Original post: social.coop (Mastodon)
this post was submitted on 06 Oct 2025
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Microblog Memes
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A place to share screenshots of Microblog posts, whether from Mastodon, tumblr, ~~Twitter~~ X, KBin, Threads or elsewhere.
Created as an evolution of White People Twitter and other tweet-capture subreddits.
RULES:
- Your post must be a screen capture of a microblog-type post that includes the UI of the site it came from, preferably also including the avatar and username of the original poster. Including relevant comments made to the original post is encouraged.
- Your post, included comments, or your title/comment should include some kind of commentary or remark on the subject of the screen capture. Your title must include at least one word relevant to your post.
- You are encouraged to provide a link back to the source of your screen capture in the body of your post.
- Current politics and news are allowed, but discouraged. There MUST be some kind of human commentary/reaction included (either by the original poster or you). Just news articles or headlines will be deleted.
- Doctored posts/images and AI are allowed, but discouraged. You MUST indicate this in your post (even if you didn't originally know). If an image is found to be fabricated or edited in any way and it is not properly labeled, it will be deleted.
- Absolutely no NSFL content.
- Be nice. Don't take anything personally. Take political debates to the appropriate communities. Take personal disagreements & arguments to private messages.
- No advertising, brand promotion, or guerrilla marketing.
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Because there is no "it" that "calls" or "identify" even less the "nature of the question".
This requires intelligence, not probability on the most likely next token.
You can do maths with words and you can write poem with numbers, either requires to actually understand, that's the linchpin, rather than parse and provide an answer based on what has been written so far.
Sure the model might string together tokens that sound very "appropriate" to the question, in the sense that it fits within the right vocabulary, and if its dataset the occurrence was just frequent enough it even be correct, but that's still not understanding even 1 single word (or token) within either the question or the corpus.
An llm can, by understanding some features of the input, predict a categorisation of the input and feed it to do different processors. This already works. It doesn't require anything beyond the capabilities llms actually have, and isn't perfect. It's a very good question why this hasn't happened here; an llm can very reliably give you the answer to "how do you sum some data in python" so it only needs to be able to do that in excel and put the result into the cell.
There are still plenty of pitfalls. This should not be one of them, so that's interesting.