If you don't already know you have issues doing so, squat down real quick. Bend your knees all the way and touch the floor. Just make sure you can do it. Okay? For me? And then stand up all the way and make sure you can balance on one foot.
Like. You don't need to blow it into some huge thing. Just. Make sure all your bits and peices still work the way you think they do.
Can you turn your head to look behind you without twisting your shoulders? What about standing on your toes? If you sit down on the floor can you get back up without using your hands?
If there was ever a tumblr post worth sending to your mom, it's this one.
Just saying, bodies are a use it or lose it kinda thing.
okay so every time I see this post crop back up in queues and notifications I end up thinking about it. Because I made the post and even I'm still doing the thing where I read the post about maintaining range of motion in my delicate meatsuit and I nod and hmm and think yeah that's a good idea and then dont move from where I'm curled up shrimp style staring at the nightmare rectangle.
So like. Thinking real hard about moving doesn't count as moving. Major bummer. Anyways. Joints.
Dunks are the ultimate form of twitter posting, they are meant to work in a sentence or two, and that requires you not to have any nuance and works better the less that you know. If you actually bother to read the article and not just the screenshot of a headline, you can't make your Epic Clapback. You'd know why the joke doesn't work.
And the rewards you reap for doing this—retweets, notes, likes—very much encourages you to not learn, not to write longer pieces, not to express complex thoughts or write anything that isn't convincing to anyone who doesn't already agree.
the next stage of this, of course, is what twitter has done over the last several years. engagement is no longer merely a little hit of heroin, engagement is directly monetized baby. Now not only can people be psychologically conditioned to dunk, but people and entities are financially incentivised to dunk, to not learn, to not express complex thoughts and so on.
People now make money off not just The Dunk, but in setting up the dunk as well! a financial ecosystem built on rage bait and cheap clapbacks.
Next up someone is going to claim that the Narnia series isn't kids books.
Kids books is probably not the best way to word it, you can enjoy them at every age, including your childhood, as you get older you may find new truths in them, but they're still good for any age.
I want you to understand this. I NEED you to understand this. My mother read me the hobbit as bedtime story, and I started pushing myself to read before pre-school so I could in fact read the hobbit for myself instead of having to wait for bedtime.
I didn't do so right away but jesus wept I PUSHED myself to learn to read SPECIFICALLY so I could read The Hobbit! It is, in fact, a children's story! And children only see page count as 'there is a lot of this fun story to read!'
Commissioning artists really makes you feel like a renaissance noble. Like you’ll get an email that just says “Hey sorry some things came up but I’m working on your piece and here’s a WIP” and suddenly you’re wearing sumptuous velvets and eating grapes on a reclining couch like “Ohoho! Take as long as you need, my Orpheus! My riches and time are a paltry price to pay to bring your beauty to the world! I am a ~Patron of the Arts!~”
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
tubi is one of our greatest warriors in the fight against streaming services costing a fortune for mediocre content. tubi has the most insane collection of movies you will ever encounter all for free. it has cult classics and questionable lifetime movies and movies that nobody except like three people on the planet have ever seen. tubi has movies that doesn’t exist. like if you just thought of a movie one day but never made it and no one ever made it it would somehow still exist on tubi. one day i will log onto tubitv dot com and i will see terribly inappropriate, overly complex, and strange on there. and i won’t even be surprised.
I'm not even opposed to fandom stuff, I have deeply fond memories of participating in fandom when I was younger. If I like something enough I'm sure I'd return to it someday. I just fucking despise the default assumption that that's everybody's life on here ("This is the FANDOM SITE" like idk maybe in 2013, for me at least it's the Complaining About The Job Market site).
The idea that anybody trying to talk about art must needs be talking about fandom is so infuriating it's like if I started talking about movies and EVERYONE thought I was talking about Toy Story 5. I didn't even mention Toy Story 5. Why are you speaking to me
Imagine if after inventing the x-ray, they tried to put x-rays everywhere to try to promote x-rays. This post isn't a metaphor that happened. This post ain't some analogy for current events. I just wanted to say in 1896 there was a massive craze for x-rays and they were placed everywhere
For many science-obsessed Victorians, X-rays were not just a fun novelty, but a potential miracle cure.
X-ray came at just the right time and had just the right properties to be a sensation:
It came at the very close of the Victorian era, a period whose science consisted in large part of making "hidden worlds" visible to the public for the first time, so it was remarkable, but fit within a well-established cultural trend worn out by amateur microscopy, photography, kinetoscope, cathode rays, etc.
If you could operate a camera, you could operate an x-ray
It seemingly confirmed the premise of fifty years of "spirit photography" that invisible phenomena could be captured on photographic plates
The association with spiritualism was also helped along by the macabre nature of the pictures it took (Anna Röntgen even described it as showing the ghost of her hand)
It was completely unexpected at a time when scientific novelties were a matter of widespread public spectacle
I’m so fucking tired of people making these big pronouncements about fantasy genre conventions and tropes and they just reference Tolkien and Gygax and maybe Lovecraft, and just absolutely skipping entirely over Robert E Howard and Michael Moorcock! They have influenced sword and sorcery vastly more than HPL, and Gygax just lifted from them and it’s Vexing watching people lecturing about the subject while avoiding these absolutely foundational writers
I've been reading the Elric of Melnibone books recently and yeah, holy shit. Chaos vs Law, planeswalking, the eight pointed star, evil MC that's sad about it—and plenty more. DnD and Warhammer (at least) wouldn't be what they are without everything that they borrowed from Moorcock.
if ur a trans girl and ur partner is not an ardent transfeminist frankly you need to dump their bitch ass. you can and will find better partners. i promise. i love you
dailybridgerton
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