Hi, its me. The warmest creature in the world. I love you. Im the warmest creature in the world and I love you so much and I need to be in your lap right now. Yes, I know about the heat wave. That's okay though because I was already the warmest creature in the world so I don't mind. I love you and you need to let me sleep in your lap right now. I'm soooo warm and I love you sooo much. If you say no you'll be saying no to a thing that love you. Let me sleep in your lap. When I fall asleep I get warmer. I love you
When I saw Chaosium was doing a new King in Yellow themed campaign, I sorta shrugged. I have a lot of love for the King, the Chambers book was an obsession of mine when I was in high school in part because it was impossible for me to find a reprint (I did, eventually), until I found my strange, probably rebound first edition in yellow boards, which I then carried around with me everywhere. The mention of Carcosa in True Detective set my brain on fire. But in a lot of ways, my relationship with the Carcosa mythos culminated with the Delta Green campaign Impossible Landscapes. I think maybe Delta Green culminated along with it in some ways. It’s just one of those things were its greatness and the way it transcended its form was immediately apparent and largely beyond debate. So, no shade, but that’s a hard act to follow.
And then I read The Sutra of Pale Leaves: Twin Suns Rising (2025) and was impressed despite my expectations. Spoilers ahead, so let me just say that I think this is worth picking up and checking out, especially if you’re intimidated by Impossible Landscapes (which is a fair reaction). But for real, if you think you’re gonna play this, stop reading.
This book, and its companion, are set in Japan during the 1980s, which is right out of the gate a dramatic shift. The way in which the authors intertwine Japanese folklore and pop culture with the tropes of the King in Yellow mythos is impressive in its own right. Two of the scenarios here use Yokai in ways that cleverly nest with themes from Chambers’ original stories. The third scenario features manga subcultures, a comic convention and cosplay and is really rather brilliant. All the themes and topics are reinforced by art in a complimentary style, so, the character portraits in the manga scenario all look like manga.
The real star of the show, however, is this manifestation of the King himself. He’s a meme who replicates himself in the minds of anyone suitably exposed to the titular book. He bends the wills of his hosts to find new ways of repeating the viral code in order to get ever more hosts (so, say, by translating the Sutra into a mass market manga). Nearly all the scenarios require players to interact with the Sutra and its derivatives to succeed, but in doing, they open their minds to the King (called here the Prince of Pale Leaves). Thus opened, the Prince knows all they do and think and, eventually, can take them over entirely. It’s an insidious and paranoid meta!
Hi, its me. The warmest creature in the world. I love you. Im the warmest creature in the world and I love you so much and I need to be in your lap right now. Yes, I know about the heat wave. That's okay though because I was already the warmest creature in the world so I don't mind. I love you and you need to let me sleep in your lap right now. I'm soooo warm and I love you sooo much. If you say no you'll be saying no to a thing that love you. Let me sleep in your lap. When I fall asleep I get warmer. I love you
Hey, we’re in line for some absurd temperatures here in the southwest this week. This is very important to know and keep in mind. Be safe, stay hydrated, stay out of the sun as much as you can.
404 has been knocking it out of the park since they started. Please support their original reporting on this! If you subscribe to nothing else I highly recommend them. Their podcast is great too.
Planning documents for "Scout" say the plan is to "make people addicted" to the tool before adding new features.
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.
i think the key difference between george lucas’s star wars and disney’s star wars is that lucas is a man with an ideology. someone with a point of view, and all that entails. which comes with ideas of revolution, anti-imperialism, challenging the status quo, cultural appropriation and racist stereotypes. complex and contradictory ideas because that’s how artists are: complex and complicated people. disney is not. disney is a corporation. a corporation can’t have ideology, because ideology defeats the purpose of profit. and when the only thing you do is to turn on the movie manufacturing machine before you sit down and plan what ideas are you trying to convey to the audience, then your results are going to be washed out corporate garbage. and because when you’re a giant corporation who only cares about selling to the widest audience possible, you can’t take sides. you can’t decide on an idea. because you want to sell your product to people who are on the entire political spectrum. which results in movies without ideology, without purpose, without soul.
I have been looking for this post for years after I came across it and it’s finally here and I need to reblog this because it is absolutely and entirely accurate.
#as I always say: lucas was making a samurai film and a ww2 flying ace film and a western film and adding laser swords#because he fundamentally LIKED samurai films and dambusters films and westerns and 40’s adventure serials#but disney are making a ‘star wars film’ and adding nothing because it already had laser swords and they have nothing else to say#xerox of a xerox baybeeeee (via harrietvane)
hey boy don't kill yourself. green's dictionary of slang is available online and allows you to explore 500 years of english vulgarity. you can search by part of speech, source, time period, etymology, and usage. there's a whole category for gay slang. they even have specific citations listed so you can see the exact context for yourself. boy did you know that in 1927 "to kneel at the altar" was slang for "to sodomize"
Princess: an effeminate and relatively youthful male homosexual or lesbian (1931-4)
Daffodil: effeminate young man (1925)
To throw a fuck into: to have sex with (1919)
Top sergeant: a masculine lesbian (1939) [‘she takes command of the girls’ privates’]
Lily: penis (1919)
Wolf: sexually aggressive man (1847); a homosexual top (1918)
Soul kiss: a deep kiss, involving putting one’s tongue into one’s partner’s mouth (1907)
Tom: a lesbian (1909); [in 'old tom'] prostitute catering to lesbians (1966)
Church mouse: a male homosexual who frequents crowded churches in order to fondle any potential sex partners. (1941)
Discover one's gender: to accept or acknowledge one’s homosexuality (1941) / Lose one's gender: To return to living as a heterosexual
Minty: a masculine lesbian (1941)
Also a lot of early 20th century vulgarity is recorded in Letter from My Father, which is a collection of letters published by a man who's dad was, in short, a major slut and human disaster who wrote about his sex life for his son. It's insane. You can find copies of it online & it's a wild fucking read (literally!) and I think a really interesting look at the life of a person who goes against our stereotypes of what people in the past were "supposed" to be like.
Anyways feel free to add y'all's favs to this post. & if you use this for gay historical fanfic please share with the class
#OH THIS IS EXTREMELY EXTREMELY HELPFUL#writing#resources#saving for later#maybe i should move my 1920s story from '25 to '27 because..... bro..........
note for writers: these are dated to the first time they were recorded, not necessarily to their first use. I imagine for many of these, they came about naturally through spoken language before they were written down anywhere. This is especially true of more underground slang because it's probably being recorded (in ways we still have) the least. So if you wanna use a term but it's a little off date-wise, give yourself some wiggle room.
also gonna take this moment to highlight two more i found recently:
Best boy: a sweetheart, a boyfriend, a husband. (1893) [w the obvious equivalent term 'best girl']
Honeydripper or honeydrips: a sexual partner (1917)
Like. Honeydripper?????? That's so horny I can't stop thinking about it. We need to bring THAT back
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