https://christopherclaudecook.com/articles Articles on production ML, AI strategy, and building AI teams. en-us https://christopherclaudecook.com/articles/ethos-knowledge-base-mcp https://christopherclaudecook.com/articles/ethos-knowledge-base-mcp Thu, 27 Aug 2026 12:00:00 GMT A language model starts every session with an empty context window, and vendor memory features solve that inside one product while fragmenting it across the rest. How a self-hosted vault attached to both ChatGPT and Claude handles retrieval, artifacts, and a semantic memory the user can watch grow. https://christopherclaudecook.com/articles/scaling-ai-team-1-to-10 https://christopherclaudecook.com/articles/scaling-ai-team-1-to-10 Sun, 23 Aug 2026 12:00:00 GMT I started ORBIS's AI capability as a department of one and grew it into a ten-engineer team delivering DoD programs and proprietary intellectual property. The management model behind that growth was servant leadership, and it carried more discipline than the name suggests. https://christopherclaudecook.com/articles/production-ml-lessons-from-defense https://christopherclaudecook.com/articles/production-ml-lessons-from-defense Sat, 01 Aug 2026 12:00:00 GMT Most ML projects fail in the distance between a validated notebook and a system running inside someone else's workflow. What years of building AI for Department of Defense inspection pipelines taught me about that distance. https://christopherclaudecook.com/articles/local-llms-secure-environments https://christopherclaudecook.com/articles/local-llms-secure-environments Wed, 20 May 2026 12:00:00 GMT For defense, healthcare, legal, and finance workloads, sending data to a commercial API is often prohibited outright. What actually works when the models have to run inside the boundary.