Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.

Elon Musk built one of the largest AI compute clusters on earth. Yann LeCun just explained why xAI now rents it out to rivals instead of winning with it. Musk has antagonized so much AI talent he structurally cannot hire the people he needs. LeCun is not a critic on the sidelines. He won the Turing Award and ran AI at Meta for a decade. When he talks about who can and cannot build a frontier lab, he is describing his own world. His verdict on xAI was blunt. He called it kind of a failure and did not soften it. His reasoning had nothing to do with money. The founding team left or was fired. There is some uncertainty about which. Either way, the people who started the company are gone. That is the part that matters. A frontier lab is its researchers. Lose them and the compute is just hardware. By March, all eleven co-founders Musk recruited in 2023 had walked out. Researchers who came from DeepMind, Google, and OpenAI. Musk himself posted that xAI was not built right the first time and had to be rebuilt from the foundations up. So Musk is left with one of the biggest clusters on earth and no way to win with it. He rents it out to other companies to recoup the cost. The most expensive infrastructure in AI, built by someone who can no longer staff it. Asked directly if xAI can compete at the frontier, LeCun gave a one word answer. No. What do you guys think about this? Source: CNBC International Live

David Sacks was one of the first people to get a full readout from the White House after the Fable ban. He went on the All-In podcast this week and told the story from the inside. It is not the story anyone is telling. Here is what actually happened. Dario went to Washington in April and told national security officials he had built a cyber weapon. He spiked cortisol levels across the entire administration. Got everyone focused. Then Anthropic quietly expanded the Mythos preview to over 50 companies without telling the White House. According to the Washington Post, at least one of those companies was flagged as a national security concern. That was the predicate. Then Fable launched. Mythos with guardrails. Anthropic's own largest partner started testing those guardrails and found a jailbreak. They escalated to the White House. The administration called Dario directly. A cabinet secretary picked up the phone personally. It should have been a five minute call. Instead, Dario argued. He said the jailbreak was not serious. Then he published a blog post trying to distinguish minor jailbreaks from major ones. This is the man who had just told Washington he built a cyber weapon. Sacks said it plainly. The trust is gone. And once you are in one of these situations it is always harder to get out than it was to avoid getting in. Anthropic spent years building credibility as the AI safety company. They burned it in a single week by refusing a phone call. WATCH THE FULL PODCAST ON @theallinpod

A doctor from Malta with degrees from Oxford, Cambridge, and Harvard coined a phrase in 1967 that ended up in the Oxford English Dictionary and became the most widely used thinking framework in corporate history. His name was Edward de Bono. The phrase was "lateral thinking." De Bono grew up in Malta and finished his undergraduate degree at 15. His nickname at school was Genius. He qualified as a doctor at 21, won a Rhodes Scholarship to Oxford, completed a PhD at Cambridge, and held faculty appointments at Oxford, Cambridge, London, and Harvard simultaneously. His father told him he had a great career in medicine and should not throw it away by writing books. He wrote 85 of them. The idea that made everything started with a simple observation about how the brain actually works. Your mind is a pattern-recognition machine. Every time you encounter a problem, your brain scans its memory for the most familiar framework it has ever used on something similar and routes your thinking straight down that groove. This is efficient. It is also the reason most people keep solving new problems the wrong way. The groove deepens every time you use it. The more experienced you become at anything, the more aggressively your brain routes you toward the same familiar paths. De Bono called this vertical thinking. You dig the same hole deeper. More logic, more analysis, more effort, all inside a frame you never question. The harder you work, the deeper into the wrong hole you go. The problem was not intelligence. No amount of better logic can correct an error that happened in perception before the logic even started. If the frame is wrong, the reasoning is wrong. Every time. A genius applying perfect logic to the wrong frame still gets the wrong answer. Lateral thinking was his answer. Not brainstorming. Not creativity in the vague sense people throw around at workshops. A specific set of deliberate techniques designed to force the brain off its established grooves and approach a problem from a direction it would never reach by digging straight down. His most useful technique was provocation. He gave it the symbol Po. A provocation is a deliberately absurd or impossible statement used not because it is true but because it breaks the pattern and forces the mind to construct new pathways around it. The classic example: a factory is polluting a river. Vertical thinking produces filters, regulations, process changes. The lateral provocation is: the factory is downstream of itself. Physically impossible. But sitting with that impossibility produces a real insight. What if the factory had to use the water at the exact point where its own discharge ends up? The incentive structure changes completely. Zero-discharge solutions become visible that conventional thinking would never reach because they lie outside the groove. The DuPont result is the number that ends every argument. One employee applied a single lateral thinking technique to their Kevlar manufacturing process. Eliminated nine steps. Saved the company $30 million a year. One person. One different way of looking at the same problem. IBM used it. McKinsey used it. Shell used it. NASA used it. Prudential used it to restructure the entire concept of life insurance, creating policies that let people access their benefits while still alive. The president of Prudential said publicly that de Bono's framework made the innovation possible. Channel 4 television in England trained its staff for two days and said they generated more new ideas in those two days than in the previous six months combined. De Bono spent the second half of his life furious about one thing. Schools were still not teaching thinking. They taught content. They taught facts. They taught students what to think rather than how to think. His frustration with this never softened. He said repeatedly that we spend enormous resources teaching children information and almost nothing teaching them what to do with it. The entire educational system was training vertical thinkers at industrial scale and then wondering why genuine innovation was so rare. He tried to fix it. His CoRT program brought thinking skills into classrooms across 20 countries. His Six Thinking Hats method was used to train juries in several US states to examine evidence more objectively. In Australia, marine biologists credited it with transforming meetings that had been paralyzed by ego and argument for years. In 2005 he was shortlisted for the Nobel Prize in Economics. He died in 2021 at 88. His most famous line contains the whole thing in one sentence. "You cannot dig a hole in a different place by digging the same hole deeper." Every person who has ever worked harder on the wrong approach without changing the approach has lived inside that sentence. Every company that poured resources into optimizing something that should have been abandoned. Every person who applied more logic to a frame that was wrong from the start. De Bono was not arguing against logic. He was arguing that logic only works once you are standing in the right place. Most people never question where they started digging. That was the only problem his entire career was trying to solve.

