Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Holy shit… someone just made DSA finally click. Not static notes Not boring pseudocode Not guessing what happens in memory Real data structures — animating step-by-step — visually. It’s called Data Structure Visualizations and it lets you watch algorithms run in real time. Here’s why this is different: Instead... of dumping theory, it shows execution live • nodes getting inserted • trees rotating • pointers moving • queues filling • stacks popping • graphs traversing • heaps rebalancing You literally see algorithms think. Everything is interactive: • Binary Search Trees • AVL Trees (with rotations) • Red-Black Trees • Heaps & Priority Queues • Graph BFS / DFS • Dijkstra & MST • Hash Tables • Tries • Sorting (Quick, Merge, Heap…) • Dynamic Programming No black box. Just input → steps → result Watch in real time: • AVL rotations balancing themselves • BFS exploring layer by layer • DFS diving deep then backtracking • Dijkstra relaxing edges step-by-step • Quick sort partition visually • Heap forming after each insert • Hash collisions resolving live This solves the biggest DSA problem: Most resources teach code → memorize → hope it works This shows input → execution → visualization → understanding Which means you finally understand: • why AVL rotates • how heap property maintains • how BFS differs from DFS • how Dijkstra actually updates distances • what happens during rehashing • how quicksort partitions • how trees rebalance Even better: You control everything Change values Insert nodes Run step-by-step Pause execution Replay algorithms Learning DSA becomes interactive Not passive Not confusing Not theoretical Just… visible. Perfect for: • DSA beginners • interview prep • visual learners • CS students • LeetCode prep • teaching algorithms • debugging understanding This is the kind of resource that makes trees, graphs, and sorting finally click. Link: We’re moving from reading DSA → watching DSA execute And once you can see algorithms… you stop memorizing and start understanding.show more

Suryansh Tiwari

34,050 subscribers

14,425 Aufrufe • vor 5 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Suryansh Tiwari

132,906 Aufrufe • vor 5 Monaten

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,206 Aufrufe • vor 5 Monaten

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.

Benjamin Bennett Alexander

18,584 Aufrufe • vor 10 Monaten

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.

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 Aufrufe • vor 5 Monaten

This explains it all. Hillary Clinton has greatly been behind the destruction of America Hillary Clinton wrote her entire college thesis on a book that explains how to turn any country into a communist dictatorship Wait until you hear this “You want to know how we got here? I'll tell you. It's people like Hillary Clinton who've been introducing Socialist Marxist ideology to the American public for decades, and they've been pushing their agenda to turn this country into a commie nation. And if you don't believe me, tell me if any of Saul Alinsky's Rules for Radicals sound familiar based on how you're living your life right now and what this country's become. It's the exact playbook on how to convert any nation into a communist dictatorship. — It was written down step-by-step. So here's how it works. It's eight steps to take any civilization and convert it to socialist Marxist ideology that's reinforced by communism, which, by the way, Hillary Clinton wrote her entire college thesis on this particular book. — The first move is to control healthcare. You control healthcare, you control people's bodies, you control their choices, and you create dependency - The second move dovetails right off the first. They jack up poverty so people can't breathe. Keep wages low, make prices high through inflation, tax f*cking everything, and then suddenly people are desperate. That's exactly what they want because when you're desperate, you beg for anyone who promises some form of relief - Then the third step is to bury a generation in debt. $20 trillion, $30 trillion. Who's keeping score again? Nobody. That's where we're at. The math is absolutely f*cking brutal on purpose and the goal is to crash the system, and then they offer the solution. - Step four in Alinsky's plot is to take guns off the table, disarm the people, and centralize enforcement. Makes everything easier when no one can push back - The fifth step is to weaponize welfare. Food, housing, income, dependency on the state. Call it compassion if you want. I call it a leash. - Then the sixth step is to own the classrooms. Control the curriculum, control the future voters, control the narrative. Kids are taught to parrot slogans and not to ask questions, which is perfect, right? At least if you want to institute socialism and a Marxist ideology and a communist dictatorship in your own f*cking country, - Then step seven is to erase God, or at least replace faith with dependency. Because faith causes people to ask questions. Dependency forces people to submit - Then finally, the eighth step. They keep the population fighting each other. Class versus class, race versus race, left versus right. While the people are busy clawing over crumbs, they rob us blind and they take over. Look, every single one of these moves is being played out in real time right now. It's not a coincidence. Not even close. It's a pattern, and if you see the pattern, good. You're awake. Don't just watch it happen. Push back. Talk about it. Share it. Wake people up. Because the worst part of this is that they knew that a society of young, strong, level-headed, logical thinking men would never have allowed any of this to take place, and that's why they came after us first.“

This explains it all. Hillary Clinton has greatly been behind the destruction of America Hillary Clinton wrote her entire college thesis on a book that explains how to turn any country into a communist dictatorship Wait until you hear this “You want to know how we got here? I'll tell you. It's people like Hillary Clinton who've been introducing Socialist Marxist ideology to the American public for decades, and they've been pushing their agenda to turn this country into a commie nation. And if you don't believe me, tell me if any of Saul Alinsky's Rules for Radicals sound familiar based on how you're living your life right now and what this country's become. It's the exact playbook on how to convert any nation into a communist dictatorship. — It was written down step-by-step. So here's how it works. It's eight steps to take any civilization and convert it to socialist Marxist ideology that's reinforced by communism, which, by the way, Hillary Clinton wrote her entire college thesis on this particular book. — The first move is to control healthcare. You control healthcare, you control people's bodies, you control their choices, and you create dependency - The second move dovetails right off the first. They jack up poverty so people can't breathe. Keep wages low, make prices high through inflation, tax fcking everything, and then suddenly people are desperate. That's exactly what they want because when you're desperate, you beg for anyone who promises some form of relief - Then the third step is to bury a generation in debt. $20 trillion, $30 trillion. Who's keeping score again? Nobody. That's where we're at. The math is absolutely fcking brutal on purpose and the goal is to crash the system, and then they offer the solution. - Step four in Alinsky's plot is to take guns off the table, disarm the people, and centralize enforcement. Makes everything easier when no one can push back - The fifth step is to weaponize welfare. Food, housing, income, dependency on the state. Call it compassion if you want. I call it a leash. - Then the sixth step is to own the classrooms. Control the curriculum, control the future voters, control the narrative. Kids are taught to parrot slogans and not to ask questions, which is perfect, right? At least if you want to institute socialism and a Marxist ideology and a communist dictatorship in your own f*cking country, - Then step seven is to erase God, or at least replace faith with dependency. Because faith causes people to ask questions. Dependency forces people to submit - Then finally, the eighth step. They keep the population fighting each other. Class versus class, race versus race, left versus right. While the people are busy clawing over crumbs, they rob us blind and they take over. Look, every single one of these moves is being played out in real time right now. It's not a coincidence. Not even close. It's a pattern, and if you see the pattern, good. You're awake. Don't just watch it happen. Push back. Talk about it. Share it. Wake people up. Because the worst part of this is that they knew that a society of young, strong, level-headed, logical thinking men would never have allowed any of this to take place, and that's why they came after us first.“

