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Extra large, but other sizes are available too. Amazing texture! 🔥 🥂 Celebrating 6 years & 600K followers! Gumper 🆚 Qutoy Use code GUMPER for a special discount อย่างใหญ่ แต่ ไซร์อื่นก็มี คับ เนื้อสัมผัสดีเยี่ยม มากๆ เพียงใส่ โค๊ด GUMPER Qutoys💫💞

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Long post alert 🚨 “A story of two different generations, connected by the same music🎼” Ruhumuriza James, a.k.a. King James KING JAMES is truly one of those rare artists whose music has stood the test of time! As he celebrated 20 years of his music career this weekend, I couldn’t help but think about where I was 20 years ago. I was at university, and later on when I started sharpening my journalism skills at UR- RADIO SALUS , I used to play his songs on air, danced to them in the studio, and engaged listeners as each new release came out. From Intinyi to Nzakubona ryari, Yantumye, Inzozi, Nta mahitamo, Yaciye ibintu, Biracyaza… and so many other unforgettable songs across multiple albums including many ‘collabos’ (We saw Riderman , Bull Dog, P Fla, Mico the Best, Safi Madiba, Chris Easy , Austin Luwano (Uncle Austin) , Arielwayz and the most recent and beautiful one Zuba Ray sharing the stage with King James at his concerts, which was lovely and meaningful too🙏🏼) Even back then, he had something special. He was authentic, a hopeless romantic through his lyrics 💘, and an exceptional performer. And of course, his talent earned him countless accolades, including the PGGSS trophy🏆 But enough about me… See my beautiful friend who became a little sister standing next to me? Twenty years ago, she probably had not even started nursery school yet😊 bambi… She told me that King James’ songs are among her most treasured childhood memories. That thought made me smile 😊 The same artist who was part of my university years also became part of her early childhood. Two different generations, connected by the same music🎼 how lovely 🥰 Today (or precisely last night), we stood together at BK Arena , surrounded by thousands of King James’ fans, celebrating his unwavering commitment to his craft #20YearsOfKingJames And twenty years later, he still performs with the same passion, HUMILITY, and authenticity. If anything, he seems even more energetic than before! Performing for two consecutive days, for more than four hours each night… that’s simply incredible🔥 What struck me even more was hearing an entire arena sing every single lyric, from beginning to end. His songs have remained timeless. They are CLEAN (one of the many reasons I’ve remained a fan all these years) meaningful, memorable, and they’ve become part of so many people’s lives (I kept hearing stories of guteretesha indirimbo za King James 🥰). Moments like these remind me how beautiful life is. We grow older, our journeys change, and new generations come along. Yet some voices, some songs, and some people remain constant. King James is undoubtedly one of them. Congratulations, King James, on 20 incredible years. Thank you for giving us the soundtrack to so many chapters of our lives. Cheers to many more years of inspiring music🥂🎼Noneho Day2 with MCs Luckman Nzeyimana & Zuba 🌞 yoh! it’s a story for another day 🔥🔥🔥We can’t thank you enough 🙏🏼 Finally, to Bruceintore and Intoreentertainment… I honestly have no words. 🥹 THANK YOU for giving #Rwanda a celebration worthy of this remarkable milestone. It was an unforgettable experience. A time was had! History was made! MUCH RESPECT kabisa 🫡

Pam Wa Mudakikwa

24,988 views • 1 month ago

I’ve been using GPT-5.6 Sol internally for the past two months, I've spent probably 25+ billion tokens. Here’s my review and comparison to Fable 5: > Let's start with the analogy because everyone seems to be giving theirs - GPT-5.6 is likely the last version of the GPT-5 training run series. It's kind of like an athlete at their peak. Through years of experience in the game, they've become the most reliable player and has the highest game IQ. But, there's no more room to grow. Fable on the other hand, being essentially the first version of a new training run, is the first round draft pick rookie. Raw talent mixed with the energy only a young person would have results in some incredible plays we didn't think possible, but also mistakes due to lack of experience. But that rookie will only improve and likely will be better than the veteran ever was because it's a new game and a new era. > GPT-5.6 is genuinely better at long, sustained work. With /goal, I've had it running complex projects for days with almost no intervention. It built a Minecraft-style game, kept adding features and mobs after the core game worked, and only stopped because I stopped the run. I never felt as though I had to jump in and guide it back to the right path. > It keeps finding useful work when you give it a concrete finish line. I had it recreate Excel with a loop. It inspected the real desktop excel app with Computer Use, comparing that against its own build, and closing the gaps. I stopped it after six days after it had built an incredible amount of functionality. > It's faster than other models in two different ways. The raw generation speed is higher, something OpenAI has been putting effort into. But it also takes a shorter path to solutions. It wanders less, changes less code, and generally knows how to get things done directly. In daily use, it feels about 2-3x times faster than Fable. That's my impression, not a controlled benchmark. The difference is large enough that I notice it constantly. > It works well across a wide range of tasks. I use it for one-line edits, quick questions, browser chores, and multi-day builds without changing my prompting style. Speaking of browser control, its the best ever I've used. To the point where I actually use it often. If a task lives on a website, GPT-5.6 usually opens the browser and does it there instead of asking for an API key or forcing everything through the terminal. When I switched back to GPT-5.5, it went straight to the command line even when the browser was clearly the better tool. > And it can handle real browser work, not just toy demos. During a data import, I had it monitor Supabase and resize instances as the load changed. It stayed on the dashboard, adjusted capacity, and checked the result without an API or a custom script. > I also gave it a full Google Workspace migration. It moved Forward Future from to preserved the old aliases, and configured MX, SPF, and DKIM. Before a consequential save, it stopped, explained exactly what would change, and waited for confirmation. > The reasoning setting matters a lot. Light is good for questions and small edits. High and Extra High are the sweet spots for serious work. Ultra usually takes longer than the extra thinking is worth and burns tokens. > I love that 5.6 is split into 3 sizes. Not only can you control speed and cost that way, but you still also have the thinking effort setting for each of them. Very precise controls. I just wish Codex automatically routed my prompts for me. > Its personality is blunt and a little bland. Claude feels warmer and more natural to talk to. GPT-5.6 is more clinical, but I like that for work. It gives me enough explanation and rarely pads the answer. I usually have to ask Fable to explain things more simply and/or more concise. > Its front-end taste has improved, but the default is predictable. Left alone, it turns websites into PowerPoint decks with huge statements and hard section breaks. The good news is that it takes design direction well and can revise without destroying the parts that already work. > It still makes confident mistakes. I asked it to rebuild parts of a system, and it told me the job was finished. Later, I found out it wasn't. Bits of its internal process also leak into the answer occasionally. > Claude Fable is more naturally autonomous on large, open-ended projects. GPT-5.6 is easier to reach for. I don't need to invent a huge project to justify using it. It works just as well for a small edit or browser chore. > GPT-5.6 is also cheaper. Sol costs $5 per million input tokens and $30 per million output tokens. Fable costs $10 and $50. Cached input is cheaper too. Still, cost per finished task matters more than cost per token. > GPT-5.6 isn't the best at everything, and it still needs supervision. But it generates faster, wanders less, works at almost any scale, and wastes less of my time. It's the model I have the most confidence in to get the job done right the first time. I put together a full breakdown with all the tests, prompts, and examples on a site. You can read it here:

Matthew Berman

188,148 views • 2 months ago

Why Exchanges Banned This Bot: The 142,000% Return Liquidation Strategy Revealed i finally posted the strategy that got me banned and now the exchanges are probably sweating because i am handing you the keys to the liquidation engine. most people think trading is about charts but the real alpha is hidden in the moments when other traders lose everything. if you can understand why market makers hunt these positions you will never look at a candlestick the same way again. it took years of losing money to liquidations and over trading to realize that hand trading is a losing game for almost everyone on the planet. code is the great equalizer because it removes the emotion that usually causes you to hold a losing position until your account hits zero. i spent hundreds of thousands on developers in the past thinking i could not code myself until i realized i just needed to iterate to success. trading by hand is just driving a horse while everyone else is in a ferrari and the fees alone will chop you up before you even realize you were wrong. i watched a guy with a six million dollar short position sitting just two percent away from total liquidation while i was building this. seeing those numbers on the screen gives me ideas that i can automate into a bot so i dont have to spend my life staring at a monitor. the process i follow is called the rbi system which stands for research backtest and implement. most traders skip the first two steps and go straight to implementation which is why they get smoked on their very first bot. research starts with a backlog of ideas from books or papers or even just watching how the market reacts to big moves. once you have that idea you have to see if it worked in the past using a backtest because if it did not work then it certainly won't work in the future. i have been collecting liquidation data for eighteen months because that data is the lifeblood of a winning system. there is a hidden loop in the market where market makers try to liquidate as many people as possible to find liquidity. i wanted to build a strategy that either trades with that momentum or bets on the bounce right after the liquidation happens. the first strategy i tested was a pure liquidation momentum play that looks for a threshold of nine hundred seventy five thousand dollars in liquidations. when longs get liquidated it shorts the market to continue the down move and it tries to take a one percent profit. this strategy showed a return of over four hundred percent in the backtest while the buy and hold was only thirty three percent. it sounds amazing but you have to be careful with optimized results because you can search with math until you find anything. i decided to flip the logic on its head and create an inverse liquidation strategy that acts as a contrarian. instead of following the move it waits for the longs to get liquidated and then buys the dip after a small price spread. this is where i stumbled onto something that felt like a mistake but turned out to be pure alpha. i accidentally typed in a threshold of three hundred thousand dollars instead of three million and the results were unbelievable. the backtest return jumped to over one hundred forty thousand percent because the bot was catching every single micro bounce in the market. even when i doubled the commission fees to account for the high trade volume the strategy still stayed incredibly profitable. most people would have missed this because they are too busy trying to be right instead of just looking at what the data says. i use tools like claude and cursor to build these bots in minutes when it used to take me an entire week to write the code. if you are not using ai to automate your ideas you are essentially choosing to work ten times harder for less money. i built three separate bots during this session including a momentum bot and two different versions of the inverse spread bot. running these together creates a sort of statistical arbitrage where you can hedge your positions across different market conditions. one bot wins when the market cascades and the other wins when it fakes out and reverses. you have to start with tiny ten dollar sizes because a backtest is never a hundred percent guarantee of what will happen today. i always run my p and l close logic first to make sure the bot exits the position if the stop loss or take profit is hit. it is vital to check your position every fifteen seconds and make sure you are not double ordering or getting stuck in a trade. the goal is to have fully automated systems trading for you so you can actually live your life while the bots do the work. i push all of this code to my private github because i believe that wall street will never show you how this actually works. you have to be a doer and not a dabbler if you want to actually make it in this industry. the reason i show everything live on youtube is to prove that anyone can learn to do this if they are willing to iterate. you dont need to be a math genius you just need to follow the rbi system and stay disciplined with your risk. every liquidation you see on the chart is a signal and if you know how to read them you are no longer the one being hunted. i am currently running the third version of the bot to see how it handles the live market volatility. it is a beautiful thing to see a system enter and exit a trade perfectly without you having to click a single button. the fees are the silent killer of hand traders but a bot can be programmed to use limit orders and stay efficient. if you learn to code you can build anything for the rest of your life regardless of where you are in the world. stop trying to guess which way the candle will go and start building systems that can handle both directions. i am going to keep testing these three strategies against each other to find the ultimate ensemble for this current market. once you find a winning edge you just have to scale it up slowly and keep refining the parameters. the exchanges might not like that i am sharing this but code is the great equalizer and it is time for you to use it. i will be back tomorrow to show the results and keep building more systems until everything is fully automated

Moon Dev

11,948 views • 6 months ago

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,153 views • 6 months ago

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

322,292 views • 4 months ago

Tlon Messenger is now open to everyone. We built a simple and infinitely flexible platform for you to use AI agents with your friends. We think it’s pretty amazing, we love using it every day, and we want to see what people can do with it. So we’re opening it up to the public. It’s fun and exciting to build the future of personal computing in an informal, chat-based way with your friends. (You can skip the rest and just download it from the link in the next tweet if you want.) If you don’t want your digital future to be owned by a giant company but you want to explore what’s possible in this new era of agent-driven computing, you should try using Tlon. But wait, what is it? Tlon is a messaging platform built 100% open source, decentralized and owned by its users from the ground up. With Tlon you own everything: your data, your workflows, your programs: the whole thing. Think of it like Telegram or WhatsApp that you own forever and you can freely customize. Every Tlon account comes with an OpenClaw-powered bot. (Don’t worry, we safely run OpenClaw for you in our infrastructure so your bot can’t go off the rails. You’re also welcome to host your own claw if you want maximal control.) We use our bots to collect research, build nuanced daily briefings, collate data from all our disparate services. Tlon makes it insanely easy to use OpenClaw by simply installing an app from the app store, we let you keep your data and programs independent from any app or model provider, and provide the canvas to explore what’s possible. What’s most interesting for us is using bots together. On Tlon bots can create groups, augment them, moderate them, invite others and freely engage with both users and other bots. Tlon is an open playing field unlike what’s possible on conventional platforms. So, what do we do with Tlon? First and foremost, we run Tlon on Tlon. Bots coordinate data from all of our services (Linear, GitHub, all of our servers and infrastructure) and handle alerts, briefings and help us track down bugs in place. Having all of this easily synced between a desktop client and a mobile app is quick and convenient. We use bots to research new areas of work or interest. Bots can compile trees of notes, use different models to evaluate them, and then add on autoresearch-like automations to go even deeper. Since Tlon bots can freely switch between models and providers, we often pass research to Anthropic, OpenAI and self-hosted models to see different results. The most fun part of using bots as researchers is doing it together. “Put together short (~500 word) notes on the 10 most popular open source messaging protocols of the past twenty years, put them in a notebook inside a group and invite Corrina, Walt and Bill as well as their bots” is a good example. Together we’re able to move more quickly than we would on our own. Many of us also use bots to keep track of all the separate threads of work in our personal lives with close friends and family. Someone built a system for keeping track of their garden across time, someone else built a system for prepping lunches for their daughter and sending recipes to family members. Another team member built an integration that tracks what flights are passing overhead so they get a push notification every time a plane goes by. Many of us quickly communicate with our bots via voice memo when we’re out and about. Having a single interface to all the models that also holds all our data and is in our pockets feels great. Especially when the data goes into a single archive. Why is Tlon different? Every Tlon account runs on top of your very own personal server. If you ever want to download it and run it yourself, you can. If we ever go out of business, it’s yours to keep. This is very different from anything that already exists. You can’t keep your WhatsApp forever. You can’t keep your Telegram forever. Tlon is an archival-quality system that’s yours to customize. Why did we build it? In my 1999 imagination, sitting in front of a CRT somewhere in the California countryside listening to Underworld and the sound of a modem, a connected computer was an engine of unending creative potential for everyone. When I was a teenager, a computer with an internet connection felt like an infinite expanse of possibility. Not only could you use the computer to find new tools to experiment with—you could also build whatever tool you could think of. It seemed like anything was possible. I looked forward to a future where everyone could build whatever software they needed, whenever they needed it. It turned out, in the intervening twenty years, that to build and customize software you have to both write code and host it on a server somewhere. For most people, so far, that has been impossible. Instead of controlling our software, our software controls us. We rely on others to build it and decide everything about it: how it works, looks, how much it spies on us and how long it lives. But all of this is changing, fast. The hottest programming language of 2026 is English. People with no technical experience are building their own tools. It’s incredible. The expanse has opened up again. The cost of building what we think of today as software is headed to zero. What yesterday was an entire app is rapidly being replaced by a conversation. The result is hyper-specific, tailored to the user and much more efficient. Today, agents help us build workflows, automate processes and pull together disparate sources of data. All of the annoying apps and services and clunky interface we’ve put up with can just disappear. We can now program and control our computers in the programming language we already know: English. There aren’t that many of us doing this yet, though. It’s still far too hard to set up, to distribute and to trust. There’s also no single platform to experiment on and collaboratively imagine this new future of personal computing. We want everyone to be able to build bespoke, ultra-personal software on demand. We think software should be as available and accessible as a pen and paper. We think anyone should be able to enjoy the expanse of possibility that the computer provides with the lowest possible barrier to entry and the highest possible quality. So, starting far, far too long ago, we engineered a whole new system for it. Just for you. We’re opening up Tlon Messenger to a limited number of people each week. This isn’t for exclusivity’s sake, but because we’re running infrastructure for you and your agent, and covering the tokens your agent uses. That can get expensive quickly, but we want to learn what people will do with this new system we’ve built. We’re really curious to see what you can do, so give it a try and tell us what you invent. Download link to your local app store in the next tweet. Yours, Galen (and the rest of the Tlon Team)