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth. The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time. Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen. Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task. When the students wrote by hand, the brain lit up everywhere at once. The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected. When the same students typed the same word, that pattern collapsed almost completely. Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG. Same word, same brain, same person, and two completely different neurological events. The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem. Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next. Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve. Van der Meer said it plainly in her interviews. Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad. Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page. A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched. The handwriting group won by a wide margin on every question that required real understanding rather than surface recall. The reason was hiding in the transcripts of what the two groups had actually written down. The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page. That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it. Two studies. Two countries. Same answer. Handwriting makes the brain work. Typing lets it coast. Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth. You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick. The fix is the thing your grandmother already knew. Pick up a pen. Write the thing down. The slower road is the faster one.

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth. The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time. Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen. Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task. When the students wrote by hand, the brain lit up everywhere at once. The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected. When the same students typed the same word, that pattern collapsed almost completely. Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG. Same word, same brain, same person, and two completely different neurological events. The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem. Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next. Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve. Van der Meer said it plainly in her interviews. Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad. Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page. A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched. The handwriting group won by a wide margin on every question that required real understanding rather than surface recall. The reason was hiding in the transcripts of what the two groups had actually written down. The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page. That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it. Two studies. Two countries. Same answer. Handwriting makes the brain work. Typing lets it coast. Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth. You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick. The fix is the thing your grandmother already knew. Pick up a pen. Write the thing down. The slower road is the faster one.

Say goodbye to Google Workspace. Someone built a complete replacement that runs on your own server, costs nothing, and has 400+ apps. It's called Nextcloud. 400,000 organizations are already running it. The German federal government switched to it. The French public sector switched to it. The European Parliament uses it. None of them are doing it to save money. Here's what one install actually gives you: → File sync and sharing that works exactly like Dropbox → Live document editing with full Word/Excel compatibility, no internet required → Video calls with end-to-end encryption and no vendor in the call with you → Photo backup with local AI face grouping, your machine, your data → Calendar and contacts synced to every device through open standards → 400+ apps including Jira-style project boards, Spotify-style music streaming, and RSS readers The architecture is the part nobody talks about. Nextcloud does not route your data through their servers. Ever. Your server talks directly to your devices. No company in the middle. No terms of service that can change overnight. No price increase email you have to swallow. Google Workspace costs $12/user/month and owns everything you put in it. Nextcloud costs whatever your VPS costs and owns nothing. 35K stars. 100% Opensource.

