god i hate how normalized diet culture and shit like bmi and calories are. bmi is based on eugenics. calories are a measurement of how much energy something gives u and not at all of how much weight or fat ull gain. diets have been proven to be harmful and ultimately unhelpful in actually losing weight. fatness has been largely proven to not be inherently unhealthy and doesnt inherently cause health issues.
if anyone has more good links to add on then please do and if anyone knows more on this stuff than me then dont hesitate to correct me!
The BMI was invented by Adolphe Quetelet, the 19th century statistician who invented phrenologist anthropometry. He wasnât just a eugenicist, he was one of the founding fathers of racist pseudoscience. Please do not listen to anything he has to say about your body.
âAnd get this: While epidemiologists use BMI to calculate national obesity rates (nearly 35 percent for adults and 18 percent for kids), the distinctions can be arbitrary. In 1998, the National Institutes of Health lowered the overweight threshold from 27.8 to 25âbranding roughly 29 million Americans as fat overnightâto match international guidelines. But critics noted that those guidelines were drafted in part by the International Obesity Task Force, whose two principal funders were companies making weight loss drugs.â
Man if y'all folks up thread thought this situation was bad back in 2021 (as I did too) you wonât BELIEVE what the 2026 cultural convo on these topics is like.
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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.
ever since i was a little girl i knew i wanted to deny location sharing and turn off personalized ads and reject all non-essential cookies and not set up siri and face ID
gender essentialism is soooo funny bc it's like "this is what women are like" and you're like "I've met women and many of them, if not the majority, have not been like that" and it's like "well women SHOULD be like that" and you're like "why should women be like that" and its like "because that's what women are like"
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#the fact that 'can prove access to an online account at least 12 years old' or even 'account to be verified is itself fully 18 years old'#AREN'T accepted methods of age verification is such a telling sign of what the real purpose of age-gating laws is:#data harvesting and deanonymization and the buildout of state-controllable ways to restrict both content and internet access itself en masse (via @shinelikethunder )
The way all the 2020s have done so far have been making me categorically against every new generation of tech that comes out is insane. Like I'm from a technological boom generation, saw the first portable phones, nokias & blackberries & flipphones etc, and the first smartphones, and the first ipods & ipads & tablets in general while still having cassettes & DVD & MP3 players around so I know how all of it work, I had computer classes in high school, I did the transition between home desktop computers to laptops and back to gaming computers. But then they started to put internet in your printer & microwave, everything has ads & AI now and every update is worst than the last. I literally loved technology and they ruined it
âď¸đ¤ itâs because the further you move toward the earthâs poles, the lower the angle of the sun is at the hottest parts of the day, meaning the radiation hits your whole body, causing it to feel 10-20 degrees warmer than the thermometer reading will tell you. People from tropical climes, aka close to the equator, are used to the sunâs radiation hitting a much smaller target- their head and shoulders.
Also the further you move toward the poles the more pronounced the difference between the length of day and night is. Worst part of a far-north (or south) heatwave is it doesnât get dark long enough for meaningful cooling.
Itâs not the heat. It very literally is the sun.
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bring back homeric epithets. call people brave-hearted, swift-footed, laughter loving and loud thundering. view the world with its rosy fingered and saffron robed dawns, its wine dark seas. make your own, walk across kiln fired earth and moss soft as sea sponges. be dew-eyed and soft-cheeked and silver-souled, deft-fingered and bright-tongued. gaze up at the many-storied stars and feel the warmth of the ancient sun, father of gods and men, as it beats down on the shimmering world, soft spun like caterpillar silk
it is rotten work. but i love you purposely and ardently, so iâll do it anyway. itâs rotten work because i donât like to see you hurt. so stain these hands, i can wash them clean. i will wash them clean so you can stain them again. tomorrow and tomorrow and tomorrow. because i chose to love you. rotten parts and all.
"I did it for you" has gotta be my favorite form of betrayal. You gave me a gift I never asked for, and now I have to look around at the world you destroyed with the knowledge that it was gift wrapped and addressed to me.
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i know this isnât part
of my blogs theme but like this
is interesting
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The eyes-in-the-front thing (usually) only applies to mammals. Crocodiles, arguably the inspiration for dragons, have eyes that look to the sides despite being a predator.
This isnât a mammalian thing. When people talk about âeyes on the frontâ or âeyes on the side,â theyâre really talking about binocular vision vs monocular vision. Binocular vision is more advantageous for predators because itâs what gives you depth perception; i.e, the distance you need to leap, lunge, or swipe to take out the fast-moving thing in front of you. Any animal that can position its eyes in a way that it has overlapping fields of vision has binocular vision. That includes a lot of predatory reptiles, including komodo dragons, monitor lizards, and chameleons.
(The eyes-in-front = predator / eyes-on-sides = prey thing holds true far more regularly for birds than it does for mammals. Consider owls, hawks, and falcons vs parrots, sparrows, and doves.)
But itâs not like binocular vision is inherently âbetterâ than monocular vision. Itâs a trade-off: you get better at leap-strike-kill, but your field of vision is commensurately restricted, meaning you see less stuff. Sometimes, the evolutionary benefit of binocular vision just doesnât outweigh the benefit of seeing the other guy coming. Very few forms of aquatic life have binocular vision unless they have eye stalks, predator or not, because if you live underwater, the threat could be coming from literally any direction, so you want as wide a field of view as you can get. If you see a predator working monocular vision, itâs a pretty safe assumption that there is something else out there dangerous enough that their survival is aided more by knowing where it is than reliably getting food inside their mouths.
For example, if you are a crocodile, there is a decent chance that a hippo will cruise up your shit and bite you in half. Iâd say that makes monocular vision worthwhile.
Which brings us back to OPâs point. Why would dragon evolution favor field of view over depth perception?
A lot of the stories Iâve read painted the biggest threats to dragons (until knights with little shiny sticks came along) as other dragons. Dragons fight each other, dragons have wars. And like fish, a dragon would need to worry about another dragon coming in from any angle. Thatâs a major point in favor of monocular vision. Moreover, you donât need depth perception in order to hunt if you can breathe fucking fire. A flamethrower is not a precision weapon. If you can torch everything in front of you, who cares if your prey is 5 feet away or 20? Burn it all and sift among the rubble for meat once everything stops moving.
Really, why would dragons have eyes on the front of their heads? Seems like theyâve got the right idea to me.