Injection for Xcode @Injection4Xcode

A TV writer with no philosophy degree read Aristotle, Kant, Bentham, and Scanlon back to back, built a sitcom around what he found, then explained in one book why the trolley problem is no longer a thought experiment and the people who need to understand this most are the ones building AI. His name is Michael Schur. He co-created Parks and Recreation and Brooklyn Nine-Nine. But The Good Place was the project that broke him open. On the surface it was a comedy about a woman who accidentally ends up in heaven. Underneath, it was a philosophy seminar. Every episode was built around a real ethical framework. He had to actually understand all of it to make any of it funny. After the show ended, he wrote the book anyway. He called it "How to Be Perfect." It begins with the most honest opening line in any philosophy book ever written: Should I punch my friend in the face for no reason? No. That is not a joke. That is his method. He starts with the obvious and builds toward the impossible. Here is the framework he built, and why the most dangerous people in tech right now are running exactly one of the four schools of thought without knowing any of the others exist. The first school is Virtue Ethics. Aristotle built it around 350 BCE. The question it asks is not "what should I do?" It asks "what kind of person should I be?" The idea: become genuinely good, and good actions will follow naturally. You build courage. You build honesty. You build practical wisdom. Then you trust the person you built. The second school is Deontology. Kant built it in the 18th century and it is the exact opposite. Kant did not care about the person. He cared about the rule. His version: act only in ways you would be comfortable turning into a universal law. If everyone lied whenever it was convenient, the concept of truth would dissolve. So you never lie. Even if the truth gets someone killed. The rule is absolute because the moment you make one exception, it stops being a rule. The third school is Utilitarianism. This is the one that should stop anyone building AI cold. Jeremy Bentham invented it in the late 1700s. The principle sounds beautiful: the right action is whichever one produces the greatest good for the greatest number of people. Pure math. Pure outcome. Intention means nothing. Only consequences count. Schur runs it through the trolley problem, the most famous thought experiment in philosophy. You are driving a runaway trolley. Five people are tied to the main track. You can pull a lever and redirect it to a side track where only one person is tied. Do nothing and five die. Pull the lever and you kill one to save five. A utilitarian says pull the lever. The math is obvious. Now the same problem with one change. You are on a bridge above the track. A large man is standing next to you. The physics are clear: if you push him off the bridge, his body stops the trolley. Five people live. He dies. The math is identical. Almost nobody will push the man. Even people who pulled the lever instantly in the first version refuse. The utilitarian has no answer for why these two situations feel different. The numbers are the same. The outcome is the same. The only thing that changed is whether you are using another human being as a tool. That gap between the math being correct and the action feeling monstrous is exactly where AI ethics collapses every single time. The fourth school is Contractualism, built by the philosopher T.M. Scanlon. It asks the question that Kant's rules and Bentham's math both miss. What principles could be justified to everyone affected? Not the majority. Not the person with the most power. Everyone. Including the one person who ends up on the shorter end of the calculation. Schur's conclusion is the part that people who live inside growth frameworks and optimization loops will resist the hardest. None of the four schools is correct on its own. Each one has a scenario where following it perfectly produces something most humans recognize as evil. Pure utilitarianism justifies harvesting one person's organs to save five dying patients. Pure deontology says you cannot lie to the murderer asking where your friend is hiding. Each system, taken to its logical extreme, becomes a machine that produces monsters while generating perfect internal justification for doing so. The way out is not picking the right framework and following it harder. The way out is using all four as lenses. Ask what Aristotle would do. Ask what Kant would allow. Do the utilitarian math. Then ask Scanlon's question: could you justify this to the person it hurts most? Where those four answers overlap, you are probably on solid ground. Where they pull in different directions, you are in territory that deserves far more than a two-hour board meeting. Schur also coined a term that has been stuck in my head since I finished the book. Moral Exhaustion. The feeling of living in an age where you can know, in real time, every ethical implication of every product you use, every company you work for, every piece of code you ship. The gap between what you know and what you can actually change becomes so large that the easiest response is to stop asking. He says that response is understandable. He also says that choosing not to ask is itself a moral choice, and the consequences of that choice scale in exact proportion to the power you hold. A person building a product one billion people will use is not operating at the scale where shrugging is a neutral act. The people who built the most consequential technologies of the last decade were not evil. Most were genuinely trying to do good. They ran the utilitarian math. They saw a billion users. They saw engagement numbers that looked like impact. They optimized for the greatest good for the greatest number and did not notice until much later that the people being turned into variables in the math were still people. Schur read 2,500 years of philosophy and the lesson he came out with fits in one sentence. You cannot use a single framework because every single framework, followed perfectly, eventually produces the wrong answer. The people who cause the most damage are not the ones who do not care about ethics. They are the ones who found one framework they liked, felt good about it, and stopped asking. The trolley problem is not a thought experiment anymore. It runs on servers. It gets optimized overnight. And the people making those decisions right now have never once asked what Scanlon would say.

