For example, if I want to activate the flashlight on my iPhone, I have to know to swipe up from the bottom left-hand corner in order to bring up a control panel where the flashlight button exists. There is absolutely nothing on the interface that indicates this is an available action. That might be fine if the action were intuitive or if it had some prominent affordance, but short of having read the iOS operation manual that is included in the box with the iPhone (oh, wait, no user manual is included in the box), there is no way to know this control exists. It is the quintessential hidden control—useful, lurking, and available—but only to those who know the trick. Want to see notifications on the iPhone? That's a separate hidden control, swiping down from the top corner this time. Apple Pay? Press an unlabeled, multifunction button twice. The number of hidden controls makes even simple operations difficult.

There’s nothing wrong with conserving our time and resources or with wanting our lives to run more smoothly. What’s wrong, Ellul argues, is that technique doesn’t accomplish these goals. Technique promises to make life more convenient, affordable, and seamless but in practice makes it more draining, expensive, and complicated. Each new technique we adopt for the sake of greater control creates problems for which we instinctively look for another technique to allay, and so on. If you want to view your child’s grade on the homework, you’ll need to set up an account with Drumblekick. You get the idea. The first reason I’d be tempted to write my screed against optimization, then, is that it doesn’t optimize. Optimization promises to cure headaches, but then it gives them.

Research suggests that subclinical ADHD is not always associated with ADHD’s signature cognitive differences like executive function difficulties, and is less likely to be associated with a family history of the disorder. Individuals with subclinical ADHD often do not experience meaningful impairments or co-occurring diagnoses and are more likely to notice benefits like creativity from their ADHD than people with moderate to severe ADHD. Yet, compared with the general population, they are at elevated risk for secondary complications from their ADHD symptoms – such as substance use disorders. They also appear to respond to treatment in similar ways to individuals meeting full ADHD criteria.

There are people who use these, apparently. And it just feels so… depressing. There are people I once respected who, apparently, don’t actually enjoy doing the thing. They would like to describe what they want and receive Whatever — some beige sludge that vaguely resembles it. That isn’t programming, though. That’s management, a fairly different job. I’m not interested in managing. I’m certainly not interested in managing this bizarre polite lying daydream machine. It feels like a vizier who has definitely been spending some time plotting my demise. It makes programming spaces feel bleaker. I don’t want to help someone who opens with “I don’t know how to do this so I asked ChatGPT and it gave me these 200 lines but it doesn’t work”. I don’t want to know how much code wasn’t actually written by anyone. I don’t want to hear how many of my colleagues think Whatever is equivalent to their own output. I don’t want to keep watching people fall for a carnival trick.

“Good tests kill flawed theories; we remain alive to guess again.” — Karl Popper

Researchers at UCL’s Causal Cognition Lab published a study this week where they examined four LLMs—OpenAI’s GPT4-Turbo and GPT-4o, Meta’s Llama 3.1, and Anthropic’s Claude 3.5—using traditional moral psychology tests. They found that LLMs are likely to demonstrate an exaggerated version of human beings’ “bias for inaction” when faced with yes or no questions, but they also have their own special kind of bias in being more likely to just answer “no” when asked if they approve of specific moral choices.

Humans had a difference of 4.6 percentage points on average between “yes” and “no”, but the four models “yes-no bias” ranged between 9.8 and 33.7%.

In other words, create the illusion of an image that extends under a sidebar, and while you won’t actually be able to see the part of the image under the sidebar, on the other hand the transparency effect applied to the sidebar will make the text on it less legible overall. A great lose-lose situation, visually, don’t you think?

Artificial intelligence” is not a technology. A chef’s knife is a technology, as are the practices around its use in the kitchen. A tank is a technology, as are the ways a tank is deployed in war. Both can kill, but one cannot meaningfully talk about a technology that encompasses both Sherman and santoku; the affordances, practices, and intentions are far too different to be brought into useful conversation.

Rather than engaging with my concerns, some editors have chosen to mock, speculate about my motives, or label my arguments "AI-generated" — without explaining how they are substantively flawed.

And there’s a logical contradiction inherent in this sort of normative smuggling, which is that the smuggling is even necessary at all. After all, if states are mostly rational and largely pursue their own interests, loudly insisting that they should do so seems a bit pointless, doesn’t it? Using realism as a way to describe the world or to predict the actions of other states is consistent with the logical system, but using it to persuade other states – or your own state – seems to defeat the purpose. If you believe realism is true, your state and every other is going to act to maximize its power, regardless of what you do or say. If they can do otherwise than there must be some significant space for institutions, customs, morals, norms or simple mistakes and suddenly the air-tight logical framework of realism begins to break down.

Be proactive in joining productive communities or make your own, on the topics you want to work on.

For example, when I decide how to spend my afternoon, I’m biased towards hopping on BrawlStars since that’s at the top of my head. Tasks that I don’t think about often, like doing laundry, are harder to surface. A community is a great way to provide a constant stream of relevant information to make the goal fresh on your mind.

It’s in our genes to seek approval. Well-known terms like accountability partner or productive competition are in fact describing the mechanics of our approval seeking tendencies boosting productivity.

I believe most highly motivated people have a good community working towards the same goals. A community can multiply your motivation while people working alone run out of steam easily.

There are infinite quotidian human experiences ripe for interpreting: putting off housework, caring about who I sit next to at a dinner party, struggling to get dressed. They stack up every day. I’m particularly fond of using them to draw ungenerous conclusions: I’m shallow, selfish, lazy, dishonest. I’ve trained myself away from defending my goodness out of fear of being delusional, but now I find myself clinging to the contrary. Might this be because, unlike more generous self-appraisals, these critiques invite more self-work, further self-focus? There’s a certain narcissism to self-deprecation, to the belief that you are exceptionally bad or wrong. The resulting “need” to turn inward enables a familiar spiral.

Regarding my taste for arranging my baby’s burp cloths for pleasure, there was truly no introspection needed. I appreciate certain color and texture combinations, I like to experience beauty because I have a soul. But my muscle for second-guessing my motivations has become so overdeveloped it jumps at every opportunity to flex.

Sometimes it surprises me how things that I consider essential in my life—things that form parts of my identity—might have never found me. Only though happenstance did I learn about these things. People happened to enter my life and introduce me to these things I love.

Review books, or write about books you read. In the same mind as keeping an archive of your path, and as a counter to the ‘letting it all wash over you as you go’ mode, taking the time to think effectively about a particular text or series of texts, whether you loved it or not, can be extremely instructive for your own approaches.

I don’t want to compare it to eating your vegetables, but some things truly are acquired tastes, and you won’t know what you like about them until down the line. Either way, tending toward books that have stood up over time instead of the fun, newfangled flavor of the week tends to be more sustaining—like, holy shit I finally read Ulysses!—and allows space for deeper understanding of the new as well in that you have context for its emergence.

Most of the arguments made against ‘difficult literature’ or ‘academic speak’ tend to come down to something like a fear of the unknown, of feeling left out by something you haven’t taken any time to understand, assuming or insisting it’s not for you—when in truth everyone—including your professors—tend to be making things up as they go along.