A Yale study tracked 3,635 people for 12 years and found that people who read books live 23 months longer than people who don't. Book readers were 20% less likely to die during the follow-up. The effect held even after researchers adjusted for income, education, health, and depression. Reading newspapers and magazines did not produce the same result. Only books did. The researchers said it was because books force the brain into deep reading and sustained focus that nothing else replicates. That is the part nobody on your timeline will tell you. The loudest voices on the internet right now are selling you the opposite. Stop reading. Watch this 90-second clip. Take this $497 course. Your brain is "too advanced" for books. Action over information. Chaos over thought. Now hold that next to what people who actually built something at scale say. Elon Musk was asked how he learned to build rockets. His answer was three words. "I read books." He was raised on Asimov's Foundation series, Heinlein, biographies of Franklin and Einstein, and the Encyclopedia Britannica which he finished at age nine. He has said books taught him more than any degree ever could. Warren Buffett spends 80% of his working day reading. He once held up a stack of paper in front of a class of students and said "Read 500 pages like this every day. That's how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it." Charlie Munger said in his entire life he never met a wise person, across a broad subject area, who didn't read constantly. Not one. Naval Ravikant said it cleaner than anyone. "The foundation of learning is reading. I don't know a smart person who doesn't read, and reads all the time." He reads one to two hours a day. He says that single habit accounts for any material success he has ever had. 3 of the most consequential thinkers of this generation built their entire edge on the same boring habit. The AI era makes this more urgent, not less. Every week another tool launches that can summarize a book in 30 seconds. Every week another influencer tells you that summaries are "good enough." They are not. A summary gives you the conclusion without the thinking that built it. You walk away with the same headline as everyone else and zero original wiring underneath. The people building real things in AI right now are reading the source material. Everyone else is repeating compressed versions back to each other and calling it insight. If you are building in AI, the leverage is not in reading more AI threads. It is in reading the books the people building AI grew up reading. 5 books that have genuinely changed how I think this year. The Almanack of Naval Ravikant. The clearest writing on wealth, leverage, and judgment ever compressed into a single book. The free PDF is on his website. Deep Work by Cal Newport. The reason most people building in AI feel busy and produce nothing. He explains exactly why and exactly what to do about it. How to Take Smart Notes by Sönke Ahrens. The Zettelkasten method that turned a German bureaucrat into the most prolific sociologist of the 20th century, rewritten for modern knowledge workers. The Beginning of Infinity by David Deutsch. If you want to understand what knowledge actually is and why human progress has no ceiling, this is the only book on the subject that matters. Range by David Epstein. The case for generalists in a world that keeps telling you to pick a lane at twenty. The most useful book I have read for thinking about a career in a domain that rewrites itself every six months. Search any one of them tonight. Buy the cheapest copy you can find. Start the first chapter before you sleep. The smartest people alive spent their entire careers telling you the same boring thing. The loudest people on the internet spent the last year telling you to ignore them and bought another car. One of these groups is building the future. The other one is hoping you do not notice. If you are someone like me who loves reading, drop your favorite book in the comments. I will read every single one.

I was paying for Meshy. Then I found this repo and cancelled my subscription the same day. Modly is a free, open source desktop app that turns any photo into a 3D model using local AI running entirely on your GPU. No cloud. No account. No credits. Drop in a photo. The app removes the background automatically. A local image-to-3D model reconstructs the mesh on your machine. Export as GLB, OBJ, STL or PLY. → Runs Hunyuan3D 2 Mini by Tencent locally, with Microsoft's Trellis and Tripo AI's TripoSG coming next → Unlimited generations, your images never touch a server → Built-in 3D viewer, collections workspace, multiple export formats → Works with Unity, Unreal, Godot, Blender, ZBrush, Cura, PrusaSlicer, Bambu Studio → Hackable extension system, swap the model or modify the pipeline yourself → Windows and Linux installers ready, macOS coming soon Cloud services charge $20 to $60 a month and upload every photo you give them. This does the same job for free on hardware you already own. 3.5k stars. MIT License. 100% Opensource.

