friend of mine recommended a video essay that's like 5 hours long about why it's currently a whole Thing for fascists to claim they only eat meat and demonize vegetables etc etc, and it's genuinely quite well researched in many places and contains some fascinating history and has made me feel very weird about my status as an adult who enjoys drinking milk. but I've not finished it and I genuinely don't know if I will because at one point the ol' "you can't say you care about animals and also eat animals" chestnut got dropped by the white vegan essayist and it put such a bad taste in my mouth that I had to leave instantly
like idk man it just feels genuinely parodic to dedicate hours to analyzing how foodways have frequently been used as white supremacist propaganda and then casually toss out an argument that pretty invariably demonizes indigenous hunting practices with 0 regard for traditional knowledge and land stewardship guiding those practices
Every time I hear a white vegan say that I want to throw the rubble of the Klamath Dams at them.
The largest dam removal in U.S. history. So successful that the salmon that haven’t run freely in the river for literally over a century came back the next year. Not steady progress over a decade as expected, the very next salmon run they came home. No one alive saw salmon in that river outside the hatcheries kept by native’s tribes and the Department of Fish And Wildlife scientists desperately trying to save them. One of the activists who helped bring the suit spoke of their dying grandmother making them promise to bring the fish home, and they didn’t know how that was possible, but they spent decades doing it.
The legal basis for successfully suing the power company who owned the dams was Indigenous treaty rights to eat the fish. Eating the fish was the one thing in a century that succeeded in being a clear and legally binding right that superseded the desires of white capital and industry. I have been going to the public interest environmental law conference off and on for 20 years where a lot of organizing for Undam The Klamath happened. I’ve heard updates about every legal tact they tried as they happened, and they tried everything.
There are healthy, thriving salmon, trout, and lamprey in a rewilding and recovering Klamath River right now as a direct result of Indigenous people eating them. Of their legally binding right to eat them. They sure as fuck care more about those fish than any white vegan ever could. They are salmon people that the Creator sent Salmon to feed, while charging the People with protecting and stewarding them. It’s not possible to learn about salmon and trout in the Pacific Northwest without learning this, and it’s unambiguously racist to dismiss this symbiotic relationship.
The white vegans are trying to get all hunting and fishing banned in Oregon, and that’s quite possibly the single most catastrophic thing that could be done to the animals here because we killed off all the major predators of creatures like deer and without anything hunting them their numbers will explode, eat all the vegetation, destroy the ecosystem, and then die along with everything else in that failing ecosystem. Controlled hunting of deer is vitally necessary for the deer to survive. This is something Native people know, but patronizing racist savior-complex white people refuse to listen to.
I have a whole collection of Oregon’s Public Broadcasting’s documentaries about Klamath River or the larger story of fish and people in the region. If you watch nothing else, watch Klamath Dams Are Coming Out, First Salmon Ceremony, After The Dams, and First Decent (Native made). It could not be clearer that the salmon are being protected and nourished by the people who eat them, and that stewardship is a massive part of their creation myths. But it contradicts political vegan dogma so they don’t listen.
#also do you have any idea how much conservation is funded by people who want to eat those animals#at least in the us anytime someone buys ammo or hunting equipment or hunting licenses that money goes directly to state wildlife agencies
“…it's currently a whole Thing for fascists to claim they only eat meat and demonize vegetables…”
“claim” is sure doing a lot of work here.
cultures (and sub-cultures) ascribe certain values to certain foods all the time; but WHICH particular values get associated with which foods changes as the culture changes, and different cultures can simultaneously attach different values to the same foods—or the same values to different foods—because (remember?) food doesn’t have any inherent moral value. right now we’ve got vegans who eat no meat 🤝 self-proclaimed carnivores who excessively eat meat, all contributing together to the rise of fascism, which should tell you it’s not and was never actually about eating meat at all. they’re just different but complementary propaganda approaches designed to appeal to and capture different populations.
white supremacists being weird about drinking milk does not make drinking milk weird.
don’t get distracted by what individual people are attempting to virtue-signal with their (claims about) their personal food choices and keep the spotlight on the causes and systems they’re actually advocating for. and against.