Work on motivations for using Facebook also supports the distinction between connection-promoting and non–connection-promoting use. A longitudinal study conducted across two time points looked at the impact of Facebook use on adolescents’ well-being. Use motivated by compensating for insufficient social networks predicted increased loneliness at follow-up, while use motivated by the desire to connect with other people predicted decreased loneliness at follow-up (Teppers, Luyckx, Klimstra, & Goossens, 2014). The authors explained these results by suggesting that compensation motives led to passive use and connection motives led to active use, but they did not measure this distinction directly. However, other research has empirically distinguished between passive Facebook use (defined as consuming information without direct exchanges) and active Facebook use (defined as activities that facilitate direct exchanges with others). Across two studies using experimental methods and experience sampling, passive Facebook use was linked to declines in well-being, while active Facebook use was not (Verduyn et al., 2015). In other words, the effect of using social network sites depended entirely on the nature of that use.

Bite-sized thoughts— especially short form video— convince you that the whole thing is right in front of you.

The challenge of reading is to navigate the narrative without the overtures of overt feelings. There is no face to latch onto, no music that sways you. Words on a page especially cannot compete with screen-time. They’re not meant to. The boredom opens up space in your mindscape to your own thoughts, opinions, and feelings.

We live in a world ran and ruled by the written word. Similar to money, right? The tool itself is not necessarily bad. However, in this iteration of world-making, proper literacy in reading and writing (as well as financial literacy) are often the difference between agency, freedom-seeking, and autonomy for self-determination and working in someone else’s world-making in abysmal conditions perpetually. Which means:If the empire wanted you to have a good relationship with reading, they would ensure one.

There is a saying in Pashto (language): A woman’s place is either inside the house or in the grave. But this is not merely a simple proverb, it is rather a law that dictates the social role of women among the Pashtun people. It means that a woman has no place outside the walls of her house

One of Yamada’s research subjects remarked that, faced with a long silence, they’d ‘fill up the silence with talk’. This pressure to speak can be so compelling that various groups have used it to their advantage. Chinese negotiators are trained to draw out silences in order to provoke their writhing Western counterparts into making concessions. And the UK’s College of Policing recommends that, during interrogations, police officers ‘Stay silent during pauses … to encourage the witness to continue.’

These norms vary by culture. The London-based Japanese linguist Haru Yamada, who has extensively explored communication differences between speakers of Japanese and of English, observes that silences that are comfortable for Japanese speakers feel ‘unbearably long’ to American speakers.

Humans obviously exist at a highly optimised state of evolution. We have conquered the planet. We reproduce prolifically and we live much easier lives with more abundance of basic resources than any other animal. Our constellation of traits combines to create a deep valley in the evolutionary landscape where reproductive outcomes are high (so high that we try to consciously control reproduction) and effort is low.

The other means of escape is a dramatic genetic change – like a beneficial mutation – that moves the species out of its valley of doom – perhaps over the crest toward a different local minimum. Evolutionary biologists call this ‘saltation’ – basically meaning big jumps in evolution, and it is (debatably) the likely way new species come about (as opposed to variation within a species, which occurs by more gradual shifts in traits over time).

This is an old tactic of capitalist work ethic ideologues. The British Treasury used it to administer emergency food aid to Ireland during the Potato Famine. On the work ethic principle that “If a man will not work, neither shall he eat,” (2 Thessalonians 3:10) the government enacted a public works employment scheme–hard labor building roads–for starving men in 1846-7. But the wage wasn’t enough to pay for the increasing cost of food. As Irish workers’ ability to perform hard labor declined from lack of food, the Treasury attributed this to laziness and insisted on paying by the task rather than the day. The enormous expense and time devoted to documenting task completion at the requisite quality for each worker led to long delays in pay and further starvation. Local relief agents kept pleading with the Treasury to relax its work and documentation requirements, explaining how these demands were diverting funds needed to feed people, causing starvation, and not catching cheaters. Their testimony fell on deaf ears. As I explain in Hijacked, the government viewed the famine as an opportunity, not a calamity. It designed relief policy for a much grander end than relieving the suffering of Irish peasants. Its real objective was to quickly revolutionize the 2-class Irish agricultural system (landlords, peasants) on the 3-class capitalist model (landlords, capitalist farmers, wage laborers) that England had taken 300 years to achieve. This model required the elimination of the peasantry–workers who enjoyed a measure of self-sufficiency because they grew their own food–to be replaced with a much smaller number of agricultural wage laborers. The potato was so nutritious and yields were so high that an Irish peasant could support a large family on a quarter-acre plot. The work ethic-obsessed English condemned the potato as the “lazy crop” because so little work was needed to grow it. In their imagination, Irish peasants were slacking off the rest of the time, and lazy Irish landlords were colluding with them by renting out tiny plots. These work ethic ideologues conveniently overlooked the fact that the peasants had to pay the rents they owed by working the landlords’ farms. Irish peasants produced enormous agricultural surpluses that England imported to feed its population. Never mind. Facts don’t matter! For the English also had their eyes trained on the property of those idle Irish landlords, which they thought should be in the hands of purportedly more enterprising English landlords. I’ll skip over the many additional grisly contortions of British welfare policy undertaken to clear the Irish estates of the peasantry and drive their owners into bankruptcy, so the English could buy them at fire-sale prices. Benjamin Jowett, Master of Balliol College, captured the spirit of the capitalist work ethic in recalling what economist and government advisor Nassau Senior had told him: that “he feared the famine in Ireland would not kill more than a million people, and that would scarcely be enough to do any good.”

You can always familiarize yourself with a strange project, no matter how badly shaped it is. I have cleared a few haunted forests in my career—progressively understanding and recovering ownership of a project, rewriting it line-by-line without indulging in the lazy start-over—and my experience is: for all the mud that can accumulate over years of careless maintenance, if you know how to look, you can always find traces of intent, you can infer the presence, the needs and constraints of previous maintainers. And you can use that to put parts of the puzzle together, to gain understanding and confidence to support some of your decisions. This wouldn’t be the case, I believe, with LLM-generated code. With LLMs it’s all random text, plausible nonsense, mocked intent. Past a certain point, the surrender would become irreversible—there’s no resurrecting that kind of project death.

Additional vocabulary to avoid using when talking about the paper  In addition to the vocabulary from Question 1 in this FAQ - please avoid using "brain scans",  "LLMs make you stop thinking", "impact negatively", "brain damage", "terrifying findings".

Lots of media and people used LLMs to summarize the paper. It adds to the noise. Your HUMAN feedback is very welcome, if you read the paper or parts of it.

Another common refrain was: “You’re an idiot, just use LaTeX,” occasionally countered by the slightly more refined: “No, you’re all idiots, just use ConTeXt.” To which I respond: Of course I’m an idiot, that’s why I have no business using either. But if you’re brave and pure of heart, I’ve been (repeatedly) told that you can achieve superior results using these tools.

A woman sat there in 1620-ish, and she made a jacket for the joy of it. Women drew on silly bugs and beasties with a fine-nibbed pen and embroidered them in not-always-realistic colours for the pleasure of owning a pretty thing and for the joy of wearing something that had given her pleasure to create.