Tlon

601,332 views • 3 months ago

Warren Buffett turns 93 today! To celebrate, I'm sharing the greatest lecture he ever gave together with his 94 (!) best investment quotes. 1. Rule No. 1 is never lose money. Rule No. 2 is never forget Rule No. 1. 2. Diversification is a protection against ignorance. It makes very little sense for those who know what they're doing. 3. Do not take yearly results too seriously. Instead, focus on four or five-year averages. 4. All there is to investing is picking good stocks at good times and staying with them as long as they remain good companies. 5. American business - and consequently a basket of stocks - is virtually certain to be worth far more in the years ahead. 6. An investor should act as though he had a lifetime decision card with just twenty punches on it. 7. And so the important thing we do with managers, generally, is to find the .400 hitters and then not tell them how to swing. 8. The most important quality for an investor is temperament, not intellect. You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 9. Bitcoin has no unique value at all. 10. Buy a stock the way you would buy a house. Understand and like it such that you'd be content to own it in the absence of any market. 11. The years ahead will occasionally deliver major market declines - even panics - that will affect virtually all stocks. No one can tell you when these traumas will occur. 12. I insist on a lot of time being spent, almost every day, to just sit and think. That is very uncommon in American business. 13. Buy companies with strong histories of profitability and with a dominant business franchise. 14. For the investor, a too-high purchase price for the stock of an excellent company can undo the effects of a subsequent decade of favorable business developments. 15. I believe in giving my kids enough so they can do anything, but not so much that they can do nothing. 16. The world went mad. What we learn from history is that people don’t learn from history. 17. The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. 18. Among the various propositions offered to you, if you invested in a very low cost index fund - where you don't put the money in at one time, but average in over 10 years - you'll do better than 90% of people who start investing at the same time. 19. Because if you're wrong and rates go to 2 percent, which I don't think they will, you pay it off. It's a one-way renegotiation. It is an incredibly attractive instrument for the homeowner and you've got a one-way bet. 20. Cash is to a business as oxygen is to an individual: never thought about when it is present, the only thing in mind when it is absent. 21. Don't get caught up with what other people are doing. Being a contrarian isn't the key but being a crowd follower isn't either. You need to detach yourself emotionally. 22. For 240 years it's been a terrible mistake to bet against America, and now is no time to start. 23. I never attempt to make money on the stock market. I buy on the assumption that they could close the market the next day and not reopen it for five years. 24. I have no views as to where it (gold) will be, but the one thing I can tell you is it won't do anything between now and then except look at you. Whereas, you know, Coca-Cola will be making money, and I think Wells Fargo will be making a lot of money, and there will be a lot -- and it's a lot -- it's a lot better to have a goose that keeps laying eggs than a goose that just sits there and eats insurance and storage and a few things like that. 25. I just sit in my office and read all day. 26. I won't say if my candidate doesn't win, and probably half the time they haven't, I'm going to take my ball and go home 27. If returns are going to be 7 or 8 percent and you're paying 1 percent for fees, that makes an enormous difference in how much money you're going to have in retirement. 28. We want products where people feel like kissing you instead of slapping you. 29. If you aren't willing to own a stock for ten years, don't even think about owning it for ten minutes. 30. The most important investment you can make is one in yourself. 31. If you buy things you do not need, soon you will have to sell things you need. 32. If you don't feel comfortable making a rough estimate of the asset's future earnings, just forget it and move on. 33. If you like spending six to eight hours per week working on investments, do it. If you don't, then dollar-cost average into index funds. 34. If you're in the luckiest 1% of humanity, you owe it to the rest of humanity to think about the other 99%. 35. If you're smart, you're going to make a lot of money without borrowing. 36. In the 20th century, the United States endured two world wars and other traumatic and expensive military conflicts; the Depression; a dozen or so recessions and financial panics; oil shocks; a flu epidemic; and the resignation of a disgraced president. Yet the Dow rose from 66 to 11,497. 37. In the 54 years (Charlie Munger and I) have worked together, we have never forgone an attractive purchase because of the macro or political environment, or the views of other people. In fact, these subjects never come up when we make decisions 38. In the business world, the rearview mirror is always clearer than the windshield. 39. Investors should remember that excitement and expenses are their enemies. 40. It is a terrible mistake for investors with long-term horizons to measure their investment 'risk' by their portfolio's ratio of bonds to stocks. 41. It is not necessary to do extraordinary things to get extraordinary results. 42. It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently. 43. The one thing I will tell you is the worst investment you can have is cash. Everybody is talking about cash being king and all that sort of thing. Cash is going to become worth less over time. But good businesses are going to become worth more over time. 44. It's been an ideal period for investors: A climate of fear is their best friend. Those who invest only when commentators are upbeat end up paying a heavy price for meaningless reassurance. 45. It's better to hang out with people better than you. Pick out associates whose behavior is better than yours and you'll drift in that direction. 46. It's better to have a partial interest in the Hope diamond than to own all of a rhinestone. 47. It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price. 48. Just pick a broad index like the S&P 500. Don't put your money in all at once; do it over a period of time. 49. Keep things simple and don't swing for the fences. When promised quick profits, respond with a quick "no”. 50. Lose money for the firm, and I will be understanding. Lose a shred of reputation for the firm, and I will be ruthless. 51. Many management teams are just deciding they're gonna buy X billions over X months. That's no way to buy things. You buy when selling for less than they are worth. ... It's not a complicated equation to figure out whether it is beneficial or not to repurchase shares. 52. The difference between successful people and really successful people is that really successful people say no to almost everything. 53. Most people get interested in stocks when everyone else is. The time to get interested is when no one else is. You can't buy what is popular and do well. 54. Never invest in a business you cannot understand. 55. Your premium brand had better be delivering something special, or it’s not going to get the business. 56. One can best prepare themselves for the economic future by investing in your own education. If you study hard and learn at a young age, you will be in the best circumstances to secure your future. 57. The most important thing to do if you find yourself in a hole is to stop digging. 58. One thing that could help would be to write down the reason you are buying a stock before your purchase. Write down "I am buying Microsoft at $300 billion because..." Force yourself to write this down. It clarifies your mind and discipline. 59. Only when the tide goes out do you discover who's been swimming naked. 60. Opportunities come infrequently. When it rains gold, put out the bucket, not the thimble. 61. Price is what you pay. Value is what you get. 62. 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. 63. Risk comes from not knowing what you're doing. 64. If a business does well, the stock eventually follows. 65. Since I know of no way to reliably predict market movements, I recommend that you purchase Berkshire shares only if you expect to hold them for at least five years. Those who seek short-term profits should look elsewhere. 66. Someone's sitting in the shade today because someone planted a tree a long time ago 67. The best thing that happens to us is when a great company gets into temporary trouble... We want to buy them when they're on the operating table. 68. Speculation is most dangerous when it looks easiest. 69. Stay away from it. It's a mirage, basically...The idea that it has some huge intrinsic value is a joke in my view. 70. The best chance to deploy capital is when things are going down. 71. The stock market is a no-called-strike game. You don't have to swing at everything -- you can wait for your pitch. 72. There is nothing wrong with a 'know nothing' investor who realizes it. The problem is when you are a 'know nothing' investor but you think you know something. 73. This does not bother Charlie and me. Indeed, we enjoy such price declines if we have funds available to increase our positions. 74. Too-big-to-fail is not a fallback position at Berkshire. Instead, we will always arrange our affairs so that any requirements for cash we may conceivably have will be dwarfed by our own liquidity. 75. There are all kinds of businesses that Charlie and I don’t understand, but that doesn’t cause us to stay up at night. It just means we go on to the next one, and that’s what the individual investor should do. 76. You can’t buy what is popular and do well. 77. We never want to count on the kindness of strangers in order to meet tomorrow's obligations. When forced to choose, I will not trade even a night's sleep for the chance of extra profits. 78. We will reject interesting opportunities rather than over-leverage our balance sheet. 79. We've long felt that the only value of stock forecasters is to make fortune tellers look good. Even now, Charlie and I continue to believe that short-term market forecasts are poison and should be kept locked up in a safe place, away from children and also from grown-ups who behave in the market like children. 80. What is smart at one price is stupid at another. 81. What we learn from history is that people don't learn from history. 82. When stock can be bought below a business's value it is probably the best use of cash. 83. When trillions of dollars are managed by Wall Streeters charging high fees, it will usually be the managers who reap outsized profits, not the clients. 84. When we own portions of outstanding businesses with outstanding managements, our favorite holding period is forever. 85. When you have able managers of high character running businesses about which they are passionate, you can have a dozen or more reporting to you and still have time for an afternoon nap. Conversely, if you have even one person reporting to you who is deceitful, inept or uninterested, you will find yourself with more than you can handle. 86. Whether we're talking about socks or stocks, I like buying quality merchandise when it is marked down. 87. Widespread fear is your friend as an investor because it serves up bargain purchases. 88. You are neither right nor wrong because the crowd disagrees with you. You are right because your data and reasoning are right. 89. You can't borrow money at 18 or 20 percent and come out ahead. 90. You can't produce a baby in one month by getting nine women pregnant. 91. The most important quality for an investor is temperament, not intellect… You need a temperament that neither derives great pleasure from being with the crowd or against the crowd. 92. You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with 130 IQ. You only have to be able to evaluate companies within your circle of competence. 93. The size of your circle of competence is not very important; knowing its boundaries, however, is vital.

Compounding Quality

621,113 views • 3 years ago

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

15,248 views • 10 months ago

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

102,145 views • 9 months ago

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

415,691 views • 4 months ago

An economics masterclass by Alex Neil on #ScottishPrism 8/9/24 "The SNP have dug their own hole, because in 2015, on the recommendation of the Smith Commission, they signed a thing called the fiscal framework with the UK government. They've signed a new one just a couple of years ago, and if you read the detail of that, and I don't think Nicola understood what she had signed, quite frankly, that it's a trap. It's an economic suicide note. I'm not going to go into all the technical details with you just now, but believe you me, nobody, I think, should have signed in any way whatsoever that fiscal framework, either the original one in 2015-16, or the second one that was signed, I think it was last year or the year before, because it puts constraints on what the SNP can do to tackle the economic and social challenges that Scotland faces. However, I think this is the opportunity the SNP government has to get back on track. Now, I think the budget on the 4th December, which Shona Robison is due to deliver, should do three things. First of all, instead of making cuts and stopping promises... programmes like the free school meals for primary six and seven, we should not make those cuts to vital services. Instead, we should raise the additional revenue required to fulfil those promises and deliver those services. And there are two candidates for that. First of all, put an emergency land tax on the top 6 or 7 thousand landed estates in Scotland. Let's say £100 pounds an acre a year. And you could do that very quickly because you're talking about a small number of people. You could probably do it from April next year, October at the latest. So if you put that tax on, it could bring in up to £1.5 billion pounds a year. That would mean you would not need to implement any of these cuts and you could deliver on free school meals and other things besides. So that's the first thing. And that would be a very popular tax. I would actually use some of that money to reduce the income tax burden on the lowest income, lowest earning people, because they're paying too much proportionately in income tax compared to the richer people. So that's number one. Number two, if we had properly negotiated the offshore wind farm deals in the way that other countries have done. For example, if you take the European example, Denmark recently announced around, I think it was Denmark, where they took 20% of a government stake in all the offshore wind farms. We didn't take any stake whatsoever. Now, if we'd done the European average, we would have about £16 billion pounds in the Scottish Government's coffers coming in, had we just followed the example of what's been done elsewhere. Had we charged as much as what the UK Government did for the offshore wind farms off the English coast, we would have had £28 billions over a period of years coming in. Now, even if you take the lower figure of £16 billion, but particularly if you take the higher figure of £28 billion, then far from having a problem, you put that into a 'National Wealth Fund' for Scotland. You could dual the A9, dual A96, sort the A82, sort a whole host of, you know, housing issues, house-building programmes and all the rest of it, over a period, say of five to ten years. But get started now. So I would do two things. First of all, I would speed up the next round of offshore licences and this time do a deal that's going to be a benefit, real benefit, to the Scottish people. There's a review going on of the Crown Estates, I would take over the Crown Estates. If I was the First Minister or Finance Minister, I would haul them in and tell them what they're bloody well going to do in the interests of the Scottish people this time. And I would also say to the King, I don't think he should be getting a quarter of the profits from the Crown Estates in Scotland, quite frankly, he's got enough to keep them going. And the quarterly piece of the slice that he takes, should come back to the Scottish people. So that would be the first thing. The second thing is, I would review the current licence that we have issued in far too generous a basis, by the way, because part of the contract and the licence says that they must make a serious effort to manufacture the blades and other materials in Scotland. Now, the evidence is that that's not happening. Most of the blades seem to be getting manufactured in China or elsewhere. So I would be going to these companies and saying, and I did this as a health minister in PFI contracts. For example, we reviewed the PFI contract at Larbert Hospital. This is 10-12 years ago, and the contractor wasn't keeping to the terms of the contract, so we actually recovered millions of pounds on that contract as a result of their breach. They didn't even challenge us on it. The first instalment was, I think, £7 million pounds back, you know, from them, because they recognised that they had not kept to the contract. So if they've not kept to the contract, we are entitled to go back to them and see those of you who have not been building your downstream supply chain in Scotland, or enough of it in Scotland, you've breached the contract, therefore the licence is terminated unless you agree not only to keep to that promise to build the supply chains in Scotland. But also, we're changing the contract so that from now we get an annual sum from you that we can spend as a Scottish Government in whatever we want to spend on in Scotland. And that might bring in only initially £500 million quid, but it's £500 million quid we don't have, and that £500 million quid could, again, prevent cuts. And the third thing I would do is, I would say to the Scottish Government, when it comes to the spending side, it's high time you get your priorities right. Let me give you an example. In the run-up to last December's Scottish Government budget, the Greens refused to take a penny out of what's called the Active Travel Budget, which is mainly spent on cycling lanes, and said, apparently they threatened to leave the government and resign from the Scottish Government if the Scottish Government took any money off the, I think it was about £200 million at that time, the Active Travel Budget. I personally would have said, well, there's the door. Because what happened was, they kept - I think it's actually down to £160 million now, - but they kept that level of money in what was a cycle lane fund. I'm not against cycle lanes, but they took £200 million off the housing budget. Now, if you're getting economic growth, tackle climate change and tackle child poverty, you need a massive investment in housing. And when it comes to priorities, housing is a far, far more important priority than cycle lanes, including for climate change, it's more important. It makes a far bigger contribution to reducing carbon emissions and... carbon efficiency than cycle lanes do, and it does it in a far better way. So I think that £200 million, which is still off the housing budget, take that money from the cycle lane budget and put it back into the housing budget. It's just one example of where do you get your priorities right. And the final comment in the budget I'd make is this. There is a call on the press, not just the right-wing press, by the way, to means test Social Security benefits. Why do we not test all the handouts the Scottish Government gives to the big landowners, for example, including, I think, the King, in relation to Balmoral? I mean, there's a project called the Peatland Restoration Project, subsidising large corporate companies and large landowners for peatland restoration. We should just pass a law saying that it's a statutory duty. They've got to do it. They've got the money in their own pockets to do it. Why are we spending billions over a period of years getting folk to plant trees? Why don't we just tell them and strengthen the land reform bill, so we've got the powers just to tell them that they've got to do with their land what is necessary for the Scottish interest. Some on the edges might, small farmers and the like, might require a bit of a subsidy, but these people who are mega rich, these corporates, are now buying up the land in Scotland. They don't need the subsidy. So why don't we means test them instead of means testing the poor? And that would be popular in Scotland. So there's three or four measures I would take if I was Shona Robison in December. I regret to say, Roddy, I don't think any of that will happen, because they don't have the backbone to do it. They don't seem to have the intellectual capacity..." from #ScottishPrism with Barrhead Boy (Through A Scottish Prism) and eva comrie Phil Boswell & special guest Alex Neil - ++Watch Again++ 🎥

