trying not to throw up poodles

the concept of star-eating microbes causing a crisis and the solution being the introduction of space wolves to space yellowstone to control the space elk population. i love a good trophic cascade

dr ryland grace immediately pitching "turn the spaceship into a giant centrifuge to generate gravity" and then not balancing his actual centrifuge later on

the entire "life is reason" scene really

grace forgetting to open the mystery alien container in a fume hood AND immediately sticking his nose in it. if we did this in chemistry lab we were executed on the spot

academia drama being one of the cornerstones of grace's character

grace switching through every spectrum setting on the microscope to try to see into the astrophages and not being able to (relatable)

also: the astrophage dying and grace going "ohhh it died..." (very relatable)

grace having his not-scientist buddy Carl to give him frank solutions when he's overthinking. yes, often the solution is just to put the box in another box

copious duct tape usage

just sticking a filter into the path of the petrova line to collect astrophage goop

eva going "so it's alive" when grace tells them the astrophage are moving, and him being like "WELL ok that could be for a lot of reasons" in the tone of someone who doesn't even know where to start explaining why that's a hasty assumption

grace referring to the first new astrophage as his and carl's baby multiple times (HIGHLY relatable)

(via bsky)

this list is not in any order, and i am not familiar with most of these artists, so an inclusion on this is not an endorsement of anything! if ive made a mistake anywhere, just let me know!

schmekel - transmasc jewish folk band (they seem to have deleted the majority of their music off most platforms, unsure why? but this link is to a playlist of re-uploads)

exiliahu - very vocally pro-palestine jewish trans man

noah finnce - british trans man, pop rock

ellyotto - canadian trans man, hyperpop

jesswar - fijian-austrailian trans man, hip hop

2am ricky - Black american trans man, hip hop/soul/jazz/house

rahim redcar - french trans man, indie/alt-pop

elio mei - american trans man, indie folk

anjimile - Black american trans man, indie folk

the oozes - queer punk band w/ a trans man lead singer

sushi soucy - transmasc, folk rock

dopamine - band of scottish transmascs

boy jr - transmasc, indie/alt rock

great grandpa - queer indie rock band w/ trans man lead singer

riotnine - transmasc punk band

the muslims - transmasc poc anti-fascist punk band

TR sun - Black american trans man, hip hop

billy tipton - american trans man, 1940s jazz star

mal blum - american trans man, indie rock/folk punk

dayflower - british transmasc "dreamcore" indie pop band

ryan cassata - american trans man, folk punk

ezra butler - british trans man, indie pop

bells larson - canadian nonbinary trans man, indie pop

sasha allen - american trans man, indie pop

boy bowser - american trans man, energetic hip hop

mikah amani - Black american trans man, folk music

jake edwards - british trans man, pop music

jakey bake - trans man, super indie/underground

king aiden - Black american trans man, indie pop

addison grace - american transmasc, indie pop

dylan and the moon - british trans man, indie folk

searows - american trans man, indie folk/bedroom pop

elio kennedy yoon - Asian-american trans man, indie pop

beverly glenn copland - Black canadian trans man, art/folk pop

REVENGEOFPARIS - nonbinary transmasc rapper

V3CTORGRAPH1CS - nonbinary transmasc, hyperpop

Um Jennifer? - american indie rock duo ; one is transmasc, the other is transfem

jigsawllie - transmasc, indie "weirdcore" vocaloid music

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2026/07/13/go-meta-meta/#meta-meta-meta

"I often wonder what the Vintners buy/One half so precious as the Goods they sell" -The Rubáiyát of Omar Khayyám

I first encountered that quote from someone extolling the virtues of bookstores, and it stuck with me, because for most of my childhood, every bookstore visit ended with me broke and wishing I'd had three times as much to spend.

As a larval hyperlexic, I just didn't understand what a bookseller could possibly buy with my money that was better than the books they already had? Of course, then I became a bookseller and discovered that Sturgeon's Law ("90% of everything is shit") applies to a bookstore's wares as much as it does to anything else. I also acquired a monthly rent obligation and discovered just how important money could be.

