Thinking in Public
Essays, frameworks, and observations on context architecture, AI product strategy, and building what matters.
How I Built a 14-Agent Software Factory on a Single VPS
While on paternity leave I stood up fourteen agents on a single $50/mo VPS, watched three vendor moves break my plan in seven days, and learned that the infrastructure is not the moat.
Why the Context Layer Loop Looks Like the Brain
The five-step Context Layer loop maps onto how memory works in the brain. Four parallels, where practitioners are converging, and three concrete fixes if your retrieval-tuning isn't fixing the actual problem.
The Composition of an Agent Team
"How many agents?" is the wrong starting question. The four-layer framework I used to compose a 14-agent software factory, with the count as the residue.
What a Context Layer Actually Is (And Why Agent Memory Isn't One)
Agent memory got the headlines. The Context Layer didn't. The four-layer separation practitioners are converging on, what actually sits inside the Context Layer, and why architecture defines infrastructure.
Market Context Wins the Deal. Situated Context Builds the Moat.
The AI industry has reduced context to what fits in the window. That's a category error. Context is a property of organizations, not model architecture — and only one of its two forms becomes a moat.
Data Is Not Context
The AI industry is obsessed with retrieval — how to get the right chunks into the context window. But retrieval is downstream. If what you're retrieving was never curated, synthesized, consolidated, prioritized, or stored intelligently, your RAG pipeline is just efficiently delivering noise. This essay defines the five-step architecture that turns raw data into decision-ready context.
The Context Layer
A newsletter on the architecture behind AI memory, context synthesis, and building products that think. Launching Q3 2026.
Want to discuss these ideas?
Get in touch →