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a taxduo centric fic based on the civil war arc!
archiveofourown.org/works/86356426…
i have two chapters out so far but the rest are all almost done hehe please check it out!
#taxduo #unstableuniverse #wemmbu #eggchan
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@kuoooka
eeeeeeehehehe (my blue checkmark was gifted by an opp i did not pay money to elon guys)
Katılım Mayıs 2026
20 Takip Edilen64 Takipçiler
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Can we not joke about wemmbu having a deuteragonist other than eggchan @everyone Please
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Commissioned Torchflower duo art for Chapter 6 of Miracle!!!!
Go read it!!!
#commissionart #uusmpfanart
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chapter 6 of my taxduo fic is out 🎉🎉thank you to @emugnsn and @pasha_rabbit for the beautiful art once again 😘😘
archiveofourown.org/works/86356426…
#wemmbuart #eggchanfanart #flamefragsfanart #lomedyfanart #taxduo #torchflowerduo
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does anyone in my network know this woman i feel like we would be good friends
Yasmine Khosrowshahi@yasminekho
Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible... And why AI hasn't actually learned to "see" the way we do. It completely changes how you think about Human Intelligence v/s Artificial Intelligence: 1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly. 2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it. 3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes. 4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired. 5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary. 6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it. 7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object. 8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs. 9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy. 10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments. 11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce. 12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer. 13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong. 14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly. 15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did. 16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built. Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.
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so i have to address this. i've been accused of ai with the recent claude artifact crack down and i will not stand for it. yes, i did use claude for one specific excerpt, but that was only because google was not loading and in hindsight, that was wrong of me, but the rest of my work is completely ai free, and i swear it. you are free to go through my source code yourself, which i have not edited or changed in any way.
i understand OP's concern, which is why i am not blaming/flaming them in any way. being vigilant for ai use is very crucial in this time, so i have to address this publically as well.
my close friends have been seeing me write Miracle for the past two months live on google docs and on discord streams, and here are my drafts in my writing software (scrivener and grammarly). and yes, grammarly does have ai for some of their features but i have NEVER used the generative ai in my work as i only use it as a grammar checker because english is not my first language.
i have devoted so much time and effort into my fic, and i will never deface my work with the use of generative ai. if i have disappointed anyone, you have my word that no ai will ever come near my writing ever again, now or in the future.
thank you so much for everyone's support on Miracle, and i will continue to write earnestly and honestly. 🫶🫶🫶
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chapter 5 of miracle just droppeddddd... BIRTHDAY CHAPTER!!! i'm very proud of this one :)
#workskin" target="_blank" rel="nofollow noopener">archiveofourown.org/works/86356426…
art by @emugnsn 🥰
#taxduo #wemmbu #eggchan #unstablesmp #unstableuniverse
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hi! it's almost the 5yr anniversary of when i first started drawing mcsr fanart ^_^
the community has been so kind to me and i wanted to give back by hosting a raffle :D plz like + rt to enter! i'll choose 3 winners to send 2 sticker sets each for free
🤍 ily mcsr comm :3
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