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Hey, you, cis girl that's very (correctly) vocal about women being allowed to talk about their periods, do you include trans women in that?
I ask because every single time I've tried to talk about it to anyone that isn't a trans woman they get fucking angry. Which has caused me to have to just suffer in silence every single month. So I really relate to cis women when they talk about literally the exact same thing; being shamed by everyone around them their whole lives for talking about their periods, so they just suffer in silence every month as it negatively impacts their work and social lives. But I don't even feel like I can voice that I am literally dealing with the same exact thing because most of y'all react like you want to throw me in front of a bus for saying it, even those of you who act like your such big great transfem allies.
I guess I'll take this opportunity to talk about trans women periods.
The first thing any tme person thinks when they hear this is always "how can trans women have periods? They don't have uteruses!"
The answer is: the uterus isn't what causes your period, it is effected by your period. What causes your period and what causes trans women's periods is the same thing: the endocrine system.
HRT changes the sex of your endocrine system. Feminizing HRT makes it a female endocrine system, giving us a 28-day hormone cycle just like cis women. At the end of that cycle, the hypothalamus floods the body with prostaglandins. Those are what cause all but one of the period symptoms, because they make muscles inflame and contract. They are what make the uterus shed its lining, they are what cause intestinal cramps, they are what cause body aches, they are what cause headaches and migraines. The only period symptom not causes by the release of prostaglandins throughout the body is depression, and that is caused by your endocrine system simply not processing as much estrogen and from simply feeling like shit.
So, the only symptoms trans women don't get every 28 days is menstrual cramps, because yes we do not menstruate since we don't have uteruses. But migraines, depression, body aches, intestinal cramps, and the infamous "period shits" don't exactly add up to us having any better of a time. Except we have to pretend that we're fine and nothing is different because no one believes that we get periods, not even cis women.
"But you can't call it a period then because that refers to MENSTRUATION!" is another one I hear all the time. This is incorrect. You use the word "period" instead of just "menstruation" because it doesn't just refer to menstruation. It refers to a period at the end of the hormone cycle where we experience a host of symptoms. And not all cis women experience all of the symptoms that encompass the period. Not all cis women get migraines, or body aches, or have severe depression. If a cis woman gets a hysterectomy she doesn't menstruate either! In that instance she experiences an identical period to what trans women experience. Yet, I doubt you'd insist that cis women who've had hysterectomies don't have periods.
Oh, another thing that I personally discovered after bottom surgery: vaginal odor changes for trans women during our periods too. I was not expecting that because I always thought it was just from menstruation. But nope, the ph levels of a trans woman's vagina are the same of as a cis woman's vagina, and it changes during our periods just the same.
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The Scales of Seduction by Rien Gray [historical fiction, retelling, erotica]: An erotic lesbian monster novella reinterpreting the Medusa myth. Romance between Medusa and a basilisk warrior. Transfem butch protagonist.
Reverse Tomboy by Auto Anon [literary fiction, autofiction, essays]: A novella about a trans woman who struggles with relationships, familial dynamics, and reclaiming her masculinity after transitioning. Comes with an essay on transfeminine butchness and masculinities. Transfem Jewish butch protagonist and author.
Hot Girls with Balls by Benedict Nguyen [litfic, satire]: A contemporary satire novel about two trans lesbian volleyball players, who have to grapple with the anxieties of stardom as well as their their own off-court relationship drama. Transfem Asian-American protagonist. Transfem author.
Little Blue Encyclopedia (for Vivian) by Hazel Jane Plante [litfic]: A trans lesbian mourns her straight trans friend, and comes to terms with her death by writing a personalized encyclopedia/love letter about the latter's favorite obscure TV show. Transfem soft butch protagonist. Transfem author.
Herculine by Grace Byron [litfic, horror]: A disaffected and traumatized young woman arrives at an all-trans girls commune founded by her toxic ex-girlfriend, only to discover something sinister afoot. Transfem butch love interest and side characters. Transfem author.
Our Monsters by Jemma Topaz (mystery, erotica, satire): In a futuristic city inhabited by monsters, a human woman tries to solve a series of grisly murders, while romancing several monster girls. Transfem butch love interest (poly romance, not the only love interest). Transfem author.
Always open to recs. Transfem butches are loved and appreciated, and I don't tolerate invisibilization of transfeminine people when speaking of butchness and lesbian masculinity.
I often talk about reality checking and going along with it when helping someone deal with delusions, but I felt as if I didn’t really make it clear to what I meant when I said it.
Recently I saw a post similar to “how to sneakily give your delusional friend a reality check when they told you not to” and honestly, that’ll just make us lose trust for you. Because we can tell when you do that.
[Image IDs: Image #1: Flowchart titled “My friend is experiencing a delusion!”
First box: Have they asked you to give them reality checks when they have a delusion. Green “yes” leads to a green box reading: Reality Check. Pink “no” leads to a pink box reading: Let it be / Go Along With It. Black “no, but im still worried” leads to a box reading: Have you asked if you can give them reality checks and they said?
Green “yes” leads to a green box reading: Reality Check. Pink “no” leads to a pink box reading: Let it be / Go Along With It. Black “they said no but im still worried” leads to box reading: Have you asked if there is anything else you can do instead.