Indeed it turns out the number of available job opportunities for translators and interpreters has actually been increasing. This is not to say that the technology isn’t good, I think it’s pretty close to as good as it can be at what it does. It’s also not to say that machine translation hasn’t changed the profession of translation: in the article linked above, Bridget Hylak, a representative from the American Translators Association, is quoted as saying “Since the advent of neural machine translation (NMT) around 2016, which marked a significant improvement over traditional machine translation like Google Translate, we [translators and interpreters] have been integrating AI into our workflows.” To explain this apparent contradiction, we need to understand what it is translators actually do because, like us programmers, they suffer from having the nature of their work consistently misunderstood by non-translators. The laity’s image of a translator is a walking dictionary and grammar reference, who substitutes words and and grammatical structures from one language to another with ease, the reality is that a translators’ and interpreters’ work is mostly about ensuring context, navigating ambiguity, and handling cultural sensitivity. This is what Google Translate cannot currently do.

In some ways, Satie feels like a long-ago ornament; at the same time, more playfully modern than our own increasingly doctrinaire era. This contradictory pulse — on the one hand, on the other — can be found in all aspects of his life. He is a one-man synthesis of Catholicism and Protestantism. He reconciles counterpoints of high culture and popular song. Founder of a church, habitué of low dives.

You tell an AI your application needs to be GDPR compliant- you aren’t really sure what that means, but the AI does- and so it dutifully constructs a workflow for data deletion. Fantastic! Now customers can click the Delete button and feel like their data is no longer retained. The next day you have another idea: “Let’s backup customer records to my home server, I don’t trust Amazon and I think we need to be in control of our own data!!” The AI complies, throwing in some effusive praise for your strategic thinking.These examples might feel too silly and contrived to be real, but I promise that developers have to talk clients down from this kind of bullshit every single day. Devs spend far more time discovering reality underlying the problem than writing code to solve it.

The sentences are well-formed, but there’s no concrete insight, no framing, no momentum.

Technology’s magnetism extended invisibly. It did not control us like an overlord cracking a whip, but as the slope of a mountain controls the water falling down it: by bending our actions toward what it made easy at the expense of what remained hard. What technology did not make easy seemed in turn harder and harder.

I like people who try very hard, and I like people who attempt to conceal their effort, but I especially like people who let all their effort show. We are all Frankenstein monsters—patchwork quilts of past experiences—trying to pass ourselves off as whole and cohesive things. Femininity in society is especially like that: how you dress, how you style your hair, how you apply makeup, how you eat, what jewelry you choose. These are learned things, earned things. Here’s what I know: if someone’s much better than you at something, they probably try much harder. You probably underestimate how much harder they try. I’m not saying that talent isn’t a meaningful differentiator, because it certainly is, but I think people generally underestimate how effort needs to be poured into talent in order to develop it. So much of getting good at anything is just pure labor: figuring out how to try and then offering up the hours. If you’re doing it wrong you can do it a thousand times and not produce any particularly interesting results. So you have to make sure you’re trying the right way.

Gasoline (John Cassell)

MySQL (My Widenius) Debian (two people named Deb and Ian)

Someone tweets a screenshot of a comment with the phrase Do you baulk at the film reviews...?Someone American asks "how long have y'all been adding a u to balk?"And then, predictably, some respondents say it's always been there, Noah Webster took it out.That's the point where someone (c'est moi!) looked into it and reported back:  The Oxford English Dictionary has it as balk. Since it comes from Old English, it only got a 'u' after French had influenced (I am tempted to say 'infected') the spelling system. The 'u' came into the word in the 1600s and 1700s, and today the spelling is very mixed in BrE.

Collection: LACE JACKETS

“Tech” no longer singularly equates to being a software engineer or even working with technology at all.

Now, we see movements like: progress, which focuses on the economic and technological drivers of social progress, and began with an editorial piece written by Stripe cofounder Patrick Collison and economist Tyler Cowen; abundance, a cousin movement that tends to emphasize social well-being and distributive outcomes, driven by the likes of media figures Derek Thompson and Ezra Klein; the New Right, who prioritize family, local communities, and strong leadership, and whose views influenced Vice President Vance; American dynamism, which emphasizes reviving public infrastructure and institu­tions that strengthen American values, spurred by a growing community of aerospace, defense, and manufacturing companies in Los Angeles and Orange County; effective altruism, a carryover from the early 2010s, rooted in rationalist and utilitarian approaches to giving; tech ethicists, who fight algorithmic bias and misinformation; and the network state, incubated by entrepreneur and investor Balaji Srinivasan, who advocates for building new, digital-first nation-states.

Of those surveyed, 82 percent thought that it was too hard to fire workers

But for those of us who identify with tech, this ideology feels increasingly out of step with the world we live in.

The sentence that now reads "And it's a deal 14 million Americans have signed up for" originally read "And it's a deal 14 million Americans have chosen for themselves."

There's a reason PCG goes to all this trouble. The company gets paid by the state every time it moves someone off of welfare and onto disability. In recent contract negotiations with Missouri, PCG asked for $2,300 per person. For Missouri, that's a deal -- every time someone goes on disability, it means Missouri no longer has to send them cash payments every month. For the nation as a whole, it means one more person added to the disability rolls.

But disability has also become a de facto welfare program for people without a lot of education or job skills. But it wasn't supposed to serve this purpose; it's not a retraining program designed to get people back onto their feet. Once people go onto disability, they almost never go back to work. Fewer than 1 percent of those who were on the federal program for disabled workers at the beginning of 2011 have returned to the workforce since then, one economist told me.

The upper middle class is responsible for the vast majority of consumer spending. The top 10% of American earners are responsible for 49.7% of consumer spending. That’s household incomes over $250,000. Include the next decile, the top 20% or those with household incomes over $165,000, and we come close to two-thirds of consumer spending.

Aggressive return to office policies cooled the flight from cities, but ultimately, the need for such aggressive return to office policies underscores that we often conflate the demand for urban living with the demand for a well-paying and high status jobs.

Between 1897 and 1925, 26 million doses of Haffkine's anti-plague vaccine were sent out from Bombay. Tests of the vaccine's efficacy showed between a 50% and 85% reduction in mortality. But "no figure" could be put on the number of lives he saved, Dr Hawgood said. "The numbers are just enormous."

Four years after Mulkowal, in 1906, the Indian government finally published its full inquiry finding Haffkine guilty. Upon reading the masses of documents, WJ Simpson, a professor at King's College, London, wrote a letter to the British Medical Journal arguing passionately that the evidence pointed to an accidental contamination of bottle 53N at the inoculation site in Punjab.

In March 1902, in the village of Mulkowal in Punjab, 19 people died from tetanus after being inoculated with Haffkine's vaccine. The 88 others inoculated that day were fine. All the evidence appeared to point to a fatal contamination of bottle 53N - prepared 41 days earlier at the Parel lab.An Indian government commission was tasked with investigating, and it discovered that Haffkine had changed the procedure for sterilising the plague vaccine, using heat instead of carbolic acid because it sped up production. The heat method had been safely in use at the world-leading Pasteur institute for two years, but it was unfamiliar to the British, and in 1903 the commission concluded that bottle 53N must have been contaminated in Haffkine's lab in Parel. Haffkine was fired as director of the plague lab and placed on leave from the Indian Civil Service.

He discovered that if he placed plague bacilli in a nutrient broth to which he had added a small quantity of clarified butter or coconut oil, the bacilli formed into a signature stalactite growth, creating microbes and toxic products on the side. He was using the same approach he had devised for the new treatment of cholera, combining the microbes with the toxic products they produced to form a single-injection vaccine.

Haffkine travelled to Bombay, where he was set up in one small room and a corridor, with one clerk and three untrained assistants, and tasked with coming up with the world's first plague vaccine from scratch.

