// dynaum.blog · notes on building with AI
Building things with AI,
written down.
Field notes from shipping software and games where the model is a real collaborator, not a demo reel. The specs, the decisions, and what actually held up.
She Gave Me a Paper Form. I Built an App.A doctor asked me to track my blood pressure on paper for a few weeks. I built the app instead. It took an afternoon, and it does the one job paper had, better.
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Training Is Over Before You Arrive
The model never learns from your chats. It arrived finished. What feels like learning inside a session is context, and it dies when you close the tab.
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The Frontier Model Is Overqualified
Most of your token bill goes to summaries, tags, and routing, all billed at genius rates. Open-weight models do the cheap tier for cents.
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Pay the Setup Tax Once
The model forgets your repo between every session, so someone re-teaches it the same facts forever. A committed, layered CLAUDE.md is how the repo briefs the model for the whole team and every session that follows.
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Stratify: The Size of What One Person Ships Now
A polyglot static-analysis engine. Five languages and six analyses through one model, reaching your terminal, CI, editor, AI agent, and dashboards. I built it solo with the spec-driven loop. The headline is not the tool. It is how little it took.
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Don't Make the Model Do a Build Step
An agent spends the first minutes of every session rebuilding a map of your codebase. A script already has that map. The model should read the facts, not re-derive them.
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Conduit: The Plumbing Every RAG Team Rebuilds
A RAG application is 20 percent retrieval logic and 80 percent data plumbing, and every team rebuilds the same 80 percent from scratch. I built an open-source engine that owns it. Today it is public.