Wall Street Apes

440,927 Aufrufe • vor 11 Monaten

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

194,171 Aufrufe • vor 5 Monaten

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 Aufrufe • vor 9 Monaten

This 6-minute video reveals how Elon Musk learns complex topics: Elon Musk: “You don’t need college for learning.” “Everything is available basically for free. You can learn anything you want for free. It is not a question of learning.” Musk starts with a blunt point: College may still have value, but not for the reason most people think. He says the real signal of college is not intelligence. It is proof that someone can work through structure: “Can somebody work hard at something, including a bunch of sort of annoying homework assignments, and still do their homework assignments, and kind of soldier through and get it done?” That, in his view, is one of the main things a degree demonstrates: Discipline. Compliance. Follow-through. Not necessarily exceptional ability. Musk pushes the idea even further: “Colleges are basically for fun and to prove you can do your chores. But they’re not for learning.” Whether or not you agree with him fully, the underlying point is hard to ignore: We live in a time when knowledge is no longer locked inside institutions. The internet has dismantled the old gatekeeping model. Today, if someone wants to learn design, engineering, writing, sales, coding, marketing, or history, they can access world-class information without ever stepping into a lecture hall. The bottleneck is no longer access to information. It is desire. Focus. Curiosity. Consistency. Musk then draws a distinction that matters: “If you’re trying to do something exceptional, there must be evidence of exceptional ability.” That line changes the whole conversation. Because in real life, people do not reward credentials alone. They reward proof. Not what you enrolled in. What you built. Not what you intended to do. What you finished. Not what you say you know. What you can demonstrate. This is why portfolios outperform claims. Why execution beats prestige. Why visible work creates leverage. Musk even says, somewhat provocatively: “I don’t consider going to college evidence of exceptional ability.” And then he points to the kinds of examples people love to cite: “Gates is a pretty smart guy, he dropped out. John was pretty smart, he dropped out. Larry Ellison, smart guy, he dropped out.” His broader message is not that everyone should leave school. It is that conventional paths are not the only paths to intelligence, capability, or impact. Then Musk moves into something even more useful: His view of how people actually learn. “Education should be as close to a video game as possible. Like a good video game. You do not need to tell your kid to play video games. They will play video games on autopilot all day.” That comparison is simple, but powerful. Why do people obsess over games? Because games are interactive. They are immersive. They provide immediate feedback. They make progress visible. They create challenge without making the challenge feel meaningless. Musk’s point is that learning should work the same way. “If you can make it interactive and engaging, then you can make education far more compelling and far easier to do.” This is where traditional education often breaks down. Students are expected to move in lockstep. Same pace. Same timeline. Same structure. Same sequence. Musk rejects that model completely: “People are not objects on an assembly line.” That may be one of the clearest criticisms in the entire transcript. Because standard education often optimizes for administration, not human variation. It is easier to manage people in batches. But easier to manage does not mean better to learn. Some people move faster in math. Some are stronger in language. Some are highly visual. Some need to touch the thing, build the thing, test the thing. And yet most systems still treat learning like synchronized marching. Musk argues for something more individualized: “Allow people to progress at the fastest pace that they can or are interested in in each subject.” That idea matters beyond school. Adults learn this way too. No one becomes exceptional by waiting for permission to move at average speed. The most effective learners usually follow interest with intensity. They go deeper where curiosity pulls them. They accelerate where energy is highest. They build momentum through engagement, not force. Musk also shares one of the most practical ideas in the transcript: “Teach problem solving, or teach to the problem, not to the tools.” Then he gives an example. If you wanted to teach someone how engines work, the traditional system might start with separate lessons on screwdrivers, wrenches, and tools. Musk thinks that is backwards. A better method is: “Here’s the engine. Now let’s take it apart.” Then the tools become relevant in context. Now the student understands *why* the screwdriver matters. Now the wrench has meaning. Now the lesson is connected to reality. This is a much bigger principle than education. People learn faster when relevance is obvious. Abstract instruction is forgettable. Applied learning sticks. When people can see the problem first, they care about the tool. That is true in business too. You do not start with theory for theory’s sake. You start with the problem that needs solving. Then you learn exactly what is required to solve it. Finally, Musk says something that quietly explains why so much education fails: “A lot of things people learn, probably there’s no point in learning them because they never use them in the future.” That may sound harsh, but most people know the feeling. They do not resist learning because they are lazy. They resist learning because it feels disconnected. They are told to memorize before they understand relevance. They are told to sit still before they become curious. They are told to absorb information before they have any reason to care. Musk’s view, underneath the provocation, is actually simple: People learn best when learning is alive. When it is tied to action. When it respects differences in pace and aptitude. When it feels engaging instead of ceremonial. When it produces visible competence, not just paper credentials. The internet made learning abundant. What matters now is whether someone can turn information into evidence. That is the real separator. Lessons I'm taking away from this clip: 1. In today’s world, access to knowledge is cheap. Proof of skill is expensive. We have crossed a point where information alone is no longer impressive because everyone has access to it. You can watch the best interviews, read the best essays, take the best online lessons, and still remain average if you never turn any of it into real work. So the advantage now is not “I know this.” The advantage is “I built this, tested this, shipped this, and can show the result.” From my perspective, this is especially true in business and personal branding. The market rewards visible competence far more than silent knowledge. 2. People learn faster when the learning feels useful, alive, and connected to a real problem. This is why so many people struggle with conventional education but thrive when they start building something for themselves. Urgency creates focus. Relevance creates retention. Once the lesson is attached to a real outcome, the brain pays attention differently. That’s why I think one of the best ways to learn anything is to start a project that forces you to use the skill in public or in real life. Learning becomes sharper when there is something at stake. It stops being passive consumption and becomes active problem-solving. 3. The smartest people are often not the ones collecting credentials. They are the ones following curiosity with discipline. Exceptional people usually do not just learn what is assigned to them. They go where their interest is strongest and then they pursue it seriously. That combination matters: curiosity without discipline goes nowhere, and discipline without curiosity becomes lifeless. The sweet spot is when someone becomes obsessed enough to keep going deeper than required. To me, that is where the real edge comes from. Not from following the default path better than everyone else, but from developing uncommon depth in something that genuinely pulls you.