Building on the work of Pasteur and Jenner, Haffkine discovered that by passing cholera bacilli through the peritoneal cavity of guinea pigs - 39 passes in total - he could produce a strengthened, or "exalted" cholera culture, which he could then attenuate using heat. An injection of the attenuated bacteria, followed later by an injection of the exalted bacteria, appeared to immunise guinea pigs against a lethal attack of the disease.

When Haffkine graduated in zoology from the University of Odessa in 1884, his reward was to be barred from taking up a professorship there because he was a Jew. He had already run into political trouble five years earlier, amid pogroms, when as a member of a local defence league he fought to stop Russian army cadets destroying a Jewish man's home. Haffkine was beaten and arrested but eventually released.

Haffkine's solution was to work with a team of Indian doctors and assistants, rather than the British - Drs Chowdry, Ghose, Chatterjee, and Dutt, among others. And he had a new trick up his sleeve in the world of vaccinology: publicly injecting himself to prove he thought his preparation was safe.

The matter admitteth those particular formes materially, and withall obli erateth or How the re­ception of formes differs in the first matter, and in the soule. blotteth out the contrary forme whereof it was before possessed: the soule of man receiues and entertaines the generall and vniuersall notions of things, free from all contagion or touch of Matter, not abolishing the contrary, or diuers formes whereof before it was posses­sed. This alone is incorporeall, immortall, [...] or immutable. This may be called the receptacle, promptuary, or store-house of all the species or kinds of things.

Not to be confused with Bud Light. An artist's impression of one of the Bude Lights installed at Trafalgar Square, London, in 1845. A Bude-Light was a very bright oil lamp (later, in its modified form, a gas lamp)

A Lesbian rule was historically a flexible mason's rule made of lead that could be bent to the curves of a molding, and used to measure or reproduce irregular curves.

The promptuary, also known as the card abacus is a calculating machine invented by the 16th-century Scottish mathematician John Napier and described in his book Rabdologiae[1] in which he also described Napier's bones. It is an extension of Napier's Bones, using two sets of rods to achieve multi-digit multiplication without the need to write down intermediate results, although some mental addition is still needed to calculate the result.

If you are fortunate or unfortunate enough to spend time around people who work for big tech firms, you will find that their views on every issue tend to be rooted in the assumption that the tech industry itself will determine the future of said issue. So discussions about the economy become, “What will AI mean for the economy?” Discussions of politics become, “How will new tech help my side win the next election?” Discussions of climate change become, “How fast can we innovate ways to capture carbon in the atmosphere?” Discussions of culture become, “Is AI making good art?”

Input Risk. An LLM does not challenge a prompt which is leading1 or whose assumptions are flawed or context is incomplete. Example: An engineer prompts, "Provide a thread-safe list implementation in C#" and receives 200 lines of flawless, correct code. It's still the wrong answer, because the question should have been, "How can I make this code thread-safe?" and whose answer is "Use System.Collections.Concurrent" and 1 line of code. The LLM is not able to recognize an instance of the XY problem2 because it was not asked to.

I’m just moving how my body wants to move. I dance because it feels good. I imagine this is what people are picking up on. Maybe it’s just fun to see someone else have fun.

Every now and then someone at a rave will approach me and say something nice about my dancing.

These days I find that going out, whether by myself or with friends, and dancing once every 3-4 weeks keeps me afloat in a way that no other intervention has.

What’s So Great About Dancing Anyways?, And How You Too Can Dance And Improve Your Mental Health For Great Success

a fellow trans woman I met on Mastodon

I did not enjoy the super gay male party; they prevented me from dancing, and I emerged from the experience a staunch believer in lesbian separatism.17 Yet, I never felt unsafe. I was annoyed but there was no tension. I felt more uncomfortable around one single dude at the Jamie XX show than I did from all the gay guys who were trying to move around me. Now I am very curious about what a predominantly femme space would be like. Those parties exist, but they seem to be way less common than the gay-man or mixed-queer-space shows.

Governed by an invisible law of statistics it’s probably not unlikely for something like this to form spontaneously, somewhere, in any given crowd.

There were just way more straight people (men) around. Are the straights OK?

More parties should tape attendees’ cell phone cameras. There is something really cool about being forced to be present in the moment.

He was at the forefront of what was retroactively called the Great Male Renunciation: a “major turning point in the history of clothing in which men relinquished their claim to adornment and beauty”. Out went silk breeches, powdered wigs, and elaborate, brightly coloured garments and in came dark colours, pants and the ever present suit – which dominates men’s fashion to this day.

Most importantly, this show taught me that I didn’t have to be totally wasted to be disinhibited, to let loose, and let the music move me. That was a new experience. At my first rave, back in May, I had assumed that I needed to be smashed in order to not be turned off by the environment. And yet here I had had a great time, and didn’t have to get destroyed to do it.

On the other hand, there is no way to win when they control every aspect of the experience. it’s simply not healthy for us to communicate on platforms that are actively adversarial to our lives. People need to get the fuck away from these platforms.

A funny thing happens when you start following rave promoters and DJs on Instagram: Instagram picks up on that right away.

A comfortable, privileged, not unpleasant vortex, don’t get me wrong. I know, I am certain, that one day I will look back on this time and think: those were the happiest years of my life. There is a book in my heart that catalogues the many trivial ways my life could get worse.

I had attended countless indie music concerts. The kind of event where you wear plaid and stand arms crossed, perfectly still, and stare, fulminating, into the soul of the performer(s).

Afterwards it brings a lightness of being, and a chill, positive attitude that takes a full day, day and a half, to dissipate.

Better sounding writing is more likely to be internally consistent. If the writer is honest, internal consistency and truth converge.But while we can't safely conclude that beautiful writing is true, it's usually safe to conclude the converse: something that seems clumsily written will usually have gotten the ideas wrong too.

An essay is a cleaned up train of thought, in the same way dialogue is cleaned up conversation, and a train of thought has a natural rhythm. So when an essay sounds good, it's not merely because it has a pleasing rhythm, but because it has its natural one. Which means you can use getting the rhythm right as a heuristic for getting the ideas right. And not just in principle: good writers do both simultaneously as a matter of course. Often I don't even distinguish between the two problems. I just think Ugh, this doesn't sound right; what do I mean to say here?

Most productivity tricks develop aversion around them.

make you feel totally overwhelmed when talking, not even sure what happened

their entire worldview seems to be derived from a single insight

mom undermines father

have thought through everything

fast when it matters, slow when it doesn’t

i’m scared that they might be better than me at everything and that i’ll be out of job if they try to do whatever it is i’m doing

I claim lists are good because they give readers an easy and predictable interface. But you can’t use lists for every subject. So, the natural question to ask is, are other structures with similar advantages?

This application uses a unique approach. It employs statistics and smart algorithms to automatically create typing lessons that match your current skill level.

Don’t worry about creativity! Yes, every permutation of every possible bit of self-help advice has probably been given before. But how did they work for you? Did you try radical honesty and find that it was a disaster and you had to add some exceptions? Did you stop drinking and find that it was better/worse/harder/easier than you expected? Even better, did you try really obvious things, like just making a big effort to be nicer to people or avoiding carbs at lunch?

List things that you think might be true, but are out of fashion or against the consensus of your in-group. Do not try to argue that your beliefs are true. Leave truth out of it. Instead, introspect and try to describe why you think you’ve come to different conclusions than others.