I'm apparently so consistently aroace that a friend of mine uses me as a measurement for whether or not something counts as "romantic," based on when I stop understanding what he's talking about.
“Many reviews are useless because, while purporting to condemn the book, they only reveal the reviewer’s dislike of the kind to which it belongs. Let bad tragedies be censured by those who love tragedy, and bad detective stories by those who love the detective story. Then we shall learn their real faults. Otherwise we shall find epics blamed for not being novels, farces for not being high comedies… Who wants to hear a particular claret abused by a fanatical teetotaler, or a particular woman by a confirmed misogynist?”
the thing about "people have talked about it you're just 21" is that it's not really "young people are stupid" it's more "if you come into an academic discussion you should know what people have said on the matter lest you talk out your ass"
actually once you do vote by mail once, it feels STUPID that in-person voting is even a thing. getting to sit with your ballot in your house, easily see your options, have time to do research and consider your choice, talk with friends and neighbors about their vote. it's especially so for small local elections and primaries where you might not have heard about different candidates and really have to ask around, attend local events, or dig into local news stories to find out who they are and what they stand for. but, of course, not allowing voters to easily research and carefully consider their votes is why there is opposition to vote by mail in the first place.
I get the sentiment, but in person voting needs to remain a thing. Not an exclusive thing, absolutely not, but the choice needs to be there.
People that don't have a residence to send a mail in ballot to need to be able to go in person if they can. People in situations with abusive spouses or partners or families need to be able to go in person and be allowed to vote without influence, if they can. People who would feel pressured to vote one way or another, or feel pressured to show their ballot to someone else if doing it at home. People that can't read or write and don't have anyone they know or trust enough to help them with the ballot can get help at the in person places. They can ask questions of the volunteers there to assist. People that are nervous to do it alone can go in person together and make a little event of it. Sometimes the mail gets misplaced or stolen.
There are a lot of reasons that in person voting is a thing and should remain a thing, just not an exclusive thing. I want to receive my ballot in the mail to my house that I own, and spend time with my wonderful spouse looking up the candidates and reading each other their stances on stuff we care about, and I want to drive my car over to the township building at my leisure and pass my completed ballot to a human being so I know it was received. But I also understand not everyone is in that kind of position, and that having more than one option on how to vote is essential actually. Voting should be as easy and accessible to as many people as possible.
mail-in voting is extremely convenient for a lot of people and mitigates a lot of hardships for others, but the secret ballot is essential and should remain available forever as the gold standard.
so please do automatically send out mail-in ballots to registered voters! but also please please please, at the same time increase in-person access by expanding early voting dates as well as voting times and adding more polling locations!
I am obsessed with fictional guys being really weird about each other. Hard at work in the plausible deniability mines. You know those pairings who would jerk each other off before they'd kiss
I think my favorite part of this post so far is the beatles fans in my notes 1. Acting like this is common knowledge & 2. Saying they got far weirder without elaborating. Alright
I wish to god I was joking or exaggerating but this is just factual beatles lore I'm so sorry for reblogging this for the 80th time op but the people need to know
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:
One of my favorite rhetorical and analytical moves is joining things together (showing that two different, seemingly unrelated ideas are aspects of the same phenomenon) and taking them apart (resolving a paradox by demonstrating that what appears to be one, contradictory thing is actually two different things that have been lumped together).
