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Fix the System, Not the Output
Correcting what the AI produced fixes today. Correcting how it works fixes every tomorrow. Re-explaining context is not just slow, it lets the goal drift. Capture the decision once.
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Don't Just Use the Model
The series says the developer owns the decisions. But a decision about a machine you treat as magic is a guess. Three real calls that came from knowing how the model works, and three readable books to get you there.
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Did It Actually Work?
Working with AI assumes output you can trust. This post measures it. Velocity is a vanity metric now. Trust is the number that survives, and three health metrics tell you whether the loop is working.
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Build Your Own Tools
A tool that used to cost a sprint now costs an afternoon. The decision of what to build is the bottleneck. A direct challenge: pick one task you did twice this week and have a custom tool for it by tomorrow night.
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Many Agents, One Chat
Once you run more than one agent locally, you become the bottleneck. A chat interface plus a vault is the upgrade. Why agentchattr and Obsidian pair so well for the personal scaling problem.
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Save the Boring Time
The biggest AI time-save is not writing features, it is the ops work that surrounds them. Three flows, two automation surfaces, and the one rule that keeps it safe.
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Pick a Framework, Any Framework
AI-DLC is one option, not the option. Four methodologies in 2026, the foundation they all share, and the worktrees-and-orchestration upgrade that scales any of them.
