On this blog you will find a range of topics. It's a wild mix of everything that catches my interest. I generally try to tag everything, but if you need something tagged that I'm not tagging yet feel free to ask.
We are against censorship and harrassment here. Fandom is for fans not creators. The only "boundary" that matters is "Do not send fanworks to creators unprompted."
Some important organisational tags you will find here:
scummy post - for all my own posts
scummy life - for personal posts, i try to remember to turn off reblogs for these but i might forget so if you see this tag please don't reblog
scummyfic - for posts about my own fics
fic rec - for fics i've read and liked and want to share
I also have a discord server for those who are interested. It's kinda quiet but it exists. It is adult only so please don't try to ask for a link if you are younger than 18. It is mostly geared towards Hermitcraft right now, but there is always room for people from other fandoms. If you want to join please ask for an invite.
If you want to create anything based on my fanfics, I have a blanket permission statement on my AO3 profile.
My own contribution to Sports AU Summer: Rowing Ranchers
In 2026, the chicest thing a gay actor can do is never explicitly come out as gay but also make it abundantly clear that he is. Coming out is too modern. Staying closeted is too old fashioned. But this method merges contemporary freedom with Old Hollywood glamour and allure, and it weeds out the dumbest people who truly don’t get it. I call it the Pascal Method.
You clearly don't go here or to queer history and signaling, or both, enough to have this conversation and I'm not going to explain it to you. You could have asked questions, you could have done even a modicum of research. You didn't and you made yourself look ignorant. Goodbye.
#I'm fucking crying#this is an instant classic#this is the next meme#i can't believe I'm here to see a baby copypasta nary two hours old#I can't#lol#i laughed way too hard#iconic
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.
5 Tiny Writing Tips That Aren’t Talked About Enough (but work for me)
These are some lowkey underrated tips I’ve seen floating around writing communities — the kind that don’t get flashy attention but seriously changed how I write.
1. Put “he/she/they” at the start of the sentence less often.
Try switching up your sentence rhythm. Instead of
“She walked to the window,”
try
“The window creaked open under her touch.”
Keeps it fresh and stops the paragraph from sounding like a checklist.
2. Don’t describe everything — describe what matters.
Instead of listing every detail in a room, pick 2–3 objects that say something.
“A half-drunk mug of tea and a knife on the table”
sets a way stronger tone than
“There was a wooden table, two chairs, and a shelf.”
3. Use beats instead of dialogue tags sometimes.
Instead of:
"I'm fine," she said.
Try:
"I'm fine." She wiped her hands on her skirt.
It helps shows emotion, and movement.
4. Write your first draft like no one will ever read it.
No pressure. No perfection. Just vibes. The point of draft one is to exist. Let it be messy and weird — future you will thank you for at least something to edit.
5. When stuck, ask: “What’s the most fun thing that could happen next?”
Not logical. Not realistic. FUN. It doesn’t have to stay — but chasing excitement can blast through writer’s block and give you ideas you actually want to write.
What’s a tip that unexpectedly helped with your writing? Let me know!! 🍒
The Phantom of the Opera: I was born with facial deformities that are so horrific that even my own mother couldn't bear to look at me.
Me: So that's why you have to live as an outcast in the basements of the opera house?
The Phantom, who has been an admired performer, palace architect, royal executioner, and chief contractor of the opera house who is able to go out in public with no alteration to his face except a false nose and mustache: No, that's a separate choice that I've made.
The European Union already forced Apple to abandon its proprietary charging port and adopt USB-C across its entire iPhone lineup. It just did something bigger. A new EU mandate requires every smartphone sold in Europe including Apple devices to feature a battery that can be replaced by the user without specialist tools, without voiding a warranty, and without sending the device to a manufacturer approved service center. Batteries must maintain a minimum capacity threshold after a set number of charge cycles and replacement parts must remain available for up to ten years after a model goes on sale.
The consumer electronics industry built its current business model around batteries that degrade, cannot be replaced at home, and create a natural upgrade cycle every two to three years. The EU just legislated that model out of existence in the world's largest regulatory market.
Apple, Samsung, and every other manufacturer now faces a choice between redesigning their devices for the European market or accepting that their current hardware architecture is no longer legally sellable there.
Given that no company walks away from European consumers voluntarily the phones are going to change and once they change for Europe the rest of the world will ask why theirs still do not.
