you know, the more i think about it, the angrier i get about how mainstream media and even people in general treated marie kondo when the life changing magic of tidying up got big. it's just so unnecessary and sad to me and i think the vast majority of people would love what she has to say if they just actually looked into it instead of maliciously memeing her to death? i'm not talking about the cutesy does it spark joy stuff but all the things portraying her as some bizarre evil cleaning dictator.
i actually read her book when i was about twelve years old, in the most shocking and probably only example of me ever being ahead of a trend, and even at twelve i really loved everything she said. at that point in time i lived in fear of my mother's threats that she would come and throw everything away while i was school, and my small and very adhd mind simply could not grasp the concept of "have less stuff". have less of WHICH stuff? how? i'd never actually been taught how to clean my room besides being told "pick up stuff" and "be organized", and as she points out multiple times, cleaning is not an intuitive thing. it's a learned behavior and skill.
anyways. her entire philosophy centers on surrounding yourself with things that you love, and only things that you love (or things that you absolutely need). she explicitly says over and over again that it is not about throwing things away, it is not about minimalism, it is not about "what is the smallest amount possible that you can survive on". she literally has a whole section where she talks about how hard it can be to throw things away when you've lived in poverty all your life and you don't have absolute confidence that you can replace something that you really needed if it gets thrown out, even though you're not likely to ever really need it--you've just been conditioned to think that because that's literally how you survive, when you're poor. she talks about how that mindset can serve and how it can damage. she talks about how minimalism is sort of a rich people thing, cause they can afford to throw everything away.
this woman really came out here and said "i want you to be surrounded by things you love and i'm going to validate your fears and your difficulties in getting to that place" and people somehow got mad at her. i don't understand it
The idea of hers that helped me the most was one of the more...shinto-y ones.
That we are the caretakers of the objects we keep and have an obligation to not only care for them, but also to *use them for their purpose.*
If you don't wear that jacket, no matter how cute it is, it is a disservice to let it rot away in your closet. Let it go on to be worn by someone else. It's not that you didn't love it enough- it's that you love it enough to let it serve its purpose, even if it's not with you.
And I think that's very freeing. It helped me, at least, with the guilt of letting go of "still usable" objects that I just wasn't using.
a thing that i particularly love about the hunger games prequels is how it shows that people have been fighting against the games since their inception
when i was younger and read the original trilogy for the first time i was so bothered how it was 74 years of games, i remember thinking how could it have gone on so long without anyone doing anything
these prequels highlight that people have been fighting from the get-go: lucy gray's defiance, reaper ripping down panem's flags to cover the fallen tributes, haymitch's games and how many others shared his ideologies - the capitol just drowns them out, they rewrite their stories so their efforts are forgotten
liberation takes time and it's built upon the actions of those in the past
childe is so funny the guy literally went to fun little tea parties with sandrone, columbina, and arlecchino and mr. "i only really feel alive when there's a blade to my throat" came out of it going "oh my god there is something deeply wrong with those people."
when you think about it, a lot of the Harbingers' opinions of each other can pretty much be summed up with "wow these people are absolutely insane. at least i'm slightly better than that," and i love that for all of them
Maybe there’s also Tanabata festival in Witch Hat Atelier’s world. I guess everything is the same except you hang your wish on a tree branch instead of a bamboo branch.
I SOOOOO love those three 🥹🥹🤲 felt THE need to sketch a bit of them
And I also have a little headcanon on their heights! Had to put Coustas back on his two for this one, lol
A little rant below the cut!
So, I feel like, despite the lack of canonical information on the exact ages of characters, oftentimes it's pretty obvious who's older/younger just from how the kids act!
My reading on those three specifically is that Coco is the oldest one: i read her as 13-14 yo, because while she might not always be confident in her abilities, it's not bc she's not mature enough: she is just _new_ to this specific field. But aside from that, she often shows Initiative, thoughtfullness and emotional maturity of someone who already has some self-awareness and understanding of nuances of both the world and the people around.
Tartah vibes like he's slightly younger: he seems to be very inspired by Coco and often follows her lead, AND he has a crush on her, which ofc is rooted strongly in just how exactly they got to know each other - but also is very common for kids looking up to their peers who are just slightly older
And Coustas to me always, both in the design and in the way he's acting, gives off a _small kid_ vibe - he's very easily distraught, easily affected, and quite often shows the lack of perspective taking towards others, which is not a personality flaw - he IS compassionate and clearly cares about his close people, but he reads like a kid who hasn't yet developped the full ability to put himself in the other people's place. Which of course is probably also rooted in his experience and quality of life overall, and is absolutely valid in his case, but that's also the reason I'm pretty sure he's NOT older than 12, and my strongest age hc for him is 11
Also, as a treat for all those who read this far: a coloured version of the kittens!
Don't like it as much bc I kinda suck at colours lol
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.