"Taking things apart" is a very useful framework for understanding AI. How do we resolve the (seeming) paradox that some skilled workers report wonderful results from their work with AI, while others are full of dire warnings about the lurking defects in their AI-assisted outputs? Simple: the first group are "centaurs" (humans who are assisted by machines) and the second are "reverse centaurs" (humans who have been pressed into service as peripherals for machines):
What are we to make of the people who've been fired by bosses who replaced them with AI, in light of the fact that AI is demonstrably not able to do their (former) jobs? Again, it's simple if you separate out two distinct phenomena: "AI can do your job" is the first. The second is: "Your boss is a credulous dolt who is infinitely horny for replacing lippy workers with pliable machines, which made him an easy mark for an AI salesman who convinced him to fire you and replace you with an AI that can't do your job":
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you. They don't have to believe in AI in order to think it's a good investment: like an investor betting that Joe Rogan can sell millions of dollars' worth of peptides to desperate young men, they are assessing the sales potential, not the merits of the thing for sale:
https://pluralistic.net/2026/08/03/andor/#either
As useful as "taking things apart" is, "putting things together" is also a very important technique for assessing, critiquing and improving AI. In a stellar essay entitled "Temperature Zero for Culture: Why Everything Is Starting to Look the Same" by the data scientist Lauren Leek, we get a top-notch example of "putting things together":
Leek's essay is one of those fabulous, wide-ranging, cross-disciplinary pieces, touching on urban design, music trends, synthetic LLM crowds, Netflix recommendation algorithms, and several other subjects, all seeking to resolve a(nother) (seeming) paradox: how is it that we have so much potential variety, but everything is so manifestly the same?
The answer is complicated and nuanced, but Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want."
This is a pervasive issue across many domains. Leek says that economists call it "performativity," while machine learning researchers call it "model collapse" and urbanists call it "placelessness."
"Performativity" describes how, once a market has been modeled by economists, that model becomes the foundation for economic policy, which pushes the market to conform to the model:
"Model collapse" describes how machine learning models that are trained on their own predictions become incredibly bland, with all variety disappearing from the system's predictions:
This is hugely consequential: it's why bias proliferates through predictive policing algorithms: train a model with data from racist stop-and-frisks and it will predict that all the weapons and drugs in a city are to be found in Black and brown peoples' pockets. Turn those predictions into recommendations telling cops where to go look for weapons and drugs and they will double down on racist stops, producing even more biased training data, which turns into still more bias in the predictions:
"Placelessness" is the urbanist's name for "when everywhere optimises toward the same template." I think of it as Flinstones Syndrome, where the same background is looped behind Fred and Barney as they drive through Bedrock. In New York City, it's Citibank-bodega-Chipotle-Walgreens; in the Chicago suburbs, it's the strip malls with a Chili's, a gas station, and a big box store.
Leek proposes that these are all expressions of the same underlying phenomenon, a failure mode of data science that takes a world of "granular personal data" and arrives at a world where "personalisation produc[es] more sameness."
To these excellent examples, I'd add another one, from the world of monetary policy: Goodhart's Law, which holds that "When a measure becomes a target, it ceases to be a good measure":
https://en.wikipedia.org/wiki/Goodhart%27s_law
Goodhart's Law captures a wide variety of phenomena. When Google first deployed Pagerank, they showed that by counting the inbound links to all the pages on the web, you could extract a signal about which pages were most important (because there was no reason to link to a page unless you found it noteworthy).
But once Pagerank became the dominant means by which web users found pages, counting links stopped being useful: first, because people used Pagerank to find the best pages and link to them, making it impossible for new pages to get the inbound links needed to supersede incumbent pages; and second, because it's easy for fraudsters to create inbound links for low-quality pages in bulk, once there's a reason to do so.
Counting inbound links was a world-beating retrospective way of predicting which page would best match a searcher's query, but once it shaped the world it sought to analyze, it ceased to be a good prospective way to predict which page would best match your queries.
Leek is a brilliant data scientist and an even better science communicator, with a knack for crisp, readily understood explanations. How can a world of granular, highly varied data turn into a world of homogeneous choices? Simple: start with a set of items ("cuisines, genres, shop types") and a standard algorithm for sorting them. Let users choose from those recommendations. The mode (average) of those choices "gets shown more, so it gets picked more, so the model grows more confident the mode is what people want, and the tails starve." Run this for a few rounds and the evenly distributed catalog of choices "collapses onto one dominant option."