A psychologist at the University of North Carolina spent 20 years proving that a single 20-second hug rewires the human cardiovascular system, and the experiment she ran is so simple you can replicate it tonight at home. Her name is Karen Grewen. She works inside the UNC School of Medicine's Department of Psychiatry. The paper that made her famous was published in 2003, and almost nobody outside her field has read it. Here is what she actually did. She recruited 183 healthy adults living with a long-term partner. She split them into two groups. The warm contact group sat together for 10 minutes holding hands while watching a romantic video. Then they stood up and hugged each other for exactly 20 seconds. The control group sat alone in a separate room for the same amount of time doing nothing. Then she made every single one of them give a public speech in front of a panel. Public speaking is one of the cleanest stressors in psychology. Heart rate spikes. Blood pressure climbs. Cortisol floods the system within minutes. It is the laboratory version of every stressful moment you have ever had at work. The people who had been hugged for 20 seconds before walking into that room had measurably lower blood pressure responses to the stress. Lower systolic. Lower diastolic. Lower heart rate increases. Everything was the same.. the speech, the panel, and fear. But this time completely different physiological response. The hug had not made the stress disappear. It had changed how the body was allowed to respond to it. Two years later Grewen ran the follow-up study that explained why. She drew blood from 38 couples before and after the same warm contact protocol and measured what was actually changing inside them. The answer was a hormone called oxytocin. Oxytocin is the chemical your body releases during childbirth, breastfeeding, and orgasm. It is the same molecule that makes a mother feel calm holding her newborn. Grewen's data showed that 20 seconds of physical contact with a trusted partner triggered a measurable spike in plasma oxytocin in both men and women, and the size of that spike directly predicted how much their blood pressure dropped. The mechanism turned out to be older than recorded history. Oxytocin binds to receptors in your heart, your blood vessels, and the part of your brainstem that controls how aggressively your nervous system reacts to threat. When the hormone shows up, the entire fight-or-flight machine downshifts. Your blood vessels widen. Your heart slows. Your cortisol production gets suppressed. This is not a feeling. This is a chemical instruction your body sends to itself that you can measure with a blood pressure cuff. The detail Grewen kept emphasizing in her interviews was the duration. Three seconds is the average length of a hug between two humans. It is too short. The hormonal cascade does not have time to start. 20 seconds is the threshold where the oxytocin actually crosses into the bloodstream in a quantity large enough to do something measurable. A follow-up study tracked 59 premenopausal women over time and found that the ones who hugged their partners most frequently had lower resting blood pressure and higher baseline oxytocin levels than the ones who did not. The effect compounded. Daily hugs produced a permanent shift in the cardiovascular baseline. A separate review of long-term partner contact research found that married adults with frequent affectionate touch had significantly lower rates of heart disease and all-cause mortality than equally healthy adults without it. The American Heart Association now cites this body of research when explaining why social isolation is treated as a cardiovascular risk factor on the same level as smoking. The most haunting line in Grewen's research is one she said in an interview after publishing the second paper. She pointed out that the average American touches another human being less than they did 50 years ago. Phones replaced eye contact. Texts replaced visits. Hugs at the door got shorter. The thing that used to regulate our cardiovascular system multiple times a day quietly disappeared from most adult lives. Your body still expects it. The hormone receptors are still there waiting. The system was designed to be reset by physical contact with people who feel safe, and the reset takes 20 seconds. You can run the experiment yourself tonight. Hug someone you love for 20 full seconds. Count it out. The first 10 will feel awkward. Around 15 something shifts. By 20 the shoulders drop, the breathing slows, the chest opens. That is not in your head. That is your bloodstream changing.

In September 1997, Apple was 90 days from bankruptcy, the stock was at $3.30, and Michael Dell had publicly said the company should be shut down and the money returned to shareholders. Steve Jobs had been back for 8 weeks. No title. No salary. Technically just an advisor. He walked on stage that month, slept three hours the night before and gave a 16-minute speech that almost nobody has watched. It is the speech that saved Apple. He did not show a product. He did not show a chip. He did not show a roadmap. He spoke about one idea. Marketing is about values. Not features. Not specs. Not megahertz. He said the world had become so noisy that no company on Earth was going to get a chance to tell people more than one thing about itself. So you had to be very clear about what that one thing was. Then he said the line almost nobody quotes from that morning. Even a great brand needs investment and caring if it is going to retain its relevance and vitality. The Apple brand had clearly suffered from neglect. He admitted on stage, to his own employees, that the company they worked for had stopped caring about the thing that made it matter. Then he ran the ad. Here is to the crazy ones. The misfits. The rebels. The troublemakers. The round pegs in the square holes. The ones who see things differently. When the tape stopped, the room was silent for a few seconds. Then they stood up. The thing he did next is what most people miss when they tell this story. He had personally called Yoko Ono to get permission to use John Lennon. He had called the estates of Einstein, Gandhi, Picasso, Edison, Amelia Earhart, Martin Luther King. Almost none of them had ever appeared in an advertisement before. Almost all of them said yes to Apple specifically, when they had said no to everyone else who had ever asked. He said on stage that morning that he did not think any other company on Earth could have run that campaign. He was probably right. The campaign broke on Sunday night during the network premiere of Toy Story on ABC. The ad ran twice. Print followed in the Wall Street Journal, the New York Times, USA Today. Billboards went up in five cities. Buses with Rosa Parks' face on them started driving through Manhattan. Apple did not announce a new computer that quarter. They announced who they were. 18 months later they shipped the iMac. 3 years later the iPod. 6 years later the iTunes Store. 10 years later the iPhone. The most valuable company in the history of capitalism was rebuilt on a 16-minute talk where the founder did not show a single product. Everyone quotes the Stanford commencement speech from 2005. The one about staying hungry and staying foolish. That one made him a philosopher. The 1997 speech is the one where he saved the company. He told his employees the company had lost its soul. He told them what the soul was. He told them they were going to spend a fortune reminding the world. Then he walked off stage and went to work. The difference between a company that dies and a company that becomes the most important company in the world is sometimes one person, on three hours of sleep, willing to stand in front of his own team and say we forgot who we are. The crazy ones changed things because somebody believed they could. That somebody was him.

