Ihtesham Ali
@ihteshamali • 48,508 subscribers
I write on technology and business. Helping you understand AI.
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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.
Ihtesham Ali2,436,521 views • 2 days ago
Sam Altman said the smartest scientists in AI are the ones who held the entire field back. The experts were the problem. This is one of the most uncomfortable things he said all night. Altman said the field was honestly held back by a generation of scientists who were too certain about what scaling would not produce. The people with the most credibility were the most wrong. Then he explained why. It was not about intelligence. It was about identity. He said when you make your identity about a particular belief, that something will work or won't work, and then the data disproves you, you get stuck. You are too attached to the belief to let it go. You cannot see the truth anymore. The smarter you are, the more confidently you defend the wrong position. He pointed at the trolls who spent years saying scaling was a dead end, a fraud, a company destined to fail. The data kept proving them wrong. They kept repeating themselves anyway. He called that a form of insanity. Then he turned it around. He said it is a reminder in both directions. Including for the people who are currently right. The lesson is not that experts are dumb. It is that the moment a belief becomes who you are, it stops being something you can update. (Watch the full talk on YouTube at Stanford Online channel)
Ihtesham Ali695,288 views • 1 month ago
Jennifer Doudna won the Nobel Prize for gene editing and went on Bloomberg to say the chatbots everyone is betting on cannot innovate at all. Every promise Silicon Valley is making about AI curing disease just hit the one person qualified to check it. She has spent her whole career inside the actual frontier of curing disease. So when she talks about what AI can and cannot do in biology, she is not guessing. She is reporting from inside the lab. Her words were blunt. She is not seeing chatbots innovate. They summarize data. They write reports. They do not come up with a brand new idea nobody has ever had. Then the interviewer pushed. So you're saying AI can't innovate? Doudna did not flinch. She does not know if it can't. She just does not see it doing it right now. This lands harder when you remember who is making the opposite case. Sam Altman says AI will eliminate disease within five years. Larry Ellison says AI will cure cancer in a 48 hour window. An OpenAI executive even floated that the company should get a cut of sales on any drug discovered through ChatGPT. Doudna answered that in two words. Good luck. Even the cancer specialists Altman is selling to keep warning that cancer is not one disease but hundreds, each needing its own cure, and that compute does not skip the years of lab work. Her reason is simpler. Biology is hard. You cannot simulate your way to an understanding of the human body. The people promising cures are the ones selling the tool. The person who actually won a Nobel building them is telling you it has not happened yet. Source: Bloomberg Originals Watch the full video on their official channel.
Ihtesham Ali458,206 views • 27 days ago
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.
Ihtesham Ali1,908,735 views • 3 months ago
Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali256,870 views • 1 month ago
Bill Ackman is openly trying to build the next Berkshire Hathaway and explained the entire playbook on All-In. It starts with a 4 billion dollar company nobody on Wall Street cares about. The company is Howard Hughes. It trades at 60 cents on the dollar. Here is the playbook he is copying. Someone went back and read every filing Warren Buffett made over 60 years. Almost all of Berkshire's value came from one thing nobody talks about. Insurance. Buffett ran an insurance company. You collect premiums today in exchange for paying claims later. That means you get money up front. Float. Most insurers obsess over the liability side. How much they might have to pay out. Buffett did the opposite. He took that float and invested it. Manage both sides well and you build a compounding, tax-efficient machine that runs for decades. So why hasn't everyone copied it? Because the people great at investing go work for hedge funds. Insurance companies can't recruit them. Buffett owned half his company and happened to be the best investor alive. Ackman is now running the same play. Instead of plowing Howard Hughes cash into real estate, he is pouring it into insurance. The goal is a trillion dollar machine compounding over 50 years. Buffett started with a failing textile mill. Ackman is starting with land nobody wanted. The playbook was never hidden. Almost nobody is built to run it. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali212,743 views • 1 month ago
China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali86,163 views • 25 days ago
I was paying $229 for Screen Studio. Then I found this repo and uninstalled it the same night. Recordly is a free, MIT licensed screen recorder and editor that ships every feature I was paying for auto-zoom animations, cursor smoothing, motion blur, native macOS capture, and a full timeline editor. → Apple-style zoom animations with automatic suggestions from cursor activity → Native ScreenCaptureKit recording on macOS 12.3+ → Cursor smoothing, motion blur, click bounce, macOS-style cursor assets → Smooth pan transitions between zoom regions → Timeline trim, speed ramps, annotations, manual zoom spans → Wallpapers, gradients, padding, rounded corners, blur, drop shadows → MP4 and GIF export at any aspect ratio → .recordly project files so you can reopen edits later macOS, Windows, Linux. Scene composition runs on PixiJS. Native Swift helpers handle macOS cursor telemetry. 8.3k stars. MIT License. 100% Opensource
