I’m lindy linda lenda or L. whatever pronouns is fine. I’m an anime fanfic writer and you can find my Ao3 account here. 18+ only.
you may request fics through ask but I tend to work semi-slowly. you may ask for a fic and get it a month later.
mostly a silly little fandom blog for the likes of blue lock, saiki k, buddy daddies, and jujutsu kaisen. I post sometimes about sk8 the infinity, haikyuu, link click, and genshin impact (and other fandoms). sometimes I write mini fics and headcanons as well.
occasionally you’ll find social justice posts because it’s important to know what’s going on in the world.
i try to be nice 🤓 but I don’t guarantee niceness when it comes to what I think is problematic so tread lightly! (zionists dni!)
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.
#WHAT a fucking read of the manic pixie dream girl#I want this desperately actually???#perfect quirky enigmatic mystery girl who has all the traits you don't but long for#and lives life with extreme confidence and whimsy doing whatever she truly wants#and she's the future you can have!! she's here because she loves you! she wants you to be happy!#she hated being you so she knows how much you hate being yourself but she's here to prove to you#that there is a joy you can attain#there is a self you will love (tags via @aethersea)
do you know where "no beta we die like x" comes from and how it is used?
The term "beta" in this context is short for "beta reader" - a person who reads a fic while it's still in the editing stage and helps the writer get it ready to post. Some betas check grammar. Some check canon compliance. Some are sensitivity readers. There are lots of things that betas can do.
So functionally, saying "no beta" means that the writer didn't get this checked by a second person before they posted it. It's a warning that there might be errors or typos etc. It's mostly used when an author has written something quickly and is posting without doing a lot of (or any) edits first.
As for where it comes from? It all started with a bumper sticker.
This image was an internet meme at one point, and it got meme'd on in the form of "no ___ we ___ like men"
Here on tumblr, one of the versions that got really popular was from now-deleted user @grec1a who created this version:
From there, it migrated to AO3 as the "no beta we die like men" tag, and very often the word men is replaced by the name of a character who dies in canon.
sometimes you’re too mentally ill to watch that show because it’ll trigger you too much. It is your job, unfortunately, to know this because I hate to break it to you, you’re the only one with your brain. you’re the only one who experiences yourself firsthand. If something is triggering you, you can and should walk away from it rather than interacting over and over and over trying to make everyone else conform to your current state of not being able to handle the thing.
Very many white LGBT people understand the idea that the societal concept that CisHet is the ‘standard’ or ‘default’ state of a human, at best, is a flattened look at the human experience, and worse a dangerous idea that leads to discrimination. Yet not very many of them seem to grasp the similar concept that thinking white people are the ‘default’ or ‘standard’ version of a person also leads to the same thing.
The entire range of human skin tones varies from so pale white that it's almost kind of blue, to so deep black that it's almost kind of blue, and I think that's beautiful. Do you love the colour of the guy.
There should definitely be more queer media, don’t get me wrong, but I fear a lot of white western sapphics are underselling how much wlw media there is because they’re not willing to consume media that doesn’t have white leads or is in any language other than English. Thailand is pumping GLs out constantly. There’s a main lesbian couple on the biggest Brazilian telenovela airing right now. You’ll have plenty of sapphic media on your plate as long as you read subtitles. Hollywood isn’t the only place where movies/shows are made.
andulkaphoto
janainaart
theartofmadeline
germansingergirl
discoholicmusic
turnnoffyourmind
areudirty