Social capital is often associated with desirable political and economic outcomes. This paper contributesto the literature exploring the “dark side” of social capital, examining the downfall of democracy ininterwar Germany. We collect new data on the density of associations in 229 German towns and cities.Denser networks of clubs and societies went hand-in-hand with a more rapid rise of the Nazi Party.Towns with one standard deviation higher association density saw at least 15% faster Nazi Party entry.All types of societies – from veteran associations to animal breeders, chess clubs and choirs – positivelypredict NS Party entry. Party membership, in turn, is correlated with electoral success. These resultssuggest that social capital aided the rise of the Nazi movement that ultimately destroyed Germany’sfirst democracy. Crucially, we examine the question when a vibrant civic society can have corrosiveeffects. We show that the effects of social capital depended on the political context – in federal stateswith more stable governments, higher association density was not associated with faster Nazi Partyentry.

The truth is people sometimes wanna have a good time with their friends and family. And to tell the truth, pizza and drinks can help with that! Most of my friends agree that Dino's is the right place to enjoy these things.

In the mathematical theory of knots, the unknot, not knot, or trivial knot, is the least knotted of all knots. Intuitively, the unknot is a closed loop of rope without a knot tied into it, unknotted.

Now, there are a million implications to outsourcing our first drafts to AI. We know people anchor on the first idea they see, influencing their future work, so even drafts that are completely rewritten will be AI-tinged. People may not be as thoughtful about what they write, or the lack of effort may mean they don’t think through problems as deeply.

+MATER+ capes weigh about 1.2 kg and are excellent to be worn in mid-season instead of a coat, or over a coat in winter to have a thicker protection from the cold. They are also perfect indoors when you sit for a long time working, reading or meditating, and they also provide a perfect shield when you dive inside yourself to work on your spells. 100% wool sustainable material

We identify a recurring set of flaws that apply broadly to predictive optimization, are hard to fix technologically, and negate its claimed benefits.

Apps, lists, and calendars can help us put our priorities in order, sure. But only we can figure out what those goals are. And setting limits on what we hope to do is philosophically painful. Every to-do list is a midlife crisis of unfulfilled promise. Winnowing away things you’ll never do in a weekly review is crucial, yet we dread it for what it says about the boundaries of existence. Our fragile psyches find it easier to build up a list of shame, freak out, and flee.

Paper forces you to repetitively rewrite tasks, as when, say, you transfer all last week’s undone to-dos to this week’s list, or when you erase and rewrite calendar events.

Time blocking forces us to wrestle directly with the angel of death. It’s natural that we then screw around less.Several researchers who study tasks told me they generally agreed that time blocking avoids the problems of to-do apps and lists. One to-do app, Reclaim, actually has an AI that estimates how long each task will take and finds a slot in your calendar. (The secret point is to show you there isn’t much room in there.)

Weeks or months later, your Todoist app is a teetering ziggurat of tasks, too painful even to behold. Omer Perchik, the creator of another app—Any.do—calls this problem “the List of Shame.”And then what do we do? You’ve probably done it: We panic, give up, and quit. We “declare to-do bankruptcy.” We toss the list away in defeat and start fresh.

And indeed, those who regularly write down their to-dos seem to possess a mind less jittery. Shamarukh Chowdhury, a PhD student in psychology at Carleton University, has found that people who create to-do lists are less likely to procrastinate than those who don’t. More delightfully yet, a study by Baylor University psychologist Michael Scullin found that people who created a to-do list fell asleep nine minutes faster, on average, than those who didn’t.

Another thing that might feel familiar: The things that IDoneThis users actually did accomplish, they did very quickly. Half of completed to-do items were done within a day of writing them down. These weren’t longer-term, complex tasks. Ten percent were done within a minute. It was almost like people were writing them down just so they had something to check off. A nice psychological boost, to be sure, but it somewhat defeated the purpose of a to-do list.

It is easy to spot the person in the room who thinks they are better than everyone. It is the person uninterested in giving any of their attention, the genuine and open-ended kind, to anyone else. This is also painful to see, because they often cannot see their own misery, how unpleasant the world is if no one is good enough to be loved.

they can be hosted on dedicated LLM hosting providers like Cerebras and Groq who can actually make money on each user inference query.

There’s a fine line between experimenting with code generation and gambling with code generation.

Although pandas is the standard for manipulating tabular data in Python and has been around since 2008, I’ve been using the relatively new polars library exclusively, and I’ve noticed that LLMs tend to hallucinate polars functions as if they were pandas functions which requires documentation deep dives to confirm which became annoying.

There is one silly technique I discovered to allow a LLM to improve my writing without having it do my writing: feed it the text of my mostly-complete blog post, and ask the LLM to pretend to be a cynical Hacker News commenter and write five distinct comments based on the blog post. This not only identifies weaker arguments for potential criticism, but it also doesn’t tell me what I should write in the post to preemptively address that negative feedback so I have to solve it organically.

Each of these projects were off-hand ideas pitched in a morning standup or a Slack DM, and yet each project only took an hour or two to complete a proof of concept (including testing) and hand off to the relevant stakeholders for evaluation. For projects such as the hierarchal labeling, without LLMs I would have needed to do more sophisticated R&D and likely would have taken days including building training datasets through manual labeling, which is not intellectually gratifying. Here, LLMs did indeed follow the Pareto principle and got me 80% of the way to a working solution, but the remaining 20% of the work iterating, testing and gathering feedback took longer.

if your philosophy finds extensive support in prime-time TV, you're merely repeating what everybody already believes.

That you got lucky at a singular moment in history and now you're an old man is not an easy set of facts to accept. So I understand — that is, I see how — one can end up associating one's best years with superficial aspects of their circumstance. You had no responsibilities, no serious consequences for failure, and the freedom to be reckless and inconsiderate. You launched small new products that didn't require building a team. If you attended school, the vast majority of your fellow students were men, and they were more or less all the same person as you.If these are the conditions under which passionate creative problem solving thrives, then of course we must recover them to make software great again. But they are not.

File

How much do I really need to know? Enough to know what I think and how I feel. Knowing any more makes both impossible.

This month, without socials, I’ve actually been able to devote time to thinking about the things I care about with some depth. I watched The Girl in the River, a documentary about an attempted honor killing in Pakistan, and it rocked me deeply. Without a timeline of competing horrors to scroll through in the background, I was able to fully devote my attention to the feelings the film brought up within me. I had capacity to do further research and free time to sit alone in my apartment, just thinking and feeling through the inhumanity of global femicide. I had space inside myself to really reflect on my own immense privilege, and it felt differently from when I would feel that pang while scrolling on socials — a pang that was immediately numbed by the next overload of information.

The services using 97-99% of AI’s energy budget are (roughly in order)Recommender Systems - Content recommendation engines and personalization models used by streaming platforms, e-commerce sites, social media feeds, and online advertising networks.Enterprise Analytics & Predictive AI - AI used in business and enterprise settings for data analytics, forecasting, and decision support.Search & Ad Targeting - The machine learning algorithms behind web search engines and online advertising networks.Computer vision - AI tasks involving image and video analysis.Voice and Audio AI - AI systems that process spoken language or audio signals.