Nevertheless, Omar Khayyám's question stuck with me, especially when I fell down a years-long rabbit-hole of learning about scams and the finance sector (but I repeat myself). Every get-rich-quick schemer will tell you that they've found the infinite money hack, which they will sell to you for a remarkably reasonable sum. Likewise, every stock picker claims they can outperform a simple low-load index fund, and all they ask of you is a few hundred basis points in exchange for multiplying your wealth beyond the dreams of Creosote. Neither one has a good answer to Khayyám's question: if you can make all the money with your amazing system, why do you need my money?

This is a question that needs to be forcefully put to AI hucksters. In their more expansive moments, the Altmans and Amodeis of the world will tell you that they're planning to teach the word-guessing program so many words that it will wake up and become god. DOGE's broccoli-haired brownshirts laughed in the faces of the NIH lifers who begged them not to vaporize their long-running cancer research projects: "General AI is around the corner and it's going to cure cancer. Cancer research is a waste of money!"

Which all raises the question: if you've truly incubated a foetal demiurge in your "AI lab," why are you offering to sell it to me? What do the AI hucksters buy/One half so precious as the Gods they sell?"

The last couple years experimenting with AI I've come to the conclusion that the main reason it works isn't a mystery because it has to be a mystery. It's a mystery because they need it to be.

That's why they've been hogging all the RAM and GPUs to the point of ordering the entire yearly worldwide supply in advance.

That's why they keep building data centres despite demand not justifying it.

That's why every open source model China releases is a hit to the stock market.

The secret sauce behind LLMs isn't really as demanding as they claim. There's waste built into it.

Every response within a chat sends the entire message chain through the robot's brain. It doesn't have loading bars, it doesn't let you calculate how many tokens you've used, it doesn't even let you see when you're crossing the limits until you're there.

It's almost like they don't want you to have control over how much you use.

ChatGPT in particular is prone to scope creep. It subtly talks you into expanding the reach of your projects to require more computation. It always delivers 90% of a project, with something missing so you ask another question.

Claude turns everything into a file creation request, even if it can deliver in plain text just fine. It's enticing to see how your simple table becomes a fancy react file or an HTML with heavy formatting.

Neither of these issues are present on DeepSeek or on local models. Which means they aren't inherent issues with LLMs.

It's not a bug, it's a feature.

They want you to keep paying unmetered rent. They do tricks to convince you to spend more than you meant making something bigger than you planned. They do this subtly, to make you think it was your idea.

The thing is, Chinese local models prove that we already have good enough AI for most things. It's plateauing in capacity, it needs to grow in efficiency and memory use. But intelligence is already at J.A.R.V.I.S levels, and can run fine on consumer grade hardware. We've reached a stability point.

The problem is you can't raise rent on stability. So you just keep throwing compute at the problem until you can justify asking for more money. Which is why they choke the memory market.

If nobody can buy memory, nobody can run local AI. If no one can run local AI, they have to keep paying unmetered rent. Development is slow, and importantly, the black box remains a black box.

That's why every memory breakthrough comes from Chinese companies. Because they're the only ones sharing their data. Capitalist companies don't want people to study how the models work.

With subscriptions, there's rent seeking. With local models, there's reverse engineering. Once enough people get their hands into the guts of the robot it won't be long before we figure out exactly how they work, and find more efficient ways to make them that don't require as many data centres.

Meanwhile, Fable and GPT 5.6 are being shadow marketed with all these rumours about how dangerous they are, and AI companies are begging for regulations from the government. Yes, they want control of those regulations, but that's not all.

What they're really after is a government ban on local AI. They want to cut off access so that they control all the intelligence.

Eventually, the goal is making contracts with large corporations to employ their robots instead of people. If no one has local AI, because they can't understand it or because it's illegal, then they can no longer compete against a faster worker that demands no labour rights, even if it does C+ work at best.

And that's where the real fun begins. And by fun I mean starving out the working class.

They aren't competing directly with their customers yet. First they have to educate them, jus like Spotify educated their consumers into forgetting how piracy works. They have to make them reliant on cloud compute, just like how Google Drive made everyone reliant on its storage before raising prices. And they want them to be ignorant of how much energy anything takes.

That's going to take one generation.

Not long ago every teenager knew how to use a computer. Now they depend on Apple for everything. It's a masterful business model. So what if we could apply it to every form of labour?