A retired plumber in Nebraska beat the CIA at predicting foreign elections in 2013. A 22 year old college dropout beat every pollster in America in 2024. A 9 billion dollar prediction market called Polymarket is now telling you whether the AI bubble bursts this year, whether the US enters a recession, and whether Anthropic overtakes OpenAI before December. They are all using the same 4-step technique a Berkeley professor proved actually works in a 20 year experiment that should have ended the careers of half the experts on television. His name is Philip Tetlock. In the late 1980s he started collecting predictions from 284 experts. Political scientists. Economists. CIA-adjacent analysts. People paid their entire careers to forecast geopolitics. Over 20 years he gathered 28,000 predictions. Then he scored them. The result destroyed an entire profession. The average expert was barely better than chance. The famous ones, the ones with the most media appearances and the loudest voices, were the worst of the group. The more confident the voice, the worse the score. Buried in his data was something nobody else had emphasized. Fewer than 2% of forecasters were dramatically better than the rest. Year after year. Across domains they had no training in. In 2011 the US intelligence community gave him his chance to prove it at scale. Still bruised from missing Iraq, IARPA ran a 4 year tournament on 500 geopolitical questions. Will North Korea launch a missile. Will Russia invade. The intelligence analysts had classified intercepts. Tetlock's team had retired plumbers and ballroom dancers. Tetlock's team won by 35 to 72 percent against the other academic teams. His top forecasters scored 30 percent better than the CIA reading classified data. A retired pipe installer in Nebraska was outpredicting the intelligence community using only the newspaper. The technique sits in his book in plain language and almost nobody applies it. It starts with a method invented by Enrico Fermi during the Manhattan Project. Fermi handed students problems that looked impossible. How many piano tuners are there in Chicago. He did not want a guess. He wanted them to break the question into smaller questions they could actually estimate. Each sub-estimate was rough. Multiplied together, they landed remarkably close to the truth. Superforecasters Fermi-ize everything. They never try to predict a complex event directly. They shatter it into smaller questions where base rates are knowable, then reassemble the pieces. The second move is the outside view. Most people, asked whether a startup will survive or a war will end by a date, dive into the specific details. Story details feel useful. They are not. Superforecasters first ask how often events of this general type happen across history. The story comes last, not first. The third move is what most people refuse to do. Superforecasters update their predictions constantly, in tiny increments. Not dramatic reversals. Small honest nudges. They move like a Bayesian. Most people move like a teenager defending a position. The fourth move is the one that hits closest. Superforecasters express predictions as actual numbers. Not "likely" or "probably." 62 percent. 18 percent. Vague language is unfalsifiable. A number forces accountability, and accountability is the engine of accuracy. Polymarket is the same idea scaled to a hundred thousand strangers with real money on the line. In the final weeks of the 2024 US presidential election, every major pollster called the race a coin flip. FiveThirtyEight had Harris at 50 to 49. Nate Silver had her at 48.6 to 47.6. Polymarket had Trump at 58 to 42 the morning of the election. By midnight, while networks still refused to call swing states, Polymarket was at 97 percent. In late 2025 the New York Stock Exchange invested 2 billion dollars in the platform. Polymarket is now valued at 9 billion. The largest stock exchange in the world is integrating prediction prices directly into the data feed traders use to make decisions. The plumber did not have classified intercepts. The college dropout did not have a polling model. Polymarket does not have an algorithm nobody else can see. They all have the same thing. A method that forces you to write a number on paper, attach a date, and let the world watch you be wrong. In 2026 the gap between people who price the future and people who narrate it is going to be the most expensive gap in the world to be on the wrong side of.

I accidentally dropped my competitor's YouTube video into NotebookLM. It told me exactly why it went viral, what his audience still wanted that he never gave them, and wrote me a script built around that gap. Here's how you can do the same in 60 seconds: Open NotebookLM. Paste the URL of the highest-performing video in your niche. The one you have been quietly jealous of every time it shows up in your feed. First prompt: "What made this video work? Break down the hook, the pacing, the emotional peaks, and the exact moment the viewer decides to stay." It does not give you vague advice. It tells you which sentence landed hardest and why. It surfaces the structure underneath the style. Second prompt: "What gaps did this video leave? What did the audience want that this video never fully delivered?" The comments section of a million-view video is a product brief your competitor wrote for you without knowing it. NotebookLM reads it like a researcher and hands you a list of unmet needs you can build your next video around. Third prompt: "Generate five viral video ideas that cover the gaps you just identified. Give me the hook, the core argument, and the emotional payoff for each one." The ideas it gives you are not generic. They are built from the actual evidence of what your audience already wanted and did not get. Your competitor cannot reverse-engineer them, because they came from studying him. Fourth prompt: "Write me the first 60 seconds of the strongest idea. Make it hook harder than the video we just analyzed." Most YouTubers spend three weeks planning and one hour studying what actually performs. This workflow inverts that entirely. The creators winning right now are not better on camera. They are better at reading the evidence their competitors accidentally left behind. You just got the playbook.

