The Right Context, Not All of It
Written deliberately as a lesson rather than a product story, taught through The Curator v3.80.0.
Everything I have published: essays and manuals from From Lab to Life, research papers, and the archive. Each work tells you where you can read it.

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Written deliberately as a lesson rather than a product story, taught through The Curator v3.80.0.
Three kinds of context, three write rules, and a project that outlives the session, the harness, the model and the machine
The confession piece about two years of hand-written handoff files. The flaw: the most degraded model writes the most important document.
The behind-the-scenes rebuild of Lumina from Gen-1 to an enterprise-grade Gen-2.
Inside the machinery that powers modern AI development, and why understanding it matters even if you never write a line of code
In the first article, I named the phases: Context Engineering, The Build Phase, Production and Deployment. That naming had an unintentional effect. By calling Phase One “Context Engineering,” I implied that context engineering was something you did once — at the beginning — and then left behind.
New essays from From Lab to Life arrive on Substack before they reach this library.
A survey of how the industry is attacking agent memory (vendor memory products, vector stores, managed memory services, file-based memory), why context windows alone fail at agent scale ("context rot"), and why a compiled Markdown…
The canonical home of the Solo era / Symphony era framing.
Two years ago, I started an experiment. I wanted to know whether I could build production-grade software products without writing most of the code myself. Not because I was lazy. Because I had thirty years of product ideas and not nearly enough development hours to explore them.
Mastering the discipline that transforms how we communicate with artificial intelligence.
A practical toolkit for context engineering at every skill level. Covers model-selection frameworks, prompt patterns, and how to match a tool's capability to the complexity of the use case.
The Claude Desktop coding-agent experiment. Documents the real productivity gains alongside the quality constraints: what worked in conversational programming, where human oversight was still required, and why context management was the deciding factor.