It's okay, just hand us your credit card and we'll do the rest. How much, you ask? Don't worry about that. Just keep your monthly payments coming. Oh, you can't afford it? You want your computer to do it? Well fuck you, we bought all the computers, so now you work for us.

Truncated text of tweet from MrPitBull, Mar 11, 2026:

She kept finding women in laboratory photographs from the 1800s. Then she read the published papers—and every single woman had vanished. Someone had erased them from history.

Yale University, 1969.

Margaret Rossiter was a graduate student studying the history of science. She was one of very few women in her program.

Every Friday afternoon, students and faculty gathered for beers and informal conversation. One week, Margaret asked a simple question: "Were there ever any women scientists?"

The faculty answered firmly: No.

Someone mentioned Marie Curie. The group dismissed it—her husband Pierre really deserved the credit.

Margaret didn't argue. But she also didn't believe them.

So she started looking.

She found a reference book called "American Men of Science"—essentially a Who's Who of scientific achievement. Despite the title, she was shocked to discover it contained entries about women. Botanists trained at Wellesley. Geologists from Vermont.

There were names. There were credentials. There were careers.

The professors had been wrong.

But Margaret's discovery was just the beginning. Because as she dug deeper into archives across the country, she found something far more disturbing.

Photograph after photograph showed women standing at laboratory benches, working with equipment, listed on research teams.

But when she read the published papers, the award citations, the official histories—those same women had disappeared. Their names were missing. Their contributions erased.

It wasn't random. It was systematic.

Women who designed experiments watched male colleagues publish results without giving them credit. Women whose discoveries were assigned to supervisors. Women listed in acknowledgments instead of as authors. Women passed over for awards that went to male collaborators who contributed far less.

Margaret realized she was witnessing a pattern that stretched across centuries.

Women had always been present in science. The record had simply pushed them aside.

She needed a name for what she was documenting.

In the early 1990s, she found it in the work of Matilda Joslyn Gage—a 19th-century suffragist who had written about this exact phenomenon in 1870.

In 1993, Margaret published a paper formally naming it: The Matilda Effect.

The term captured something that had been hidden in plain sight for generations. Once you knew the term, you saw it everywhere.

Her dissertation became a lifelong mission.

For more than 30 years, Margaret researched and wrote her landmark three-volume series: Women Scientists in America. She examined letters, institutional policies, individual careers. She gathered undeniable evidence that women in science had been consistently under-credited and structurally excluded.

Her work faced resistance. Many dismissed women's history as political rather than academic. Others insisted she was exaggerating.

Margaret didn't argue emotionally. She presented data. Documented cases. Patterns repeated across decades and institutions.

Eventually, the evidence became undeniable.

Her research helped restore recognition to scientists who had been erased:

Rosalind Franklin, whose X-ray work revealed DNA's structure—credit went to Watson and Crick.

Lise Meitner, who explained nuclear fission—omitted from the Nobel Prize.

Nettie Stevens, who discovered sex chromosomes—received little credit.

Cecilia Payne-Gaposchkin, who discovered stars are made of hydrogen—initially dismissed.

And countless others whose names had nearly vanished.

Margaret changed the narrative. Science was no longer just the story of solitary male geniuses. It became a story of collaboration that included women who had been written out.

When I asked why, a student answered honestly: It was too long, and she kept losing track of what the paper was about. This was not a remedial class: These were students who had cleared the admissions process and written essays good enough to get them here. Yet a routine academic reading assignment had defeated them.

Every generation of professors has complained that their students cannot read. The lament is usually overblown, but data have caught up to anecdote, and what I am seeing in my classroom is no longer a hunch. There is a measurable, generational collapse in sustained reading and writing, and the academy is responding to it with improvisation and exhaustion rather than the structural overhaul it requires.

In February 2024, Adam Kotsko, who teaches in the Shimer Great Books School at North Central College, wrote in Slate that students who once handled 30 pages of reading per class meeting now seem “intimidated by anything over 10 pages and seem to walk away from readings of as little as 20 pages with no real understanding.” Crucially, he added that this is “not a matter of laziness on the part of the students” but of underlying skills they were never given a chance to build.

