I’m kinda surprised that nalbinding isn’t as popular as crochet and knitting tbh because it has an even lower barrier of entry tools wise and unlike crochet and knitting it makes fabric that you can cut.
I feel like part of it might be casual people are generally aware of the existence of crochet and knitting, even if they don’t know very much about either, but have never heard of nalbinding
Yeah I hadn’t heard of it until recently and I ordered a big bone needle for myself to try it out and that should be arriving soon.
I was surprised that I’d never heard of it though. It’s older than knitting and crocheting and even though it’s been done all over the world it’s super relevant to Nordic culture and my grandmother and I are both into keeping in touch with our roots a bit so I’m surprised I’ve never heard of it.
It seems like the sort of thing that would be popular even if not as popular as crocheting and knitting, considering the low barrier of entry.
You also don’t need a bunch of different sized needles for nalbinding or whatever. The size of the stitch is controlled either completely freehand or by pulling it against one of your fingers. Most people who have a lot of nalbinding needles seem to either have tried out wood, bone, and metal ones to see which kind they liked or they enjoy carving wood or bone and like making their own needles as an extra hobby.
It’s also a lot easier to freehand and adjust as you go than crochet or knitting and you mostly go by inches instead of rows and number of stitches so a large number of accessories like stitch markers or whatever isn’t really necessary.
Maybe the lack of accessories also makes it unpopular idk. People do like collecting things in their nests.
I've been wanting to do so, I cannot find anyone who can teach me, and any books I can find on it are Ass in the Visual Learning department. Otherwise I'd be making the hell outta some nalbinded fabric
I thought this would be kind of a niche post to make but I was quickly reminded that I’m on tumblr, the website full of gay people with one billion hobbies.
there will never be anything as funny as the mutual disbelief between long form and short form fic writers about each other's style.
short form writers look at people writing 100k+ fics as though this is some sort of talent given as part of a fae bargain, that the commitment required shows some sort of ungodly mental fortitude.
meanwhile long form writers look at people writing 1000 word one shots like god I would cut off my left nipple to be able to say anything concisely. i would love to play with multiple ideas. free me from the shackles of this child I have birthed. i love them but I now must take them to t-ball and doctor's appointments and they're going to destroy everything I own.
Sometimes you hear a song and a fic pops into your head full formed. This is a trap. The fic may be fully formed in your brain, but you still Have to write it down. This is an important step that most people forget about.
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.
in other developments re german/anglo cultural exchange on breadstuffs, this image was posted to a facebook group yesterday
the following events ensued:
1. predictable lively discussion on the preparation of Wienerschnitzel, in which natives and wurstaboos are pro-puff and everybody else is like *confused dog head tilt* why wouldn’t you want the crust to stay ~attached to the thing you put it on? as with other fried foods?
2. thirty “Bad Schnitzel is my band name” jokes
3. thirty “Bad Schnitzel is my stripper name” jokes
4. one “ah yes, Bad Schnitzel! a lovely spa town” joke
I am so sick and tired of having to charge stuff every day of my miserable life!! My phone, my watch, my headphones, my vibrator, my backup vibrator, my emergency vibrator, the ancient cursed amulet of darkness that whispers to me....it's too much!!!
i am about to bestow upon you the secret butter technique. i am sorry, but it is french. i am sorry again, this only works with cow butter. i am certain plant based butters wouldn’t work, and alternative animal butters may or may not work
has this ever been you: you have a nicely steamed vegetable, or maybe you want to make the best butter noodles, but you know that if you put butter on those it’ll just melt and you end with kind of greasy noodles or vegetables? don’t you wish it was instead a luscious buttery glaze?
introducing: beurre monté
you will take a small sauce pan, and begin heating it with 1-2 tablespoons of water (use very little water) and bring it to a hard simmer or boil
turn the heat down slightly, and add Butter. how much? however much you dare. (start with 3-4 tablespoons and go from there)
you are going to either whisk Aggressively or you can pick up the saucepan, still holding it over the heat, and swirl aggressively so the butter is skating around the sides of the pan
done correctly, you will have liquid butter that is still emulsified. you have made Butter Sauce. season it with a little salt, and toss whatever you want in it.
if you’re butter splits, i’m sorry. you didn’t agitate it enough to maintain the emulsion, and now you have melted butter.
you can use this knowledge to make other sauces by swapping out the water for another liquid. white wine becomes beurre blanc. red wine is beurre rogue.
you want to CUM? sweat minced shallot in a tiny bit of butter, add white wine and cook it out until it’s reduced by about half. then whisk butter in hard. a few flecks of minced thyme or fennel frond stirred thru, and you eat that with a nice seared fish? or scallop? or even shrimp? wow. you will Nut
your boxed mac and cheese game can also be elevated by cooking your pasta and making a beurre monté first, tossing your pasta in that and adding the cheese packet. wow. hey; you’ll cum
Bro why did you censor the snake's cloaca on the snake anatomy post??? It's a snake?
I didn't censor anything, what -
oh. Oh, no. That's meant to be a line to show where the tail begins. Oh no, now I look like some weird prude.
Yeah, that's meant to help people grasp the anatomy and visualize how small the tail is in relation to the torso. Not meant to be some kind of weird snake privacy screen
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