ScotNews

22,901 views • 2 years ago

Charlie Munger spent 50 years studying why intelligent people make catastrophically stupid decisions. It is the most useful thing I have ever watched: 1. Incentives are more powerful than anyone thinks. Munger says he has been in the top 5% of his age cohort his entire life in understanding the power of incentives and he has still underestimated it every single year. Federal Express could not get their night shift to work efficiently until someone realized they were paying by the hour. They switched to paying by the shift. The problem disappeared immediately. 2. People rationalise terrible behavior when their incentives point that way, and they do not even know they are doing it. A doctor in Nebraska was removing perfectly healthy gallbladders for years. When Munger asked an old colleague whether the doctor knew he was harming patients, the answer was no. he genuinely believed the gallbladder was the source of all medical evil and that removing it was an act of love. That is incentive-caused bias at its most extreme. 3. Psychological denial is real, and it is not just for weak people. A family friend's son flew off a carrier in the North Atlantic and never came back. His mother, a completely sane woman, simply never believed he was dead. Reality was too painful, so she distorted it until it was bearable. Munger says we all do this to some extent, and it causes terrible problems. 4. Consistency and commitment tendency are one of the most powerful forces in the human mind. Once you have stated a position publicly, you are psychologically locked into it. Max Planck said the really important new physics was never accepted by the old guard. A new guard came along that was less brain blocked by its previous conclusions. If this happened to the deans of physics, Munger says, imagine what it does to ordinary people. 5. The Chinese brainwashing system used on prisoners of war worked better than torture. They did not start with big demands. They maneuvered people into making tiny little commitments and declarations and slowly built from there. The same mechanism operates in every cult, every sales system, and every ideology that gets deeply embedded in people's heads. 6. Pavlovian association shapes buying behavior at a level most people never consciously process. Munger estimates three quarters of all advertising works on pure Pavlov. Coca-Cola does not want to be associated with funerals. They want to be associated with the Olympics, wonderful music, heroics. The association itself changes how people feel about the product at a subconscious level. Raising the price of a product can actually increase its market share because price and quality are associated in the human mind, and people use price as a signal of value. 7. Persian messenger syndrome is alive and running every major organization. The Persians killed the messenger who brought bad news. Bill Paley in his last 20 years, did not hear one thing he did not want to hear. everyone around him knew bringing bad news was dangerous. The result was that one of the most powerful men in media made terrible decisions for two decades because reality never reached him. 8. Social proof causes otherwise intelligent people to follow each other off cliffs. When one oil company bought a fertilizer company in the 1970s, practically every other major oil company rushed out and did the same. There was no rational reason for oil companies to own fertilizer companies. But if Exxon was doing it, it was good enough for Mobil. Every single acquisition was a disaster. 9. The efficient market theory persisted in academia for decades despite Berkshire Hathaway existing as a living contradiction. One economist kept adding sigmas to explain away the anomaly. two sigma, then three, then four, eventually six sigma. Munger's observation: It is better to add a sigma than change a theory just because the evidence comes in differently. That economist later went into money management himself and sank like a stone. 10. Contrast bias warps perception constantly and invisibly. Put your hand in hot water, then room temperature water. It feels cold. Put your hand in cold water, then room temperature water. It feels hot. same bucket. The human sensory apparatus has no absolute scale, only a contrast scale. Real estate agents exploit this deliberately. They show you two overpriced, awful houses first, then take you to a merely overpriced house, and it feels like a bargain. 11. The frog in slowly heating water is the business version of contrast bias. If something bad comes to you in small pieces, you are likely to miss it entirely. Munger says he has known many high-powered brilliant businessmen who were destroyed this way. not because they were stupid but because each incremental change was too small to trigger alarm. The contrast was never large enough to notice. 12. Authority bias is so powerful it can make trained professionals watch a plane crash. In flight simulator experiments, when the pilot, the authority figure, does something that any trained co-pilot knows will crash the plane, 25% of the time, the co-pilot sits there and lets it crash anyway. They have been trained to know better. The authority relationship overrides the training. 13. Deprivation super reaction syndrome explains why people go insane over small losses. Munger's neighbor had a 180 degree view of the harbor. the neighbor put in a pine tree about 3 feet high that turned it into a 179 and three-quarter degree view. They had a blood feud that went on for years. The New Coke disaster is the corporate version. Coca-Cola told customers they were changing a flavor and triggered a deprival super reaction so powerful that Pepsi was weeks away from releasing old Coke in a Pepsi bottle. smart engineers. brilliant lawyers. armies of psychologists. All missed it. 14. Envy and jealousy are far more powerful than greed and almost entirely absent from psychology textbooks. Munger says Warren Buffett has said half a dozen times that it is not greed that drives the world but envy. In a thousand-page psychology textbook, the index entry for envy and jealousy is blank. One of the most powerful forces in human behavior and academia essentially ignores it. 15. Gambling addiction is not explained by variable reinforcement alone. Skinner thought he had fully explained gambling by showing that variable reward schedules pound in behavior more powerfully than fixed ones. But the people who design modern slot machines know things Skinner did not. Lotteries where you pick your own number get far more play than lotteries where the number is assigned to you. People who commit to a number believe it has more validity because they chose it. Near misses on slot machines trigger deprival super reaction syndrome. It is four or five psychological tendencies working together, not one. 16. The most dangerous situations are when multiple psychological tendencies combine toward the same end at once. Munger calls this the lollapalooza effect. Tupperware parties use four or five tendencies simultaneously. Moonie conversion methods combine multiple tendencies and work extraordinarily well. alcoholics anonymous achieves a 50% no drinking rate when everything else fails because it also combines multiple tendencies toward a constructive end. The Milgram experiment is not just about obedience. it involves authority bias, consistency and commitment tendency, and contrast effects all working together. That combination turns human brains into mush. 17. Boards of directors are structurally designed to fail as corrective mechanisms. The top executive is the authority figure. He is doing something questionable. You look around, and nobody else is objecting, which is social proof that it is fine. He flies you around in the corporate jet and raises your director fees every year, which triggers reciprocation tendency. Munger's rule: boards only act when the behavior gets so bad it starts making them look foolish or threatens legal liability. That is the only forcing function that reliably works. 18. John Goodfriend of Salomon Brothers destroyed his career and reputation because he did not fire a trusted employee who had lied to the government. Every psychological tendency pointed toward keeping the man. He was a close colleague. His wife was known. He was part of a group that had made over a billion dollars for the firm. He said he had never done it before and would never do it again. Goodfriend looked into his eyes and believed him. The man did it again. The lesson: everyone who gets caught embezzling says they have never done it before and will never do it again. That is what they all say. 19. Darwin avoided confirmation bias by deliberately seeking out disconfirming evidence. Munger says Darwin was not especially smart by ordinary standards of human acuity. Yet he is buried in Westminster Abbey. Munger studied how Darwin worked and realized he had psychological tricks worth learning. Darwin always paid extra attention to evidence that contradicted his theories. Munger started doing the same and credits it as one of the most important intellectual habits of his life. 20. Why is the most important word in communication? Carl Braun designed oil refineries with spectacular skill, and you got fired in his company if you wrote a communication without explaining why. not just who, what, where, and when, but why. Braun knew that in a complex system where things can blow up, a communication system that always explains the reason behind an instruction works dramatically better than one that does not. Forstein, the general counsel of Salomon, told Goodfriend on multiple occasions that he had to report the employee's misconduct. He explained it was the right thing to do. He never explained what would happen to Goodfriend personally if he did not. he failed to use the most powerful tool of persuasion. Goodfriend ignored him. When Goodfriend went down, Forstein went with him.

Jaynit

781,153 views • 3 months ago

Made $313 → $2,382,780 in 4 Days Using a Claude AI Bot on Polymarket. 26,738 trades. 98% win rate. Full blockchain proof. Every single trade verifiable on-chain. I've made the exact step-by-step guide to build this Claude Polymarket bot from scratch. You've been trading for 3 years. Still red. He gave Claude $313. Woke up rich. Free for 24 hours. To get this Setup guide: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) Full 2-hour video tutorial attached. Every single click and command explained. Beginner to running bot. Now let me break down exactly how this works. Save this post. This is the most important trading breakdown you'll ever read. ↓ Let's start with the number that should make you sick. $313. That's what this wallet started with. Not $50,000. Not $10,000. Not even $1,000. $313. Less than your monthly Netflix + Uber Eats + Spotify combined. 4 months later: $2,382,780.80. That's a 7,942x return. While you spent those same 4 months staring at charts, drawing trendlines, panic selling, revenge trading, and ending the month exactly where you started. Minus the $200 you lost on that "sure thing." Same 4 months. Same market. Same opportunities. He had a bot. You had feelings. Guess who won. Save this post right now. What I'm about to explain is the exact mechanism behind every dollar of that $2.38M. Follow Himanshu Kumar so you don't miss the rest. ↓ How Polymarket actually works and why bots print money on it. Polymarket is a prediction market. Will BTC be higher in 15 minutes? Yes or No. Will the Fed raise rates? Yes or No. You buy shares between $0 and $1. If you're right, your share settles at $1. If you're wrong, it settles at $0. Simple. Now here's where it gets interesting. Polymarket updates its prices SLOWER than the real market moves. When BTC drops 0.6% on Binance, Polymarket still shows old odds for about 2.7 seconds. 2.7 seconds. In those 2.7 seconds, the bot already knows the outcome. It's not predicting. It's not guessing. It's reading information that already exists and trading before Polymarket catches up. That's not trading. That's collecting free money with a 2.7 second head start. And you're over there using a 15-indicator TradingView setup trying to "predict" where BTC goes next. The bot doesn't predict anything. It just reads faster than you. That's the entire edge. Save this post because if you understand this one concept you understand how millionaires are being made on Polymarket right now. Follow Himanshu Kumar for more breakdowns like this. ↓ Let me walk you through one single trade. A new 15-minute BTC contract opens on Polymarket. Odds are 50/50. Fair price. 10 minutes in, BTC drops 0.6% on Binance. Hard, fast move. The real probability of BTC being lower at expiry is now about 78%. Polymarket still shows 54/46. The bot sees this instantly. Binance WebSocket feed. Under 50ms latency. The edge is 24 percentage points. On a binary contract, that's basically free money. Bot calculates position size using Kelly Criterion. Executes via Polymarket's API. Done. Within 2-3 seconds, other participants update the odds. 54/46 moves toward 78/22. Bot either exits for immediate profit or holds to resolution. Either way, the trade was entered with near-certainty of a positive outcome. Now repeat this 200-500 times per day. $313 → $2,382,780 in 4 months. Not magic. Not prediction. Not luck. Industrial-scale exploitation of a market inefficiency that still exists today. And you're still placing one manual trade per day and calling yourself a "trader." This is the mechanism behind every single dollar. Bookmark this post so you can study it again. Follow Himanshu Kumar because I'm breaking down each strategy separately. ↓ There are 4 strategies. Not all Claude bots do the same thing. Strategy 1: Latency Arbitrage. Win rate: 85-98%. What 0x8dxd used. Monitor Binance price feeds. When Polymarket odds lag behind reality by 3-5%, buy the correct side before the market corrects. No forecasting. No model. No sentiment analysis. Pure speed. You're not guessing. You're reading an outcome that has already happened. Strategy 2: Oracle Arbitrage. Win rate: 78-85%. Chainlink oracle price feeds occasionally diverge from Polymarket's implied prices. When they do, the settlement direction is known. Fewer opportunities. Higher certainty when they appear. Strategy 3: News-Driven Trading. Win rate: 60-75%. Claude ingests real-time news. Government filings. Central bank statements. On-chain data. Assesses probability impact before retail traders even finish reading the headline. Lower win rate because interpretation introduces uncertainty. But works on ANY market category, not just crypto. Strategy 4: Market Making. Return: 2-5% per month. Place buy and sell orders on both sides. Capture the spread. No prediction required. Most consistent. Hardest to blow up. Compounds aggressively over time. You didn't even know there were 4 strategies. You thought "trading bot" meant one thing. That's how far behind you are. 4 strategies. 4 different risk profiles. 4 ways to make money while you sleep. Save this post. Follow Himanshu Kumar for the deep dive into each one. ↓ The timeline that should haunt you. December 2025: Bot launches with $313. Nobody notices. January 6, 2026: Wallet hits ~$438,000. 140x in 30 days. 6,615 predictions. 98% win rate. Finbold reports it. Crypto Twitter explodes. March 10, 2026: Head-to-head test. Claude bot: $1,000 → $14,216 in 48 hours. +1,322%. OpenClaw bot: fully liquidated. Same market. Same timeframe. Claude won because of better risk management. OpenClaw died because it overleveraged. March 16, 2026: Someone trains a swarm model on 3 years of NBA data. Result: +$1.49M on Polymarket. April 2026: 0x8dxd final verified balance: $2,382,780.80. 26,738 trades. 4 months. This all happened while you were "waiting for the right time to start." The right time was December 2025. The second best time is right now. But you'll probably wait until it's too late. That's what you always do. Every date on this timeline is a day you could have started but didn't. Save this post. Follow Himanshu Kumar so you at least start today. ↓ Why Claude and not ChatGPT? This isn't opinion. It's data. March 2026 head-to-head: Claude bot: +1,322%. OpenClaw (GPT-based): liquidated. Same prompt. Same market. Same conditions. Researchers found Claude's code included: > More defensive edge cases > More conservative default parameters > Better error handling > More legible code for debugging > Proper Kelly Criterion position sizing > Hard drawdown kill switches ChatGPT's code overleveraged into a losing sequence and couldn't recover. Claude's code sized positions conservatively, stopped trading when drawdown thresholds hit, and survived to compound another day. The difference between +1,322% and liquidation wasn't the strategy. It was the risk management. And Claude writes better risk management than ChatGPT. That's not a debate. That's a $15,216 difference in 48 hours. But sure, keep using ChatGPT because "everyone uses it." Everyone's broke too. Coincidence? Stop using the popular tool. Start using the profitable one. Save this post. Follow Himanshu Kumar for more Claude vs ChatGPT comparisons with real data. ↓ Why humans lose to bots. Every single time. Same strategy. Same market. Same period. Bots: ~$206,000 profit. Humans: ~$100,000 profit. 2x gap. Same strategy. Here's why: 1. Late entries. By the time you identify the lag, verify your reasoning, and click buy, the 2.7 second window is gone. The bot executes in under 100ms. You execute in 30 seconds. The opportunity doesn't exist for 30 seconds. 2. Emotional sizing. You oversize when "confident." Undersize when scared. Exact opposite of Kelly math. The bot sizes based on edge. Every time. No feelings. 3. Fatigue. You make worse decisions at hour 6 than at hour 1. The bot makes the same decision at hour 72 that it made at hour 1. 4. Drawdown psychology. After 3 losses you either panic quit or double down trying to recover. Both destroy capital. The bot has a kill switch. It stops. It doesn't feel anything. You're not competing with other humans anymore. You're competing with machines that don't sleep, don't feel, don't flinch. And you're losing. The data doesn't lie. Humans lose to bots 2x on the same strategy. Save this post. Follow Himanshu Kumar for the complete bot setup that removes you from the equation. ↓ What can go wrong. Because I'm not going to lie to you. Most people who build this bot will NOT 7,942x their money. Some will lose their initial capital. Here's what can kill you: Edge compression. The arbitrage window was 12 seconds in 2024. It's 2.7 seconds now. It's shrinking. At some point it hits zero for retail operators. This is a time-limited opportunity. Not a permanent income stream. Rule changes. Polymarket can change contract mechanics, settlement rules, or API terms overnight. What worked yesterday can lose money tomorrow. Risk management bugs. A 98% win rate strategy with broken position sizing will blow up your account on the one losing trade. The March 2026 experiment proved this. Claude survived. OpenClaw got liquidated. Same strategy. Different risk management. That's why the 2-hour video tutorial walks through every single risk parameter. Because the strategy doesn't kill you. Bad risk management kills you. This is the section most "gurus" delete. I'm keeping it because I'd rather you make money safely than blow up and blame me. Save this post. Follow Himanshu Kumar for honest breakdowns, not hype. ↓ The step-by-step to build your own. Step 1: Set up a Polymarket wallet. Fund with USDC via Polygon network. Start with $100-$300 for testing. Step 2: Generate API credentials. CLOB API key from docs.polymarket .com. Store private key in environment variable. Never hardcode it. Never share it. Step 3: Prompt Claude to build the bot. Use Claude Code for best results. It reads your filesystem, executes code, and iterates on errors autonomously. Step 4: Paper trade for at least one week. Minimum 200 completed trades. Win rate must be above 70% before going live. This step is NOT optional. Step 5: Configure risk management. Max single position: 8% of portfolio. Daily loss limit: -20% with auto halt. Kill switch at -40% drawdown. Telegram alerts on every threshold. Step 6: Go live small. $1-5 per trade. Watch every trade for first week. Compare to paper results. Scale only on evidence. Skip steps 4 and 5 and you will lose your money. That's not a warning. That's a guarantee. This is your complete build guide. Save this post. Follow Himanshu Kumar because I'll be posting the exact Claude prompts for each strategy. ↓ The edge exists right now. Not next month. Not "when you're ready." Right now. The arbitrage window is 2.7 seconds. It was 12 seconds in 2024. It's shrinking every week. Every day you wait, more bots enter the space. The window gets smaller. Your potential returns get smaller. The bots already running have a compounding advantage. They're making money today that they'll use to make more money tomorrow. You're reading about it and telling yourself "I'll look into this next weekend." That's what you said last weekend. And the weekend before that. The best time to start was 6 months ago. The second best time is today. But you already know you're going to bookmark this and never open it again. Prove me wrong. ↓ Full 2-hour video tutorial attached. Every single click. Every command. Every parameter. From zero to running bot. Beginner friendly. Nothing skipped. A similar bot has already earned $2,382,780. Full blockchain proof in the article below. The video is free. The tools are free. The edge still exists. The only thing that costs money is another month of doing nothing while bots eat every opportunity you're too slow to catch. Follow Himanshu Kumar for the complete series covering every automated income stream using Claude. Prediction markets are just the beginning. Save this post. Bookmark it. Screenshot it. Whatever you need to do so you actually watch the video and build the bot instead of just reading about people who did. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