Ihtesham Ali240,397 views • 3 months ago
This TED Talk changed my life. When I was 16, I was the kid at school nobody listened to. I would say something in class. Silence. Someone else would say the exact same thing two minutes later. The room would react. I thought it was confidence. I thought it was personality. I thought some people were just born with the ability to command a room and I wasn't one of them. I was wrong about all of it. Then I found this. Julian Treasure has spent his career studying one of the most powerful instruments on earth — the human voice. Not music. Not machines. Your voice. The one thing you use every single day and have almost certainly never been taught to use properly. Here is the framework that made me audit every conversation I had been having since I was that kid in school. He opens with what he calls the seven deadly sins of speaking. Not as metaphor. As a literal checklist of habits that cause people to tune you out before you finish your first sentence. Gossip. Judging. Negativity. Complaining. Excuses. Exaggeration. And dogmatism delivering your opinions as facts and expecting people to simply accept them. Most people commit at least three of these every day. Some do all seven before lunch. But the insight that stopped me cold was not the list of sins. It was what he said we are actually competing against every time we open our mouths. Noise. We live inside an environment of constant, aggressive, badly designed noise. Open offices. Restaurants built for aesthetics not acoustics. Phones that fracture every thought. And into that environment we send our words and then wonder why nothing lands the way we intended. The problem is almost never what you are saying. It is everything surrounding how you are saying it. His framework for doing it right spells a single word: HAIL. Honesty, authenticity, integrity, and love. Not love in the soft sense. Love as genuinely wishing the person in front of you well because if you actually want good things for someone, it becomes almost impossible to judge them at the same time. Then he opened what he called the toolbox. And this is the part nobody talks about when they share this talk. Register. Timbre. Prosody. Pace. Silence. Pitch. Volume. These are not performance tricks. They are instruments. Sitting inside you right now, completely unplayed, because nobody ever told you they existed. The research on register alone is striking. We vote for politicians with deeper voices. Not because of their policies. Because depth signals authority at a neurological level that moves faster than rational thought. Your voice is landing on people's nervous systems before their minds have processed a single word you said. The 16 year old version of me didn't have a confidence problem. He had a toolbox he didn't know existed. Nobody taught us that the voice is an instrument. Nobody told us to use its registers deliberately, to let silence do the work that words cannot, to understand that how you say something rewires how people feel about what you said. The most important TED Talk about communication isn't about what you say. It's about everything you've been doing with your voice your entire life without ever once stopping to look at it.
Ihtesham Ali75,732 views • 3 months ago
A Carnegie Mellon professor walked onto a stage in 2007 and gave an hour-long lecture to 400 people about achieving your childhood dreams. He did not tell the room that the entire talk was actually written for his three kids, who would grow up without him. His name was Randy Pausch. The date was September 18, 2007. The video has since passed 20 million views, and the book that followed spent more than a hundred weeks on the New York Times bestseller list. Pausch was 46 years old, had been diagnosed with terminal pancreatic cancer a month earlier, and had been told he had three to six months of good health left. He did not walk onto that stage to talk about dying. He walked onto it to teach a single lesson hidden inside another one. Here is what I missed the first time I watched it. Pausch opened by doing push-ups on stage. He told the audience he was in phenomenally good shape, in better shape than most of them, and anyone who wanted to cry or pity him was welcome to get down and match him. The room laughed. Then he said the line that sets up the entire hour to come. We cannot change the cards we are dealt. Just how we play the hand. That was the frame. Everything after it was a demonstration. The lecture was officially titled Really Achieving Your Childhood Dreams, and Pausch did spend the first 40 minutes working through his actual childhood list. Zero gravity. Playing in the NFL. Writing an entry in the World Book Encyclopedia. Being Captain Kirk. Becoming a Disney Imagineer. He walked the audience through which ones he got, which ones he didn't, and what the gap between wanting and getting had actually taught him. The framework inside those 40 minutes is the part most people remember, and it is the one Pausch delivered with the most