When I first started reading about this, I assumed people worried about climate thought that AI was using way more energy than the companies were telling them, but it seems like everyone on both sides of the debate agrees 3 Wh is a reasonable upper bound guess. It’s very strange to have such a strong disagreement about such a clear amount of energy that the climate movement would have never worried about with any previous technology.

Generation after generation, claims of burgeoning loneliness make sense to Americans because most of us share a deep, nostalgic, powerful  intuition that modern life is a fall from grace, that where once we lived in warm, harmonious, tight-knit communities—says this collective memory—we now live in cold, discordant, and lonesome spaces. No amount of historical or sociological research to the contrary will shake such a deeply-felt conviction. I suspect that there is yet an additional reason, too, for the power of the loneliness meme today: It sidesteps class and material conditions. Loneliness is an emotional pain that, while more common among the poor, the infirm, and the angst-ridden teens, has been felt by every modern American at some time. Making this common experience a focus of our public concern avoids confronting harsh, worldly, and politically volatile inequities in our society. As I said in Part 1, a loneliness agenda also means less attention and fewer resources to deal with other “epidemics”—gun violence, school failure, opioid addiction, maternal mortality, homelessness, climate migration—and, you know, real, non-metaphoric epidemics.

Even when explicitly prompted for accuracy, most LLMs produced broader generalizations of scientific results than those in the original texts, with DeepSeek, ChatGPT-4o, and LLaMA 3.3 70B overgeneralizing in 26 to 73% of cases.

Economists and sociologists measure intergenerational inequality by comparing parents’ economic outcomes with those of their adult children. We estimate a person’s likely economic situation based on their parents’ economic situation, taken at similar points in their lives (for example, incomes at age 30).

It’s just as bad now, she said; it’s still only ten percent women, and we still don’t talk to each other. It was disappointing then to hear that so little had changed. Now it’s been another ten years, and it’s slightly better: the proportion of women getting computer science degrees is now closer to 20%. I have to imagine that, with social media, they’re talking to each other now. I hope it’s helped.

One of the questions the organizer asked: it’s one thing to be an A student, but what if you’re a woman and an average student? Would you feel okay in the program if you were getting C-pluses? The women in the room all agreed: they were doing well academically, which helped them feel like they belonged there. But if they’d been getting C-pluses (the mandated class average by which all grades were curved), they would have dropped out. I knew instantly that was true of me as well. I needed to cling to pride in my achievements, because otherwise the loneliness would have been too much.

I also started reading a lot of feminist writers, in particular Simone de Beauvoir and Germaine Greer1. I had a feeling that in order to survive in computing I would need to cultivate within myself an unshakable sense of my right to be there.

When I told people I was going to study Computer Science, once in a while they’d raise an eyebrow and say with some skepticism: isn’t that mostly guys? The number I heard bandied around was ten percent — ten percent of computer science students were women. And that proved to be accurate. Every time I entered a new classroom, I counted them. Fifty-person classes had five women. Hundred-person classes had eight to ten. In one of the intro classes, the professor announced during the first lecture that the class had a 40% failure rate. One of the few other women in the class got up right then, grabbed her laptop, and walked out. I noticed that the women never sat together, but were always scattered across the classroom. I didn’t try to meet them.

For this reason, an important signal to keep track of is: where is your taste the best?I’ve noticed a lot of people underestimate their own taste, because they expect having good taste to feel like being very smart or competent or good at things. Unfortunately, I am here to tell you that, at least if you are similar to me, you will never feel smart, competent, or good at things; instead, you will just start feeling more and more like everyone else mysteriously sucks at them.For this reason, the prompt I suggest here is: what does it seem like everyone else is mysteriously bad at? That’s probably a sign that you have good taste there.

The way to prepare people to accept that is to convince them that their minds, and their relationships, are not really theirs—that the truth of those things lies in statistics. Then you can give those objects back to them, with guardrails: optimize your conversations using science.

Baker shows how American business culture and psychology have formed a crucible for the weirdest excesses of exploitation in the modern economy, a royal lineage into which Brooks’s book fits cleanly.

As the foregoing makes clear, this particular brand of twaddle is relentlessly fixated on improving economic outcomes above all else.

The claim here is that the experiments she cites in which people systematically ask more questions can tell us more about our actions than we can ever know, so long as they are properly randomized and controlled, the results dispassionately interpreted.

If we follow the steps laid out in the acronym of the book’s title—Topics, Asking, Levity, Kindness—we’ll be on track to optimize conversation. It’s just not really clear what that means. Thankfully, she’s got a parade of scientific studies to help make things clear.

Ophelia tells her father about how a half-naked, deranged-looking Hamlet barged into her room, physically assaulted her, and ran off into the night.  Since we never hear anything about Polonius’ wife, we must assume that the strain of raising two children on his own has rendered him non compos mentis, as this leads him to believe that Hamlet is in love and not a booze-shattered date rapist.  He decides to tell the King and Queen about it, because Ophelia’s personal life is apparently the business of everyone in the Danish royal family.

Prop 65 is often presented as a story of overregulation, but I think it's a matter of underregulation. Rather than simply telling you that there's a potential carcinogen nearby and leaving you to figure out whether you've exceeded your risk threshold, a useful regulatory framework would require firms to use their products in ways that minimize cancer risk. For example, if a product ships with a chemical that is potentially carcinogenic for a couple weeks after it is manufactured, then the law could require the manufacturer to air out the product for 14 days before shipping it to the wholesaler. "Caveat emptor" has its place – say, at a yard-sale, or when buying lemonade from a kid raising money for a school trip – but routine shopping shouldn't be a life-or-death matter than you can only survive if you are willing and able to review extensive, peer-reviewed, paywalled toxicology literature. When a product poses a serious threat to our health, it should either be prohibited, or have its use proscribed, so that a reasonable, prudent person doing normal things doesn't have to worry that they've missed a potentially lethal gotcha. In other words, transparency is nice, but it's not enough.

I do not want the audience of my writing, blog or otherwise, to be search engine users who found it because the terms were best-fit, or anonymous reposters on Reddit, or even people scrolling the public timeline on fedi.

We respond to each other and play off of each other’s ideas, and we do it in community. It feels, dare I even say it, LiveJournalish.

he worst part is that even though population structure itself is random, the GWAS orients all of that structure to match the phenotypes we actually observe, which makes the (false) genetic findings appear eerily plausible: genetically taller in the North and shorter in the South just like we see with our eyes! People sometimes ask why population stratification would just happen to line up so well with what we see phenotypically, but that is exactly what population stratification does: it lines up random genetic fluctuations with the observed phenotype in a way that then persists in independent samples.

With fewer experienced civil servants managing the contracts, and fewer contractors available to bid on them, each with more market power, costs have naturally soared. Other factors contribute to the problem, including the military’s penchant for stuffing weapons systems with fragile, unproven, and unnecessary high-tech tools (“everything bagel” policymaking was invented in the Pentagon, not the Biden administration). What doesn’t explain the exploding prices and slow delivery of weapons systems? Permitting. Perhaps not coincidentally, abundance liberals have had little to say about the subject.

