im posting this here despite the website being extremely white centered, I want people to understand how in this country it's basically ok to murder and victimize black people, especially women and children in the name of "self defense" and white America will reward you for your antiblackness.
Who are the hated popular group in high schools these days? Like, how we always complained on our myspaces and livejournals about “preps.” Who are the “preps” of today?
It’s p much cozies vs. modders, cheerhards, fruit babies, et cetera, plus you’ve got your muppet girls, wraiths, mechanoids and whatnot. Cozies only really get on with each other, though they might date a muppet girl or two. Fruit babies are cool with anyone who’s not a cozy. Modders always want to date mechanoids, of course, but they rarely get a chance to. Modders stay underappreciated, honestly. Cheerhards are vicious, razors in their pompons and all that, going around shoving everyone else into lockers (except for wraiths, who are incorporeal).
I paid serious banknote for this 411, use it well.
Israel's attacks have been relentless since the eve of Eid al-Adha on Tuesday, May 26. Just yesterday the head of anesthesia at Al-Yafa Medical Hospital was killed in an attack that injured three others. Over 50% of hospitals in Gaza are nonfunctional. Even when Palestinians survive attacks, treatment is limited and medical bills are expensive.
This targeting of healthcare workers and infrastructure is the reason my friend Fadel needs to raise funds for medical evacuation to a hospital outside of Gaza to surgically remove shrapnel that embedded in his body during the Israeli airstrike that destroyed his home while he was inside.
Fadel (@fadel-dani) is currently struggling to afford his hospital bills. He is being treated for severe anemia and malnutrition as a result of long term food insecurity and being unable to afford his monthly medication to treat his blood disorder. He suffers from frequent fainting and collapses unconscious, leaving his family terrified that he has died from starvation. Please help him.
My friends ,You are our last hope. If you leave us now, we will not survive. Please stand by us before it is too late, Please don't ignore what we're going through.
Please don't stop, please don't stop. Your Help help me a lot. The medication is very expensive, and my condition is very serious. Please stand by me now now, please.
I think overall there will probably be devastating long term effects on the movie industry from a horror movie based on a youtube video based on a 4chan post making 7 times it's budget on opening weekend but I think the movie itself is probably preddy good
what studios are going to take away from this, unfortunately, is not that building practical sets instead of green screen stages and taking a risk on a 20 year old first time director can sometimes pay off and elevate a really thin script into something more substantial. they are going to start asking themselves how do we make a movie out of that one post that says notice how there was no wednesday this week
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.
If I could sit down every single new sewist who wants to learn and improve their skills I would tell them the following things
Read the manual. Work through the manual from front to back. Read the troubleshooting section even when you're not having a problem. Read it. Keep it handy. Maybe download a PDF too, just in case the hard copy walks off.
Buy an iron and ironing board. They don't have to be fancy, a bigger board is only better if it is stable. An regular Ikea board has worked for me for years. A $20 iron from Walmart will noticeably improve your work. Press every seam before another seam intersects it. Don't just iron at the end.
Sewing machine needles have sizes AND types and you need to match them to your fabric. They also wear down. The often quoted rule is 8 hours of run time and its a pretty good rule.
The problem is almost never the tension settings, and even less often is it the bobbin tension. The problem is almost always your threading or needle.
Clean your machine. Do not use canned air. Take off the plate under the foot and use a brush and or a keyboard vacuum to get the fluff out.
“he was talking about emperor Valerian, whom the Persians allegedly skinned, stuffed, and kept on display, right?” wrong, that would be too obvious. he roasted Trump by comparing him to Philip the Arab