A child prodigy who finished his Harvard degree at 14 and his PhD at 17 sat down in 1948 and wrote a single book that invented the entire conceptual vocabulary we still use to talk about AI, robotics, self-driving cars, and reinforcement learning. He never got the credit. Most people have never heard his name. His name was Norbert Wiener. The book was called Cybernetics. Every feedback loop running inside every system you interact with today traces back to one problem he was handed during World War II. The problem was this: how do you aim a gun at a fast-moving airplane? By the time your shell arrives, the plane is somewhere else. You cannot aim at where the plane is. You have to aim at where the plane will be. And the plane's pilot, knowing this, is constantly changing course to make that prediction wrong. Wiener spent years on this. What he built to solve it was not a better gun. It was a new science. He noticed something that nobody had formally described before. The gun system and the human nervous system were solving the same problem using the same method. You observe where the target is. You compare it to where you want to hit. You calculate the gap. You correct. You observe again. He called that loop feedback. Not in the casual sense people use it today. In the precise mathematical sense. A signal goes out. The result comes back. The system compares the result to the goal. The gap between them drives the next action. The loop closes. That mechanism, exactly as Wiener described it in 1948, is what runs inside every thermostat, every autopilot, every cruise control system, and every AI training loop on the planet right now. When GPT-4 learned to answer questions better, it was doing feedback. When AlphaGo learned to play Go, it was doing feedback. When a self-driving car adjusts its steering because it drifted two inches toward the curb, it is doing feedback. The word they all use, the concept underneath the word, the mathematics formalizing the concept, all of it came from one book written by a child prodigy in 1948 who was trying to figure out how to shoot down a plane. The deeper insight was what he proved about living systems and machines. Before Wiener, biology and engineering were treated as completely separate domains. Organisms adapted. Machines calculated. The idea that you could describe both using the same mathematical framework was not just unusual. It was considered a category error. Wiener proved it anyway. He showed that a brain correcting a reaching movement and a missile correcting its trajectory were running mathematically identical control loops. The hardware was different. The math was the same. Living systems and engineered systems obeyed the same laws once you understood what those laws actually were. He named the field after the Greek word for steersman. Kubernetes. Cybernetics. The person who holds the rudder, reads the water, and adjusts constantly to hold a course through a current that is always pushing the ship somewhere else. That is the mental image he wanted. Not a machine that executes instructions. A system that responds to its own results. The third thing he did is the part almost nobody connects to modern AI. In 1948, Wiener spent an entire chapter of Cybernetics warning about what would happen when machines that learn from feedback were given control over consequential decisions. He described the displacement of workers not as a distant possibility but as a near-term certainty. He wrote about the ethical risks of building systems that optimize for measurable proxies of human values rather than actual human values. He described in plain language what alignment researchers today call Goodhart's Law without using that name, 25 years before Charles Goodhart published anything. He was a mathematician in 1948 writing about problems that AI safety researchers are still trying to solve in 2026. The book is dense in places. The equations are real and the sections on statistical mechanics require actual attention. But Wiener knew this, which is why in 1950 he published The Human Use of Human Beings, which is the same book with all the math removed. Same ideas. Same warnings. Written for anyone who reads English. That second book has been in print for 75 years and almost nobody in tech has read it. Wiener died in 1964 at a conference in Stockholm. He collapsed mid-conversation between sessions. He was 69. He did not live to see a personal computer. He did not live to see the internet. He never saw reinforcement learning, neural networks, or the AI systems that run almost entirely on the mathematical architecture he designed while trying to solve a World War II gunnery problem. Every AI lab in the world today is building systems that run on his framework. Almost none of the people building those systems know his name. The field he founded, cybernetics, mostly disappeared as a word. The ideas did not disappear. They dissolved into every other field. Control theory. Cognitive science. Computer science. Neuroscience. AI. They each took a piece of what he built and called it their own terminology. The word that survived is the one that proves he invented it. Feedback. You use it every day. You use it in code reviews, in meetings, in conversations about AI performance. Every time you use it in the technical sense, meaning a signal that closes a loop between output and goal, you are using the exact definition Wiener wrote down in 1948. He gave the word its meaning. Most people using it have never heard of him. The Human Use of Human Beings is free on archive. Cybernetics is in print and available anywhere books are sold. His major essays are in academic archives at no cost. The man who built the foundation of modern AI was writing about its dangers before the first commercial computer existed. Most people building AI today have never read a word he wrote.

