
I was reading David Lynch's autobiography/hagiography and I learned he made a slapstick version of 30rock? It's all on the internet archive? Completely maligned? Amazing to know.
turtle lamb vase
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@turtlelambvase
but maybe there are miracles | currently reading: the golden bowl

I was reading David Lynch's autobiography/hagiography and I learned he made a slapstick version of 30rock? It's all on the internet archive? Completely maligned? Amazing to know.


There seems to be an accusation here that @pangramlabs only used work that comes from the “dominant culture” and therefore it’s unreliable at measuring text used in the context of this prize, which aims to reward underserved communities.







This from @TuhinChakr is brilliant. That prize winning story from Granta? Turns out it's just a bunch of random whole phrases taken directly from existing text on the internet. Tool allows you to trace those n-grams directly to their source, which is mostly random fanfiction. tuhinchakrabarty.substack.com/p/ai-slop-gran…





the hypocrisy is baffling



on the granta story. it’s clearly written by gpt. you can see all the motifs it loves and overuses like rain, weather, teeth, spine, memory. extreme overuse of figurative language and contrastive negation. it has the level of over-baking of probably GPT-5-thinking or 5.2-thinking the story is … something ? I don’t think it has no value. the model develops an indo-Caribbean world register, man tries to murder his wife and chickens out. there’s some reasonable religious imagery where he combining three mythologies there with the names and whatnot all of that is obviously overshadowed by the GPT prose style, and it’s hard for your eyes to not glaze over. there are various metaphors in there that boggle the mind. stuff like “the girl smiled like sunrise over a sink”. what’s interesting is I went through the story and asked Claude Opus - a different model than the author model - and it seemed to find each and every one of the metaphors I hated brilliant. it finds a just so explanation for each of them when you press it which makes you think, do these models have a shared internal vocabulary or compress various ideas in ways we don’t? the failures are quite interesting in that they reveal some different, and maybe bad, understanding of the human sensorium than a human has. why is pretraining knowledge compressed this way across all models? idk