The Chronicle of Higher Education’s 2024 investigation found the same pattern across institutions as different as the Stevens Institute of Technology and Wellesley College, where the average SAT exceeds 1400. Nicholaus Gutierrez, an assistant professor at Wellesley, told The Chronicle that the baseline for what students consider a reasonable amount of work has dropped so noticeably that he has cut his readings accordingly; a 750-word essay now strikes many students as long. At Stevens, the science and technology studies associate professor Theresa MacPhail described following the mantra of “meet your students where they are” for so long that she has begun to feel “like a cruise director organizing games of shuffleboard.”

Worse, the national data tell the same story in colder language. On the 2011 National Assessment of Educational Progress (NAEP) writing assessment, which is the most recent comprehensive writing benchmark, only 24 percent of 12th graders reached the Proficient level, and just 3 percent reached Advanced; another 21 percent scored below Basic. The reading side of the ledger is worse, and getting worse fast: The 2024 NAEP results released in September 2025 show 12th-grade reading scores at the lowest level recorded since the assessment began in 1992. Thirty-two percent of 12th graders now score below NAEP Basic in reading, meaning that, in the assessment’s own language, they likely “cannot draw general conclusions based on concepts presented explicitly in a text.” And yet more than half of these same seniors reported being accepted to a four-year college. That last sentence is the whole problem in one line: We are admitting a cohort that cannot read at a college level and are pretending otherwise.

Why is this happening? One reason, of course, is smartphones.

I came into teaching as a skeptic of the anti-smartphone argument: I had a phone in my pocket throughout high school and college in the 2010s, and I read long books anyway. I now think I was wrong, because the neuroscience has caught up. In a 2017 paper, Adrian F. Ward and colleagues at the University of Texas at Austin’s McCombs School of Business showed that the mere presence of a participant’s smartphone — whether that be face down, powered off, untouched, or across the desk out of vision — measurably reduces available working memory and fluid intelligence on cognitive tests, with the largest effects on the most phone-dependent users. A 2022 study by Motoyasu Honma and colleagues at Japan’s Showa University used near-infrared spectroscopy to compare reading on a smartphone with reading the same passage on paper, and found that smartphone reading produced overactivity in the prefrontal cortex, suppressed sigh generation, and led to general lower comprehension scores; the authors argued that the sigh inhibition and prefrontal overload were causally linked to the comprehension decline.

So when a student tells me they “kept losing track” of a 20-page article, I have to acknowledge that they may be describing a measurable neurological condition. The neural pathways that support sustained attention are built by use, and they atrophy without it. Your body is a use-it-or-lose-it system, and the brain is no exception.

Another reason for the decline in student reading capability is increasing reliance on generative AI. In June 2025, Nataliya Kosmyna and colleagues at the MIT Media Lab released a preprint titled “Your Brain on ChatGPT.” They divided 54 participants into three groups writing SAT-style essays — one using ChatGPT, the second group using a search engine, the last group using nothing — and monitored brain activity with a 32-channel EEG. The ChatGPT group showed the lowest neural connectivity of the three, with up to 55 percent reduced connectivity compared with the brain-only group, and “consistently underperformed at neural, linguistic, and behavioral levels.” Eighty-three percent of LLM users could not quote a single line from essays they had written minutes earlier. When the LLM group was forced to write without AI in a follow-up session, their brain activity did not bounce back to baseline; the researchers coined the term “cognitive debt” for the lingering deficit.

This is the first neurophysiological evidence that early reliance on LLMs measurably alters the brain’s engagement with writing tasks, and it is consistent with what those of us in front of classrooms are watching happen in real time. When I assign analysis, I am not trying to extract a polished product; I am trying to put the student’s mind through resistance in order to make it stronger. Offloading the struggle to a chatbot does not “free students up for higher-order work.” It deprives them of building the strength to do any substantial cognitive work at all.