A Russian biophysicist spent 30 years proving that shining red light on a cell could double its energy, and almost nobody believed her until a tech billionaire named Bryan Johnson made her work the most searched biohack on the internet. Her name was Tiina Karu. She worked in a Moscow lab through the 1980s and 1990s, and the discovery she defended for decades sat in journals nobody read while the rest of medicine ignored her. The whole thing started by accident. In 1967, a Hungarian doctor named Endre Mester was trying to use a new device called a laser to burn tumors out of mice. His laser was broken. It did not have enough power to burn anything. He used it anyway. The mice grew their hair back faster than the control group. Their wounds healed faster too. He had no idea why. Tiina Karu picked up his work and asked the question that mattered. Why does this happen. She ran experiments for 20 years. Different wavelengths. Different doses. Measuring what happens inside the cell when red light hits it. The answer she landed on was almost too specific to be true. The thing in your body that responds to red light is one enzyme. Cytochrome c oxidase. It sits inside your mitochondria. Mitochondria are the part of your cell that makes energy. They take oxygen and food and turn it into a molecule called ATP, which is the fuel your cells run on. Your body makes 40 to 70 kilograms of ATP every single day just to keep you alive. If your mitochondria slow down, you age faster, heal slower, lose hair, lose muscle, and get inflamed easier. Cytochrome c oxidase does most of the work. It contains copper and iron atoms. Those atoms happen to absorb light at very specific colors. Red light at 630 to 670 nanometers. Near-infrared light at 810 to 850 nanometers. Other colors do almost nothing. Blue does not work. Green does not work. The biology is locked to those two windows because that is what the metal inside the enzyme can physically catch. When a red photon hits that enzyme, three things happen. The enzyme runs faster. ATP production jumps 30 to 40% within minutes. Nitric oxide gets released. Blood vessels widen. More oxygen and nutrients flow in. A small stress signal goes off inside the cell that tells it to repair itself. The same signal it gets after exercise. Red light is not adding anything to the cell. It is just unlocking work the cell was already trying to do. For 30 years almost nobody outside her field cared. Red light therapy lived inside dental clinics for mouth ulcers and physical therapy offices for tendonitis. Medical schools did not teach it. The science sat in obscure journals. Then the evidence started piling up. A 2024 review of 18 trials confirmed red light speeds up wound healing. Another 2024 review found it lowered inflammation markers by 38% over 4 weeks. Athletes using red light before training had 45% less muscle soreness the next day. Seven separate trials on hair loss showed visible regrowth in every single one. A 2024 study found 15 minutes of red light before a meal cut blood sugar spikes by 27.7%. In March 2026, Nature published a 4,000 word feature on red light therapy. The most respected scientific journal on Earth officially admitted there was real biology under the hype. That was the moment the field crossed from fringe to mainstream. Bryan Johnson is the reason the average person now knows any of this exists. He uses a red light cap on his scalp for 6 minutes daily and a full-body panel three times a week. He posted his hair regrowth photos and his skin scans, and the algorithm did the rest. Red light masks went from biohacker forums to Sephora shelves in two years. Tiina Karu died in 2019. She did not live to see Nature validate her. She did not live to see a billionaire turn the enzyme she identified into a billion dollar industry. Every red light mask, panel, cap, and bed on the planet right now is just a way to deliver the photons she proved mattered. The wavelengths were always there. The enzyme was always there. The biology was always real. It just took a Hungarian doctor with a broken laser, a Russian scientist nobody listened to, and one tech billionaire willing to stand in front of a glowing panel for the world to finally pay attention.

10 things NotebookLM does that ChatGPT and Claude literally cannot: 1. Find ideas across 50 books 2. Surface field contradictions 3. Predict exam questions 4. Turn textbooks into podcasts 5. Simulate a PhD reviewer 6. Map how ideas evolved 7. Find gaps a field ignores 8. Mock interview you 9. Decode earnings calls 10. Write lit reviews fast Open the article (save this) 👇

A 25-year-old MIT PhD student stood in front of a classroom in January 2018 and started teaching the most ambitious deep learning course on the planet. His name is Alexander Amini. He's been teaching it every single January for the last 8 years, and the entire course is uploaded to YouTube for free within weeks of being recorded on campus. Here's what almost nobody tells you about this course. MIT 6.S191 was never designed to be a watered down public version of an internal class. It is the internal class. The same lectures the on-campus students sit through are the lectures uploaded to YouTube. The same labs the on-campus students submit are the labs you can run in Google Colab from your laptop. The same problem sets. The same projects. The same guest lectures from researchers at OpenAI, Google DeepMind, and NVIDIA. The only thing you don't get is the MIT credential. Everything else is identical. Amini and his co-instructor Ava Soleimany rebuild the course every single year because the field moves so fast that last year's lectures are already half obsolete. The 2026 version covers the architecture of frontier LLMs, modern RLHF, multimodal models, and diffusion in a way that did not exist in any curriculum even 18 months ago. A self-taught engineer in Lagos, a high schooler in Karachi, and a working software developer in Berlin can all open the same playlist tonight and be learning from the same instructors as a 22-year-old paying $60,000 a year to sit in a Cambridge auditorium. This is the most quietly democratizing thing happening in technical education and almost nobody outside the field has heard of it. The course is at The lectures are on YouTube. Both are free. Most people will scroll past this post. The few who open the link will be in a different position by March than they are tonight.