A 2023 Yale Law and Economics study of highway resurfacing projects in all 50 states found that two variables overwhelmingly explain cost overruns. The first is bureaucratic “capacity”—that is, the number, skill level, and experience of employees at state departments of transportation—which has generally declined in recent years. This drop has led state DOTs to rely on outside consultants to plan and oversee the resurfacing projects. The second variable is a fall in the number of contractors available to bid on the projects. This is due largely to industry consolidation, which has shrunk the number of construction firms in 70 percent of U.S. states. The Yale researchers found that outsourcing infrastructure planning increased costs by 20 percent per mile, while each additional bidder on a project corresponded to an 8.3 percent reduction in cost.

t cannot be that people need to move to a handful of elite coastal cities to produce abundance. The growth of regional inequality of opportunity that the authors’ own scarcity mind-set represents is a real problem, and has little to do with land use regulation and everything to do with the deregulatory push from the 1970s to the 2020s and the resulting concentration of power and shift of resources from the real economy to the financial sector.

Cowen has been a protégé and friend of Thiel’s, citing him as a major influence. He advocates a form of government called “State Capacity Libertarianism” which, unlike anarcho-capitalism, includes a strong state apparatus. In Cowen’s words: “a good strong state should see the maintenance and extension of capitalism as one of its primary duties, in many cases its #1 duty.” He explicitly credits Thiel for inspiring this idea: “You will note the influence of Peter Thiel on State Capacity Libertarianism,” Cowen writes, “though I have never heard him frame the issues in this way.”

Patri Friedman, grandson of neoliberal high-priest, Milton Friedman, doesn’t worry about losing his Tesla key—it’s implanted in his hand. “I’ve gotten in the habit of getting crazy medical treatments here, ” he tells an interviewer from Reason Magazine, showing off a subdermal ID chip under a bandage. “Two trips ago I got gene therapy to make me stronger and faster. Last trip I had my mouth bacteria replaced with genetically engineered ones so I’ll never get cavities.”

I don’t personally understand why policy arguments need to be coupled with historical narratives to be compelling. If some aspect of the status quo is bad, then that is true regardless of whether it used to be less bad and regardless of how it got to be that way. But I am clearly an outlier in this regard. Whether because people are hardwired to love stories or because stories do actually illuminate something that I seem unable to comprehend, building a historical narrative around one’s policy preferences appears to be an indispensable part of stoking a political movement or, at least, selling a book.

In the conclusion of the book, they make it very clear that they want Abundance to be part of a vanguardist movement that remakes the Democratic party and then the political order. To achieve something as grandiose as that, the authors are forced to pair the policy ideas with a specific declinist historical narrative, contestable ideological commitments, and a utopian vision of the future. Not surprisingly, it is these aspects of the book that have drawn the most attention from critics.

Often I see creative people share personal projects and get their spirits weakened by “how’s it different?” The question can be well-meaning; it isn’t necessarily cynical! It’s just misunderstanding the goal. On the other hand, I see people submit cool work to academic research venues and get confused by, or even chafe at, the stringent requirement of situating the work in context. I used to be pretty dismissive of Related Work sections myself, until I went through grad school and realized how valuable they are to the world.

Starting with just the minimal thing we need for our particular use case, adding things immediately as we come across new requirements. Ending up with a tool that’s molded to our needs like a leather shoe,

Claude wasn’t perfect—it did get stuck one time when I asked it to add a flamegraph view of the stack trace changing over time. Perhaps I could have prodded it into building this better, or even resorted to building it myself. But instead I just decided to abandon that idea and carry on. AI development works well when your requirements are flexible and you’re OK changing course to work within the current limits of the model.

Whether you’re an AI-programming skeptic or an enthusiast, the reality is that many programming tasks are beyond the reach of today’s models. But many decent dev tools are actually quite easy for AI to build, and can help the rest of the programming go smoother. In general, these days any time I’m spending more than a minute staring at a JSON blob, I consider whether it’s worth building a custom UI for it.

For brands and their leaders, this decoupling has profound implications.

How do we value an employee, a member of the community, a partner, a leader, a teacher, a political movement, an education, a lifestyle, a brand, a product, or an idea when we can no longer point to work and dedication as a reliable measure of shared value?

AI means anyone can be a master, so your hard work doesn’t win you more accolades.

it’s hard to know how we should celebrate work as it relates to the body (which is perhaps the most profitable moral highground, as evidenced by the nearly $7T wellness industry).

Most men can’t afford to leave masculinity behind—it’s the expectation anywhere from construction sites to the offices of private equity firms, and if you don’t fit the mold then you will miss out on economic opportunities.

The right wing’s war on men consists of inducing insatiable demands in them–for love, respect, money, muscles–and then robbing them of any ability to even partially fill those demands, either by depriving them of the financial means or by convincing them that the only way to meet these absurd standards is by absurd means.

The Miller family are not poor, and the nondescript Northern town the series is set in is not deprived. Eddie runs a plumbing company, taking on out-of-hours emergency work—this is the kind of job that pays well beyond the median wage, and in monetary terms could place the family well inside the middle classes. England does not have a particularly logical class system however, so the fact that Eddie has a thick Scouse accent and enjoys sports means that no matter his wealth and position as a business owner with multiple employees he will always be regarded as working class.

Through the DARPA award, Szablowski will work with Miao-Hsueh Chen, an associate professor of pediatric nutrition at Baylor College of Medicine and an expert on BAT, to enhance nonshivering thermogenesis. A drug that boosts BAT response could help first responders treat victims of hypothermia and even lower the cost of arctic exploration. “If you have a drug that makes brown fat more active, then instead of having to spend weeks and weeks adapting to cold, you can perform better within hours,” he said.

The cynicism our current moment inspires appears to be, regrettably, universal. For millennials, who watched the better-world-by-design ship go down in real time, it’s hard-earned. We saw the idealist fantasy of creative autonomy, social impact, and purpose-driven work slowly unravel over the past decade, and are now left holding the bag. Gen Z designers have the same pessimism, but arrived at it from a different angle. They’re entering the field already skeptical, shaped by a job market in freefall and constant warnings of their own obsolescence. But the result is the same: an industry full of people who care deeply, but feel let down. As Shar Biggers describes it, designers are “realising that much of their work is being used to push for profit rather than change, making the rich richer, and being manipulated for misinformation. I’m constantly meeting designers who are looking to do work they believe in, and they’ve yet to find an opportunity to do that. And when they do, even that lets them down for numerous reasons.”

Here’s the difference: good, dense academic writing appears designed to be read multiple times. To be sat with. Butler and Bhabha want you to wrestle with their words, puzzling through them. I’m even convinced they would like readers to argue with their ideas (and cite them, of course). True academic disagreement is a sincere form of flattery.On the other hand, AI writing is designed to never be read again.

Suggestive sentences can be interpreted in multiple ways, thereby increasing their opportunities to be cited by scholars. If a sentence is suggestive while remaining ambiguous, then future academics can use it for their own work. It’s the quoting game of modern academic scholarship. A game that is full of citation chains where the more citations in the chain, the better.

To approximate an argument, AI writing bots use the word “and” instead of argumentative language. These chatbots avoid words such as:ThereforeTherebyAccordinglyConsequentlyFollowing this line of reasoning

Listing and cataloguing triangulate meaning for something that it is trying to calculate.

In seven of the eight sentences, there are lists of 3+ concepts/phrases/words (I did it again!—I used backslashes here to avoid sounding like AI).

What got my “AI radar” up was how the sentence made sense the first time I read it but, after thinking about it, the sentence was so complex that it overloaded my brain. In fact, it has 9 concepts in a 35-word sentence. In other words, there are so many concepts packed into a single sentence that it’s hard to keep track of them

will find useful.

