They don't tell you this but besides the Beloved Mutual there's also the Longterm Follower who you don't follow back but they're always in your notes and you're kind of watching them. Checking on the longterm follower's bio every so often like turning over a log. She's trans now good for her
Realizing I'm somebody's longterm follower who they don't follow back but check in on every so often. This is what's happening when you see the [Following] in your notes
how would you feel if you woke up tomorrow and find out exactly 100 of the world’s richest people died of heart attacks at exactly noon universal time. can you imagine the theories. light is absolutely a loser for not doing this
[ID: Reply from elumind that says: “Do the richest one every week and see next in line lose their shit and try to get rid of the money. I think of this almost daily.” /end ID.]
The notes on this are wild because people are legit passionately arguing about why this wouldn’t work. No one said it would work. They said he’s a loser for not doing it.
The first one stands up and draws a massive A on the nearest wall before dropping dead.
Exactly one week later, Thursday at 3:13 PM, the next one looks up, blank-faced, and uses a car key to scratch the word ‘CAMEL’ into the side of their car. There are memes.
The week after that, in the middle of an interview, the third victim turns to the camera and says ‘THROUGH.’ He drops dead.
The man who writes “EYE” is in a private underground bunker. Enough radiation shielding to survive a direct nuclear strike. There are fifteen guards posted at the door- surveillance confirms not one of them left their post.
By the time “NEEDLE” is scratched into the upholstery of a private yacht, people are starting to give money away.
Like most of us I’ve thought extensively on this since I first saw Death Note and came to the conclusion that the most likely reaction would be people creating more byzantine ways of keeping hold of their resources while not technically counting them as personal resources and not technically being so rich. With enough shell companies, fake charities, and resources stashed in secret or illegal places or the bank accounts of relatives, people could keep most of what they have while dropping right off any list of wealthiest people. The wealthy are often experts at this for tax fraud reasons. Light’s response, of course, would be to start taking these things into account, seeking out hackers and accountants and various other experts to keep track of the actual wealthiest, and the wealthy (many of whom would be willing to risk their lives to stay that way) would use the dying as a metric for what the mysterious killer was using to score wealth and try to find ever more secret methods of resource hoarding. An accountancy arms race would be underway.
I’m not saying it’s a bad idea. I’m saying it would make a fantastic Death Note rewrite. Instead of Light making stupid mistakes against L, he could actually put his genius to work in Death Note: The Accountancy Wars.
“A queer-focused TTRPG that employs art and dream elements to paint narratives about finding yourself and fighting your demons. Community, cooperation, rampant self-love, nightmarish enemies, and the beauty of embracing what you've been taught to ignore.”
lmfao i am too old to be reading posts from people sincerely pissed off that their art keeps getting comparisons to one of the modern masters of illustration. oh poor baby. that must be so hard
"i hate when people compare me to other artists" your audience likely lacks the vocabulary to articulate what those similarities are and why they resonate with the art bc arts education has been in the toilet for 60 years. they are complimenting you using language available to them under a framework unique to their personal experiences. what you want is a collection of dolls with a pull string you can use to elicit specific praises.
its been long enough now i can say the artist who the person who spurred this post was freaking out over being compared to was yoshitaka amano. can you imagine lmfao
[Description: a looping bouncy animation of grace and Rocky from project hail mary. Grace throws himself onto rocky for a hug, smiling and nuzzling his face to the top of Rocky's xenonite covered carapace. Rocky brings a claw up to ruffle Grace's hair and grace throws himself even more on top of rocky, rocky wrapping his arms around grace. End description.]
Love that Ryland Grace is the opposite of so many male protagonist "heroes" in media and yet he's still so incredibly brave, resilient, and strong. That flimsy little science teacher saves the day.
But he also,
Throws fits when things don't go his way. Not a "I'm a bad bitch" destroying everything-type fit, but tossing a trash can, breaking a screen-type fit.
Cries. A lot.
Pleads. He begs.
Doesn't answer the call to action.
Shows weakness. Being a coward and being fearful are two things he defines himself with.
Doesn't end up with the girl. In fact, that girl isn't even interested and he isn't, either.
Cherishes friendship over a romantic plot or something stereotypically brave like, "I'm going to save Rocky so I can save his world." No, he wants to save his friend, first and foremost.
Squeaks. He squeals. He screams, loudly and very high pitched. He whines. He complains. He physically struggles to open a jar. He's clumsy as hell. He makes some of the least graceful noises one can make.
Is not afraid to be the primitive species lowkey.
I love him and everything he stands for as a male protagonist. Men need to know that they can be just like Ryland Grace and still be just as much of a hero and a man.
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.
jbbartram-illu
vaporwave
stormborns
theartofmadeline
ellievsbear