This 6-minute video reveals how Elon Musk learns complex topics: Elon Musk: “You don’t need college for learning.” “Everything is available basically for free. You can learn anything you want for free. It is not a question of learning.” Musk starts with a blunt point: College may still have value, but not for the reason most people think. He says the real signal of college is not intelligence. It is proof that someone can work through structure: “Can somebody work hard at something, including a bunch of sort of annoying homework assignments, and still do their homework assignments, and kind of soldier through and get it done?” That, in his view, is one of the main things a degree demonstrates: Discipline. Compliance. Follow-through. Not necessarily exceptional ability. Musk pushes the idea even further: “Colleges are basically for fun and to prove you can do your chores. But they’re not for learning.” Whether or not you agree with him fully, the underlying point is hard to ignore: We live in a time when knowledge is no longer locked inside institutions. The internet has dismantled the old gatekeeping model. Today, if someone wants to learn design, engineering, writing, sales, coding, marketing, or history, they can access world-class information without ever stepping into a lecture hall. The bottleneck is no longer access to information. It is desire. Focus. Curiosity. Consistency. Musk then draws a distinction that matters: “If you’re trying to do something exceptional, there must be evidence of exceptional ability.” That line changes the whole conversation. Because in real life, people do not reward credentials alone. They reward proof. Not what you enrolled in. What you built. Not what you intended to do. What you finished. Not what you say you know. What you can demonstrate. This is why portfolios outperform claims. Why execution beats prestige. Why visible work creates leverage. Musk even says, somewhat provocatively: “I don’t consider going to college evidence of exceptional ability.” And then he points to the kinds of examples people love to cite: “Gates is a pretty smart guy, he dropped out. John was pretty smart, he dropped out. Larry Ellison, smart guy, he dropped out.” His broader message is not that everyone should leave school. It is that conventional paths are not the only paths to intelligence, capability, or impact. Then Musk moves into something even more useful: His view of how people actually learn. “Education should be as close to a video game as possible. Like a good video game. You do not need to tell your kid to play video games. They will play video games on autopilot all day.” That comparison is simple, but powerful. Why do people obsess over games? Because games are interactive. They are immersive. They provide immediate feedback. They make progress visible. They create challenge without making the challenge feel meaningless. Musk’s point is that learning should work the same way. “If you can make it interactive and engaging, then you can make education far more compelling and far easier to do.” This is where traditional education often breaks down. Students are expected to move in lockstep. Same pace. Same timeline. Same structure. Same sequence. Musk rejects that model completely: “People are not objects on an assembly line.” That may be one of the clearest criticisms in the entire transcript. Because standard education often optimizes for administration, not human variation. It is easier to manage people in batches. But easier to manage does not mean better to learn. Some people move faster in math. Some are stronger in language. Some are highly visual. Some need to touch the thing, build the thing, test the thing. And yet most systems still treat learning like synchronized marching. Musk argues for something more individualized: “Allow people to progress at the fastest pace that they can or are interested in in each subject.” That idea matters beyond school. Adults learn this way too. No one becomes exceptional by waiting for permission to move at average speed. The most effective learners usually follow interest with intensity. They go deeper where curiosity pulls them. They accelerate where energy is highest. They build momentum through engagement, not force. Musk also shares one of the most practical ideas in the transcript: “Teach problem solving, or teach to the problem, not to the tools.” Then he gives an example. If you wanted to teach someone how engines work, the traditional system might start with separate lessons on screwdrivers, wrenches, and tools. Musk thinks that is backwards. A better method is: “Here’s the engine. Now let’s take it apart.” Then the tools become relevant in context. Now the student understands why the screwdriver matters. Now the wrench has meaning. Now the lesson is connected to reality. This is a much bigger principle than education. People learn faster when relevance is obvious. Abstract instruction is forgettable. Applied learning sticks. When people can see the problem first, they care about the tool. That is true in business too. You do not start with theory for theory’s sake. You start with the problem that needs solving. Then you learn exactly what is required to solve it. Finally, Musk says something that quietly explains why so much education fails: “A lot of things people learn, probably there’s no point in learning them because they never use them in the future.” That may sound harsh, but most people know the feeling. They do not resist learning because they are lazy. They resist learning because it feels disconnected. They are told to memorize before they understand relevance. They are told to sit still before they become curious. They are told to absorb information before they have any reason to care. Musk’s view, underneath the provocation, is actually simple: People learn best when learning is alive. When it is tied to action. When it respects differences in pace and aptitude. When it feels engaging instead of ceremonial. When it produces visible competence, not just paper credentials. The internet made learning abundant. What matters now is whether someone can turn information into evidence. That is the real separator. Lessons I'm taking away from this clip: 1. In today’s world, access to knowledge is cheap. Proof of skill is expensive. We have crossed a point where information alone is no longer impressive because everyone has access to it. You can watch the best interviews, read the best essays, take the best online lessons, and still remain average if you never turn any of it into real work. So the advantage now is not “I know this.” The advantage is “I built this, tested this, shipped this, and can show the result.” From my perspective, this is especially true in business and personal branding. The market rewards visible competence far more than silent knowledge. 2. People learn faster when the learning feels useful, alive, and connected to a real problem. This is why so many people struggle with conventional education but thrive when they start building something for themselves. Urgency creates focus. Relevance creates retention. Once the lesson is attached to a real outcome, the brain pays attention differently. That’s why I think one of the best ways to learn anything is to start a project that forces you to use the skill in public or in real life. Learning becomes sharper when there is something at stake. It stops being passive consumption and becomes active problem-solving. 3. The smartest people are often not the ones collecting credentials. They are the ones following curiosity with discipline. Exceptional people usually do not just learn what is assigned to them. They go where their interest is strongest and then they pursue it seriously. That combination matters: curiosity without discipline goes nowhere, and discipline without curiosity becomes lifeless. The sweet spot is when someone becomes obsessed enough to keep going deeper than required. To me, that is where the real edge comes from. Not from following the default path better than everyone else, but from developing uncommon depth in something that genuinely pulls you.