So in one respect, Nordhaus was right: technology did eventually save the whales. But a) it happened more than a century later than he suggested, by which point whale stocks were much lower than in the 1860s, b) the technology in question wasn’t derived from crude oil and c) it was only developed in response to a government-enforced ban on whale hunting.

When I used to blog about programming languages, I found that simply saying that I liked some programming language, any language at all, would get me in surprisingly serious hot water. People would yell in threads, digital spittle flying everywhere. I didn’t get what was going on. All this, just because I said I liked a language?After several years I figured out that it’s because they felt if people listened to me, then everyone would switch to that language, and then the senior devs would have to learn it too. They equated having to learn something new – and I mean really new, sort of like starting over – with losing their job and their health insurance and going bankrupt and dying outside a hospital on the steps. It’s just human nature at work, in the face of big uncertain change.

Once you have tried coding agents, and figured out how to be effective with them, you will never want to go back. They are going to stomp chat coding. And the great thing is, with agents you are still vibe coding. That’s why it’s not a modality: You can vibe code with any non-manual AI modality: chat, agents, clusters. As long as AI is doing the work, you’re vibing! The only difference with agents is that you don’t rendezvous with them as often.Now that agents have emerged, we can start to see patterns. Each successive modality wave, beginning with chat, is conservatively about 5x as productive as the previous wave. Chat can be 5x as productive as manual coding, agents can be 5x as productive as chat, and so on. Note that each wave would probably grow to be 10x as productive as its predecessor, if left unchallenged and given time to mature. But they keep getting flattened by new, even faster modalities.

Brief note about the meaning of "vibe coding": In this post, I assume that vibe coding will grow up and people will use it for real engineering, with the "turn your brain off" version of it sticking around just for prototyping and fun projects. For me, vibe coding just means letting the AI do the work. How closely you choose to pay attention to the AI's work depends solely on the problem at hand. For production, you pay attention; for prototypes, you chill. Either way, it’s vibe coding if you didn’t write it by hand.

This is the 83rd edition of People and Blogs, the series where I ask interesting people to talk about themselves and their blogs. Today we have Maya and her blog, maya.land

When you appear in this story in step 8 above, this pressure may compel you to proceed forward, that is, to implement the new requirement by changing the existing code. Attempting to do so, however, is brutal. The code no longer represents a single, common abstraction, but has instead become a condition-laden procedure which interleaves a number of vaguely associated ideas. It is hard to understand and easy to break. If you find yourself in this situation, resist being driven by sunk costs. When dealing with the wrong abstraction, the fastest way forward is back. Do the following: Re-introduce duplication by inlining the abstracted code back into every caller. Within each caller, use the parameters being passed to determine the subset of the inlined code that this specific caller executes. Delete the bits that aren't needed for this particular caller. This removes both the abstraction and the conditionals, and reduces each caller to only the code it needs. When you rewind decisions in this way, it's common to find that although each caller ostensibly invoked a shared abstraction, the code they were running was fairly unique. Once you completely remove the old abstraction you can start anew, re-isolating duplication and re-extracting abstractions.

If the LLM is consistently doing things you don’t like, you need to change its culture: you need to put it in a different part of the latent space. This could be adding a rule to your Cursor rules (modifying the prompt), but it can also be refactoring existing code to follow the style you want the LLM to follow, since LLMs are trained to predict the next token in context. The fine-tune, the prompt and the codebase are the culture. One you can’t change, and the codebase is a lot bigger than the prompt and ultimately will have a dominating effect.

The general word on the street is you should use a reasoning model for debugging (actually, people tend to prefer using models like Grok 3 or DeepSeek-R1 for this sort of problem). Personally, when I do AI coding, I am still maintaining a pretty detailed mental model of how the code is supposed to work, so I think it’s pretty reasonable to also try to root cause it yourself (you don’t have to fix it, if you tell the model what’s wrong it will usually do the right thing).

Help the model do the right thing: make sure there documentation it can reference, and make sure the model can find it (e.g., via prompting, or just putting it where the model expects it to be). Avoid asking for major changes (where misalignment on the actual goal is more likely to be harmful) without contextualizing how it should fit together with the project as a whole.

The eternal debate between dynamic and static type systems concerns the tradeoff between ease of prototyping and long term maintainability. The rise of LLMs greatly reduces the pressure to choose a language that is good at prototyping, since the LLM can cover up for boilerplate and refactors. Choose accordingly. You will want an agent setup where the LLM is informed about type errors after changes they make, so they can easily tell what other files they need to update when doing refactors. Be careful about your token costs.Unfortunately, the training corpus highly emphasizes Python and JavaScript. Their typed offerings are workable, but as both are gradual type systems you will need to carefully setup the typechecker settings to be strict (or carefully prompt your LLM to setup the settings properly).In principle, Rust should be a good target language for LLMs. However, LLMs are not as good at generating Rust as they are for Python/JavaScript.

The Walking Skeleton is the minimum, crappy implementation of an end-to-end system that has all of the pieces you need. The point is to get the end-to-end system working first, and only then start improving the various pieces.

Ideally, an agentic model would know to do a web search and find the docs it needs. However, Sonnet does not currently support web search, so you have to manually feed it documentation pages as needed.

Do not make files that are too large, if your RAG system for feeding code context can only operate on a per-file level, you will blow out your context;

Often, your problem space is constrained enough that once you write down all of the requirements, the solution is uniquely determined; without the requirements, it’s easy to devolve into a haze of arguing over particular solutions.

Look for opportunity in places where previously people had written off a problem as “too much work”. Make sure you inspect what the LLM is actually doing though, because it will happily keep doing the same thing over and over, unlike a human who would get bored and look for a better way.

Preparatory Refactoring says that you should first refactor to make a change easy, and then make the change. The refactor change can be quite involved, but because it is semantics preserving, it is easier to evaluate than the change itself.Current LLMs, without a plan that says they should refactor first, don’t decompose changes in this way.

Black box testing says that you should test the functionality of a component without knowing its internal structure. By default, LLMs have difficulty abiding with this, because by default the implementation file will be put into the context, or the agent will have been tuned to pull up the implementation to understand how to interface with it.

Usually, it is best to avoid getting into this situation in the first place. For example, it’s very popular to come up with a plan with a reasoning model to feed to the coding model. The planning phase can avoid asking the coding model to do something that is ill advised without further preparation.

He coded divine truths in a language that our idolatrous culture could comprehend. Warhol may not have been a paragon of sanctity, but his work was receptive to divine grace.

Warhol, unlike Baudrillard, was not a nihilist. He believed in a transcendent deity who would turn bread and wine into his own body and blood. Surely that same God could also bestow some sacramental value on mundane realities like a soup can, a Brillo pad, a banana, and a gunshot wound. His works that juxtapose product packaging with religious imagery are tinged with the awareness that Christ’s love can redeem both human weakness and the banality of the everyday (see Last Supper/Be Somebody With a Body, The Last Supper (Dove), and Raphael Madonna-$6.99).

Similarly, the subject of Warhol’s Catholic faith has fed the debate about where to draw the line between sincere and performative belief.

Remember those bullet points for sending emails? One day we'll just send bullet points as emails. We'll reach business speak protocol version 2.0. That which was verbose becomes terse. No more time wasted translating thoughts and injecting platitudes.