force. He called it the brick wall. He said the brick walls in your life are there for a reason. They are not there to keep you out. They are there to give you a chance to show how badly you want something. They are there to stop the people who do not want it badly enough. They are there to stop the other people. Read that again slowly. He is not saying brick walls are a test you have to pass. He is saying brick walls are a filter nature uses to separate the people who actually want a thing from the people who only like the idea of wanting it. That is a completely different claim. Most people treat obstacles as unfair. Pausch argued obstacles are the mechanism by which desire gets proven, and without that mechanism the whole concept of wanting something would be meaningless. Every dream he achieved, he achieved by treating the wall as a signal that he was close, not a signal that he should stop. The second framework he taught the audience is the one almost nobody teaches in any classroom. He called it the head fake. He pulled it from football. Coaches teach young kids to tackle by having them run drills that look like they are about tackling, but the real lesson being embedded is teamwork, grit, how to take a hit and get back up. The kid thinks they are learning football. They are actually learning something much larger, and they will not realize it until years later. Pausch said the best teaching in the world is head fake teaching. You get people to learn the thing they need by dressing it up as the thing they already want. This is the technique behind Alice, the programming software he built at Carnegie Mellon. Kids thought they were making animated movies and games. They were actually learning to code. Pausch said one of his proudest claims to fame was that he had taught programming to a generation of students who had no idea they were being taught programming at all. And then, with about three minutes left in the lecture, he ran a head fake on the room. He asked the audience if they had figured out the first head fake of the talk itself. The room went quiet. He said the lecture was never actually about how to achieve your childhood dreams. It was about how to lead your life. If you lead your life the right way, the karma takes care of itself and the dreams come to you anyway. Then he asked if they had figured out the second head fake. Even quieter. He said the talk was not for the four hundred people in the room. It was for his three kids. Dylan was six. Logan was three. Chloe was eighteen months. They would grow up without their father, and he knew it. Pausch had spent an hour on stage pretending to give career advice to strangers because he needed to record something his children could watch when they were old enough to understand who their dad had been. The entire architecture of the lecture was a message in a bottle disguised as a keynote. The filtered brick-wall philosophy, the football stories, the dreams he chased and the ones he missed, the line about playing the hand you are dealt, all of it was something a father wanted three small children to internalize after he was no longer there to say it in person. That is the moment the video stops being a lecture and starts being something else entirely. Pausch died on July 25, 2008, ten months after giving it. His final sentence on stage was that he had given the talk tonight, and then he walked off. The applause lasted nearly a minute before the camera cut. Most professors spend their entire careers trying to say one true thing their students will remember for a week. He said one true thing his children will remember for the rest of their lives, and the rest of the world is still watching the footage.
Ihtesham Ali56,615 views • 3 months ago
Demis Hassabis described a world where central banks stop guessing on interest rates and run hundreds of thousands of simulated economies first. He wants to turn economics into a hard science. He thinks the way we run the economy is almost primitive. Right now the Fed moves rates half a percent and waits to find out if it caused a recession. His words: "oh, whoops. Maybe we shouldn't have done that." We make trillion dollar decisions and check the results after the damage is done. The data backs him harder than he said it. A study of more than 16,000 forecasts from the Philadelphia Fed's own survey found professional forecasters were 53% confident and 23% accurate. The people steering the economy are wrong roughly three times out of four. Hassabis wants to run the economy the way AlphaGo runs a board. Before a single move, AlphaGo simulates tens of thousands of paths and picks the most promising one. He wants the same for monetary policy. Simulate hundreds of thousands of trajectories, adjust the big levers in each one, then act on the aggregate. The reason we can't today is that the social sciences can't rerun the experiment. You get one timeline. You can't test a recession a thousand times in a controlled way. His fix is an AI that learns the simulation from the data, because we don't understand the economy well enough to code it by hand. Every central bank on earth is flying with one attempt and no rehearsal. Hassabis is describing the rewind button they have never had. --- Watch the full interview at Semafor on YouTube
Ihtesham Ali15,140 views • 28 days ago
🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.
Ihtesham Ali31,051 views • 3 months ago
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