Yasmine Khosrowshahi

34,213 Aufrufe • vor 5 Monaten

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 —> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 —> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 —> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 —> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 —> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 —> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 —> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 —> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.

Sulfur

25,540 Aufrufe • vor 1 Monat

Terence Tao, Professor of Mathematics at UCLA and Fields Medalist, on why nobody can fully explain why LLMs work: Tao starts with the mechanics, which are no mystery at all. You gather an enormous amount of text and you fit a curve to it. "The magic of LLMs is that if you train these LLMs on enough data — so trillions and trillions of data points — and you really try to fit as good a curve as possible, and this takes like millions and millions of dollars of computing power and months and months of time, then suddenly, even when you iterate, it stays coherent. It begins to sound not like monkeys but it actually sounds like a human speaking." That is the entire recipe: data, compute, time, curve-fitting. None of it obviously adds up to fluent English. Then Tao says the part that most people building these systems move past quickly: "And somehow we don't fully understand why that's the case." The admission comes from one of the most capable living mathematicians, and the gap he describes sits at the centre of the field. His best account of what's happening puts the mystery in the language rather than the machine: "What seems to be true is that language, like English or other natural languages, contains a lot of hidden patterns that we're not consciously aware of. I mean, we know some of the laws of English, there's laws of grammar and things, but there are sort of unspoken, unwritten rules of language that humans pick up." It relocates the question: the structure was always latent in the text, and the model found it. Why enough curve-fitting surfaces that structure is still unanswered. Tao reaches for a child to explain it: "A human child, even though they're not taught what a noun is, what a verb is or whatever, they can pick up what order English words go in just by continual exposure to the language." Which is honest about the limits of the explanation, because we can't fully account for how children do it either. From there, the unexplained behaviour compounds. Exposure to language turns out to be enough to produce something that looks like reasoning: "It seems like you can teach these models to also pick up patterns in language to the point where you can give them math questions. The answer to 2 plus 3 is — and they will say five." And once a model handles language at all, you can push it into resembling self-correction: "Once you have a little bit of ability to speak English, you can kind of go in loops and sort of check your work and make fewer mistakes, and you can prompt these models to proceed step by step and not say something unless it's been double checked and so forth. And so they become a little bit smarter, quote unquote, to the point where they can solve many, many complicated tasks." The scare quotes around "smarter" carry the whole argument. Tao does not concede that the unexplained fluency implies anything underneath it: "But they're still just guessing the next word to say. It's not really grounded in any deep understanding of the real world. It's just that they have seen the patterns in the English language or other language that they've absorbed so well."

Terence Tao, Professor of Mathematics at UCLA and Fields Medalist, on why nobody can fully explain why LLMs work: Tao starts with the mechanics, which are no mystery at all. You gather an enormous amount of text and you fit a curve to it. "The magic of LLMs is that if you train these LLMs on enough data — so trillions and trillions of data points — and you really try to fit as good a curve as possible, and this takes like millions and millions of dollars of computing power and months and months of time, then suddenly, even when you iterate, it stays coherent. It begins to sound not like monkeys but it actually sounds like a human speaking." That is the entire recipe: data, compute, time, curve-fitting. None of it obviously adds up to fluent English. Then Tao says the part that most people building these systems move past quickly: "And somehow we don't fully understand why that's the case." The admission comes from one of the most capable living mathematicians, and the gap he describes sits at the centre of the field. His best account of what's happening puts the mystery in the language rather than the machine: "What seems to be true is that language, like English or other natural languages, contains a lot of hidden patterns that we're not consciously aware of. I mean, we know some of the laws of English, there's laws of grammar and things, but there are sort of unspoken, unwritten rules of language that humans pick up." It relocates the question: the structure was always latent in the text, and the model found it. Why enough curve-fitting surfaces that structure is still unanswered. Tao reaches for a child to explain it: "A human child, even though they're not taught what a noun is, what a verb is or whatever, they can pick up what order English words go in just by continual exposure to the language." Which is honest about the limits of the explanation, because we can't fully account for how children do it either. From there, the unexplained behaviour compounds. Exposure to language turns out to be enough to produce something that looks like reasoning: "It seems like you can teach these models to also pick up patterns in language to the point where you can give them math questions. The answer to 2 plus 3 is — and they will say five." And once a model handles language at all, you can push it into resembling self-correction: "Once you have a little bit of ability to speak English, you can kind of go in loops and sort of check your work and make fewer mistakes, and you can prompt these models to proceed step by step and not say something unless it's been double checked and so forth. And so they become a little bit smarter, quote unquote, to the point where they can solve many, many complicated tasks." The scare quotes around "smarter" carry the whole argument. Tao does not concede that the unexplained fluency implies anything underneath it: "But they're still just guessing the next word to say. It's not really grounded in any deep understanding of the real world. It's just that they have seen the patterns in the English language or other language that they've absorbed so well."

Big Brain AI

190,391 Aufrufe • vor 9 Tagen

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

Dwarkesh Patel

1,052,518 Aufrufe • vor 11 Monaten

Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) *my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be *focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃

Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃

Ace

45,581 Aufrufe • vor 10 Monaten

$Real estate has a simple problem that people don’t always say out loud: it’s not designed for partial participation. You either have enough money to buy in properly, or you don’t. There’s usually no “in-between.” And once you do invest, your money is tied up for a long time. Selling isn’t instant and flexibility is limited. So even though real estate is seen as a solid way to build wealth, a lot of people are effectively locked out... not by lack of interest but by how the system is structured. That’s the gap APARTCHAIN is focused on. APARTCHAIN is a platform that turns real estate into something you can invest in fractionally. Instead of buying an entire property, the ownership is divided into digital shares (tokens), and investors can buy a portion that fits their budget. So rather than needing large capital, you’re able to take smaller positions in actual properties. Here’s how it works in practice: • APARTCHAIN acquires real estate • The property is split into multiple ownership shares • Investors buy those shares on-chain • Rental income from the property is distributed to shareholders • When the property is eventually sold, any profit is also shared So your return comes from two places: ongoing rental income and potential appreciation when the property is sold. Now, fractional real estate isn’t a brand-new idea. What makes APARTCHAIN different is how it’s positioned. It operates within Kazakhstan’s regulatory framework, with oversight connected to the country’s national financial authority. That’s a key detail because a lot of tokenization platforms operate without clear local regulation. Here, the structure is built to align with an existing legal system, not bypass it. On the technical side, it runs on a blockchain network designed for low fees and fast transactions. That means buying, holding or transferring your share doesn’t come with the heavy costs or delays typically associated with traditional property processes. There’s also no strict lock-in at the protocol level... you’re not forced to hold your position for a fixed period. But in reality, your ability to exit depends on the secondary market, which is still developing. So liquidity exists, but it’s not fully mature yet. It’s also worth being clear about the risks. Property values can go up or down. Rental income isn’t guaranteed and because this system relies on smart contracts, there’s a technical layer that traditional real estate doesn’t have. On top of that, the platform itself is still growing. Property inventory is limited for now and the resale market for shares is still building. That said, it’s not just an idea on paper... APARTCHAIN has already completed at least one full investment cycle... acquiring a property, generating returns, and exiting. That matters because it shows the model can actually function beyond theory. So at the core, this isn’t really about “changing real estate” in some dramatic way. It’s about removing the all-or-nothing barrier that’s always surrounded it. And that leaves a simple question: if you could start building exposure to real estate without needing to go all in from day one, would more people actually step in earlier or would they still wait until it feels “big enough” to matter? Superteam Kazakhstan || APARTCHAIN$

Real estate has a simple problem that people don’t always say out loud: it’s not designed for partial participation. You either have enough money to buy in properly, or you don’t. There’s usually no “in-between.” And once you do invest, your money is tied up for a long time. Selling isn’t instant and flexibility is limited. So even though real estate is seen as a solid way to build wealth, a lot of people are effectively locked out... not by lack of interest but by how the system is structured. That’s the gap APARTCHAIN is focused on. APARTCHAIN is a platform that turns real estate into something you can invest in fractionally. Instead of buying an entire property, the ownership is divided into digital shares (tokens), and investors can buy a portion that fits their budget. So rather than needing large capital, you’re able to take smaller positions in actual properties. Here’s how it works in practice: • APARTCHAIN acquires real estate • The property is split into multiple ownership shares • Investors buy those shares on-chain • Rental income from the property is distributed to shareholders • When the property is eventually sold, any profit is also shared So your return comes from two places: ongoing rental income and potential appreciation when the property is sold. Now, fractional real estate isn’t a brand-new idea. What makes APARTCHAIN different is how it’s positioned. It operates within Kazakhstan’s regulatory framework, with oversight connected to the country’s national financial authority. That’s a key detail because a lot of tokenization platforms operate without clear local regulation. Here, the structure is built to align with an existing legal system, not bypass it. On the technical side, it runs on a blockchain network designed for low fees and fast transactions. That means buying, holding or transferring your share doesn’t come with the heavy costs or delays typically associated with traditional property processes. There’s also no strict lock-in at the protocol level... you’re not forced to hold your position for a fixed period. But in reality, your ability to exit depends on the secondary market, which is still developing. So liquidity exists, but it’s not fully mature yet. It’s also worth being clear about the risks. Property values can go up or down. Rental income isn’t guaranteed and because this system relies on smart contracts, there’s a technical layer that traditional real estate doesn’t have. On top of that, the platform itself is still growing. Property inventory is limited for now and the resale market for shares is still building. That said, it’s not just an idea on paper... APARTCHAIN has already completed at least one full investment cycle... acquiring a property, generating returns, and exiting. That matters because it shows the model can actually function beyond theory. So at the core, this isn’t really about “changing real estate” in some dramatic way. It’s about removing the all-or-nothing barrier that’s always surrounded it. And that leaves a simple question: if you could start building exposure to real estate without needing to go all in from day one, would more people actually step in earlier or would they still wait until it feels “big enough” to matter? Superteam Kazakhstan || APARTCHAIN

