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The limiting factor for any AI personal assistant is signal quality. Generic models work from what a user types in a session and little else.
Muse, Meta's newly unveiled agent, draws from a user's WhatsApp chat history and Instagram activity to generate customized suggestions, placing Meta's social data infrastructure at the center of the personalization loop.
Where this sits in the stack The constraint for a personalization engine is context. Without a durable read on a user's actual behavior, recommendations default to the generic.
That is the problem Muse is designed to address, and the design choice is architectural: pull the signal from where the user already lives, rather than ask them to build fresh context inside a new application.
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