Eva Stratt does NOT need a redemption arc. Eva Stratt did EVERYTHING WRONG so that no-one else would have to. Her hands are permanently stained with blood so humanity gets to keep on living.
All those "spend just 35 minutes doing a bunch of really hard exercises and you'll feel great forever!" videos are really discouraging. I usually end up doing like, 2 minutes of some of his while waiting for my shower to heat up, and ykw?
That's 2 minutes more than I would have done otherwise.
oh hey I have a thing like that last one that I like to do sometimes, but I do it lying down in bed. it's a very satisfying pop/shift in my hips/lower back, but doesn't always work. maybe I will try his version and see if it is more effective for me
You know how canaries were historically brought into coal mines, because if the mine was full of carbon monoxide the canary would die first and the miners would be able to escape before they died too?
I just found the greatest thing.
This is a canary resuscitator.
When the miners notice the canary getting sick with carbon monoxide poisoning, they can close that circular hatch so no more gas gets into the canary cage, and open the valve on that oxygen tank to keep the canary breathing. In other words, they made a spacesuit for birds.
By immediately giving the canary access to clean air, the miners can save it from the poison. The bird lives. To be clear, this is not for economic purposes, this was specifically created because the miners felt bad and wanted to save the bird.
Isn’t that just the perfect demonstration of what humans are like? We started sacrificing small creatures to save ourselves, and then felt bad and spent our valuable resources on saving the critters too. Because yeah the canary was the only way to test for CO, but it’s a living creature too, dammit!
watching sinners with an inflation calculator open in a second tab so i can understand just what kinda money the smokestack twins are throwing around. nerdiest possible movie experience i think.
Okay coming out of lurking for this because among the many great features of Sinners is you don't actually have to go outside of the movie to understand what kind of money they're throwing around. The movie tells you itself.
In the scene where Smoke teaches the young girl how to negotiate, they're standing in front of of a cafe. The shot of them negotiating is framed so that you see a sign in the cafe window advertising a Ham and Eggs breakfast - in other words, a full meal - for 25 cents. The editing makes sure to put that sign back into frame whenever the question of the value of money arises in their discussion.
Smoke offers her 10 cents a minute and asks if that works for her. She says yes. He says no, it does not and tells her to negotiate higher. The 25 cent sign is framed in the shot when he tells her no, reminding us *why* it's not a good value.
She comes back with 50 cents - which the sign has informed us is the cost of *two* meals. Smoke tells her that's too much and counters with 20, which is just under a full meal but we now know that's a fairly respectable price because we just got the high/low contrast of 10 being too little and 50 being too much.
The negotiation ends with her getting 20 cents per minute and we now know 1) 25 cents is the cost of a filling meal in this environment 2) This girl only needs to do five minutes of work to be able to feed herself for a over day (20 cents per minute times five is a dollar, which is four meals) 3) Smoke has the kind of money to throw around that over a day's worth of food for someone can be to him - as it is to our modern eyes - mere pocket change and 4) Smoke's the kind of person who can both be a violent gangster but also care about teaching this girl how to look out for herself so that one day maybe she too can throw over a day's worth of food around like pocket change.
Combined with 5) you can now use that 25 cents = a meal to do the math every other time money gets mentioned in the movie to understand just how much cash the Smoke Stack boys are dealing with.
And that's just ONE detail which, thanks to props (Hannah Beachler), editing (Michael P Shawver), and cinematography (Autumn Durald Arkapaw), told you almost everything you needed to know about how finances work in this environment. This movie is unfair to all other films in how fucking good it is.
your world is dying. your husband presumably dies in space so now you're a widow and your world is still dying. then 50-ish years later your husband everyone assumed was dead pulls up in a random spaceship full of a highly volatile atmosphere with the solution to your impending apocalypse and none of the original crew from his mission and instead with a wet, blobby alien that he pack bonded with and almost died for and is now emotionally codependent on that now wont stop complaining about the temperature of the water in its enclosure. your name is adrian and you dont know how you got here
I think about how hayao miyazaki said that love is two people inspiring each other to live. and to live doesn’t just mean to be alive. living involves finding beauty in the simple moments of being. so to inspire someone to be in awe of the simplicity of living? that’s special
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 actually do think we should discourage women from becoming housewives. Do not become financially dependent on a man. That's how a lot of women ended up dead over the years. A man gets violent suddenly and you have to choose between homelessness or potentially dying at his hand because you have an enormous gap in your resume and no degrees or certifications or anything that will help you pursue a career that will allow you to be financially independent. He owns your bank account. His name is probably the one on the car. Try and leave and he can report it stolen. Where will you go then?
As a principled feminist I'm often tempted to say shit like "are men capable of higher thought" but then I have to remember not to perpetuate gender essentialism and change it to "why do men choose not to think about anything"
Steal from everything you love. watched a movie and thought "wow that scene hit different"? figure out why and use it. read a book where the banter made you kick your feet? study it. saw a tiktok that made you Feel Things? that's research. keep a notes app full of random lines you'll never use. screenshot tumblr posts at 4am for "inspiration." your influences should be obvious and chaotic. remix everything. that's not theft that's apprenticeship.
all i want for 2026 is that gigantic rancid AI bubble to finally burst in such a catastrophic way that the consequences will be so good and i'll never have to see another AI generated image ever again
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