Jessica♡🛡

105,405 Aufrufe • vor 4 Monaten

He didn't run. That's the detail nobody expects. Every instinct in a wild, broken animal screams "run" the second a human gets close. Flight is the oldest survival code there is. And yet — he didn't run. Watch the video and you'll understand why that single fact should terrify you more than comfort you. Because an animal that doesn't run from you anymore has already given up on the idea that running matters. He has already decided, somewhere in that starving, exhausted brain, that whatever comes next can't possibly be worse than what came before. Sit with that for a second. What does it take to break that instinct? What does it take to make a living creature so depleted, so past the point of hope, that self-preservation itself shuts off? We're not talking about a dog that was "a little scared." We're talking about a dog who had already made peace with dying alone in the grass, hidden from a world that had already decided he didn't matter. And then someone showed up anyway. This is the part of the internet nobody warns you about. Not the cute part. Not the "aww" part. The part where you realize how close "almost too late" actually is — and how many of these moments are happening right now, in ditches and fields and abandoned lots, with nobody filming, nobody coming, nobody ever finding out. This one was found. I'm not going to walk you through what happens in the footage. I'm not going to spoil the moment his body language shifts, or the second you can physically see the exact heartbeat where "prey" turns into "please." Words can't carry that anyway. You have to watch it happen in real time, frame by frame, to feel what it actually is: the most fragile, most honest negotiation on earth — a terrified animal deciding, right in front of a camera, whether trust is worth the risk one more time. Here's what almost nobody talks about when it comes to strays like this. Dogs abandoned long enough don't behave like pets. They behave like wild animals, because that's exactly what they've had to become. The friendliness gets stripped away first — that's a luxury, and luxuries are the first thing survival deletes. What's left underneath is raw calculation: threat or not a threat, food or not food, safe or not safe. Every second spent deciding wrong could be the last second they get. So when you see an animal in that state hold still — when you see it let a stranger's hand get closer than four legs and thirty years of abandonment should ever allow — you're not looking at "cute." You're looking at the single bravest decision an animal without language, without hope, without any promise of a good outcome, is capable of making. That's what's buried in this video. That's the part that will actually get you. And here's the twist that makes this whole thing so much heavier once you know it: this isn't rare. This is happening at a scale most people never let themselves think about. Estimates on free-roaming and abandoned dogs worldwide run into the hundreds of millions. Hundreds of millions of versions of this exact moment — animals lying in grass, behind dumpsters, under bridges, past the point of hoping anyone comes — and only a fraction of them ever get a camera pointed at them, let alone a hand extended. Every viral rescue video you've ever scrolled past is a survivor's story. For every one of these, there are dozens that never get told, because nobody showed up in time to tell them. That's not meant to guilt you. It's meant to explain why this specific video hits different the second you actually watch it instead of skimming past it. You're not watching content. You're watching one of the rare good outcomes in a numbers game that is brutally stacked against good outcomes. Now — about the moment itself. There's a very specific window in every rescue like this. Rescuers call it different things, but it comes down to the same handful of seconds: the animal has to choose, right then, whether the human in front of it is a predator or a possibility. There's no in-between. No negotiation period. No "let me think about it." It happens in the space of a breath, and everything after depends on which way it breaks. You can see it happen in this footage. You can actually watch the exact moment where the decision gets made — where you'd swear the animal's whole nervous system recalibrates in real time. If you've never seen that moment up close, you don't actually know what "trust" looks like at its most primal. Most of us only ever encounter trust after it's already established, already comfortable, already taken for granted. This is trust being built from absolute zero, live, on camera, with everything on the line. I've watched hundreds of these rescue videos over the years — the genuinely real ones, not the staged reels that flood every feed now with fake "before" shots and suspiciously perfect lighting. The real ones all share this same fingerprint: a pause. A held breath. A moment where absolutely nothing happens except two creatures deciding, silently, whether the next few seconds are going to be safe. This video has that pause. And it's the reason you need to actually watch it instead of just reading about it. Because here's the thing text can never replicate: body language. The tension in a spine before it releases. The exact angle of an ear that tells you, before anything else does, whether fear is winning or losing. None of that survives translation into words. You either watch it happen, or you miss it completely. Let's talk about what "broken" actually costs an animal, physically, because most people underestimate it wildly. Extended abandonment doesn't just mean hunger. It means the body starts making impossible trade-offs. Muscle gets sacrificed for basic organ function. Coat and skin — usually the first thing to show damage — become secondary priorities compared to keeping a heart beating and lungs working. By the time visible damage shows up on the outside, the inside has usually already been compromising for weeks, sometimes months. Vets who specialize in stray and feral rescue will tell you the same thing over and over: what you see on the surface is never the full story. The surface is the last thing the body protects. If the outside already looks that rough, you almost don't want to know what's happening underneath. That's the stakes this video is actually operating at, even if the softness of the moment makes it easy to forget. This isn't a feel-good clip about a slightly dirty dog getting a bath. This is triage. This is the line between "made it" and "didn't," captured completely by accident, because someone happened to have a phone out at exactly the right moment. And that's maybe the most unsettling part of all of this, if you actually think about it for more than five seconds: how much of survival — for an animal with zero ability to ask for help — comes down to pure, dumb chance. Right place. Right time. Right person, willing to stop instead of walk past. How many times has "walking past" been the actual ending to a story like this one? We'll never know, because those endings don't get filmed. They don't get posted. They don't get millions of views and thousands of comments. They just... end, quietly, in a field somewhere, with nobody ever finding out there was a story there at all. This time, someone stopped. I want to be straight with you about something else, because it's the part that actually separates a real rescue from a manufactured one, and it matters more than people think. You can tell — almost instantly — when a rescue video is authentic versus when it's been engineered for engagement. Authentic ones are messy. Uncomfortable. Slow in places where a scripted video would cut. The animal doesn't hit its emotional beats on cue. There's confusion, hesitation, sometimes a step backward before the step forward. Real fear doesn't resolve on a content creator's timeline. It resolves on the animal's timeline, whenever that ends up being — thirty seconds, three minutes, sometimes far longer than anyone filming has patience for. This one has that texture. That's what makes it worth your two minutes instead of just another manufactured "rescue" clip built for a fake reaction. There's a reason rescue footage — the real kind — keeps outperforming almost everything else on this platform, and it's not because people are shallow or looking for cheap emotion. It's because this content taps into something most of us don't get nearly enough exposure to anymore: raw, unscripted stakes. Something is genuinely at risk. Something genuinely uncertain is happening. In a feed built almost entirely out of performance, irony, and content calculated down to the frame, a moment like this lands like a gut punch precisely because nobody could have staged the way it actually plays out. You can't fake that pause. You can't script that flinch, or the moment right after it, when the flinch stops. That's why you need to watch this instead of scrolling past the description of it. Because I can tell you it happened. I can't make you feel it happening. That gap — between knowing about something and actually witnessing it — is the entire reason video exists as a medium in the first place. Let's talk for a second about what happens after a moment like this, because most people watch these videos and never think past the ending card. Rescue is not resolution. It's the very first data point in a much longer, much harder process that almost nobody films because it isn't visually dramatic enough to go viral. Quarantine periods. Vet visits. Bloodwork. The slow, exhausting process of convincing a nervous system that's been running on high alert for months that it's actually allowed to relax now. Some animals take days to decompress. Some take literal years. Trust, once it's been broken at the level this video hints at, doesn't rebuild on anyone's convenient schedule. That's the part that never gets the same reach. The three-months-later update, the "he finally slept through the night" post, the "he let a stranger pet him for the first time" milestone that would look like nothing to anyone who didn't know the whole story. Those posts get a fraction of the views the rescue moment gets, even though they're arguably the more important part of the story. The internet loves a beginning. It's much worse at sticking around for the middle. So if this video moves you — and it will, if you actually watch it instead of skimming past — do something with that feeling beyond just scrolling to the next post. Rescues like this don't happen because of luck alone. They happen because someone, somewhere, decided that stopping mattered more than being on time to wherever they were headed. They happen because someone funded a vet bill, fostered an animal mid-recovery, drove two hours to pick up a dog that wasn't even theirs yet. None of that is glamorous. None of it goes viral on its own. But all of it is the actual machinery behind every single one of these videos you've ever watched and felt something about. If there's one thing I'd want you to take from this before you hit play, it's this: pay attention to the exact moment things shift. Don't just watch for the "aww." Watch for the decision. Watch for the specific second an animal that has every biological reason to run instead chooses to stay. That's the whole story, compressed into a handful of frames. Everything else — the outcome, the relief, the ending — is just what happens after that decision gets made. Most people will watch this video for the ending. Watch it for the middle instead. That's where the real thing is happening. And once you've seen it — once you've actually watched that shift happen in real time instead of reading about it secondhand — you'll understand why videos like this stop people mid-scroll every single time, no matter how many of them they've already seen. Because no matter how many of these you watch, that exact moment never