@OpenAIDevs Has nobody seen #InjectionIII/#InjectionNext? (works on a device, MCPs your actual screen and can send taps) - Hire me for a while! github.com/johnno1962/Inj…

Just plugged in a 20 year iPod nano (MA489LL). No lag. No loading indicators. UI is practically instant. No touchscreen. I plugged in my Bose headphones and played Black Eyed Peas like 2006 was yesterday. Just fucking worked. Incredible. Technology has gotten worse in so many ways. Younger people have no idea.

@neeratanden dem incompetence led us to trump twice. the left has never stopped opposing trump. this insane convo only exists on this fucking website. most ppl are mad at dems for not doing enough. all of the spoxppl for the party on this stupid website are out of touch w the masses.

A McKinsey consultant with no PhD, no AI background, and no academic position quietly built the most-watched deep learning course on Earth and gave the entire thing away for free. I opened the first lesson at 1am and could not believe a single human had taught this many people without ever charging a cent for it. His name is Jeremy Howard. The course is called "Practical Deep Learning for Coders" For most of his career, Jeremy Howard had no business teaching artificial intelligence to anyone. He spent 8 years as a management consultant at McKinsey and AT Kearney. He started an email company called FastMail in Australia and ran it for years. He built an insurance pricing startup that got acquired by Lexis-Nexis. None of that was AI work. None of it was academic. He did not have a doctorate in computer science or mathematics or anything else. He was a businessman who happened to be very good at writing code. Then in 2010 and 2011, he became the top ranked competitor in data science contests on Kaggle, beating teams of PhDs from the most credentialed labs in the world. He was eventually made president of the company. And what he saw from that seat was the thing that ended up changing his life. The best deep learning research on Earth was being done by maybe a few hundred people in five or six elite labs in San Francisco, London, and Toronto. To get into those labs, you needed a PhD from Stanford or MIT, a recommendation from a tenured professor, and access to expensive GPUs that no individual could afford. The practical knowledge of how to actually train these models was almost never written down. It lived inside the heads of a small priesthood, and the priesthood was almost entirely closed to outsiders. In 2016 Jeremy Howard and Rachel Thomas decided to break that gate. They founded fast.ai with one mission. Take the techniques that the elite labs were using and teach them to anyone in the world who could write a Python loop. No PhD required. No advanced math required. No expensive hardware required. Just a laptop, an internet connection, and the willingness to actually finish the lessons. The way they did it was the part that almost nobody in academia had ever tried. Every other deep learning course on the planet started with theory. You learned linear algebra. You learned multivariate calculus. You learned probability theory. You spent 6 months on the foundations before you ever got near a working neural network. By the time most students reached the part where they could actually build something, they had quit. Howard inverted the entire curriculum. Lesson one of his course is not theory. It is a working image classifier that you train in 15 minutes on a model that can distinguish dog breeds with 99 percent accuracy. You build the thing first. You make it work first. Only then, once you have proven to yourself that you can actually do this, do you start peeling back the layers to understand why it works. His justification was simple. The reason most people quit learning hard things is not that the material is too difficult. It is that the curriculum is structured to make them feel stupid for as long as possible before they ever get to do anything interesting. Howard refused to do that to his students. He believed that if you could see something working with your own hands on day one, you would have the motivation to fight through the hard math three months later. And he was right. The course has now been viewed over 6 million times. His students include a Canadian dairy farmer who used the course to build an AI system to monitor the health of his goats. They include a French math teacher and a network of doctors in Africa. They include people who walked into the lessons knowing nothing about AI and walked out building production systems at Google Brain, OpenAI, Adobe, Amazon, and Tesla. In 2018, Jeremy Howard and a young researcher named Sebastian Ruder published a paper called "Universal Language Model Fine-tuning." It introduced a transfer learning technique for language models that worked so well it cut error rates on text classification by 24 percent on the hardest benchmarks in the field. That technique, refined and scaled by the labs that came after, became the foundation of how every major language model on Earth is trained today. ChatGPT. Claude. Gemini. The fine-tuning step that makes them useful traces back to the methodology in that paper. The man who co-wrote it had no PhD. He had been teaching the same ideas to strangers on the internet for free a year before he ever published the paper. His students were already building with it before the elite labs had even read it. The best things on the internet are almost never the ones with paywalls in front of them. They are the ones built by people who decided the gate was the problem and then quietly walked around it.