This is intrinsic in the choices we make in designing recommendation algorithms, tilting them towards the likelihood of a successful recommendation. A recommender that wants to succeed every time will make the safest possible recommendations, "so an algorithm that is uncertain about you, and it is always at least a little uncertain, hedges toward the average."
Then she busts out a beautiful, perfect little statistics aphorism: "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
This is how recommendation systems – and other analytical tools that produce predictions that are then turned into action – force so many diverse phenomena (streets, markets, media recommendations) into sameness. The fact that these recommenders are self-fulfilling prophecies means that "they don't have to be right," only "listened to."
This explains the sameness of so many of London's high streets. Leek examines 640 shopping streets, characterizing 18,000 food places spread out across them, flagging all the chain restaurants. Her analysis shows that any two London streets will, on average, share about half of their "food profile."
Obviously, this is most pronounced on streets with chain outlets, and it doesn't take that many chain outlets before a street's sameness shoots up: "A relatively small number of repeated names is enough to make otherwise different streets resemble one another more." So why do streets with chains resemble one another so much? Because the chains use an algorithm (weighting footfall, proximity to train stations, demographics, and competitors) to decide where to put their restaurants. If a street with a Gail's Bakery on it feels like every other street with a Gail's Bakery, that's because Gail's only puts its restaurants in places that have highly similar characteristics, measured to a high degree of accuracy and controlled by a narrow set of tolerances.
In other words, every street that feels like it should have a Gail's will eventually get a Gail's, whereupon that street will feel even more like all the other streets that have a Gail's, because it will share one more common factor with those other streets (a Gail's).
Leek points here to her earlier work on pub closures in the UK. The UK has experienced an epidemic of pub closures, with thousands of pubs disappearing since 2016:
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This same phenomenon acts on culture. In an age of music recommendation algorithms, hit songs are changing; today's songs use a smaller vocabulary of unique words and repeat those words more often:
Vocabulary richness, distinct words relative to length, has fallen by more than a quarter since the early 1960s, while the share of repeated lines has climbed by nearly a third. The modern hit says less and says it more often, because the hook that works gets repeated.
But that's not the whole story! While each song resembles itself more ("saying less more often"), within that constraint, there's far more variety today than before: a given song's (constrained) vocabulary has grown more distinct when compared to all the other songs' vocabularies. Songs repeat the words they use, but the words repeated in songs are getting more different.
For Leek, this is the key to understanding the whole phenomenon and (more importantly) doing something about it. Music recommendation systems optimized for a singable hook, but did not optimize on any of the other variables in songs, so those dimensions acquired a broader range, even as the optmized variable got flatter and narrower.
This means that the tendency of recommenders to "flatten the world" isn't a single blunt outcome: it depends on which dimension we choose to flatten through recommendation, and who chooses to flatten that dimension.
A media recommender optimizes for consumption, showing you a tractable set of things it believes you'll watch, read or listen to. When you choose from among this limited set, the recommender takes note of that fact and shows you more of the same, pushing everything to a greige median. All the movies, books and songs you might have liked that were omitted from that initial set are excluded from being recommended in the future. The features of that media that you might have appreciated "decay out of consideration." They are never tested for desirability. The model collapses.
How badly does it collapse? Leek cites Movietweetings' data on which movies people watch: out of a million public movie ratings, half relate to the top 2% of movies in the set. There's 38,000 films in the set, but just 380 titles account for 40% of the ratings. Leek argues (persuasively) that this isn't because recommenders are good at "knowing your taste" – rather, they are good at "narrowing the menu."
Leek relates this to her work on creating LLM "personas" – synthetic populations meant to mimic the tastes and proclivities of real groups of people, that you can interrogate "before you spend money asking actual humans." While this would be useful for many applications, "it fails in exactly the way this whole essay is about."
Leek went to enormous lengths to reproduce the traits that make people interesting to study in aggregate, painstakingly replicating the ways that social connections, psychological outlook and demographic factors predict people's beliefs. The result was a set of LLM personas with "elaborate stories" about how they differed from one another, but whose survey responses about planned actions were homogeneous in a way that real populations are not.
