Our Smallest, Most Efficient Model: Muse Ember

Re:Our Smallest, Most Efficient Model: Muse Ember by AlecSchueler on Tuesday November 10, 2026 @04:00AM (#50437756)

> The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training.

I can see that for stuff like facing right because characters in images generally do face right, and roads generally do have dashed white line separators. But putting a fish in a basket seems like a stretch of creativity—one that I would rather it not make unless prompted to—so it's surprising that every single model now seems to insist on it.

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Re:Our Smallest, Most Efficient Model: Muse Ember by dstu on Tuesday November 10, 2026 @05:06AM (#50438179)

The models do all seem to converge in ways that, on occasion, wouldn’t seem obvious. But I suppose since they’re all products of the same core training data it’s inevitable that when they’re given the opportunity to fill in the blanks they come to the same conclusions. This seems to be true regardless of what modality you’re working with.

If there isn’t a concerted effort against it the world is going to start to look and feel ever more homogenous.

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