gets less powerful. It just reminds you, over and over, of how much is riding on someone simply choosing to stop. Let's go back to the beginning for a second, because there's a question almost nobody asks about videos like this, and it's the one that actually matters most. How long was he out there before anyone found him? Nobody in the video knows the answer. Nobody watching it will ever know the answer. That's the part that sits with you long after the clip ends — not the moment of rescue itself, but the enormous, silent, unfilmed stretch of time that came before it. Days? Weeks? However long it was, it was long enough to erase every part of him except the will to keep breathing. Long enough that hiding in overgrown grass, invisible to a world that had stopped looking for him, had become the only strategy left. That's the actual horror hiding underneath a video that, on the surface, looks gentle. Everything soft about the footage is only possible because of everything brutal that happened just outside the frame, in the hours and days the camera never captured. This is why I keep telling you not to treat this as passive scrolling material. There is an entire invisible story sitting behind every second of visible footage, and your brain fills in almost none of it unless you slow down and actually let the video play out in full, without skipping ahead, without half-watching while doing five other things. Now, let's talk about why content like this spreads the way it does — because understanding the mechanics actually makes the moment hit harder, not softer. Platforms reward exactly one thing above everything else: retention. Not likes. Not shares. Not comments. Time spent actually watching, second by second, without looking away. And there is almost nothing on this entire platform that earns retention like genuine, unscripted animal footage where the outcome isn't obvious from frame one. Think about why that is. A cooking video, you already know the dish is going to come out fine. A prank video, you already know it's a joke. A dance video, a fashion video, a "get ready with me" video — the outcome was never in question to begin with. But a video like this one? The outcome is not guaranteed. For the first several seconds, you genuinely do not know which way it's going to go. That uncertainty is exactly what makes your eyes stay locked on the screen instead of your thumb flicking to the next post. That's not manipulation. That's not a trick. That's just what happens when something real, with real stakes, ends up on a feed built almost entirely out of things that were never actually uncertain in the first place. Here's something worth sitting with: most people, if you stopped them and asked directly, would say they care about animal welfare. Genuinely, sincerely, most people mean it when they say that. And yet the actual scale of the stray and abandoned animal crisis worldwide remains something most of the same people have almost no real information about. Not because they don't care — because nobody ever hands them the numbers in a way that actually lands. So here they are, stated plainly, without softening them: hundreds of millions of dogs live as strays globally, a significant share of them in conditions of chronic hunger, untreated injury, and disease. Shelters in country after country report the same pattern every single year — intake numbers that outpace adoption numbers, resources that fall short of need, and volunteers stretched thin trying to hold together a system that was never built to handle the actual scale of the problem. None of that is abstract when you're watching one specific dog in one specific patch of grass. It becomes very real, very fast, once you understand that what you're looking at isn't an isolated incident. It's one visible thread pulled out of an enormous, mostly invisible pattern. That's part of why this particular video deserves more than a passive scroll-past. It's not just "a nice moment." It's a small, rare, filmed exception to something that is happening constantly, relentlessly, without cameras, without rescuers, without any kind of happy resolution at all. Let's talk about the rescuer for a second, because they never get enough credit in videos like this, and it's worth correcting that. Whoever is behind that camera — whoever made the decision to stop, to approach slowly, to read an animal's body language carefully enough to know when to move closer and when to hold back — did something that looks simple on video and is genuinely difficult in real life. Approaching a frightened, possibly injured, possibly defensive stray animal correctly is a skill. Get it wrong, and you can panic the animal into fleeing somewhere far worse, or hurting itself trying to escape, or in rare cases, defending itself the only way it knows how. The people who do this well, over and over, in situation after situation, are operating on a mix of instinct and hard-earned experience that most of us will never develop, because most of us will never need to. That instinct is exactly why this rescue plays out the way it does instead of ending in a chase, a panic, or worse. Give that person their due before you close this tab. What you're watching is competence dressed up as tenderness. It looks soft. It is not easy. Now — a quick myth-versus-reality breakdown, because misconceptions about rescue moments like this one are everywhere, and they change how people watch this kind of footage. Myth: a starving, frightened stray will always be aggressive toward humans. Reality: fear responses vary enormously animal to animal. Some go defensive. Many, especially those who were once someone's pet before being abandoned, retain a flicker of learned trust toward people, buried under the fear, waiting for a reason to resurface. Myth: rescue is basically instant — approach, comfort, done. Reality: what you see in a two-minute clip is often the result of a much slower, more careful approach happening just before the camera starts rolling, sometimes minutes, sometimes far longer. Myth: once rescued, an animal is "fine." Reality: rescue is step one of dozens. Medical evaluation, parasite treatment, nutritional rehabilitation, and behavioral decompression all come after, often over a period of weeks or months, invisible to anyone who only ever saw the viral clip. Keep those in mind while you watch. They completely change what you're actually looking at. One more thing before you go press play, because it's the detail that changes how the whole video reads. Location matters more than people think. An animal hidden deep in overgrown grass, off any visible path, away from foot traffic, isn't there by accident. Animals in that condition instinctively seek concealment — it's one of the last functioning defense mechanisms left once running is no longer a realistic option. Being found at all, in a spot chosen specifically to not be found, is already a low-probability event before a single second of footage even starts. That's the invisible math sitting underneath this entire video. The odds of this exact outcome happening were never good. And yet here we are. If you've read this far without watching the video yet, that's honestly a little bit funny, because everything above was building toward one single, simple, unavoidable conclusion: None of this — the psychology, the statistics, the survival instinct, the odds stacked against him — means anything close to what it means once you actually see it unfold in real time, on his face, in his body, in the exact second everything changes. Reading about a moment like this is the trailer. Watching it is the movie. Let me leave you with one last thing, because it's the part people usually forget by the time they close the app and move on with their day. Every single one of us has scrolled past a moment like this before. Not this exact video — but this exact shape of moment. The thumbnail that looked heavy. The caption that hinted at something hard. The three seconds of hesitation before deciding whether today was a day you had the emotional bandwidth to watch an animal suffer before things got better. Most days, most people, scroll past. That's not a character flaw. That's just what an endless feed trains you to do — protect your attention, protect your mood, keep moving. But here's the thing about this specific kind of video that makes stopping worth it, every single time: the discomfort at the start is never the point. It's the toll you pay to get to the part that actually matters — the shift, the decision, the moment fear loses. Skip the discomfort, and you skip the entire reason the video exists in the first place. You end up with secondhand information about a moment that was never designed to be understood secondhand. Think about the last time a video actually changed your mood for the rest of the day. Not entertained you — changed something. Made you call someone. Made you donate somewhere you'd never donated before. Made you look at your own dog curled up on the couch and feel something different for a second. Those moments are rare precisely because most content isn't built to carry that kind of weight. Most content is built to be forgotten by the next scroll. This isn't most content. There's a reason rescue footage keeps circulating years after it's first posted, resurfacing on different accounts, different platforms, different captions, over and over, long after the original context is gone. It's because moments like this don't expire. A joke gets old. A trend dies in a week. But the exact second an abandoned, broken animal decides to trust a stranger anyway — that doesn't age. It hits the same way in five years that it hits today, because the thing it's tapping into isn't a trend. It's something much older than any platform, any algorithm, any feed. It's the oldest story there is, really, just told without words: something was broken, someone showed up anyway, and against every reasonable expectation, that was enough. We don't get many unscripted, unforced examples of that anymore. Almost everything in front of us now is curated, edited, angled for a reaction. This isn't. This is just what actually happened, captured because a phone happened to be there, no different than it would have looked if no camera existed at all. That's rare. That's worth two minutes of your undivided attention instead of a half-watched scroll-by. So here's the actual ask, plain and simple: don't just watch this one and move on like it's any other post in your feed today. Watch it properly. Let it play all the way through without skipping ahead to see how it ends. Notice the exact second his body changes. Notice what happens right after that. And then, if it moves you even a fraction as much as it should, do one small thing with that feeling before it fades — share it with someone, support a rescue near you, or just remember, the next time you see a stray on the side of a road, that stopping is always an option. It's always been an option. Somebody just has to choose to take it. That choice is the entire video. Everything else is just what happens after it gets made. One last thought, and then I'll let you go watch it instead of reading about it any longer. Somewhere out there, right now, there's another version of this exact scene playing out with no camera anywhere near it. No rescuer. No hand reaching out slowly through tall grass. No moment where fear loses. Just an animal, alone, running out of time in silence. We can't fix that with a video. Nobody's pretending we can. But we can make sure the ones that do get found, the ones that do get a camera and a rescuer and a happy ending, actually get seen — properly seen, not scrolled past in half a second on the way to something louder and easier. This is one of the good ones. One of the rare stories where showing up actually happened in time. Don't waste that by treating it like background noise. He didn't run. Now go find out why.

