Elon Musk is a white supremacist with a breeding kink who manipulates women into bringing more white babies into the world. He donated millions personally to Trump's campaign, led the DOGE takeover, ruined Twitter, publicly shames and disowns his trans daughter, and has been responsible for the rapid normalization of actual Nazi shit in public discourse.
No, Taylor Swift isn't "Elon Musk for girls."
Taylor Swift is a billionaire and undoubtedly a terrible person just like all billionaires. But comparing the two just so you can make a sly misogynistic comment about silly vain women and their silly vain interests is actually part of the fascist rot this shit contributes to.
The fact is, women aren't fascists at anywhere near the same rate as men, and people fucking hate that.
And one more thing, it's fucking tone deaf to say this shit after Elon publicly threatened to rape Taylor. They're the exact same though.
the thing about misogyny is that femininity is both devalued AND forced on women (and those misgendered as women). if you fail to meet the arbitrary standard of Womanly Charm you're ugly and unwanted and hate yourself and if you succeed you're shallow and brainless and weak. if you don't try to conform you're lazy and if you do you're vain. it is not possible to beat this system. feminism needs both "feminine doesn't mean weak or less-than" AND "women don't have to be feminine to have value", and if you are fighting for one of these the people fighting for the other are not your enemies, nor are they privileged over you.
this post is inclusive of trans people, including but not limited to trans women and transfeminine people. exclusionary sentiment is not welcome.
In Greece, Meteora features a cluster of ancient Eastern Orthodox monasteries built atop immense, natural sandstone rock pillars that rise like giant fingers toward the sky, seemingly suspended in mid-air. Video and description by Our Hidden Earth.
Cool, so you want natural fiber costumes with no/nuanced corset slander, people wearing colors, historical hairstyles, people wearing hats or headcoverings and long sleeves outside during the day, no potatoes or pumpkins in pre-columbian Europe, actors with textured skin and wrinkles, minimal makeup, consulting HEMA groups and weapons scholars for all the weapons and fight scenes, a good soundtrack that includes traditional instruments?
Oh, you mean you want 100% white people. Even in crowd scenes in port cities. There's a different word for that.
The original flag of Estonia survived Soviet occupation thanks to two members of Estonian Students Society, who hid it behind their farmhouse’s chimney wall for 50 years.
Nowadays, the original flag of Estonia is on proud display at the Estonian National Museum in Tartu.
At the risk of sounding anti-intellectual, I think that college should be free and also not a requirement for employment outside of highly specialized career fields
technically you can, if you don't care about degrees.
Free Harvard courses.
Free Courses from Stanford.
Free Courses from MIT.
Free courses from Yale.
Free courses from Princeton.
Free courses on Coursera.
Free Courses on EDx
Free Courses on Alison
For paid, there's The Great Courses+/Wonderium. 20$ a month for unlimited courses.
When searching, the phrases you're looking for are Massive Open Online Courses (MOOCs), or you can do a general search of say, "free online college courses."
Oh, and so you don't get surprised like I did, have an avoid: Hillsdale College is a conservative Christian site and not a valid MOOC place. Sign up with them and you will get things like THIS IS WHY THE LEFT IS TURNING YOUR KIDS TRANS AND GAY in your inbox.
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.
On fun thing about everyone using ai to do their data analysis or write articles or whatever is it's really exposing how many people weren't doing their jobs this whole time.
"We just found out that the ai tool we've been using to make business decisions for 3 months has been hallucinating all the sales data!" okay well it was stupid to use ai for this, why didn't you learn how glitchy it was before implementing it, but far more importantly, what you've just told me is that nobody bothered to check the real numbers for 3 months. "This guy has been using ai to write 9 magazine articles for us and the quoted info was all fake, he has been fired" no editor checked the first eight? "I used ai to write this report and the math was wrong" you didn't check the math? In your own report? Everyone is just telling on themselves re: how their work was always wildly unreliable and they already weren't doing their jobs.
Awhile back I was looking at some marketing nonsense about some company trying to create, like, virtual versions of workers that could help with their jobs, and obviously the actual target was employers (who they wanted to convince that they could replace all their workers with bots and save on salaries) but they were pretending to market it to the workers themselves, and their whole pitch was like "imagine if this digital version of you went to all your meetings and stuff while you hung out at the beach, and emailed you a meeting summary later".
And I was just like. Meetings where a bunch of people send their AIs to meet and then send them summaries? If no people need to be at the meeting, why is there a fucking meeting? This is Email With Extra Steps. If all that's happening in the meeting is people reporting a bit of information, that can just be a report. All this does is admit that you don't actually need the meetings. You don't need to have robot-only meetings you can just cancel the whole thing.
Students writing papers with AI that teachers then read and grade with AI while media sharing platforms are full of ai slop made so that other ais will watch the slop and inflate viewer numbers. While people send ais to meetings to summarise information given by other people's ais at the meetings. People prompting ai to write messages to their friends which are summarised by a different ai and the friend tells ai to write a message back to be ai summarised again. I saw a reddit post the other day about a guy who couldn't figure out why his bride stormed out of their wedding while he was reading her ai-generated wedding vows. Inaccurate ai-generated shit slips into publication because the editors already weren't checking (assuming the writers were) and now the writers aren't checking (assuming the editor will). It's fascinating to see just how much people are already just nor participating and now they're finding easier ways to not participate. You don't have to get married. The boss doesn't have to schedule these meetings. Every time someone is like "ai fucked this thing up" I'm like. Sounds like you were already not invested in doing it.
every day it just concerns me how little compassion people have. no compassion for those living in the global south. no compassion for immigrants. no compassion for disabled ppl. no compassion for addicts. no compassion for prisoners. no compassion for children. like holy shit ...
i made a separate post about this but actually there are plenty of people cough white people who care about animals more than they ever do human people . not what i'm talking about make your own post
lord knows I'm sick of people using "kink" as some magic shield against engaging with their own ideas and actions but it's wild to single out BDSM practicioners as people who "keep murdering women" and to do so by citing an eleven year old incident when I can name like, a half a dozen women murdered in nice Christian relations in the last few years. Sorry this is just another example of why it's hard to take radfems seriously
the thing about fairytale love is that it is not easy. it is love that needs to be fought for, and i don't mean by fighting the dragon and getting a spouse as the prize, i mean that fairytales that tell a story of marital rite of passage often feature themes of wrong assumptions, misunderstanding, broken trust, renegotiation, learning from one's mistakes, hard work and acceptance. the girl is in despair because she is betrothed to a serpent, the lad is shameful because he has to marry a frog, but they have to learn to find the person underneath; the husband burns his wife's feathery coat, the wife goes against her husband's wishes and takes a peak at his human form while he is asleep; and then they are gone. they are gone because their spouse has proven to be untrustworthy, because they broke a customary taboo. and now you have to venture east of the sun, west of the moon, wear down seven iron shoes, chew down seven iron breads. you have to defeat her abusive warlock father, you have to negotiate with his mother-witch. you have to earn their trust back, to accept them for what they are: a person. the idea that fairytale love is always an easy love, always love at first sight, with no inter-personal hardships or conflict, mostly comes from modern media that parodies fairytales, not the fairytales themselves.
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