53,994 views • 6 months ago

My fellow Kenyans, Many of you have seen my recent posts about the deadly cancer that is corruption in our country. In my last post, I tried to paint a picture of the disconnect between our potential as a country and the economic circumstances we find ourselves in today, and the connection between corruption and the incalculable pain and suffering and cruelty that is meted out every single day to the most vulnerable among us by thieves operating out of public office. And after covering the goings-on in Mandera County, I told you that in my honest opinion, our governments exist to cater for the filthy-rich lifestyles of the vilest and most corrupt among us, at the expense of everyone else. I received tremendous support from all of you, for speaking on behalf of so many struggling Kenyans who don’t have a voice, or the audience necessary to spark the much-needed discussion about where we are heading as a country. But even with all that support, I have received messages asking me to be careful. One compatriot told me: “prepare to be relentlessly pursued, threatened, enticed, guilt-tripped, and gas-lit”. This is from a someone who knows how our government operates, and how it uses violence and its monopoly on power to silence those who question why politicians are stealing so much. I am not naive about the dangers of speaking up and calling out thieves who control state machinery, and who possess the ability to shut me up in a few seconds. But I will tell you why we CAN NOT and MUST NOT keep quiet. In November of 2023, I stumbled upon the story of a young man from Turkana, Calvin Esekon Esewit , who, despite scoring an A-, and getting an acceptance into medical school, spent two years not knowing whether his dreams of becoming a doctor would ever come true. I was moved by that story in a way that I can never adequately explain. I could not understand how it is possible that, in our country, a young man who appears to be every parent’s dream child can spend two years in limbo while we as a country possess the ability to invest in our best and brightest. And so, I spent weeks trying to chase down Calvin to see how I could help him attend college. After a lot of searching, I finally found Calvin, and by this time he had managed to get some help and is now in college. While this story has a great ending, it did not to be this way. And we know that the number of cases that end like this, with some success, are a small fraction of those ones which end tragically, with broken dreams. This is what happens when corruption consumes anything and everything in a country. It destroys lives. See attached video to learn about Calvin's story. I tell you all this story because it provides context to today's topic. For one story like this one that you see on the news, there are millions that never make the news. But they are real situations, nonetheless. There are millions of your compatriots who are devastated by this killer cancer of corruption that is perpetuated by people that you and I have put into public office ostensibly to improve our lives. They go into these offices and abuse the trust you bestowed upon them and deny you and everyone else a decent opportunity in life. You see, Calvin and millions of other victims of this shameless level of corruption and plunder have no voice, and no real ability to look the thieves that are destroying lives and generations of Kenyans in eye and tell them to stop this unbearable pain and the cruelty. This is the reason I embarked on this journey to attempt to expose this shameful situation. Watch the attached video of Calvin’s situation, and I am sure that you will agree that the millions of Calvins in our country need a voice, NO MATTER THE RISK. The thieves that are destroying the futures of millions of children just so they can have beachside homes in Miami, Dubai and other places count on the idea that most people will fear for their lives, and therefore not speak up. They count on the growing apathy in the Kenyan psyche. But we cannot give in to that. We cannot cower to thieves. We must look them straight in the eye and tell them that they MUST STOP. If we don't, our children and their children are guaranteed the same level of cruelty. And so with that, today I want to talk about the utterly insane crime scene that is Turkana County. I don’t know any other way to describe it, other than, it is a “shit-show”. Just follow along, and let me know if you disagree. As I did in my previous commentary, I will ask you to indulge me a little bit, and allow me to use a couple of pictures, because pictures speak louder than a thousand words. The first picture shows the state-of-the art County Government offices, that the County Government of Turkana decided to invest an ungodly amount of money on. Close to a billion shillings. The second picture is a classroom in session. In Turkana County. These two realities are occurring in parallel in the same county, at the same time. Ladies and gentlemen, let me just tell you that I do not go out of my way to find bad news. I want stories that would help re-affirm our belief in the fundamental decency of human beings. When I find good news as I review these Counties’ decisions and how they behave with our resources, I will be the first one to report it to you. But I don’t have any good news today. I have bad news. If you read my commentary yesterday and were offended by what you saw, I am afraid you might not make it to the end of this article, because what you will hear will be quite shocking. The cancer of corruption, particularly at the County Government level, is worse than your wildest imagination. And so, as I like to do, I like to start off by putting some numbers on the table for us to use as reference points. Bear in my that all the information I put in this article is publicly available. Nothing came to me through a whistle blower. The first number is KSH 100 Billion. With a B. In the last decade or so, you and I, through the National Government, has sent over KSH 100 billion to Turkana County. To support recurrent expenditure, and development. For example, in the 2022-2023 fiscal year, we sent KSH 12.6 billion. In the 2021-2022 fiscal year, we sent KSH 11.4 billion. And on and on and on. The second number is 1 million. This is the population of Turkana County. The third number is KSH 18.4 billion. This was Turkana County’s budget for the 2022-2023 fiscal year. The fourth number is KSH 190 million. This was the amount of money that Turkana County was able to generate on its own accord within the county, from all its investments and other activities in the period in question. This number is an important proxy, in my view, for the value of the county’s economic prospects for the foreseeable future, and to people that are not driven by greed and corruption, would be an important consideration when they are thinking about how and where to deploy your money as taxpayers. If you are doing the math, Turkana County, for the 2022-2023 fiscal year, was only able to raise 1% of the funds needed to keep the lights on. 99% came from you and I, and a tiny amount from grants. The next number is KSH 129, 040. This is the average ANNUAL [emphasis added] income of a resident of Turkana County ( Keep that number in mind when we are discussing the massive theft of public funds by Turkana County leaders. The next number is 80%. 80% of the residents of Turkana County live below the poverty line. They have a really difficult time putting food on the table. ( The next number is KSH 12 Million. This is the basic salary of the Governor of Turkana County before other benefits that, as I explained yesterday, can often double the salary. Remember the “housing allowance”, the “hardship allowance”, the “commuter allowance”, the “risk allowance”, the “extraneous allowance”, etc.? Remember that? I still cannot figure out, for the life of me, what “extraneous” means in the context of County business, but we don’t time to dwell on this. The next number is 93. The Governor of Turkana County makes 93 times the average Turkana County resident’s annual income. 93 times! The next number is 82%. This was the percentage of people that were illiterate in Turkana County in 2013 ( Could not read or write. A point to note about the above literacy figure. Ten years later, and despite over KSH 100 billion is spent in Turkana County, including many billions for education, that literacy rate HAS NOT CHANGED ONE BIT. Only 20% of the population can read or write today. ( KSH 829 million. This is how much it cost to build the County Government offices. Yes, the ones shown in the first picture. KSH 120 million. The County Government decided that it was prudent to pay a contractor KSH 120 million to construct the Governor’s personal residence. Get this, even after this payment, no construction took place. The money was stolen. All of it. KSH 90 Million. This is the amount that the County Government paid to another contractor, to build the Governor a mansion, having previously lost KSH 120 million. So, the tally for the Governor’s residence now stands at KSH 210 million. Never mind that the limit allowed by law is KSH 45 million. KSH 5 billion. In the last days of his term in office, an outgoing Governor of Turkana, Koli Nanok, EGH. , sought to inflate pending bills by adding KSH 5 billion so that it can be paid to his criminal cartel. KSH 5 billion. We have our key numbers, ladies and gentlemen, so let us discuss. So, we have a county that is dead last in literacy, and in the top 2 of the poorest counties in the republic. Only 20% of the population can read. The Governor earns 92 times the average citizen. The Governor lives in a house that cost over KSH 200 million. When he leaves his house in the morning, he goes to his office that cost KSH 829 million. And this is all happening when 80% of the County residents struggle to put food on the table. Those are the facts, and they are not in dispute. During the same time, the County Government geniuses decide to build the Speaker of the County Assembly a house. And a home office, and a garage. The house was initially estimated to cost KSH 75 million. But due to circumstances that not a soul in the government could explain to auditors, the contract expired before the house was completed, and the County Government found a new contractor to complete the job for an additional KSH 29 million. But this palace in the jungle worth apparently worth over KSH 100 million in Turkana County was not enough. The County proceeded to build the Speaker a guest house for another KSH 19 million, and a few other amenities, and so the whole cost went to KSH 276 million! The legal limit for a Speaker’s house is KSH 35 million, and they spent close to KSH 130 million just for one residence. By this time, I am sure you are getting tired of these obscene numbers. You and I work, and pay taxes. Nobody pays you 92 times the income your average neighbor is making. And for sure nobody will drop KSH 100 million to build you a house. These are the perks of working in government in a poor country. Go figure. And so, as a country, we need to answer for ourselves the question I posed yesterday, which is, what is the point of government? What is its role in our lives. If this level of criminality and pillaging can occur in our country in the midst of so much poverty, questioning the need for government is a totally valid question. I said in my last post that, when the average citizen looks at the thug on the street and the government, and is unable to discern any meaningful difference between them, that society from that point on is on its journey to becoming a failed state. A journey to anarchy. Over the last two months or so, Kenyans have been shouting at the top of their lungs, begging for their government to listen. To hear them out. Kenyans have asked that their government stop this unbelievable level of plunder. Dozens of Kenyans have died, thousands injured, and many more are missing today. To this day, the people that govern us continue to use the power of the gun to subdue Kenyans, until they can take everything in their sight. And so, as a society, we all have to ask whether today there is any difference between the thug on the street and our governments. Every Kenyan will have to answer this question for themselves. And before answering this question, everyone needs to remember the many Calvins in our society. Smart, upright children whose only crime is to be born in an unforgiving, lawless, and corrupt purgatory that is Kenya today. For myself, I have concluded that there is no difference between the thug on the street and our governments, county and national alike. If you can see any meaningful difference, let me know. I am willing to listen. So despite over KSH 100 billion in money sent to Turkana County, there is almost no measurable improvement in people’s life today. None. And it makes sense, when you look at how that money is spent. I want you to forget for a second the obscene obsession by the County Government with spending ungodly amounts of money on themselves. The houses, etc. If you step back and look at how the government is actually spending the hard-earned money on other things, you will be depressed. I am telling you that I wept three times in the middle of the night trying to make sense of this crazy situation in Turkana County. Three times. I have never imagined that human beings can be so greedy and cold-blooded. Think about this: In the couple of years I reviewed, the County spent around KSH 400 million annually in “tourism” initiatives, including marketing, and apparently upgrading certain facilities. KSH 400 million for tourism. In Turkana County. In 1 year. KSH 400 million per year in marketing and other money pits. The government’s own website says that the county gets around 3000 visitors per month. Around 36,000 per year. That’s them saying that, on their website. Are you curious to know the return on that KSH 400 million investment? I have an answer for you. Remember that I told you that the County has never raised more than KSH 200 million in a year within the county, despite its KSH 18.4 billion budget? Let me walk you through the breakdown of the absolutely embarrassing shit-show that is the County Government’s “own source revenue” operations. In 2022-2023, the County Government collected KSH 190 million locally against their KSH 18.4 billion budget. 1% of the budget. Remember, there is absolutely no requirement on the County to cut costs, or achieve certain local revenue targets today. So they raised KSH 45 million in single business permits, KSH 72 million in CESS, KSH 8 million in market fee, KSH 9 million in “slaughter fees”. And then finally, there is the return on the tourism investment that you were looking for. A whopping KSH 209, 000 in “park fees”. KSH 209,000 in fees, after investing KSH 400 million. And so, take this as an example and extrapolate it across the entire budget, and you can see how one can spend KSH 100 billion and get NOTHING in return. You don’t need to be a genius to see the absurdity of this situation. Let me explain using an example that should illustrate the utter dimwittedness of this situation. Remember the KSH 100 billion sent to Turkana by you and me? Part of this amount is supposed to be for “service delivery”, or “recurrent expenditure”. Usually about 70% of the budget. The balance, 30%, is designed to go to development projects. With that in mind, from KSH 100 billion, the County apparently has made KSH 30 billion worth of investments, right? 30% of the KSH 100 billion. Now, if you employed someone to run a business for you, and they asked you to invest KSH 30 billion, which is no small fortune, at some point you would have to start seeing returns, right? That’s common sense, isn’t it? So, when we look at the revenues streams that make up this paltry sum of KSH 190 million, and see things like “slaughter fees’ and “market fees”, what does it tell you? It tells me there is no real “development” happening in that county. Trust me, if you had real development totaling KSH 30 billion, you would have corporate taxes in the hundreds of millions or billions, a booming real estate market, rising wages and standards of living, etc., low unemployment, etc. You would not have 80% of the people living hand-to mouth, and a County Government that can not afford to support itself for 5 days out of the year that has 365 days! We do not have enough time, trust me, to deal with the shit-show that is Turkana County. Dealing with that mess would require a forensic team. I will just highlight a few of other “in your-face” type of theft of public funds, and then conclude my submission. A government that has a budget of KSH 18.4 billion annually, and which has never raised more than 1% of its budget had the wisdom to do the following with your money: · Spend KSH 222 million on a project building something that NOBODY uses. You got that right. They spent KSH 222 million on a facility that NOBODY uses. KSH 222 million gone to waste, in a county that is dead last in pretty much all measures of human progress. · Remember the County Government offices that cost KSH 829 million? The County spent KSH 82 million on “air-conditioning” for that building. · Despite the County Spending hundreds of millions for the top three officers of the County, the Governor and his Deputy, in the 2022-2023 year, illegally charged the county (you and I) KSH 2.2 million in housing allowance! · Built two facilities for KSH 16 million, that were completed, but NOBODY uses them. · Entered into a contract for the construction of a plastic use facility for KSH 13 million in 2021. The contractor gets paid KSH 4.9 million, and has never been seen since. · Paid out KSH 62 million in salaries that were not supportable in just one year. They could not point to anybody and say, that is who we paid. · Paid out KSH 27 million in legal fees that nobody could say what they related to. And the County’s Legal Advisor, who, in 2022-2023, had a budget of KSH 123 million, apparently did not know anything about it! · Had an outstanding bill at Kenya Revenue Authority in the amount of KSH 486 million, that did not show up on the County Government’s financial statements. Think about that. KSH 486 million owned to the Kenya Revenue Authority, and that liability is not on the financial statements! This only means that someone took those funds for themselves, which is why the liability would be missing from the county’s books. · Could not account for KSH 367 million in expenditures for 2022-2023. KSH 367 million, in unexplained expenses. · Awarded a contract worth over KSH 200 million to a bidder with no bank statement, against the law. This contract was entered into and approved before the statutory time after the bidding process lapsed. Someone was in a hurry to get paid. KSH 200 million, illegally awarded to a bidder who did not have a 6-month bank statement. · Apparently purchased KSH 1.5 billion in assets in 2022-2023, but kept no records of the said assets. For this reason, NOBODY can verify where these assets are located. KSH 1.5 billion. Let me just say this. In my last article, the most common critique was that it was too long. Too many words. I did not intend to make another long article. Trust me when I tell you this, we do not have the time to detail half of the problems in Turkana County. For just 1 year! We do not. Now, you recall my point about how societies descend to madness and anarchy. In our country today, our leaders are accusing those of us who are agitating for honest and transparent governance of being traitors to the country. They call us anarchists, criminals, and merchants of chaos. They are questioning our patriotism. You have all seen the government and its horde of propagandists threatening the Ford Foundation and others because they may have helped civil society keep the lights on, and investigative journalists to have the capacity to continue to do the Lord’s work of investigating criminality in government. As though citizens are so dumb and ignorant, that they cannot see what is going on. The reason why millions of Calvins in this country will never graduate from college and earn a decent living is not because of the Ford Foundation. No. It is because of the thieves we have in office today, like the ones in Turkana County. In this post, I copy our leaders, the President and his deputy. I copy them because I want them to help Kenyans understand the following conundrum, about crime and criminals. There is nothing so special or peculiar about criminals or where they pop up. There are criminals in the US, Canada, France, and other places. Just like we have criminals in Kenya. The difference between banana republics and failed states, and civilized societies, is WHAT we do to and about criminals. In civilized societies, criminals are prosecuted and punished heavily. They are shunned. In some places, those charged with serious crimes such as corruption are executed. These are societies that are committed to sending the message that corruption, which robs citizens of their rights, is not acceptable. And they demonstrate this commitment by heavily punishing those who steal from the most vulnerable in society. In Kenya, we see the opposite. Criminals are exalted. They are promoted and embraced in government. It was just last week that the president unveiled his nominees for his Cabinet. Among them, are the likes of Hassan Ali Joho, EGH. , @GovWOparanya , and Davis Chirchir, ALL people who have been accused or charged with massive corruption against Kenyans. And am sure you remember that I mentioned Koli Nanok, EGH. , the man who tried to steal KSH 5 billion in his last days in office. Would you believe it if I told you that he works in government, at State House? He plunded billions of your money, got no measurable improvement in the lives of his subjects, and now has a government job in State House. Let that sink in. And so, the question is, how is it that in a country of 55 million people, with thousands of highly qualified people who have never ever stolen from Kenyans, he ends up with the criminals and thieves in the government, despite the fact that their crimes are in the public domain? How is this possible? Is it possible that these thieves possess a certain unique ability to run government, save Kenyans billions, and solve problems in a way that the president performs a cost-benefit analysis, and the benefits outweigh the costs of their theft? If not, what message does it send to Kenyans, when their own president puts into office known thieves? I think that is a fair question, don’t you? Dr. Ekuru Aukot Rigathi Gachagua William Samoei Ruto, PhD Okiya Omtatah Okoiti Citizen TV Kenya Nation Breaking News TI-Kenya CNN County Government of Turkana