@unusual_whales "once, men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them." Dune, 14.

Has anybody coined the term "Large Brother" for the upcoming widespread use of LLMs in surveillance?

Thank you @AOC for stating the obvious: the anti-tax obsession is effectively a tax on young American workers just starting out in the economy.

NOTUS @NOTUSreports

a month ago

The differences between Rep. AOC’s and Sen. Bernie Sanders’ approach to tax cut plans are indicative of the divides Democrats must bridge as they form their agenda going into 2028. notus.org/economy/democr…

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Nobody could figure out why the abandoned Hendricks apple orchard suddenly bloomed in April 2019. The trees hadn't produced fruit in eleven years. County agriculture office sent two inspectors. They found sixty thousand honeybees working the property - a massive colony that had escaped from Tomás Vega's apiary three miles south. Tomás had reported the swarm missing in March. He expected them dead. Instead they'd colonized the hollow barn on the Hendricks lot and cross-pollinated every surviving tree. That October, the orchard produced twenty-two tons of Cortland apples. The Hendricks family offered Tomás a permanent lease. He moved his entire operation there the following spring.

Trump, the biggest failed strategist in US history, has plunged America into a strategic catastrophe. During his self-proclaimed “victory parade” against Iran, he squandered at least 45% of the US's precision-guided missile arsenal in just seven weeks—including half of all THAAD missiles and nearly 50% of Patriot interceptor missiles. This isn't some fake news blog reporting this, but CNN, citing a CSIS analysis and internal Pentagon data. The result? An “imminent risk” of munitions depletion should a real conflict erupt in the coming years—for example, with China. Trump has ruined the US defense capability for years to come. And for what? For nothing. No regime change in Iran. No destroyed nuclear program. No strategic breakthrough. Just a shaky ceasefire that gives the mullahs time to rearm while America stands naked. Trump, the great “Art of the Deal” master, has once again only produced hot air – and in doing so, burned through the most expensive and scarce weapons in the USA like a pubescent boy with fireworks.