This, Leek writes, is the same force that homogenizes other data-driven predictors. Because she'd ordered her LLM to reproduce the statistically validated relationships between different factors that predict a person's beliefs, each synthetic persona was a homogenized average. It's like the paradox of "The Average Man," where military uniforms sized to the average of all service personnel fit no one, because no one is average:
https://archive.org/details/DTIC_AD0010203
The thing is (as Leek points out) the idea that synthetic personas are a good way to understand the preferences of a real population is not a harmless delusion: it's a product that's being actively sold to governments, campaigning politicians and marketers. It's a self-fulfilling prophecy that drives governance, political campaigns and product design to the same homogeneous median that is making every shopping street in London feel the same.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms. As anyone whose favorite banana went extinct can tell you, homogeneity works well, but diversity fails well:
https://en.wikipedia.org/wiki/Gros_Michel
The brittleness of algorithm-induced homogeneity is compounded by the fact that recommenders obscure the true preferences of people. If you watch two Scandinavian crime dramas after Netflix recommends them to you, it will keep showing you more Scandy crime for the next decade – even if there's another kind of programming that you'd vastly prefer (if only you knew about it). This means that decision-makers who choose which shows will get made in the future will keep on funding their safe Danish detectives, to the exclusion of whatever might emerge from the same weird attractor that produced the K-Pop Demon Hunter fortune.
Transpose this failure mode onto states, bank managers and landlords, and we see whole ranges of policies, businesses and activities that never come into existence, despite the popularity, prosperity and joy they might bring us.
But Leek doesn't end with this worrisome note. Instead, she identifies this whole thing – model collapse, placelessness, performativity, even Goodhart's Law – as an expression of one of the best-understood tradeoffs in computer science: "exploration vs exploitation":
Any system learning from feedback has to divide its effort between exploiting what already scores well and exploring options it hasn’t tried, in case they’re better.
Computer scientists have long understood that focusing on exploitation to the exclusion of exploration is a trap that locks you into "the first decent option" so you can never discover the best one.
Which means that this algorithmic homogeneity has a well-understood corrective: "forcing exploration back in." The problem is that markets hate this kind of exploration. A company that lives and dies by how many clicks it gets is never going to sacrifice 20% of its traffic by showing its users weird, untested options that score worse than the median because these weird things have never had a chance to prove that they are desirable.
This is a classic market failure, and, as Leek points out, there are regulatory responses in the UK (the Digital Markets, Competition and Consumers Act) and the EU (the Digital Services Act), both of which require the largest platforms to open up their recommendation systems, but so far, regulators have focused on "online harms" rather than variety (though the DSA does require platforms to offer algorithmic recommendations that are not based on your personal traits).
Leek identifies this willingness of states to set conditions for algorithm design as a means by which "exploration" can be forced back into the system. She's also bullish on interoperability, so that users can leave platforms with bad recommenders, without losing access to their media or social circles. As she writes, "the deepest discipline on a feed that has trapped you is the credible ability to leave it and take your data with you." I couldn't agree more:
She's less hopeful about individual responses. Demanding that you be an "adventurous consumer" is a way of letting systems off the hook. When every street has the same restaurants and every bookshop has the same books and the people in your life are all locked into one of two social media platforms, "choosing wisely" only gets you so far. Shopping isn't politics!
Leek is a superb writer. After reading this piece yesterday, I sent it to half a dozen people and then read everything else in Leek's newsletter archives. Not only is it all brilliant, but I also realized that she'd written one of the most memorable articles about cities and platforms I've read in the last year, "How Google Maps quietly allocates survival across London’s restaurants – and how I built a dashboard to see through it":
I should have added Leek's newsletter to my RSS reader when I read that last December. I've rectified that oversight! What a fantastic thinker, scientist and communicator! If she isn't being relentlessly pestered by editors and literary agents offering her a book deal, then it really does prove that the recommender systems are elevating the bland median over the thoroughly, delightfully spiky outliers.
