale.

Apr 6

The seven levels of agentic engineering

From autocomplete to goal-driven autonomy: a working taxonomy for how AI agents are actually being used to build software in 2026 -- and how to tell which level you are at.

Apr 4

Building foundations that outlast you

At some point in every growing organization, someone becomes the person who sees the whole system. Here is the playbook for doing that job well -- and making yourself unnecessary, which is the whole point.

Apr 2

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Available for contracts · Q2 2026

Real
systems,
shipped.

Engineering lead. 11+ years building distributed systems at scale — TypeScript, Go, Ruby, AWS. Available for contract engagements, fractional roles, and full-time positions.

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— Stack TypeScript · Go · Ruby · gRPC / GraphQL · AWS · DynamoDB · Terraform · React · Vue · Hotwire · Claude · MCP · agents · 11+ years shipping production systems