A MIT professor who built the world's first neural network machine said something about intelligence that nobody in Silicon Valley wants to admit. His name was Marvin Minsky. He co-founded MIT's artificial intelligence lab with John McCarthy in 1959. He built SNARC the first randomly wired neural network learning machine in 1951, as a graduate student at Princeton. He won the Turing Award. He advised Stanley Kubrick on 2001: A Space Odyssey. Isaac Asimov, who was not a modest man, said Minsky was one of only two people he would admit were more intelligent than him. In 1986, after decades of building machines that could think, Minsky published a book about something far more unsettling. How humans think. And why we are wrong about almost everything we believe about it. The book is called The Society of Mind. It has 270 essays. Each one is a page long. Together they build a single argument that most people, when they first encounter it, reject immediately because it is too uncomfortable to accept. The argument is this: you do not have a mind. You have thousands of them. What you experience as a single, unified self making clear-headed decisions is not a thinker. It is an outcome. The result of hundreds of tiny, specialized, mostly mindless agents competing, negotiating, overriding, and occasionally cooperating with each other beneath the surface of your awareness. You do not decide things. You are what is left over after the arguing stops. Minsky was precise about this. He wrote that the power of intelligence stems from our vast diversity, not from any single perfect principle. He called this the trick that makes us intelligent, and then immediately added: the trick is that there is no trick. There is no central processor. No ghost in the machine. No unified self sitting behind your eyes, calmly evaluating options and choosing rationally. There is only the parliament. And the parliament is always in session. This reframing destroys the standard explanation for every failure of self-control. The reason you procrastinate is not laziness. It is that the agent in you that understands long-term consequences is losing an argument to the agent that wants comfort right now, and neither of those agents has a decisive vote. The reason you change your mind the moment someone pushes back is not weakness. It is that the social agent, the one that monitors status and belonging, just outweighed the analytical one. The reason willpower fails is not a character flaw. It is that you sent one small agent into a fight against dozens, and you called that discipline. Minsky had a specific line that breaks this open completely. He said: in general, we are least aware of what our minds do best. The things you do with the most apparent ease, reading a face, walking through a crowded room, understanding a sentence, catching a ball, are not simple at all. They are the products of staggeringly complex agent networks that run so smoothly, so far below conscious access, that you experience them as effortless. The things that feel like work, the logical arguments, the deliberate choices, the careful plans, are actually the clumsy surface layer, the small fraction of mental activity you can observe at all. You have been taking credit for the wrong parts of your own intelligence. The practical implication is the one that most productivity advice misses entirely. If your decisions are not made by a single rational self but by whichever coalition of agents happens to win the moment, then the game is not about training yourself to be more disciplined. The game is about designing the environment so that the right agents win without needing a fight. This is why removing your phone from the room works better than deciding not to check it. This is why writing one task on an index card works better than building a sophisticated system. This is why commitment devices beat motivation every time. You are not strengthening your will. You are changing the conditions of the argument so that the outcome you want becomes the path of least resistance. Minsky spent his entire career building machines that could imitate intelligence. What he discovered in the process was that natural intelligence, the kind running inside every human brain on earth, is nothing like what we think it is. It is not a single flame burning in a single chamber. It is a city. Loud, chaotic, full of competing interests, with no mayor. The people who understand this stop trying to win the argument through force of will. They learn to build a better city instead.

These little AIs are all very clever but it remains to be seen if they can make any money. All I've seen so far is the RideShare app playbook of using venture capital to undercut the local cab firm out of business to jack up prices later except this time the cab firm is all of us

Everyone sharing this depolarization research on AI seems to avoid the fact AI is a radically isolating experience. Sure, it is RL’ed to be close to the center, and given where we are coming from right now, this might seem appealing. But I do believe we as a society ought to find a way to discuss and constructively disagree on uncomfortable topics rather than find escape in a median opinion that someone (even with best intention) chose to be the truth. It does require a degree of civil discourse that disappeared from mainstream western culture, but I believe it is a mistake to swing all the way to individual, assuming it won’t have stagnant side effects long term.

Rob Wiblin @robertwiblin

2 months ago

Really great news I would say: "Social media is populist and polarising. AI may be the opposite." – @jburnmurdoch in the FT

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By Karl Bose, “CEO said a thing” journalism: karlbode.com/ceo-said-a-thi…

I really don’t think you understand how insane it is that a foreign country is using the aid we give them to buy off our politicians.

🚨WATCH: TRUMP FAILS IN IRAN

You can now run Ollama using MLX as a backend 🚀

ollama @ollama

2 months ago

Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework. This change unlocks much faster performance to accelerate demanding work on macOS: - Personal assistants like OpenClaw - Coding agents like Claude Code, OpenCode,

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I'm usually not one to write thought pieces without much technical depth. But here we go. Slow the fuck down. mariozechner.at/posts/2026-03-…

Claude gives a pretty interesting perspective of "why SwiftUI List performs worse than AppKit equivalents", after reading the assembly x.com/jacobtechtaver…

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Jacob Bartlett @jacobtechtavern

3 months ago

Even in iOS 26, SwiftUI is dramatically outclassed by UIKit when it comes to scroll performance of very complex UIs. But why is this performance different? Perhaps you’re thinking what I’m thinking. Isn’t List implemented via UICollectionView under the hood? Here’s our UIKit

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