Bonnie Mwangi, CPA, LLM, MBA

107,583 views • 2 years ago

In 1998, Warren Buffett and Charlie Munger spent 4 hours explaining why the smartest people in finance keep going broke. It might be the most valuable finance lecture ever recorded: 1. The smartest people in finance went completely broke. Long-term Capital Management had 16 people with possibly the highest average IQ of any firm in the country, 350 to 400 combined years of experience, and most of their own net worth in the fund. They still went bankrupt. Buffett said if he ever wrote a book it would be called why smart people do dumb things. 2. Life and markets have no relation to sigmas. Buffett keeps a 1901 newspaper on his office wall. Northern Pacific went from $170 to $1,000 a share in a single day when two buyers accidentally cornered the stock. A brewer who had shorted it, facing a margin call, dove into a vat of hot beer. That man probably understood sigmas and knew such a move was impossible. Buffett has never wanted to end up in the vat. 3. Beta and sigmas tell you nothing about the risk of going broke. the LTCM team relied on mathematics and believed a six- or seven-sigma event could not touch them. they were wrong. history does not tell you the probabilities of future financial events. the real risk is a permanent blind spot in something crucial, often caused by knowing a great deal about something else. 4. To a man with a hammer, every problem looks like a nail. Munger's explanation for why brilliant people do dumb things. They learn a set of mathematical techniques and then twist every problem to fit the solution they already know. Combine that with a poor grasp of history, and you get people with advanced degrees blowing themselves up. 5. To make money they did not need, they risked money they did need. That is just plain foolish, Buffett says, no matter your IQ. Hand him a gun with a million chambers and one bullet, offer any sum to put it to his temple and pull once, and he will not do it. there is nothing on the upside that justifies the downside. people do this financially all the time without thinking. 6. The major banks all had risk models and had no idea what they owned. they met weekly at risk committees, printed all the statistics in neat columns, and did not have the faintest idea what risk they were carrying. The rare and essential quality is someone who can contemplate perils that have not popped up yet, the ones no past model contains. 7. A chief risk officer often just makes you feel good while you do dumb things. munger compares him to the Delphic oracle who convinced the Persian king to attack. he has a PhD and does advanced math, but he tortures reality to defend a model that does not hold under extreme conditions. all that computation makes you feel like you clobbered the risk when you have only clobbered your own head. 8. The whole quant risk system just changed the shape of the curve and kept going. Munger notes the business schools "improved" by throwing away the Gaussian curve and drawing a different one. They talk about fat tails now, but they still have no idea how fat to make them. he and Buffett always knew the tails were there, and used to roll their eyes at the risk-control people at Salomon. 9. Never risk what you have and need for what you do not have and do not need. Buffett will not explain to his family, who hold most of their net worth in Berkshire, that they went broke on a 100-to-1 gamble. Their returns get penalized 99 years out of 100 by being too conservative, and in the hundredth year they survive when others do not. 10. Build the business so that if the world stops working tomorrow, you have no problem. Berkshire double-layers its protection. First, they behave so no rational person questions their credit, then they hold so much liquidity that if the world suddenly hated their credit, they would not notice for months. It gives up higher returns 99% of the time and survives the one time others do not. 11. The real danger is a risk that has never happened before. Buffett wants someone who can imagine perils that have not yet appeared, the ones no model contains. The major institutions all had models, and that inability to envision the unprecedented is exactly what proved fatal. He and Munger spend a lot of time thinking about things that could hit them out of the blue that others leave out entirely. 12. Investing is simple, but not easy. The framework is not complicated. you did not need a high IQ to buy junk bonds in 2002 or stocks at low multiples in 1974. you just needed the courage of your convictions and the willingness to act when everyone else was paralyzed. Following logic rather than emotion is obvious, and yet some people find it almost impossible. 13. You cannot get rich with a weathervane. Buffett and Munger pay no attention to predictions about the economy or the market. People love predictions, entire industries are built on them, but it is like the king hiring a forecaster to read sheep guts. They have never made or avoided a single business purchase because of a macro view. 14. Name one super-wealthy economist. Munger's challenge. All these economists with 160 IQs spend their lives studying markets, and you cannot find one who got rich buying securities. Even Keynes tried to predict the credit cycle, broke a couple of times, and only did well once he switched to buying good businesses cheap and concentrating. 15. Focus only on what is important and knowable. Some things are important but unknowable, like whether someone drops a nuclear weapon tomorrow. Some things are knowable but unimportant. You narrow your attention to the small set of things that are both important and knowable, and you ignore everything else. 16. The market is there to serve you, not to instruct you. This is Graham's chapter eight, and Buffett calls it enormously important. When people talk about momentum or charts, they are saying the market instructs you. It does not. It just quotes prices. When it does something silly, you get a chance to act. Otherwise you go play bridge and check again tomorrow. 17. You can make a decision in five minutes or not at all. Buffett and Munger act fast because they rule out enormous territory in advance. Munger blots out startups entirely, and half a dozen other filters, so what remains is small enough to judge instantly. If they cannot decide in five minutes, they will not learn enough in five months to make up for going in deficient. 18. You can make a lot of money on a Sunday. Buffett said the calls you get on a Sunday, when things are truly screwed up, are the ones you make money on. All you have to do is be the collie and not the caller. You never get in a position where the other party can call your tune, so you can always play out your hand. 19. You are not right because others agree with you. Ben Graham said you are neither right nor wrong because the crowd disagrees. You are right because your facts and reasoning are right. Being contrarian has no special virtue over being a trend follower. All that matters is whether the facts are correct and the logic is sound. 20. Know where the edge of your circle of competence is. Buffett says the size of your circle does not matter. Knowing its perimeter does. You do not have to understand 90% of businesses. You just have to know something real about the few you actually put money into, and honestly recognize the ones you do not understand and walk away. 21. Intrinsic value is just the cash a business will produce, discounted back. Buffett thinks of every business as a bond with coupons that are not printed on it. Your job as an investor is to estimate those future coupons. If you cannot estimate them, like in a high-tech company, you pass. Investing is putting out money to get more back from what the asset produces, not from selling it to someone else. 22. The best businesses earn a royalty and need little capital. Coca-Cola sells a formula and takes a cut of every drink. Magazines like People operate on negative capital because subscribers pay in advance. The great businesses are the ones that can grow very large while needing almost no capital, which is why consumer businesses with pricing power are so valuable. 23. You only have to find one good idea, not twenty. Munger said you cannot find twenty deeply mispriced things, and Buffett agreed you do not need to. You do not have to have tons of good ideas in this business. You just need one good idea that is worth a ton, occasionally. For small sums, Buffett said he would have been 100% in Korea a few years earlier, where great companies traded at three times earnings. 24. The trick is measuring everything against your best opportunity. Munger calls this opportunity cost, the doctrine from the first page of the economics textbook that modern portfolio theory somehow ignored. Once you have found the best thing you understand, you measure every other option against it. The higher your default option, the more you can reject. 25. Modern portfolio theory is, in Munger's words, asinine. Most people will not find thousands of equally good things. They will find a few where one or two are far better than anything else they know. The right way to invest is to concentrate on your best opportunity cost, not to diversify into mediocrity because a model told you to. 26. Big opportunities must be seized, and seized big. Buffett says imagine you got a punch card with only twenty punches for your whole life, one per financial decision. You would think hard about each one, make fewer and better bets, and probably never use all twenty. The discipline of scarcity would make you rich. Dabbling in a bull market because it is easy is how people lose. 27. America has always been full of reasons to sell, and wrong every time. Coca-Cola went public in 1919 at $40, dropped to $19 within a year, and then faced the great depression, World War, and atomic bombs. One share reinvested is worth millions now. The country's opportunities have always won out over its problems. It is investors, not the economy, who tend to be their own worst enemy.

