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"
As the fog returns this evening, it presents unique and compelling photographic opportunities. This image captures the 1877 Tall Ship Elissa re-entering Galveston Harbor. This is just another stunning view from the Galveston Ferry. 12/23/25 TXDot
Author/illustrator Trung Le Nguyen has been live posting reading Pride and Prejudice for the first time on bluesky and just hit the first proposal. The replies are basically the sickos meme
Cassandra: YOU ARE ALL GOING TO REGRET THIS SO MUCH YOU DON’T EVEN KNOW.
Odysseus: Regret it why?
Cassandra: You won’t believe me if I tell you. If I prophecy, nobody believes me. That is my curse.
Odysseus: … I’m Nobody. Fill me in.
*A couple of months later*
Odysseus: HELLO PENELOPE, I AM HERE PRECISELY ON TIME AND NOT YEARS LATE incidentally I rescued and adopted a Trojan seer while I was away, she’s great, got me home really fast, Cassandra this is your new mother who’s not going to treat you like shit.
Penelope: … I’m going to need more details, but okay, sure.
Further context: Durham city council (Reform UK) cut funding and support for Pride. The Durham Miner's Association and other trade unions raised enough money for Durham Pride 2026 to go ahead - a direct call back to when Lesbian and Gays Support the Miners (LGSM) raised money for mining communities when Margaret Thatcher seized union funding during the miner strikes of 1984-85.
At the 1985 Labour party meet, the motion to support LGBT rights as a party was passed due to a block vote from mining unions.
Stephen Guy, the chair of the Durham Miners’ Association, said that when it became apparent Durham Pride was under threat, he took it upon himself to “encourage the trade union movement to step up and do the right thing, and stand shoulder to shoulder with the LGBT+ community […] They not only raised funds for us, but came to our communities, uplifted our spirits when they were down, and showed their solidarity.”
Once when I was in undergrad, someone described something as “problematic” in class and our professor was like, “That’s cool, but ‘problematic’ doesn’t really mean anything. It means that the thing you’re describing has a problem, and in and of itself that’s not bad. Art, especially, should always have problems, or else it’s not interesting and not art, either. It sounds like you’re trying to say that this is bad, but you don’t want to say ‘bad.’ Is that right?”
So from then on whenever one of us called something problematic, he would make us talk it out until we could name the “bad” thing we were hinting at. In this particular class, 7/10 it was some type of oppression, and the remainder was like, “I’m uncomfortable because this is very new/confusing/pushing boundaries that made me feel safe.”
Once we stopped calling things “problematic” and stopping at that, class got way more interesting and... we all had to say, like, “that’s racist” or “that’s misogynistic” or “ew capitalism gross” out loud, which a lot of us had never done in a classroom before. Or we had to be like, “Uhhh... I’m not sure what’s so bad?” and confront our own beliefs and that was maybe even more useful.
Anyway. Whenever I see the word problematic, I can’t help but think of this professor being like, “Good starting point, now let’s get specific.” I think when we have to commit to saying “that’s ___” it requires a lot more careful thought about the truth and impact and complexities of whatever we’re claiming. Sometimes there really is some bullshit afoot, and also sometimes it’s art, and it should be full of problems, because that’s what art is.
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.
The cops very clearly planted evidence on him because they had to make an arrest because all eyes were on them and whoever actually did the deed was making them look stupid.
Why would the real killer hero have kept the weapon on his person and traveled two states over while carrying it and a manifesto in his bag, conveniently turning the crime into a federal matter? The same guy whose bag they found in a park, filled with monopoly money? Why did the police turn off their bodycams, take Luigi's stuff, drive a block away, turn their bodycams back on, go back into the restaurant, and then arrest him?
From the moment of his arrest, even left-of-center media has been presuming his guilt without examining anything (e.g. calling him "the killer" instead of "alleged" or "accused") and then when I say he didn't do it, the nearest person chimes in with some quip that tells me they think he did do it but should go free anyway. Don't get me wrong, I would have the same attitude if he had done it. But he didn't. It makes me feel like the only sane person in the world, even among my staunchly leftist friends.
if this happens to you, this might be a sign that you actually do need rest. if you cannot do the thing, but youre sitting in one hunched over position, all tensed up over your phone, this can be harmful to your body. it takes a lot of muscle effort to hold the positions it takes to sit and scroll on your phone, and this is part of why you still end up exhausted after a day of seemingly doing nothing.
instead, if you cannot do the thing, treat yourself as if you need rest. you are too sick to do the thing. go lie down, get cozy, and try to relax. worst case scenario, youre letting yourself take a break. best case scenario, youve at least broken the cycle and you might even actually be able to do the thing after lying down for a bit!
but yeah. if you cannot convince your body to do the thing, then maybe what it really needs is rest.
I know that people are just sharing their own personal favourite exercises on this post but the number of them that are for some reason framed as if they are advice to me and then recommending shit that is either impossible or completely irrelevant to me is wild.
Like I know they're not *actually* giving advice, just making conversation about how they like swimming or hiking or whatever. They're not actually talking down to me under the impression that I'm the biggest idiot on the planet Earth who doesn't know how to try dancing or go for a walk. But the framing trips me up every time.
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