fuck "slavic doll", fuck "slavic bimbo", fuck "oooh slavs are so mean and cold they got the scary Slavic Stare", fuck you romanticizing our pain and generational trauma, fuck off with only representing our countries with those old post-soviet dilapidated housing as if no time has passed since the 90s and we didn't progress at all, fuck off with reducing us all to some old Russian stereotypes, fuck off with your fetishising aesthetic build off of 5 emaciated models from the 2000s, some photos of fur coats and ushankas thrown in the mix and the usual coquette bullshit you just try to rewrite in cyrillic because it' "different" and "exotic", yet still fits a white supremacist beauty standard.
Tying your bullshit anorexia and starvation romanticisation to an ethnic group that has experienced so many famines, crisises, wars, back to back poverty for generations is disgusting.
When a cop pulls you over and asks what you’re up to, he isn’t interested in the goings-on of your day. He’s asking you, “Can you tell me something I can use to hurt you?”
When a hiring manager asks about a gap in your resume or why you left your last job, she isn’t chatting out of curiosity. She’s looking for a reason not to hire you.
Even when your doctor asks you questions about your thoughts of self-harming, they aren’t necessarily really asking you about this for your own benefit because sometimes they’re asking, “Would you like to be abducted and forcibly restrained for a minimum of a few days?”
The difference here is power. Power changes things because what’s associated is the threat of violence, direct or indirect. A guy holding a gun asking, “That’s a nice phone. Can I see it?” is not paying a compliment or making a request but giving a pretty direct threat while mugging you.
It’s not even autism; it’s just understanding realistic social dynamics.
Your landlord is not your friend. They don’t have to live up to their handshake agreements and verbal contracts. You should not be revealing information to them that they can use to hurt you, even if you think they won’t.
Snitching is not telling the truth, and covering for someone else by deceit is not lying (better to shut the fuck up, though). Because power transmutes social relations with the magic of violence, implied and direct.
Despite ICJ genocide warnings, 51 nations kept arming Israel, even after pledging to halt support.
A months-long Al Jazeera investigation has found that military-related goods originating from at least 51 countries and self-governing territories continued entering Israel after the ICJ’s warning of a plausible risk of genocide in Gaza.
Based primarily on an analysis of Israeli Tax Authority (ITA) import data between 2022 and 2025, and supported by customs records and freedom of information requests, the investigation traced military supply chains linked to countries across Europe, Asia, North America and South America. All named countries are signatories to the Genocide Convention.
In some cases, the military-related goods originated from countries that had formally imposed arms embargoes on Israel or had partially suspended arms supplies to the country.
In fact, according to the ITA data, arms imports increased after the ICJ ruling, with the largest share falling under the category of munitions.
The five largest countries of origin for military-related goods entering Israel - the United States, India, Romania, Taiwan and the Czech Republic - all recorded increased shipments during the war.
[...]
Patrick Wilken, a researcher at Amnesty International, said the scale of Israel’s military campaign made such international support indispensable.
“There is no way that Israel alone could have sustained the intensity of its massive bombardment across the Gaza Strip,” he said. “Israel has relied on a global supply chain of arms, munitions and support services, primarily supplied by the United States but supported by many other states.”
As the war continued, weapons-related imports to Israel increased. The data appears to bear that out.
[...]
Neve Gordon, an Israeli professor of international law and human rights at Queen Mary University of London, told Al Jazeera that “country after country has contributed to the evisceration of the international legal order by feigning blindness and refusing to abide by their legal obligations".
“This is myopic. The very states that helped build this legal order, and invoke it constantly, are now playing a central role in its dismantlement.”
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.
baratheonpilled
dededos
pixelskylines
jacobvanloon