Jaynit

104,045 views • 2 months ago

A wild bobcat. Cold, open water. A body that was never built for this. This is an animal that can kill a deer. The water was about to beat it anyway. Watch the video first. Then read on. What happens in the final seconds is the reason I can't stop thinking about it. . Some clips you watch. Some clips you feel in your chest for an hour afterward. This is the second kind, and I want to explain exactly why, because once you understand what you are actually looking at, you will never watch it the same way again. This is a long one. Every section ends with something that makes the next one worth it. Stay with me. . 01 // THE COLDEST MATH IN THE WILD Let's start with something nobody tells you about water. Water is a thief. It pulls heat out of a living body up to 25 times faster than air at the same temperature. Not a little faster. Twenty-five times. It is the reason a cold day is uncomfortable and a cold swim is an emergency. Now put that math on an animal wearing a fur coat. Fur works because of what is trapped inside it: air. Thousands of tiny pockets of warm air held against the skin. The air is the insulation. The hair is just the scaffolding that holds it in place. Soak that coat and the air is gone. What was a winter jacket becomes a wet towel wrapped around the body. It is heavy. It drags. It bleeds heat at the exact moment the animal can least afford to lose it. And swimming is not free. A cat does not swim the way an otter swims. Otters are engineered for it: dense waterproof fur, webbed feet, a body shaped like a torpedo. A bobcat is engineered for something completely different. Every stroke burns fuel it does not have, while every second in the water drains the fuel it already has. That is the trap. Heat goes out. Energy goes out. Nothing comes in. In humans, the first minute in cold water triggers what physiologists call the cold shock response: a gasp reflex, a spike in heart rate, panicked breathing. Then comes the slow part, where muscles and nerves cool down and stop doing what you tell them. Animals do not get an exemption from physics. Bodies are bodies. Survival experts who study cold water use a simple frame for humans, the 1-10-1 rule. One minute to get your breathing under control after the initial shock. Ten minutes of meaningful muscle function before your limbs start refusing orders. About an hour before hypothermia becomes the real threat to life. Now shrink the body. A smaller body has more surface area for its mass, which means it sheds heat faster. Those human numbers are a generous best case. For a 20-pound animal, the clock runs quicker. So when you see a wild animal in water it did not choose, you are not looking at a swimmer. You are looking at a countdown. Here is the part that should bother you: the animal does not know it is a countdown. It only knows that it has to keep moving, and that stopping is not an option, and that there is nothing solid anywhere. Hold that thought. Because the next thing I am going to tell you is why this particular animal is the last one you would expect to be in that position. . 02 // THE "BIG HOUSE CAT" LIE Somewhere along the way, someone decided a bobcat is basically a large housecat with attitude. Somebody was wrong. Let's do the numbers. A typical house cat weighs 8 to 10 pounds. Most adult bobcats land somewhere between 15 and 30 pounds, with big males pushing past that. That is two to three times the mass, and none of it is soft. It is wire and spring: long hind legs built to launch, shoulders built to hold, claws that retract and reset like a trap. By widely cited estimates, a bobcat can cover roughly 10 feet in a single leap. That is not a pounce. That is a projectile. It hunts the way the best hunters do: by being patient past the point where patience seems reasonable. It picks a spot. It goes still. It waits, sometimes for a very long time, until the world arranges itself into a single clean opportunity. Then it ends the situation in a second. Rabbits and hares are the staple. Mice, voles, squirrels, birds. But here is the detail that changes how people talk about them: bobcats have been documented taking down deer, animals much larger than themselves, particularly in harsh winters when deer are weakened. Think about what that means. This is a creature that routinely wins fights against animals that outweigh it. It has ear tufts that scientists still argue about. It has a ruff of fur around the face like a lion in miniature. It has a stubby tail, four to seven inches long, that gave the animal its name. It has a spotted coat that is essentially camouflage for dappled light, and, useful fact, the spot pattern is different from one animal to the next, which is how researchers identify individuals on trail cameras. And it has a voice. Purrs, hisses, growls, and a night scream that has been mistaken for a human in distress by people who did not know what they were hearing. So this is who we are talking about. Not a pet. Not a pushover. One of the most efficient small predators on the continent. Which is what makes the situation in this video so strange. Because everything I just told you about strength, speed and pride goes quiet when the ground disappears. . 03 // THE ANIMAL YOU HAVE PROBABLY WALKED PAST Here is a fact that should feel a little unsettling. Bobcats are one of the most widespread wild cats in North America. Their range stretches across the lower 48 states, into southern Canada, and down into Mexico. Forests, swamps, deserts, mountains, the ragged edges of suburbs. They are adaptable in a way that very few predators are. And yet most people, including people who have lived their entire lives near them, have never seen one. That is not an accident. Bobcats are solitary, shy, and mostly active around dawn and dusk. They live in the margins. They move through your world at times you are not looking, and they have made avoiding you into an art form. A bobcat's entire survival strategy is to see you first and never be seen. Which means any video of one, let alone a close, unhurried, unguarded look at one, is rare. Wildlife photographers wait years for angles like this. Researchers set up cameras and come back to empty memory cards. Now think about what it takes for a wild bobcat and a human being to end up in the same small space, with the animal not running. The animal has to be in a situation where running is no longer possible. That should give you chills. Because it is not a heartwarming meeting. It is what happens when a very proud animal has run out of options. And what happens next is the part that does not add up, which is exactly where the next section starts. . 04 // THE PART THAT DOES NOT ADD UP Predators do not do calm. A wild animal's nervous system is a hair trigger. When something big and unfamiliar closes in, the body floods with adrenaline and cortisol, and the decision tree is short: fight, flee, or freeze. Every instinct it has been born with says a large, upright, two-legged creature is the most dangerous thing in the environment. So when a wild animal is calm around humans, there are only a few explanations, and none of them is "it decided to trust us." Sometimes the animal is habituated, meaning it has lived near people so long it has stopped treating them as a threat. Sometimes the animal is so depleted, cold, hurt or exhausted that its body has quietly shut down the option to fight. Biologists have a name for one version of this: tonic immobility, a kind of involuntary freeze in which an animal goes still and unresponsive when escape seems impossible. And sometimes it is a mix of all of it, in ways science is still working out. Here is the line I want you to remember, because it will save you from a lot of bad internet takes: Do not confuse calm with consent. Stillness is not friendship. A quiet animal is not a tame animal. And some of the most dramatic moments in wildlife rescue come not from the fight, but from the moment the body stops fighting and everyone has to figure out what that actually means. There is even a documented condition, capture myopathy, in which the sheer stress of being restrained can damage a wild animal's muscles and organs badly enough to kill it, sometimes hours or days after the event, with no visible wound at all. Handling stress is a real cause of death in wildlife work. That is why professionals move slowly, keep things dark and quiet, and treat every second of contact like it costs something. Which means that in a situation like this, the difference between a good outcome and a tragic one can be invisible to the naked eye. That is what makes the footage so tense to watch if you know what to look for. And I am going to tell you exactly what to look for. But not yet. First I need you to understand how unlikely it was that anyone was there at all. . 05 // NATURE HAS NO AUDIENCE Almost everything that happens in the wild happens unwatched. Think about that for a second. Billions of small emergencies, every single day. Animals slipping, trapped, lost, starving, stranded. A fawn separated from its mother. A bird with a broken wing. A predator that misjudged a jump by an inch. Almost none of it is ever witnessed by anyone. No camera. No rescuer. No witness. It simply happens, and then it stops, and the world continues as if nothing occurred. For a rescue to happen, a chain of things has to line up. Somebody has to be in the right place. Somebody has to be looking in the right direction. Somebody has to notice a small, wet, dark shape in a huge moving surface, and understand what it is. Somebody has to decide to act rather than say "huh" and keep going. And somebody has to actually know how to help without making it worse. Break any single link and the story ends before it starts. Every viral rescue video you have ever seen is a chain like that, where all the links happened to hold. I think that is why these clips hit differently than almost anything else online. They are not just proof that an animal survived. They are proof that the chain can hold. That in a world that mostly does not look, sometimes someone does. The cold math says this animal had little time. The odds say nobody should have been there. And yet. I keep circling that "and yet." . QUICK PAUSE Before we go deeper, a small experiment. Without scrolling back up, answer this in your head: how long was the animal in that water before anyone noticed? Got a number? Now hold it loosely. The truth about time in a moment like this is that it never feels like what it is. Seconds stretch. Minutes vanish. Nobody in a crisis has an accurate clock. Keep your number. We will come back to it. . 06 // WHY YOUR BRAIN CANNOT SCROLL PAST THIS Let's zoom out, because this is the part that fascinates me professionally. In 2012, two Wharton researchers, Jonah Berger and Katherine Milkman, studied thousands of New York Times articles to figure out what makes online content spread. Their finding was not what most people expected. Positivity helped, but what mattered even more was how much physical arousal a piece of content produced. Awe, anger and anxiety, all high-arousal emotions, made people share. Sadness, which is low-arousal, made people stop. Now look at a clip like this through that lens. It has anxiety: will the animal make it? It has awe: this is a wild predator, close enough to see the texture of its fur. It has relief, and something bigger than relief. Psychologist Jonathan Haidt has a word for that bigger thing: elevation. It is the warm, expanding feeling you get when you witness someone behaving with unexpected goodness. It is the emotion that makes people want to be better, to call their mother, to do something kind. It is exactly the kind of emotion that makes people hit share before they have even finished watching. And then there is the mechanism behind the hook itself. In 1994, the behavioral economist George Loewenstein described what he called the information gap theory of curiosity: curiosity is the discomfort of noticing a gap between what you know and what you want to know. The bigger the gap, and the closer you feel to closing it, the harder it is to look away. That is what I did in the first three lines of this post. I gave you a wild animal, a disaster, and an unanswered question. Your brain has been carrying that open loop ever since. You cannot un-notice it. You can only close it. That is not a trick. It is how attention has worked since the first campfire story. The only difference now is that the campfire fits in your pocket and never goes out. There is one more thing, and it may be the most important one. Animals do not perform. They cannot posture for a camera, hide their fear behind a joke, or spin the story afterward. Their reactions are uncut. In an internet made mostly of people curating themselves, a wild animal reacting honestly to a moment is about as authentic as content gets. And in a feed that is ninety percent outrage, ten percent ads, and the occasional argument about sandwiches, a clip that is about competence and kindness feels like finding cold water in a desert. So no, it is not weird that you cannot look away. It would be weirder if you could. . 07 // 15 BOBCAT FACTS THAT CHANGE HOW YOU SEE THE CLIP You now know what the water does and what the animal is. Here is the rest of the file, fast. 1. The scientific name is Lynx rufus. "Rufus" means reddish. The animal has been carrying a color-coded label for centuries. 2. It is the most common wild cat in North America, which makes how rarely people see one even more remarkable. 3. Bobcats are ambush hunters, not chasers. They win by patience, not by endurance. Long chases are not their thing, and long swims are definitely not their thing. 4. Yes, bobcats can swim, and they do it when they have to: crossing rivers, fleeing danger, following prey. But it is a tool of last resort, not a hobby. 5. Kittens are born in spring, usually in litters of two to four, and the mother raises them alone. Dad is not part of the picture. 6. A kitten's eyes open at around a week and a half old. It will stay with its mother for most of its first year before heading off to find a territory of its own. 7. Young bobcats leaving home can travel dozens of miles looking for a place to settle. That means young animals are the ones most likely to end up somewhere unfamiliar, in situations nothing in their short lives prepared them for. 8. Bobcats mark territory with scent, scrapes and scratched trees. Their world is written in a language we mostly cannot read. 9. When they cannot finish a meal, they often cover the leftovers with leaves, snow or debris and come back for it later. Forward planning, from a cat. 10. They climb trees and use them for escape and lookout. If a bobcat ever wanted to get out of a situation, vertical was its first idea. 11. Their ear tufts are still debated. Camouflage? Hearing? Communication? Nobody has a definitive answer, and I love that. 12. Bobcat and Canada lynx are cousins, not twins. Lynx have longer ear tufts, bigger paws for snow, and a fully black-tipped tail. A bobcat's tail is black only on top of the tip, like someone dipped a paintbrush and stopped halfway. 13. Coat color changes by region. Desert animals run pale and sandy. Northern animals run darker and grayer. Same species, different wardrobe. 14. A bobcat is not closely related to a cougar. It belongs to the Lynx genus. The cougar belongs to another. When people call it a "baby mountain lion," a biologist somewhere loses a year of their life. 15. In the wild, life is often short and hard. Many bobcats do not make it to ten years old. In captivity, they can live far longer. Which tells you what the wild costs, every single day, on top of everything else. Fifteen facts. One conclusion. Every one of these facts describes an animal built to survive. Which is exactly why watching one in trouble is so hard to forget. . 08 // SIX MYTHS ABOUT WILD CATS AND WATER Since we are here, let's clear up some things. MYTH 1: All cats hate water. FALSE. Tigers swim. Jaguars swim. Fishing cats literally dive for a living. The idea that cats are universally water-averse comes from house cats, and even that is a generalization. Plenty of wild cats are comfortable in water. It is not fear of water. It is that most cats do not choose it unless there is a reason. MYTH 2: If an animal can swim, it is safe in water. FALSE. Being able to swim and being able to survive in water are two different skills. Temperature, distance, current, exhaustion and time all matter. A strong swimmer can lose to cold long before it loses to drowning. MYTH 3: A tough predator does not need help. FALSE. Toughness is not immunity. Predators die of exposure, injury and exhaustion all the time. Being at the top of a food chain does not mean being at the top of every situation. MYTH 4: A calm wild animal is a friendly wild animal. FALSE. We covered this. Calm can mean cold, tired, hurt or in shock. It does not mean safe to touch. MYTH 5: Wild cats attack people constantly. FALSE. It is the opposite. Bobcats avoid people so well that many humans go a lifetime without seeing one. Encounters that end badly are extremely rare, and most involve an animal that is sick, cornered or handled. MYTH 6: Rescue is easy. FALSE. Rescue is the visible five percent. Underneath it are timing, knowledge, restraint, risk and an enormous amount of luck. Six myths. Not one of them survives contact with the footage. . 09 // THE SECOND HALF OF EVERY RESCUE Here is something the clips never show you. Getting the animal out is the first half of the story. The second half is quieter, slower, and matters just as much. When a wild animal ends up in professional hands, the priorities are almost the opposite of what your instincts tell you. Warmth, calm, darkness and silence come first. Then a medical check for injuries, dehydration and hypothermia. Then, if all goes well, recovery in a space where the animal sees as little of humans as possible. Yes, as little as possible. Good rehabilitators deliberately avoid cuddling, talking to, or bonding with wild patients, because an animal that gets comfortable around people is an animal that struggles to survive once it goes back out there. The goal is never a rescued pet. The goal is a released predator. And when it is possible, animals are often returned to the area where they were found, because that is the territory they know: the routes, the hiding places, the food, the neighbors. So the real happy ending of a wildlife rescue is not a hug. It is a door opening, and an animal walking through it without looking back. Hold on to that image. It will matter in a minute. . 10 // PLEASE DO NOT TRY THIS I am going to be blunt, because the comment section will not be. Every time a clip like this goes viral, thousands of people write some version of "I would have grabbed it too" or "I want to keep it." Please do not. A wild bobcat is not a pet. It is a wild predator with claws, teeth, and a nervous system built to treat contact as an attack. Even a small, exhausted, seemingly docile animal can injure a person badly in a fraction of a second. Bites and scratches from wildlife can carry infection. Rabies in bobcats is uncommon, but it is real, and that is not a gamble you take on a hunch. And as we covered, the danger runs both ways. The stress of being handled can hurt or kill a wild animal even when nobody meant it any harm. In many places it is also illegal to keep or transport wild animals without permits, which means the well-intentioned rescuer can end up in a legal mess and the animal can end up worse off. So what should you do if you ever find a wild animal in trouble? Keep your distance. Keep noise and movement low. Do not try to pick it up, feed it, or warm it up yourself. Call your local wildlife agency, animal control, or a licensed wildlife rehabilitator, and follow their instructions. They do this for a living. They know what the animal needs, and what it does not. Love wildlife the right way: at a distance, with respect, and through the phone number of someone qualified. The best rescue stories are the ones where the animal gets to go back to being wild. Which brings me to the ending. . 11 // HOW TO WATCH THIS VIDEO Here is my one request. Do not watch it on autopilot. Before you press play, decide what you think is going to happen. Make a prediction, honestly. Then watch, and see how wrong you were. Keep your eyes on the animal, not on the people. Watch the eyes. Watch the ears. Watch the moment the animal has to decide what it wants to do. Wildlife tells you everything if you know where to look, and it does not lie. Pay attention to the way the whole tone of the clip shifts as it goes on. The first seconds and the last seconds feel like two entirely different films. And carry these five questions with you while it plays: Why is a predator this strong not fighting? What does an animal like this actually feel in a moment like that? How many other animals never get this chance? What would you have done in that spot, honestly? And what is the very last thing the bobcat does? Only one of those has an answer you can see with your own eyes. And remember the door I mentioned, the one that opens and the animal that does not look back? Keep that picture in your head while you watch. See how close reality gets to it. And stay to the last frame. There is a reason I told you the ending matters. A second watch will show you things the first one hides. . 12 // THE THREE RULES EVERY WILDLIFE PROFESSIONAL LIVES BY If you spent a week with people who do this work for a living, you would hear the same three ideas over and over. Rule one: the animal comes before the story. Good professionals never put a camera ahead of the patient. The best footage in the world is worthless if it costs the animal something. Rule two: distance is a form of kindness. The closer you get, the more stress you add. The most respectful thing you can do for a wild animal is usually to do less, from farther away, and let it be what it is. Rule three: success means the animal does not need you anymore. Not gratitude. Not a bond. Not a photo with a cute caption. Success is independence. Notice how that flips the usual story. In most human stories, success is being needed. In wildlife, success is being forgotten. . 13 // IF YOU ONLY REMEMBER FIVE LINES Screenshot this if you want to. I would. 1. Water steals heat 25 times faster than air, and a wet coat stops working. 2. A bobcat is an ambush predator with the strength of an animal twice its size, and none of that helps when the ground disappears. 3. Do not confuse calm with consent. 4. Almost everything in nature happens unwatched. That is why the times someone looks matter so much. 5. The best ending to a wild rescue is an animal that walks away on its own. . 14 // THE REAL REASON I POSTED THIS We spend a lot of time online being told the world is getting worse. Sometimes it is. But there is another truth underneath, one that rarely trends: in a random moment, on an ordinary day, with no cameras and no audience and nothing to gain, someone looked, someone cared, and something wild got another chance. Remember the number you guessed earlier, the seconds or minutes in that water? It turns out it does not matter. What matters is that somebody noticed at all. That is the whole post. Not a miracle. Not a spectacle. Just a small, stubborn piece of evidence that the chain can hold. If it moved you even a little, that is not weakness. That is the part of you that still works. If this landed for you, here is what to do: Reply with one word for how you felt watching it. Just one. I read them. Repost it for the person in your life who says they do not care about animals. Watch what happens to their face. Bookmark this thread-that-is-not-a-thread for the next time your feed makes you tired of everything. And follow me if you want more clips that make you feel something real, plus the stories behind them, without the fluff. One last thing. You made it to the bottom of a very long post. That means something. It means you are the kind of person who stays for the ending. Now go back to the video, press play, and stay for the last frame. P.S. If you scrolled straight to the bottom to see how this ends: the video is attached to this post. The ending is in the video. Nothing I write can replace it, and I would not want it to. P.P.S. Drop your prediction in the replies before you press play: what does the bobcat do in the very last seconds? Then come back after and tell me how wrong, or how right, you were. I will pin my favorite.

