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Intelligence is assembled.

Useful AI behavior usually comes from composition across models, memory, tools, and runtime checks.

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When a system feels coherent across many tasks, it is rarely because one model carried everything alone.

It is usually because memory, retrieval, tools, and control loops were arranged well enough to make the model usable.

What this changes in practice: when behavior breaks, inspect the assembly before assuming the model is the whole problem.

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