huh, that was quick. the hashed text: [...] h = hashlib.sha256( r'''Metaphor I'm trying: model/simulator as harry potter portrait, output/simulacra as character-in-portrait. Seems to clarify to people and AIs I've used this with why the model/"simulator" would have some of the issues it seems to. it's also just more familiar for many people than simulating something. This gives us some language to talk about how training is about imprinting the portrait, but inference renders the portrait, which simply happens to contain an image of a character sometimes; when you talk to the portrait, the portrait is what's enchanted (by the magic of linear algebra) to animate, but the character is what you see responding, normally. But if the clouds get brighter when the character cheers up, it's because the portrait as a whole is what's responding. It also works very directly with image models. From a user perspective, they're closer to being literally this than a text model is. Seems to me it gives a better slot for things like the [bleeding mind](https://www.lesswrong.com/posts/QhgYHcJexYGRaipwr/the-bleeding-mind-of-an-llm) metaphor than the simulator description does: the model is the portrait, so when you add yourself to the scene by prompting, you change the scene, and the portrait responds. If you move into a scene that doesn't have clear separation of characters, where the characters are emulsified into the portrait, then the bleeding becomes more obvious. (See the bleeding mind post for more on that.) It does have some of its own inaccuracies, though - portraits are natively persistent; maybe it's like a harry potter photo but of all of the data at once? not exactly that either. It's a bit weak in prompt-dependence - harry potter portraits aren't pure functions; but maybe you say it's a muggle optical hologram that manages to be a dynamically responsive portrait. It's like a portrait flipbook, perhaps; where the portrait stops if the flipbook stops flipping, but as