A community college professor taught the same study skills lecture for 30 years, and the video quietly became one of the most watched educational recordings on the internet. His name is Marty Lobdell. He spent his career as a psychology professor watching students fail not because they were lazy, but because nobody had ever taught them how their brain actually works under the pressure of learning something hard. The lecture is called "Study Less Study Smart." Over 10 million views. Passed around in Reddit threads, Discord servers, and university study groups for over a decade. And the core insight buried inside it has been sitting in cognitive psychology research for years, waiting for someone to explain it in plain language. Here is the framework that completely changed how I think about effort. Your brain does not sustain focus the way you think it does. Studies tracking real students found that the average learner hits a wall somewhere between 25 and 30 minutes. After that, efficiency doesn't just decline. It collapses. You're still sitting at your desk, still looking at the page, but almost nothing is going in. Lobdell illustrated this with a student he knew personally. She set a goal of studying 6 hours a night, 5 nights a week, to pull herself out of academic probation. Thirty hours of studying per week. She failed every single class that quarter. She wasn't failing because she lacked effort. She was failing because she had confused time spent near books with time spent actually learning. The 25-minute crash hit her at 6:30pm every night. She spent the next five and a half hours sitting in the wreckage of her own focus and calling it studying. The fix sounds almost too simple. The moment you feel the slide, stop. Take five minutes. Do something that actually gives you a small reward. Then go back. That five-minute reset returns you to near full efficiency. Across a six-hour window, the difference is not marginal. It is the difference between thirty minutes of real learning and five and a half hours of it. The second thing he taught destroyed something I had believed about how memory actually works. Highlighting feels productive. Going back over your notes and recognizing everything feels like knowing. But recognition and recollection are two completely different cognitive processes, and your brain is very good at making you confuse them. You can see something you've read before and feel completely certain you understand it, even when you couldn't reconstruct a single sentence from memory if the page were blank. He proved this live in the room. He read 13 random letters to his audience. Almost nobody could recall them. Then he rearranged the same 13 letters into two words: Happy Thursday. The whole room got all 13 without effort. Same letters. Same count. The only thing that changed was meaning. The brain stores meaning. Not repetition. The moment new information connects to something you already understand, the retention changes entirely. This is what the cognitive psychology literature calls elaborative encoding, and it is the mechanism underneath every effective study technique. The third principle was the one that hit me hardest, and the one almost nobody applies. Lobdell cited research showing that 80 percent of your study time should be spent in active recitation, not passive reading. Close the material. Say it back in your own words. Teach it to someone else, or to an empty chair if no one is around. The struggle of retrieval is where the actual learning happens. Reading your notes again is watching someone else do the work. His parting line has stayed with me longer than almost anything else I have read about learning. He told the room that if what he shared didn't change their behavior, they hadn't actually learned it. It would just live in their heads as something they had heard once and felt good about. He was right. And most people leave every lecture exactly like that. The students who remember everything aren't putting in more hours. They stopped confusing the feeling of studying with the fact of it.

I accidentally discovered how to compress a semester of learning into 48 hours. A grad student at MIT showed me his NotebookLM setup. I thought he was just organized. Then I watched him pass a qualifying exam on a subject he'd never studied before. Here's exactly what he did: First: he didn't upload a textbook. He uploaded 6 textbooks, 15 research papers, and every lecture transcript he could find on the subject. Then he asked NotebookLM one question: "What are the 5 core mental models that every expert in this field shares?" Not "summarize this." Not "explain this topic." Mental models. The stuff that takes professors years to develop. But the next part is what broke my brain. He followed up with: "Now show me the 3 places where experts in this field fundamentally disagree, and what each side's strongest argument is." In 20 minutes he had a map of the entire intellectual landscape of the field: the debates, the consensus, the open questions. Most students spend a full semester just figuring out what those debates even are. Then he did something I've never seen before. He asked: "Generate 10 questions that would expose whether someone deeply understands this subject versus someone who just memorized facts." He spent the next 6 hours answering those questions using the source material. Every wrong answer triggered a follow-up: "Explain why this is wrong and what I'm missing." By hour 48, he could hold a conversation with his thesis advisor without getting destroyed. The tool didn't change. The questions did. Most people treat NotebookLM like a fancy highlighter. These students are using it like a private tutor who has read everything ever written on the subject. The difference between a semester and 48 hours isn't the amount of content. It's knowing which questions to ask.