There is a final factor that is contributing to this decline in reading skills, and that is that the students arriving in my classroom today are the first cohort to have experienced Common Core-influenced reading instruction across the entirety of their K–12 schooling. Whatever the standards’ original intent, the on-the-ground implementation in many districts replaced sustained reading with the practice of pulling “evidence” from disconnected short passages, the same format used on the standardized tests that increasingly determine school funding. The education scholar Natalie Wexler, among others, has documented this pivot in detail: Students drilled on “finding the main idea” in two-paragraph excerpts never build the stamina or background knowledge that longform reading requires. The pandemic then added fuel to a fire that was already burning. NAEP scores for 13-year-olds dropped sharply in 2022 and have not recovered. A 2023 EdWeek survey found that 24 percent of secondary-school administrators described pandemic learning loss in English and language arts as “severe or very severe.”

In July 2025, the journalist Mary Harrington argued in The New York Times that “thinking is becoming a luxury good.” The ability to read deeply and reason at length is fragmenting along class lines as ultra-processed digital media replaces text in everyday life, much as ultra-processed food has replaced cooking. Her longer treatment of the subject in First Things makes the more provocative case that we are witnessing the end of print culture itself, and with it the end of the cognitive substrate on which modern liberal democracy was built.

I see this stratification in the classroom and on the page every week. My students from districts that protected sustained reading through small class sizes, strict phone policies, and faculty who refused to teach to the test all arrive with their attention relatively intact. My students from districts that surrendered to devices and standardized testing arrive cognitively winded. A democracy that requires a literate electorate is now training one fraction of that electorate out of literacy while marketing to the other a “deep work” lifestyle as a luxury good. The students who cannot read a 20-page article today are the voters who will not be able to read a bill, or the jurors who cannot follow a closing argument, tomorrow.

I do what I can in my own classroom to address the problems. I break 20-page articles into two halves and assign the first half with explicit analytical tasks. I require exploratory writing before formal drafts. I model (visibly, on the board) how to track an argument across pages or distinguish a source’s claim from my own analysis. I make structured peer review explicit, because the workshop format I used to take for granted now collapses into “this is good” and “maybe add more details” the moment I step back.

But I want to be plain about the limits of what an individual instructor can do, and all of these solutions have costs. Scaffolding a 20-page article into halves compromises the integrity of the argument I am asking students to engage, just as modeling note-taking in a credit-bearing rhetoric course is using a college slot to teach a middle-school skill. None of the syllabi I teach are designed to deliver this type of cognitive rehabilitation, and pretending otherwise has produced credential inflation. We cannot keep conferring degrees on students who cannot do what the degree is supposed to certify.

I’m afraid I don’t have answers. I do, however, have some questions that may point us in the right direction. If higher education is going to respond to the reading crisis as a structural problem rather than a private burden carried by composition instructors and adjuncts, it has to stop avoiding the following questions: If a majority of incoming students cannot read at a level the curriculum requires, are we admitting students we cannot serve, or offering a curriculum we cannot provide?

Why are first-year writing and reading-intensive general-education courses still the most adjunctified, lowest-paid, highest-load corner of the university, at the precise moment when their work has become the most important work the institution does? What is the responsible institutional response for AI usage: Is it a syllabus statement, or a sequencing principle that requires students to demonstrate the cognitive work themselves before AI assistance is permitted?

Why are most college classrooms still phone-permissive by default? K–12 districts from Florida to California are now banning phones bell to bell; higher education has somehow lagged behind the public schools. Universities benefit from a pipeline they did not build and refuse to repair. What would it mean for a university system to invest seriously in the reading instruction happening in the high schools that feed it, rather than treating remediation as something to be quietly outsourced to first-year composition instructors?

The thing I am no longer willing to do is pretend this is a temporary adjustment period, or that “students will adapt.” They will not adapt on their own. The conditions that produced this collapse are still in place: the phones, the algorithmic feeds, the test-prep excerpts, staffing models that load the reading-intensive work onto the most precarious faculty, and now the chatbots that finish students’ sentences before they’ve even begun to think of them. If we want literate citizens, we will have to rebuild the conditions for literacy deliberately, against the grain of every incentive currently pointed the other way. I know the academy has the will to do that. It also has the obligation."

— Tyler Jagt, 1 June 2026, "My Students Can’t Read"

Whose habit was constantly mewing

He mewed too hard

At a sexual bard

Ever noticed Otabek got a tiger shirt, like Yurio's? I only saw it peeking through when I cleaned up this scan. The boys match!