Every 'normal' adult is fussing around Pugsley and Wednesday because "poor children that must be so hard for you to see mom and dad break up like this"
But the kid are absolutely unfazed, arguing that "it's alright they will be together again soon". The normie are so sad for the "children clinging to vain hopes" until Morticia and Gomez get married again two weeks after the divorce.
In the meantime Mama and Uncle Fester fight about which one of them will go to whose custody.
They pretend to argue in court and at meeting with lawyers over the splitting of the properties but that's mostly Gomez insisting to leave more and more thing to his wife in an angry voice.
At home they decided not to talk to each other so Lurch has to (begrudgingly) transmit messages from one to the other, even when they are sat on either side of the table.
That works (more or less) then Morticia says one word in french and Gomez run to cover her with kisses until Morticia remind him that they are spliting (that's the only moment he seems to regret the whole thing)
at some point in your life you will be boiling fruit, water, sugar, and lemon juice in a pot to make a syrup or jam. the instructions will tell you to simmer for a certain amt of time. your timer will go off and you will look at the pot and go, "hm, this doesn't look thick enough. maybe i'll let it go for another 10 minutes." this is the devil speaking. it's only so liquid right now because it is at boiling point. it will thicken when it cools down. learn from the follies of my youth and do not let this happen to you
at some point in your life you will be making a sauce or a stew in which you need to add cornstarch to thicken it. and you will prepare a slurry of starch in cold water and think "this looks like way too little starch to thicken this amount of liquid." this is the devil speaking. cornstarch instantly polymerizes at 95°C and if you add too much it will turn into an impossibly thick goop.
at some point in your life you will be making some sort of cream based dessert that requires gelatin to thicken it. and you will soak some gelatin sheets in water and think "this is too few gelatin sheets for this amount of cream." this is the devil speaking. it will thicken in the fridge and if you add too much you will end up with milk jelly
at some point in your life you will be baking cookies. you will take the sheet out after twelve minutes as the recipe instructs and the cookies will still be glistening and soft. "these don't seem cooked enough," you will think to yourself, "i should place them back into the oven until their edges are nice and golden." this is the devil talking. this is how you get dry, overdone cookies. the cookies will continue to bake on the warm sheet for several more minutes and then harden up after sitting on a rack for a while. trust the process. trust the process.
at some point in your life you will be adding a small pasta to a soup and you will think "that is not enough small pasta." this is the devil talking. the pasta will absorb the stock and expand. this is how you end up with a soup that is a solid mass of soggy ditalini.
At some point in your life you will be adding garlic to a dish and you will think "that is not enough garlic." These are angels speaking. They are correct. Add more garlic.
i personally believe that maximus was the former captain of the guard who was cursed into horse form pretty recently, and everyone’s having trouble adjusting to it.
like, he’s entitled to sick leave, he really ought to take a couple months to break the curse and come to terms with whatever the fuck just happened to him, but instead of embarking on a journey of self-discovery and healing he just keeps showing up to work. no one can get captain maximus to go on his fucking voyage of self actualization and fix the curse because he’s obsessed with catching flinn ryder. everyone really fucking hopes that when he finally catches this guy the curse might be broken anyway, but it isn’t.
he just keeps showing up to work. he glares at the stablehands until they saddle him up. everyone’s gone over the regulations a dozen times but there’s nothing there saying you’re not allowed to saddle and ride the captain of the guard if he makes you do it. his former second in command rides him around like ‘sir i really don’t know about this’ and he’s just like ‘are we going to catch some criminals or what.’
you can see in the movie that everyone in the royal guard defers to this horse. it’s absolutely because that’s their boss. and secondarily because now he’s two thousand pounds of percheron.
there is no evidence against this theory and you cannot prove me wrong.
Recently, I was standing outside my son’s classroom waiting to talk to his teacher. I stood on one side of the hallway, not even close to the center. At some point, a man came walking along. I was standing right in his path, but the hallway was empty, so I logically expected him to swerve around me. Instead he kept walking right toward me, got to me, and stopped, as if waiting for me to get out of his way. I didn’t; I just smiled politely at him. He finally walked around me, clearly annoyed that I hadn’t leapt out of his manly path.
Now I’m wishing I’d leapt aside, taken off my jacket and laid it on the floor before him, then bowed deeply and said, “My Liege!”
I also work at a college campus. I smack shoulders sometimes, but I find that if I stare straight ahead and follow the advice below, people get the heck out of the way.
Honestly this post changed how I carry myself when walking alone in public, or in a situation where I’m the one leading. People definitely move for the murder gaze.
Confirmed. I once had to rush back inside a convention hall as the con was closing in order to a retrieve a sick friend’s medication, and I didn’t understand why people in the crowd were jumping out of my way (literally—one guy vaulted a table) until I realized I was dressed as the Winter Soldier and doing the Murder Walk because that’s just how I walk in those boots. I got the meds, got out, and made a mental note.
I repeated the experiment later, wearing the boots but otherwise my usual clothing and mimicking the expression I thought I’d had at that moment. People parted like I was Charlton Heston.
I now wear that style of boots whenever possible. I recently had a man do a double-take as I walked by and ask me, politely, where I had served because I “looked like a soldier.” I’m not current or former military. I was wearing a flowy purple peasant top and looked as un-soldierlike as possible.
Moral of the story: wear comfortable shoes, square your shoulders, and walk like you’ve been sent to murder Captain America.
IT’S BACK!!!!!! I was searching for this to show my daughter the other day and couldn’t find it. I’m so glad IT’S BACK!! I will always reblog the Murder Strut!!
In case you were wondering, yes you can do this in a wheelchair. Same look in your eyes and let ‘em know you will run them down. Just picture yourself in a sports car accelerating towards someone with the intention of flattening them.
If there’s anything more satisfying than watching Abled men leap out of my way when they realize I’m not moving for them, I can’t think of it atm.
I can actually answer this! So the latex/rubber they used, while standard for Hollywood at the time, reacted REALLY BADLY to being doused in pouring water nonstop for an entire day of shooting. They ended up corroding, which caused them to stink really badly and glob together at the seams. The original plan was to hand out masks to various crew members on the final day of shooting as souvenirs, but the sopping wet, melting, rotting rubber got so gross that by the end of that shooting day they’d already thrown most of them out. Somewhere in a landfill are hundreds of disgusting, bloated, slimey Hugo weaving heads fused together into a nightmarish rotting amalgam :)
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.
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