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Practical decisions behind useful software. Start with a workflow, define its boundaries, and make the result checkable.
ENGINEERING CHECKLIST
A practical checklist for choosing an AI workflow, defining tool permissions, retaining human approval, and testing failures before expanding automation.
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A practical checklist for private GPU workflows: node readiness, versioned inputs, job states, failure handling, output delivery, and project boundaries.
Read guide →ENGINEERING CHECKLIST
What a remote AI coding interface should preserve: device and session identity, concrete approval previews, instruction delivery, and reconnect behavior.
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