why did elementary and middle school teachers get so mad when you finished your work early and did silent reading? I was an A/B student and some of them would be so pissed when I finished my test within 15 minutes and whipped out my N. D. Wilson novel or whatever
Everybody knows elementary and middle school students need to pretend to be slaving away at math problems for the full 8 hour school day to prepare them for the torture of adult employment.
When asked teachers would say it's because it makes the other kids feel bad
Boo fucking hoo
The constant bullying made me feel bad but nothing was ever done about that was it teacher? Let the other kids take the full hour to take the exam, I'm done in 30 and got the answers correct so just let me sit here and read quietly, I've earned it!
if your animal is lying on the floor, furniture etc, it’s important to take a picture of them. then, if they move or shift in any way, it’s important to take another picture. with this technique, you can take many pictures of your animal
With so many elections coming up worldwide it's probably a good time to remind everyone that tumblr once got infested with agents trying to convince everyone not to vote, or not to vote left because the candidates weren't morally pure enough.
Also a reminder that they were better at tumblr than most of us, comrade interloper was great at memeing. Like, the talent!
Anyway don't fall for it. There is no morally pure option.
and since I still regularly come across the argument of "those blogs couldn't have been psyops, they were leftist minorities who posted about activism," here's a reminder that the people behind these blogs will position themselves as whoever they need to be to get the most credibility in the space they're trying to infiltrate, and they will post whatever they need to post in order to establish that cover
they will do a lot of that, actually; most of their posts are usually a mix of popular memes (since that's what gains followers) and reasonable-sounding posts about social and political issues (since that's what "proves" their legitimacy and establishes their cover), and in between all of that is the occasional post about how voting is useless and the progressive candidate is Just As Bad, Actually
these blogs are meant to be indistinguishable from real people in left-leaning communities because that's the entire point
so this is also a reminder that you cannot use someone's identity or sociopolitical views as a substitute for thinking critically about what you're reading, especially now that we know for a fact that this has happened here before
O'Sullivan, D., Byers, D. (2017, October 13). Exclusive: Even Pokémon Go used by extensive Russian-linked meddling effort. CNN Business. https://money.cnn.com/2017/10/12/media/dont-shoot-us-russia-pokemon-go/index.html
Silverman, C. (2018, February 6). Russian Trolls Ran Wild On Tumblr And The Company Refuses To Say Anything About It. BuzzFeed News. https://www.buzzfeednews.com/article/craigsilverman/russian-trolls-ran-wild-on-tumblr-and-the-company-refuses#.kq6pLQ5q6
Collins, B., Russell, J. (2018, March 1). Russians Used Reddit and Tumblr to Troll the 2016 Election. The Daily Beast. https://www.thedailybeast.com/russians-used-reddit-and-tumblr-to-troll-the-2016-election
Hi Tumblr,
We’re all grappling with the influence that state-sponsored disinformation campaigns can have on our political conversations—and
Democracy requires transparency and an informed electorate, and we take our responsibilities very seriously. We aggressively monitor Tumblr
Ewing, P. (2018, March 27). Tumblr's Ban Of Russian Accounts Adds Detail To Targeting Of Black Americans. NPR. https://www.npr.org/2018/03/27/597021235/tumblrs-ban-of-russian-accounts-adds-detail-to-targeting-of-black-americans
Feldman, B. (2018, March 26). Tumblr Is, Almost by Accident, Our Best Glimpse of How Russian Trolls Work. New York. https://nymag.com/intelligencer/2018/03/tumblr-is-our-best-glimpse-of-how-russian-trolls-work.html
Jones, R. (2018, December 18). Here's How Russian Trolls Turned Social Media Into a Weapon While Tech Giants Played Dumb. Gizmodo. https://gizmodo.com/heres-how-russian-trolls-turned-social-media-into-a-wea-1831144317
Neill Hoch, I. (2020). Russian Internet Research Agency Disinformation Activities on Tumblr: Identity, Privacy, and Ambivalence. Social Media + Society, 6(4). https://doi.org/10.1177/2056305120961783
Abstract: On 24 March 2018, Tumblr terminated 84 user accounts identified as being "linked to Internet Research Agency or IRA (a group closely tied to the Russian government) posing as members of the Tumblr community." In response, Tumblr deleted the blogs and accounts of these 84 users but allowed reblogs of their posts to continue to circulate openly on the platform. Through a case study of posts originating with one IRA account, Lagonegirl, and qualitative interviews with 13 Tumblr users, this article considers the platform conventions and social norms that were utilized by the Lagonegirl account to facilitate its distribution of disinformation. Posing as a Black woman concerned with social justice but also sharing humorous posts that resonated with Millennials, Lagonegirl's performance shows overlap with existing work on "Left Troll" IRA Twitter accounts while demonstrating platform specificity in the construction of posts.
alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
TUMBLR ADS ARE NOT SUPPOSED TO AUTO-PLAY AUDIO! THAT IS A BUG AND YOU SHOULD REPORT IT!
"This ad is auto-playing audio" is literally on the drop down menu for reporting an ad. Tumblr isn't trying to implement this! Don't protest this "new policy", cause it's not one.
They are not supposed to automatically redirect you without you clicking them, they are not supposed to cause a pop-up, they are not supposed to freeze your screen.
This is all bugs or malicious advertising which is also against tumblers ad policy. You should report all ads which do this.
Let’s get rid of those horrible monopoly ads, together.
btw it's so fucking stupid you can be anxious physically in your body even after you've decided mentally you don't care. I'm supposed to be in charge here
Bookriot 100 Must-Read Sci-Fi Fantasy Novels By Female Authors
Bookriot 100 Must-Read Sci-Fi Fantasy Novels By Female Authors
ok let's do another one! this time we have Bookriot's 100 sci-fi fantasy novels by female authors.
just to pre-empt a few possible objections to the list, it was published in 2016 and:
JK Rowling's public transphobia downward spiral began in 2018.
All Systems Red (The Murderbot Diaries, Martha Wells) was published in 2017 and Gideon the Ninth (Tamsyn Muir) was published in 2019.
The Fifth Season (NK Jemisin) was published in 2015 & given that the editor included a different NK Jemisin I presume Fifth Season simply hadn't hit all-time classic status yet.
The editor stated upfront that they included 1 book per author.
I have no idea what We Have Always Lived in the Castle (Shirley Jackson) is doing there given that it categorically is not SFF.
How many of Bookriot's 100 sci-fi fantasy novels by female authors have you read?
Femslash February Day 28 Edith and Helen from Even Though I Knew The End by C.L. Polk. (Reference under cut)
I hope you guys enjoyed my Femslash February of 2026. I know I had a lot of fun and I am really proud because this is the first time I've been on time every day for a monthly challenge. I'll be taking some time off from creative things for a little while just to avoid burn out. But see you around soon!
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.