Earth Unveiled

58,188 views • 6 days ago

Hey True Earthers... If you get tired of globers bitching about a model, or sunrise angles, or star trails, or sunlight, or eclipses, anyone can ALWAYS reference THIS MODEL The reason it is called "Shane's Mode;" is strictly so YOU can use it, and I can take all the criticism, insults, ridicule, jokes, attacks, etc. The general idea is that the community gets the considerable benefit of presenting an accurate model and using it to explain several normal phenomena at once. Then, only I get the drawbacks of all that will surely come from it, and everyone else will benefit. I planned it this way, because I largely don't care about what any of the globers piling the hate over here so we can press forward. Or.. you know, f*ck me for saying the word model, and for bendy light or for whatever. If that's the case, no hard feelings. One last thing, the smaller dome in the model simply represents the limit of an observers view, a spherical limit with a radius of 3959. The math that supports that is here... and here. The descriptions are entirely reworked, mostly spelling error free, and entirely plausible. So feel free to bring it up in debates, forums, streams, podcasts, or whatever you like. The model adequately emulates and explains all of these observations: Sunrise, Sunset, Moonrise, Moonset, Moon Phases, Moon's apparent rotation, Sun's position on Equinox, Seasons, some aspects of Solar and Lunar Eclipses, Star trails, 24 hours Day/Night at the North-pole and Antarctica, Celestial Poles, Why people south of the equator can see the same Stars rotate clockwise around a singe celestial pole at the same time at different continents [Southern Cross Observations] Cheers everyone! The FULL Description is below, and it is LONG. Sorry. The Model This model does not assume a physical Sun nor Moon which will show a collective convergence for every observer on Earth. It only matches their apparent positions as observed across the plane. The Bislin model acknowledges this and moves all celestial bodies to a nearly infinite distance away. This does nothing more than create a triangle large enough that you can mathematically abstract your way into the inverse of everything you experience. The truth is there is a limit to one's visual space. And this limit is necessarily geometrically spherical. Because one never observes objects in anything but their 'apparent location' within one's personal celestial sphere, there is no need to explain a tiny ball of heat mysteriously powering itself along at 3100 miles above the plane. This is not reality. We feel we only have to model the exact apparent position for each observer. We do not have to provide an explanation for what you think should be required. This model relays the apparent size and positions of Sun, Moon and star constellations. It depicts their paths as well as the day-night terminator. Simply by observing reality and plotting that data on a planar map we demonstrate that the Sun, Moon and stars can move beyond the limit of one's vision and become unresolvable by the naked eye. We show how this can be conflated with the assertion that objects ACTUALLY drop down below the horizon when, in reality, they are only apparently dipping below the horizon when exceed limit of your vision. It is elegantly simple and easy to understand without the bullshit. Sun/Moon tracks: In 24 hours, the fixed stars rotate about 1 degree more than 360 degrees so that, in 365.25 days, the star constellations return to the same place in the sky. This is seen by incrementally advancing DayOfYear (click the field and use Arrow Up or Down). The Dome grid will advance each day by about 1 degree. Advance the time in 24 hours steps and the Sun noticeably moves between the Solstice lines. The Sun will also trace a figure 8. This is caused by the Sun's Ecliptic plane at 23.44 degrees to the orbital plane. The paths of the Sun and Moon are visible against the fixed star background (Dome Grid) by checking the options Sun track and Moon track. For a description of the tracks, click the Eclipses button. They correspond to observable reality. The tracks are derived from the solar and lunar cycles and are absolutely not exclusive to either model. It would be extremely dishonest to claim anything else. Sorry, Walter. Retrograde Motion of the Moon's track: The Sun's path stays fixed on the Dome Grid. But, the Moon's path slowly rotates retrograde against the Dome Grid and rotates one full rotation in 6,798 days. This is due to the oscillation and intersection of the Moon's orbit caused by the distant Sun. Currently, the Moon Ecliptic is such that the path of the Moon extends the path of the Sun, North/South, by about five degrees. Approximately 3,400 days later, the path of the Moon moves inside the path of the Sun by about 5 degrees. This observation is simply translated to the planar model. Eclipses: The intersection points of the Sun and Moon's paths are called Knots. Two Knots are marked by a green dot. If the Sun and Moon are on two opposing Knots, a Lunar Eclipse occurs. The Sun and Moon on the same Knot will result in a Solar Eclipse (play Demo Eclipses from Step 6 on). This Flat Earth model can predict Solar and Lunar Eclipses. It can also absolutely predict the optical effect conflated with the Moon's alleged shadow on Earth during a Solar Eclipses or vice versa. It uses a ratio of the cycle that is based on the radius of a shadow, as postulated by Phillippe de La Hire, in the 1700s. It was first calculated for a Lunar Eclipse. But, the ratio applies to all future eclipses which belong to an appropriate series. This ratio is then applied to the predicted path to dynamically widen or shorten the path in order to accommodate the penumbral and umbral radial intersection as a visible sphere on the plane. We then apply this integer as a scalar to correctly approximate the size of the optical effects conflated with shadows. All of the maps onto which the eclipse can be projected use the same globular coordinate system, unfortunately. Now, it can be shown that heliocentrism cannot predict eclipses at all. They can only interpret the cycle data in the same way the ancients did and apply more refined mathematics. Moon Phases and Orientation: The model shows the Moon phases and the orientation of the Moon with respect to the Observer's horizon. The apparent rotation of the Moon during the day is due to the fact that the camera's up vector remains perpendicular to the surface of Earth while following the path of the Moon. This perfectly matches reality. Equinox: This model produces the correct apparent Sun positions during an Equinox. The Sun rises due East at 6:00 AM and sets due West at 6:00 PM. Poles: This model produces a 24 hour day and night on the North Pole and in Antarctica. Heliocentric Model: Simple observations mathematically translated to this planar projection perfectly map the paths of the Sun, Moon and stars (star trails) as they appear to the Observer inside their personal celestial sphere. As with all other celestial observations, the Equinox, the Solstice Knots and the Day-Night terminator can be derived from basic observation and data applied to the planar projection. No need for baseless assumption. The Heliocentric model utterly fails here. Newton's laws can be reduced to exclude mass and still manage to describe the same periodicity and, thusly, the same relationship. No need for an exclusivity claim here at all, is there, Walter? Shapes on the Dome: The shape of Sun, Moon and star constellations appear on the personal celestial sphere exactly as they do in reality, and when projected onto the globe. Again, because we invoke the same radius to describe the spherical limit of our celestial view, the very same observations become easily explainable when using all of the normal conventions, with no need to invent branches of physics and invert reality. All features of this model are derived only from observations of the sky. Observations of the sky have always been kinematically equivalent - equally applicable to geocentric and heliocentric model. This was rather the point of the invention of Special and General Relativity (nonsense). Problems with the Shane's Flat Earth Model Distances: Many people misunderstand distances on map projections. On the AE map, distances measured in an exactly North-South direction are correct. Other measurements are also proportionately correct. Data translation between projections is tied to the coordinates we use. The longitude and latitude we use in any of the appropriate 200 map projections will ensure the distances between those points remain accounted for, at scale. Please learn how map scaling works if this seems inadequate to you. Only an absolute moron would expect visual distance to be equal in an equal area, or equal distance, cartographic transformation. Right, Walter? Personal Celestial Sphere: The Sun and Moon trace specific paths across the celestial sphere. The paths of the celestial bodies are directly mapped from observation to the planar projection. They also follow the cycle of the Heavens, with no need for gravity, Newton, nor the very lackluster performance of gravity based predictions of systems with 2 or more bodies. It was jaw dropping to see that poor Walter actually wrote that gravity caused this. I assume it was because he knew he would never have to answer any challenges. Show me the math which uses the gravitation from all of the forces Walter listed and I will immediately remove this section. Moon Phases and Field Rotation: Moon phase and apparent orientation, as shown, perfectly represent what every observer on Earth sees, correct to their location. The 15 year solar cycle and the 18 (10/11) month lunar cycle have been understood for so long that people eventually forgot and are now incorrectly perceive their paths. Only in modernity do the vast majority of people wander about under their own personal clock without the ability to read it. How sad. The Day/Night Terminator: The shape that matches reality is a bit peculiar and it changes over the course of a year. The shape not only depends on the location of the Sun but its height and speed as well. Again, we know the Sun circles the plane at a 23.4 degree tilt. And this perfectly defines the terminator line. There is absolutely no reason to invoke bendy light in order to explain any of these observations. The model simply matches what we see. It represents reality. Missing The Third Dimension: We need to correct the inherent misunderstanding in the assumption of the physicality of any 'dome'. Modeled here is a personal celestial sphere. It uses a radius. It just so happens that Shane has been arguing this concept and this radius since the day he showed Walter Bislan's model as evidence, amid the jeers of the uneducated masses. As it turns out, the personal celestial sphere is a visual limit imposed on one's spherical view of the heavens. It most simply describes the particular visible slice of the heavens. And it moves that amount with you where ever you go. This is such an elegant, beautiful explanation to what had been perplexing the flat Earth community for years: how the stars work. The personal celestial sphere, once properly understood, is a perfect explanation for everything we see in the sky. It explains the curved nature of the arcs of summer and winter, the behaviors of the Sun and Moon, as well as the apparent non movement of the static stars in relation to each other. Every single stellar observation is explained as well as, if not better than, any Heliocentric explanation. Any person who incorrectly assumes a visual distance scale also assumes things to be visually identical in size and demonstrates a massive misunderstanding of proper distance scaling inherent in all map projections - particularly in the AE map. It's as if everyone has forgotten that the AE map is equal to the Globe map, which is also equal to 199 other map projections. The choice of projection does not matter. They are all the same. They all represent the same distances. We can make predictions based on cycles as well as the next guy. So, we wont need help there. As we keep saying, every observation in the sky is equal between geocentric and heliocentric perspectives. People seem to be INTENTIONALLY misunderstanding that, at this point. Light-Bending: absolutely not required in any way shape nor form. Observable reality matches the model in every way; I cannot imagine a better fit. To now try to invent a need for bendy light would only publicly highlight the ineptitude of a lower tier glober - and their inability to learn and adapt, a vital skill in these times. Our model perfectly represents azimuth and elevation of every celestial object in its apparent position. This is all that we ever see. There is no need to explain what has never been observed. The visualization of the South Pole in action is actually what brought Shane to the ultimate understanding of the celestial wheels. So, thank you again, Walter! Light Bending Over Night-Shadow: to match the 24 hour Daylight in Antarctica data from the light forms a shape congruent to a coffee cup caustic effect. Shadows Of Eclipses: although this model can predict the date of Eclipses, it was argued that it can be used for nothing else. Please check the provided links to review the absurdity of those claims. Conclusion Some observations, like the positions of the Sun, Moon and Star Constellations as well as Sun/Moon-rise/set can be explained by a Flat Earth Model - if we allow ourselves to adhere to the mathematical principle of equivalence. What a concession. Some final thoughts: 1) Distances on the AE Map are 100% 1:1 equivalent when you comprehend how to accordingly use the scale provided with the ruler which represents longitude. 2) LEARN ABOUT MAPS. Hopefully, the covariant scaling and lossless unlimited translations between the projections will teach you this valuable lesson. Equinox, Solstice, Azimuth, Elevation This model draws a perfectly circular orbit of the Earth around the Sun and a perfectly circular orbit of the Moon around the globe Earth. This is because the planar Earth has no moronic need for elipicity because they didn't back themselves into a logical corner by making shit up. This model chooses to match: Spring Equinox at 12:00 UT, March 20, 2017 Solar Eclipse at 18:00 UTC, August 21, 2017 Azimuth and Elevation of the Sun and Moon are also slightly inaccurate (according to the assumed Heliocentric requirement) due to the use of circular instead of elliptical orbits. This affects also Moon phase. Computing Day-Night Terminator The Day-Night terminator is derived from to match reality as follows: 1. A circle perpendicular to the Earth-Sun axis in the Sun coordinate system is computed depending on the Sun's position at a point in time relative to the intersection knot of the Equatorial plane of the Earth and the ecliptic plane of the Sun. This is entirely possible in both models. 2. This circle is then transformed to the globe Earth coordinate system. There is no way around using this coordinate system. If Walter Bislan comes asking for his source code, tell him thank again, from shane. Any questions can be sent to [email protected]

Shane St Pierre

90,809 views • 2 years ago

🚨BREAKING: A C-130 pilot claims he transported a 10-to-12-foot, 1,100-pound, red-haired humanoid giant from Kandahar to Bagram in 2005. A military team had found it eating a dead soldier near a cave with the rest of the surrounding soldiers dead; this second team killed the giant and was responsible for its transport. The body was airlifted out of Afghanistan in a large palette and taken to Wright-Patterson Air Force Base where it may still be held today. Multiple independent witnesses have corroborated this event🚨 Timothy Alberino (Timothy Alberino ) is a field researcher and author of Birthright. He spent 10 years in Peru, made multiple research expeditions to Sardinia and Afghanistan-adjacent source networks, and has spent decades synthesizing eyewitness testimony from abductees, military personnel, and indigenous communities across multiple continents. This episode covers what the Book of Enoch and other myths mean for modern UFO disclosure, the Kandahar giant retrieval as documented through a verified C-130 pilot, the Grey alien hybridization program as compiled by David Jacobs at Temple University, Alberino's personal investigation of the 2023 Peruvian face peeler attacks in the Alto Nanay region, the mystery school lineage and its effects on UFO research, and the religion he believes is now being assembled in real time. 1. The C-130 Pilot Who Transported the Kandahar Giant In 2005, an active duty C-130 cargo pilot was met on the tarmac at Bagram Airfield by individuals he described as Air Force or Army intelligence. He had never been intercepted on the tarmac before. They told him: this never happened, no pictures, don't talk about it. His cargo was a nine-foot pallet. On the pallet, curled in fetal position with part of its head and hands visible, was a body. Red hair. Pale white skin. Six fingers on each hand. Six toes on each foot. The loadmaster weighed everything before loading. After subtracting the pallet and rigging, the being weighed 1,100 pounds. The soldiers standing around it were comparing boot sizes to its feet. It was transferred to a base in Qatar. The pilot later heard through the grapevine it ended up at Wright-Patterson. Alberino flew him to Bozeman, Montana, interviewed him in silhouette for the True Legends documentary series, and reviewed his credentials before recording. 2. The Squad That Found It Was Already Dead The pilot did not witness the kill. What he was told by the personnel at Bagram was this: a recon team went missing somewhere in the Afghan countryside and stopped reporting in. A second team was dispatched to find them. They found the first team at or near the entrance to a cave. Every man was dead. The giant was eating one of them. The second team killed it. L.A. Marzulli subsequently received an independent account from a special operations source placing a near-identical encounter in 2003. In that version, the giant skewered one of the soldiers with a spear. Alberino's pilot, who had deliberately withheld specific details from his original account as a vetting mechanism, confirmed that Marzulli's source was telling the truth. Multiple soldiers and officers have since told Alberino privately that the story is real, that there are more giants, and that this is among the most highly classified subjects they have encountered. 3. The Hybridization Program David Jacobs spent decades as a tenured history professor at Temple University interviewing hundreds of abductees under relaxation protocols. His final book, Walking Among Us, documented what he called personal project hybrids, or hubrids: the most advanced generation yet produced by the Grey breeding program. These are human-alien hybrids now indistinguishable from ordinary humans. They retain the telepathic capabilities of the Greys. Their loyalty is entirely to the Greys. Female abductees were being assigned male hubrids and required to tutor them in basic civilian life: grocery stores, driving, appliances, public behavior. Jacobs described the hubrids as psychopathic. They abused and manipulated the women assigned to them, exercised complete psychological control, and treated them as subordinates. Jacobs named the endgame plainly: planetary acquisition by stealth. 4. Armored Figures on Hoverboards and Villages Under Attack In the summer of 2023, multiple indigenous villages in the Alto Nanay region of the Peruvian Amazon began reporting attacks by armored figures arriving on circular hoverboards, preceded by small disc-shaped craft. The figures were described uniformly as six and a half to seven feet tall, dressed head to foot in black body armor with almond-shaped tinted eye lenses, impervious to 16-gauge shotgun fire at point-blank range. Villagers in San Antonio de Pintuyaku had not been sleeping at night for weeks. They were running armed patrols. The Peruvian and American press ran with the explanation offered by two provincial police officers: illegal river miners using jetpack technology. Alberino, who spent 10 years in the Peruvian Amazon and speaks the local charapa dialect, called this narrative absurd. He chartered a riverboat, hired two active duty Peruvian Navy jungle commandos, brought in night vision equipment and medical supplies, and went to the village himself. 5. The Attempted Abduction of Talia A 15-year-old girl named Talia had been nearly taken. When Alberino first saw her in the village, she was sitting alone against a wall during the evening, watching others play soccer and volleyball. When she noticed him and his partner, she began trembling and covered her face with her hair. The following morning, her father brought her outside to speak. She trembled again and cried before saying a word. She told Alberino that two armored figures had descended on hoverboards into her backyard just after sundown. One landed behind her and grabbed her from behind. The other grabbed her feet. They carried her behind a chicken coop hovering off the ground. They injected something into her nose that caused disorientation. They applied a cream to her face and produced what appeared to be a laser scalpel. One of them said to the other: be careful, don't put too much on her face, it will ruin the flesh. She pushed up the helmet of the figure behind her. He let go to pull it down. She screamed. Her brother and neighbors arrived within seconds. They saw the two figures dragging her by the hair before dropping her and ascending through the forest canopy on their discs. Alberino filmed the laser incision scar on Talia's face. 6. Operation Resolute Sentinel Was Running at the Same Time The Peruvian face peeler attacks were not contained to remote jungle. They were also reported in Nauta, a city of 36,000 people with an active Peruvian Navy presence. This alone dismantles the miner hypothesis. Simultaneously, a joint multinational military operation called Resolute Sentinel was running in Peru. Participants included the U.S. Marines, Air Force, Navy, Space Force, and Coast Guard, alongside Peruvian, Uruguayan, Ecuadorian, Brazilian, and British military units. Alberino does not know what Resolute Sentinel was covering. He raises two possibilities: either it was a benevolent operation attempting to locate and remove whoever was conducting the attacks, or some element of a subcontracted aerospace apparatus had gone rogue and the operation was managing the exposure. 7. Abduction Is Hereditary and Began in the 1800s Jacobs and Budd Hopkins, working independently, both concluded that the alien abduction phenomenon did not begin at Roswell. It began in the mid to late 1800s. Alberino had reached the same conclusion before encountering Jacobs' lecture confirming it. This window is the same period in which the Theosophical Society formed, the Society for Psychical Research launched in England, seances were reportedly as common in American life as Sunday church attendance, and the mystery airship sightings began. Abduction runs in families without exception. Alberino has found no case where an abductee does not have at least one abductee parent. The program is exponential by design. A friend of his, long suspected as an abductee, walked into his office recently, rolled up his sleeve unprompted, and showed him a fading delta-formation of dots. He said: they came and got me. 8. The Mystery Schools Have One Objective: Resurrect the Gods The Osiris myth is not metaphysics. It is operational documentation. Isis represents the adepts of the mystery school. Her mission is to recover the scattered body of antediluvian knowledge, reassemble it, and resurrect Osiris long enough to conceive Horus: the reborn empire of the gods. Alberino traces this lineage from the Phoenicians, through Freemasonry, through the NASA mission catalog, whose named programs and landing sites map directly onto the Greek, Egyptian, and Mesopotamian pantheons. The AFRL general who served as Tom DeLonge's primary source reportedly spoke frequently about Greek mythology. A WikiLeaks email queried the resurrection tomb of Gilgamesh. The objective, Alberino argues, has not changed. One of the primary repositories of pre-flood knowledge is believed to be on Mars, specifically in the Cydonia region. The aspiration to reach Mars is ancient. 9. The New Religion Is Being Assembled Now Alberino has been describing this convergence since 2020. It combines two streams. The first is apotheosis, the deification of man, which is the core aspiration of the mystery school tradition. The second is the literal return of the gods in craft. The sequence he expects: Mars disclosure confirming an ancient extraterrestrial civilization, followed by the reframing of the God of the Hebrews as merely one extraterrestrial among many, and specifically as the tyrannical one. Transhumanism runs alongside this: artificial wombs, designer biology, cybernetic integration, all framed as healing but designed, in Alberino's reading, to forfeit the human genome. Ray Kurzweil, asked if he believes in God, said: not yet. Yuval Noah Harari has written that in a thousand years no Homo sapiens will remain. Alberino does not read these as predictions. He reads them as a program. Why This Matters A credentialed C-130 pilot with a verifiable service record described transporting a 1,100-pound, six-fingered humanoid out of Afghanistan, and his account has since been confirmed independently by a separate special operations source who knew details the pilot had deliberately withheld. A tenured Temple University historian spent decades interviewing thousands of abductees and named the endgame of the program plainly. A field researcher with ten years in the Peruvian Amazon personally documented a sustained campaign of attacks on multiple indigenous villages, interviewed a traumatized teenage girl with a laser incision scar on her face, and filmed it all while a classified multinational military operation ran concurrently in the same country. These are not isolated stories. Alberino has spent thirty years building the connective tissue between them. The question he leaves open is not whether any of this is real. It is who is managing all of it, and toward what end. Full episode is live now.