Green “yes” leads to green box reading: Do that instead. Pink “no” leads to a pink box reading: Let it be / Go Along With It.
Image #2: Pink box with text reading: What Letting it Be / Going Along With It Is Not: (red “X” bullet points)
adding new information to the delusion / trying to send your friend deeper into the delusion
“that must be scary to believe something that isn’t real”
“keep that to yourself, the more you talk about it the more you’ll encourage yourself”
dismissing your friend’s delusions, saying “everyone thinks like that once in awhile.”
trying to distract your friend from their delusion or act as if it’s a game
What Letting it Be / Going Along With It Is: (blue checkmark bullet points)
allowing your friend to vent or express themselves ( sometimes just talking about their delusions out loud can help them question it )
“I’m so sorry that you’re going through that”
encouraging your friend to express their delusions through poetry, song, art, or other non-harmful things they enjoy
“If that becomes too over whelming for you, you can call me and ask for comfort or a distraction”
allowing your delusional friend to feel safe around you, allowing them to healthily express themselves
Image #3: Green box with text reading: What Reality Checking Is Not: (red “X” bullet points)
“Snap out of it, it’s just a delusion.”
“That’s impossible.”
“Think about it for a second, then you’ll snap out of it”
forcing your friend into the hospital
calling the police on your friend
trying to fix or stop the delusion
What Reality Checking Is: (blue “checkmark” bullet points)
“Could you describe the evidence you have for your belief?”
“How firm are you with this belief? Would you be okay with other suggestions on what could be happening?”
“You’re going to be okay, what you’re fearing won’t hurt you.”
“Can we take a step back? Is what you’re going through effecting those around you?”
“Has what you believe happened to anyone else? What is the likeliness of this being real?”
Helping your friend question the delusion or become aware of how the delusion is affecting them.
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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.
This story from a reddit thread abt "why do dpairs stick together" has been haunting me btw. Especially the explicit acknowledgement that being nhl tier is being able to achieve 12 years of Learning Each Other in a couple of praccys
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I WAS FUCKING WONDERING WHAT THOSE DIGITAL PRICE TAGS WERE ABOUT SUDDENLY i had hoped they were so the workers didn't have to finagle those little papers into the slider part anymore 😭
Hi, yes, that is the OFFICIAL excuse made to me by the guy replacing the paper tags with digital ones at my local Walmart, but the end goal is to remove the numbers off the shelf entirely, replacing them with QR codes that you have to scan with the app…. Which requires your login information….. and also stores your card information so even if you didn’t use your Walmart account at the physical checkout, if you used a card they recognize, they assign that purchase to your Walmart account purchase history.
I explained very clearly to the manager my issue with the meat section not having the price tags listed, and they claimed it was only going to be for the meat, since meat is by weight, and the price of each item is printed on the packs of each item.
Sure. That’s how they get their foot in the door. Fast forward not even two weeks, and here we are:
Bar codes. No prices, no item descriptions. No price stickers on the individual items. Heck, not even the name of the item that is SUPPOSED to be there.
No. The only way to see the price is to scan it on your phone app, which is also recording what you looked at recently, as a way of gauging what you might be looking for in the future.
So here’s what we’re gonna do gang:
Every time you go into a store that has implemented these price-less tags:
Take 1-3 items up to the cash register. Ask the cashier for the price, or hit the price check item on the self checkout, which will likely call over the attendant.
Express that you didn’t actually want it, you just couldn’t see on the shelf how much it was.
POLITELY, AND WITH A THANK YOU FOR THE PRICE CONFIRMATION, Give the items to the cashier or attendant to put back.
When they inevitably try to push the app, politely decline. If pressed for why not, say you don’t want to have to carry your phone in-hand the whole time you are shopping in order to see how much things cost. (Not having cell service or data to use the app is NOT a valid excuse, as stores already often have complimentary WiFi AND more stores will provide WiFi rather than give up on this push for surveillance pricing)
If it’s a shelf-stable item, the cashier will have to set it aside, taking up room in their limited operating space, and eventually pass it off to someone to put in a holding area to put back later. If it’s a fridge/freezer item, it might have to get tossed due to food product sale regulations.
In either case, you are making it a pain in the ass for them to have these digital bar codes. Tie up the checkouts. Give the employees more busywork that the company has to pay them to do. Hurt their bottom line having to toss the pint of ice cream you carried around in your cart for 20 minutes before giving it back to the cashier.
Yes, call your reps. Yes, push for more legislation like this in more places. But also take an extra minute out of your shopping trip to MAKE IT HURT for companies to pull this shit.
I've seen some people in the notes express (very fair) concern that this is only going to inconvenience already under-paid laborers, and not have any impact on corporate. While I can't speak for every company or every store, I do work in a grocery store and I can tell you this is precisely the kind of thing that would have an impact, especially if people are doing it en masse. Stores absolutely track their shrink numbers, and they do draw distinctions between what gets stolen, damaged, or wasted for other reasons. If people are making it clear that the reason they're bringing things to the cashier is that the prices are not adequately represented on the displays, and rather than improving business it's wasting product, slowing down transactions, and causing confusion and mistrust in customers, that is a language that shareholders speak.
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