Highlighting a few especially key parts (bolding mine):
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you.
[…] Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want.”
[…] "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms.
“…billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you…”
↓
“…[sellers] who believe that [customers] can be sold [a product] and don’t care if [that product] is defective, because that’s [the customer’s] problem after he buys the [product]—”
fraud. you’ve literally just defined fraud.
a third of the economy is currently running on digitial snake oil peddled by billionaire tech quacks.
like. obviously everything else here is important. but tbh it all fades into the background compared to the scale of the blatant fraudulent scheme at the heart of it all.
People really need to take a wider view of this paranoia about age gaps and realise this is how we lose the ability to build communities. You need to be able to realise that people can have things in common with you even if they grew up in a different time/place/culture. You also need to realise you can build communities with people who don’t have obvious things in common with you, that people can have the same goals and needs as you even if in most ways they’re very unlike you. Now, more than ever, we need to be able to work together to have any chance to stand ip against the few who have so, so much more power, money and influence than any of us do individually. We need to form communities that reach across age (and class and race and sexuality and so on…).
I... I have coworkers who are from 26 to like. 56. we have work in common?? the work we all do?? together?? commonly? all of us, at all of our ages???
Also I don't care what I have in common. I can strike up a conversation over plenty of shit I don't have in common. That's how you exchange information on topics. Learn To Ask A Question And A Follow-Up Question.
do these people not talk to their family members?
I mean my niece and nephew are some of the coolest people out there and they are 30 years younger than me.
Do they have nothing in common with their parents or aunts and uncles?
My community elders in the fiber community are awesome and I have learned so much from them. And regularly do social things together.
Also not for nothing but. I was 26 once. for a whole year. we have 25 years prior to that in common as well, I was all those ages too.
ALSO also not for nothing but............... you can have conversations with people who have little in common with you. That is, in fact, a GREAT way to learn about things you don't know about, at any age.
If you only ever associate with a narrow age range of your peers, chances are actually a lot higher than you won't have anything EXCEPT your age in common, as people are vastly varied in their interests and hobbies and knowledge. Going online may HELP to find people in a narrow age bracket but a) people generally aren't yelling their age from the rooftops and b) are you going to exclude someone you have a lot in common with just because they're a bit older or younger than you?
And I DO mean "a bit." The difference in age between 26 and 32 is six damn years. My youngest sibling is 5 years younger than me. I'm currently 41, and my neighbors down the street are in their 60s. They've got 20+ years on me, and we talk all the time about raising fowl and life problems and what's going on in our lives. Outside of farming, we don't have a ton in common, but that's actually great! I love to hear about whatever nonsense is going on in their lives that isn't going on in mine. The things we don't have in common are just as interesting as the things we do.
Like it just astounds me that people think like the OG tweet up there. Have you metamorphosed into someone so completely different in just 6 years that you can no longer relate to anyone that age ever again? You cannot think of one single thing that you might be able to talk about with someone of an age you practically just were? Like I know a lot can happen in 6 years, believe me, and idk about anyone else, but I certainly haven't blacked out and woken up a brand new me who doesn't remember what was happening 6 years ago. I have have hobby projects I'm still currently working on that are older than that. Yeesh.
I do think the ability to emoji-react is a net win for human communication. not only does it give you an outlet for 'I see and acknowledge this but don't have a verbal response' but it also adds a pleasing alethiometer element to things
my coworker announces that he's off to the dentist. someone reacts with a tooth emoji. is this a statement of dentist solidarity? a wish for my coworker to return with more (or fewer?) teeth than he set out with? simple word association? who can say
no, no, come back here and tell me how stupid it is to talk about how the power dynamics inherent to christianity are built upon the rhetoric that failure is unavoidable and there is never enough you can do to make up for it
David Foster Wallace, "Tennis Player Michael Joyce’s Professional Artistry as a Paradigm of Certain Stuff about Choice, Freedom, Limitation, Joy, Grotesquerie, and Human Completeness" in String Theory
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