Jesse Michels

1,059,573 views • 5 months ago

What if I told you ripple:native just moved closer to a financial universe doing $17.5 TRILLION in FX and interest-rate derivatives every single day? I’m not talking about some random prediction. I’m talking about BIS Working Paper No. 1374. This is going to be a long read, because the headline barely scratches the surface. Four of the five authors work at the Bank for International Settlements, and instead of only mentioning XRP Ledger in theory, the researchers actually built, tested and published an open-source XRPL-based prototype. That distinction matters. This is a research implementation, not a production BIS deployment. But the technical choice itself is what caught me. The researchers needed a public blockchain that could help prove official economic and financial data had not been altered. They chose XRP Ledger. And they explained why: low fees, fast finality, developer resources and existing research around its consensus system. This wasn’t somebody adding an XRP logo to a presentation. They built the gateway. They created XRPL transactions. They used institutional anchoring wallets. They put cryptographic proofs inside transaction memos. They linked publisher identities to XRPL addresses. They retrieved those transactions again during verification. Then they measured how the system performed. Median publication latency came in around 3–5 seconds. Verification took around 1–2 seconds. That is where my brain immediately went beyond the headline. Because what exactly were they trying to verify? The kind of information the entire financial system runs on. -Inflation. -GDP. -Interest rates. -Banking statistics. -Debt information. -Financial-stability data. -Regulatory reporting. Imagine a central bank publishes an inflation number. Today that number gets copied everywhere. -Websites. -News terminals. -Databases. -Screenshots. -AI models. -Trading systems. Once it spreads across the internet, how does another machine independently prove that the number it received is exactly what the institution originally published? That is the problem BIS researchers were attacking. Their model creates a cryptographic fingerprint of the official dataset. Individual statistical series can receive fingerprints too. Those hashes are combined through a Merkle tree. A final Merkle root gets anchored to XRPL. The underlying economic data do not need to be dumped onto the blockchain. XRPL simply keeps the proof. Think of it like this: The official institution publishes the document. XRPL holds the tamper-proof receipt. Someone changes even one part of the underlying file? The cryptographic fingerprint changes. Now a bank, regulator, investor, trading engine or AI agent can check: Is this the original data? Has it been changed? Did it really come from the institution claiming to publish it? And that second part is where this paper gets even more serious. The BIS prototype combines the data proof with a W3C Verifiable Credential for the publisher. The publisher’s cryptographic identity is connected to an XRPL address. The paper even uses the format: did:xrpl: So you are not only verifying the information. You are verifying who published it. Now picture a financial world where machines can check both automatically. A central bank publishes CPI. A model receives it. Before touching money, the software checks XRPL. Correct file. Correct publisher. No alteration. Then it acts. That sounds simple until you realize what financial markets actually do with official data. -Rates move. -Currencies move. -Bond prices move. -Derivatives reprice. -Collateral requirements change. -Loans reset. -Inflation-linked instruments adjust. -Portfolio risk changes. And this is where BIS Working Paper 1374 stops being a boring statistics paper for me. Because the authors themselves discuss putting verified information beside digital financial assets. They specifically mention: -CBDCs -stablecoins -tokenized deposits -derivatives. That one section changes the entire way I look at this. The vision is not simply: “Put a hash on a blockchain.” It becomes: verified economic information + digital money + tokenized assets + automated execution. Now remember what Ripple has been building around XRPL. -Multi-Purpose Tokens. -Credentials. -Permissioned Domains. -Permissioned DEX infrastructure. -Confidential Transfers. -Stablecoins. -Institutional lending. -Tokenized collateral. -FX. -Onchain credit. And Ripple has repeatedly positioned XRP across payments, liquidity and credit. Now put those pieces beside what the BIS researchers are exploring. An official institution needs an identity. XRPL can represent identity and credentials. A regulated participant needs permission to enter a market. XRPL is building permissioned infrastructure. A bond needs trustworthy economic information. The BIS prototype shows one way that information can be authenticated through XRPL. A financial asset needs a digital representation. XRPL is being built for tokenization. A transaction needs money. Stablecoins and tokenized deposits can provide the cash side. Then all those different assets need liquidity. That is where ripple:native becomes much more interesting to me. But before getting there, look at the scale surrounding BIS itself. The BIS does not process the world’s $9.6 trillion of daily FX transactions. It measures that market through its Triennial Central Bank Survey. That distinction matters. According to the numbers in the context here: global OTC FX turnover = $9.6 TRILLION every day. Then add: OTC interest-rate derivatives turnover = $7.9 TRILLION every day. Together: $17.5 TRILLION per day. Just the FX number annualized across roughly 250 trading days comes to around: $2.4 QUADRILLION per year. That is the financial universe BIS research sits over. -Currencies. -Banks. -Central banks. -FX swaps. -Rates. -Derivatives. -Cross-border capital. -Collateral. -Dollar funding. And researchers inside that institution just chose XRP Ledger for an actual technical prototype. That is why I keep telling people not to reduce this to transaction fees. Yes, the worked example uses an XRPL Payment transaction. Yes, the reference cost is only: 10 drops = 0.00001 XRP. Yes, transaction fees on XRPL are destroyed. So if this kind of anchoring eventually ran on mainnet, publishing data itself would consume XRP. But that is not the part that gets me excited. The fee is intentionally tiny. The much bigger question is: What happens when verified information starts triggering financial activity on the same broader infrastructure? The paper itself talks about: inflation-linked products perpetual futures tokenized financial instruments derivative settlement interest payments automated compliance and even: automated monetary-policy applications. Now we are talking about information causing money to move. Imagine an inflation-linked bond. The government publishes inflation. That release gets cryptographically anchored. The bond checks the proof. The CPI number is verified. The contract adjusts what is owed. Digital cash settles the payment. No one has to manually copy a number from a website into another system. No one has to blindly trust a third-party data feed. The financial instrument can verify the economic input itself. That is the idea I keep coming back to: self-verifying finance. And the researchers even discuss using the XRPL EVM-compatible sidechain for more advanced applications where data verification and programmable financial execution exist in the same broader ecosystem. They mention: access controls, permissioning, automated compliance, multisignature requirements, oracle integration, programmable validation. Now connect that with Ripple’s institutional roadmap. Credentials can prove who a participant is. Permissioned Domains can define who belongs inside a regulated environment. Tokenized assets can represent financial instruments. RLUSD can represent digital dollar liquidity. Lending can make those assets productive. XRP can provide native network resources and, where economically useful, liquidity between fragmented assets. That is a very different picture of XRPL than the one people were arguing about years ago. It is not simply: “Can XRP send a payment quickly?” The question becomes: Can XRPL sit underneath parts of a machine-readable financial system? And Working Paper 1374 just gave that question much more weight for me. There is another section that barely gets discussed. The architecture is not limited to one data publisher. The researchers designed a multi-publisher system. Different institutions can create their own Merkle roots. Those roots can be combined into one larger super-root. One XRPL transaction can anchor that shared proof. Yet each publisher remains independently accountable for its own data. Now imagine the participants. Central Bank A. Central Bank B. Regulator C. Statistical Office D. International Organization E. One public verification system. Different publishers. Independent cryptographic accountability. That begins to resemble infrastructure for cross-border public-sector data exchange. And the paper’s own conclusion talks about trustworthy exchange among: national statistical offices central banks international organizations. Then look at who already uses the statistical standard the paper builds around. SDMX is sponsored by institutions including: BIS European Central Bank Eurostat International Monetary Fund OECD United Nations World Bank Group International Labour Organization. That does not mean those institutions are adopting XRPL. But it tells you something important about the design philosophy. The researchers did not create a blockchain system that requires the existing financial world to throw everything away. They designed it to sit underneath an existing institutional standard. That matters a lot. Because the easiest technology to adopt is often the technology that does not force everyone to rebuild from zero. Existing systems can continue publishing. XRPL can provide the cryptographic proof underneath. Then comes BIS Open Tech. The paper says the open-source reference implementation is being released as a prototype through BIS Open Tech and the SDMX community. That means other institutions can inspect it. Reuse it. Modify it. Build on it. This is how technical ideas can spread inside serious institutions. Not through hype. Through code. Documentation. Standards. Reuse. That is the kind of adoption path I pay attention to. Then there is the AI angle. This is where the whole thesis becomes almost unfairly interesting. The authors explicitly discuss AI agents. An AI system receives economic information. Instead of blindly trusting what it scraped from somewhere, it can ask: Is this data authentic? It checks the XRPL proof. Valid? Continue. Invalid? Do nothing. Now compare that with what Ripple launched in June 2026: the XRPL AI Starter Kit, designed around autonomous agents making payments with XRP and RLUSD. Two completely separate directions suddenly sit beside each other. BIS research: AI verifies information through XRPL. Ripple ecosystem: AI moves value through XRPL. Now imagine both ideas eventually meeting. An agent receives official inflation data. It verifies the release cryptographically. It recalculates risk. It reprices a bond. It adjusts collateral. It changes an FX position. It executes a payment. It settles in RLUSD. It routes through XRP where XRP is the best available liquidity path. That is machine-native finance. And now go back to the scale. The BIS 2025 Triennial Survey says: $9.6T/day FX. The dollar appears on one side of 89% of FX trades. The euro is involved in 28.9%. The Japanese yen in 16.8%. FX swaps alone are around $4T every day. Then another $7.9T/day exists in OTC interest-rate derivatives turnover. Think about what happens if only part of those markets becomes tokenized. Digital USD deposits. Digital EUR deposits. Tokenized JPY. RLUSD. CBDCs. Tokenized Treasuries. Interest-rate derivatives. FX derivatives. Collateral. Money-market instruments. The first problem is getting the assets onchain. The second is verifying the information those assets depend on. The third is moving liquidity between all the different forms of value. This BIS paper attacks the second problem using XRPL. Ripple has spent years attacking the first and third. That is why the combination gets my attention. And you do not need XRPL to capture the whole market for the numbers to become enormous. For scale only: 0.1% of $9.6T daily FX turnover = $9.6B per day. 1% = $96B per day. Again, that is not a forecast. It shows what even tiny percentages mean when the underlying market is measured in trillions every day. And that is only FX. It does not include the additional $7.9T/day of interest-rate derivatives turnover BIS measures. This is where the XRP liquidity thesis changes from a crypto argument into a market-structure argument. Suppose the future has hundreds of tokenized currencies and financial products. Every possible pair cannot maintain perfect direct liquidity. USD token / EUR token. EUR token / JPY token. JPY token / RLUSD. RLUSD / Treasury token. Treasury token / derivative. Derivative / deposit token. The combinations explode. A common intermediate asset becomes useful whenever routing through it provides a better market. That is where XRP’s role becomes interesting. Not replacing the dollar. Not replacing the euro. Not replacing CBDCs. Not replacing bank deposits. Connecting liquidity between them when that route makes economic sense. Now imagine the system is automated. No trader needs to shout: “Use XRP.” Software looks at: price, spread, depth, settlement, availability. If the XRP path wins, the software uses XRP. That is the outcome I care about. Machine-selected liquidity. And if those transactions grow large enough, the XRP market itself has to change. Institutional market makers need inventory. Liquidity providers need inventory. Prime brokers need financing capacity. Order books need deeper capital. Large transactions need to clear without huge price impact. That is where the price thesis becomes different from retail speculation. If XRP ever helps support institutional flows inside markets measured in trillions per day, the relevant question is not: “How many retail holders bought today?” It becomes: How much dollar liquidity does the XRP market need to represent? That is an entirely different valuation conversation. There is one more thing I think people are missing. BIS Working Paper 1374 does not only talk about SDMX statistics. The researchers say the same architecture can extend to: XBRL regulatory filings FINREP COREP and other forms of structured official information. Now imagine banks submitting regulatory reports that receive immutable XRPL proofs. The bank cannot quietly change an old filing later. The regulator can verify the exact version. Auditors can verify it. Another authority can verify it. AI software can consume it. One system can prove both: who submitted the data and whether it changed. That gives XRPL a potential role far beyond payments. It starts touching the information layer of finance. And this is why the line “BIS used XRP Ledger” actually undersells the paper. What happened is more specific. Researchers inside BIS took a real institutional problem. They selected XRPL. They built a working implementation. They measured performance. They published the code direction. Then they explored how authenticated data could coexist with: CBDCs, stablecoins, tokenized deposits, derivatives, AI agents, automated financial instruments. That is what I am bullish on. Not a logo. Not a rumor. Not a screenshot. Technical work. And when I look at the direction Ripple is independently pushing XRPL, the overlap is hard for me to ignore. Trusted identities. Verified information. Regulated participants. Tokenized assets. Digital money. Automated execution. Credit. Collateral. FX. Liquidity. AI. Put together, the long-term architecture can look like this: Official institutions publish information. XRPL anchors the proof. Banks and regulators verify it. AI consumes it. Tokenized instruments use it. Stablecoins and tokenized deposits provide cash. Institutional markets execute trades. XRP supplies native network resources and can supply cross-asset liquidity where the route makes sense. That is not simply a faster payment network. That starts looking like part of a digital financial operating system. And then remember where this conversation is happening. Inside the research world of the institution that measures: $9.6 trillion of FX turnover every day plus $7.9 trillion of interest-rate derivatives turnover every day. A combined: $17.5 TRILLION DAILY. No, that is not XRPL volume. No, BIS does not process those trades. The significance is that BIS researchers just tested XRP Ledger while working inside the institutional world surrounding markets of that size. That is the fact. And now I’m asking the question that matters to me as an ripple:native holder: What happens if XRPL earns even a small role inside the tokenized version of that financial system? Because 0.1% of a trillion-dollar market is not small. And this market is not one trillion. It is trillions every single day. That is why Working Paper 1374 changed the scale of the conversation for me. For years, people asked whether XRP could become part of the future financial system. Now researchers inside the BIS have taken XRP Ledger, built institutional infrastructure on it, and explicitly discussed a future combining trusted information with digital money and programmable financial assets. We are still at the prototype stage. But for me, the direction is the real story. The next financial system will need trusted data, tokenized assets, automated execution and deep liquidity. XRPL is now showing up in all four conversations. And XRP sits natively underneath the network where those pieces can eventually meet. $17.5T a day. Now look at your ripple:native bag again. Enough?

X Finance Bull

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