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I love how Zohran Mamdani is wearing a suit everywhere. And if he has anything else he puts it ON TOP of the suit. A basketball jersey. A high-vis vest. All worn over the suit. He’s like the mayor character in a cartoon who’s always dressed as The Mayor. If I didn’t know who he was and he biked past me in NYC I’d be like holy shit was that the mayor
Not to bring the serious to a very fun post, but this reaction is exactly what Mamdani is working for with his image, because in a very real way the most effective way for him to be The Mayor is if he looks like The Mayor.
This is a man who is VIOLENTLY aware that when it comes to conservatives, he is a Muslim first, a Brown Man second, an Immigrant third, a Socialist fourth, and a human a very distant fifth, if considered at all. He was also a young adult during the Obama Years and will have seen Republicans rip Obama to shreds for wearing a tan suit instead of a dark one and use literally ANY excuse they could to try and degrade his image.
Despite the fact that a mayor who wears a T-shirt and jeans might "seem more approachable" in the eyes of the average American, Zohran Mamdani knows that someone with his profile fundamentally cannot get away with that the way his White colleagues can. He has instead put in the effort to look professional and BE approachable, because not only does it make it easier for him to reach and represent his constituents, it forces everyone, including both his opponents and establishment Democrats, to engage with the work he is doing instead of judging his image. The fact that he is always seen in a suit and is recognisably The Mayor is, while also something he has fun with, a deliberate choice to ensure he is as inarguably A Professional Politician To Be Taken Seriously. The added humour of e.g. the hi-vis is a bonus, only achievable because he works so hard to Look Like The Mayor.
i got a fucking. advertisement on youtube. from google ai. saying. without sarcasm and with complete sincerity. "if shakespeare is too hard for you, you can always have our ai explain it to you." im gonna throw up. im gonna throw a molotov cocktail. if i see that ad again im reporting it for hate speech. how fucking dare you. i will kill you with my bare hands. with my exit pursued by a bear hands. i will tear google headquarters down brick by brick. im going to start biting people.
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its actually easy to de-enshittify your digital experience all you need to do is install this browser extension and this browser extension and this browser extension and input this custom script into the advanced box and go into your system settings and reconfigure all these options you didnt know existed and change your entire workflow and switch to this alternative operating system and this alternative web browser and this alternative chat client and this alternative word processor and this alternative- sorry that one turned out to be malware delete that one okay now double check your task manager for unwanted background processes and element block these ads and invest in a good VPN and append all your searches with AI blocking keywords and wait a few years until everything you just did becomes shitty too so you can do it all over again okay kitten. its literally that easy.
disabled ppl we need to start lying to nosy people okay? you tell me i'm too young to need a cane and i will tell you point blank that maybe you should tell that to the guy who ran me over. you don't get an explanation of my health issues you get lies and depending on how much of an asshole i want to be that lie will be anything from a humble car crash to a 1 billion lions attack. mind yr business.
"i could never live like that" well maybe you'll have to because this happened overnight. yeah you heard me i was the most able bodied man in the world but then one morning bam i woke up disabled. yeah you could have that too. there's no cure either you'll just wake up one morning and now you have to live like me
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The KIDS act (which contains KOSA) passed the US house, so I would recommend calling your senators and telling them to vote in opposition. I don't think I need to stress the importance of being able to use the internet freely and without privacy barriers being breached.
There are many scripts online you can follow, many ways to tweak your message to fit your senator (ESPECIALLY if they are up for reelection this year). Whether it's a recording you reach or a real person, be straightforward, don't argue, just let your rep know that you are a constituent and you oppose the KIDS act, etc.
Here is where you can search for your senator... call, email, fax, bring them physical letters, show up in person to their offices if you are able.
I really do wish meeting new people when you're autistic didn't result in the person viewing you as secretly evil for at least a month before realizing you just act slightly different than others without ulterior motive. I get that people meet a lot of assholes in life but omg. I didn't do anything
Reasons for hope: Lots of amazing people did a ton of work to make this fantastic, fully interactive resource available - because no matter how bleak things seem, there are millions, and millions of people doing everything they can to protect both the world and their own communities.
You can use this to view and subscribe to updates, project statuses, and for at least some of them even whole dossiers. This is an amazing resource, I highly recommend checking it out
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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.