He didn't run. That's the detail nobody expects. Every instinct in a wild, broken animal screams "run" the second a human gets close. Flight is the oldest survival code there is. And yet — he didn't run. Watch the video and you'll understand why that single fact should terrify you more than comfort you. Because an animal that doesn't run from you anymore has already given up on the idea that running matters. He has already decided, somewhere in that starving, exhausted brain, that whatever comes next can't possibly be worse than what came before. Sit with that for a second. What does it take to break that instinct? What does it take to make a living creature so depleted, so past the point of hope, that self-preservation itself shuts off? We're not talking about a dog that was "a little scared." We're talking about a dog who had already made peace with dying alone in the grass, hidden from a world that had already decided he didn't matter. And then someone showed up anyway. This is the part of the internet nobody warns you about. Not the cute part. Not the "aww" part. The part where you realize how close "almost too late" actually is — and how many of these moments are happening right now, in ditches and fields and abandoned lots, with nobody filming, nobody coming, nobody ever finding out. This one was found. I'm not going to walk you through what happens in the footage. I'm not going to spoil the moment his body language shifts, or the second you can physically see the exact heartbeat where "prey" turns into "please." Words can't carry that anyway. You have to watch it happen in real time, frame by frame, to feel what it actually is: the most fragile, most honest negotiation on earth — a terrified animal deciding, right in front of a camera, whether trust is worth the risk one more time. Here's what almost nobody talks about when it comes to strays like this. Dogs abandoned long enough don't behave like pets. They behave like wild animals, because that's exactly what they've had to become. The friendliness gets stripped away first — that's a luxury, and luxuries are the first thing survival deletes. What's left underneath is raw calculation: threat or not a threat, food or not food, safe or not safe. Every second spent deciding wrong could be the last second they get. So when you see an animal in that state hold still — when you see it let a stranger's hand get closer than four legs and thirty years of abandonment should ever allow — you're not looking at "cute." You're looking at the single bravest decision an animal without language, without hope, without any promise of a good outcome, is capable of making. That's what's buried in this video. That's the part that will actually get you. And here's the twist that makes this whole thing so much heavier once you know it: this isn't rare. This is happening at a scale most people never let themselves think about. Estimates on free-roaming and abandoned dogs worldwide run into the hundreds of millions. Hundreds of millions of versions of this exact moment — animals lying in grass, behind dumpsters, under bridges, past the point of hoping anyone comes — and only a fraction of them ever get a camera pointed at them, let alone a hand extended. Every viral rescue video you've ever scrolled past is a survivor's story. For every one of these, there are dozens that never get told, because nobody showed up in time to tell them. That's not meant to guilt you. It's meant to explain why this specific video hits different the second you actually watch it instead of skimming past it. You're not watching content. You're watching one of the rare good outcomes in a numbers game that is brutally stacked against good outcomes. Now — about the moment itself. There's a very specific window in every rescue like this. Rescuers call it different things, but it comes down to the same handful of seconds: the animal has to choose, right then, whether the human in front of it is a predator or a possibility. There's no in-between. No negotiation period. No "let me think about it." It happens in the space of a breath, and everything after depends on which way it breaks. You can see it happen in this footage. You can actually watch the exact moment where the decision gets made — where you'd swear the animal's whole nervous system recalibrates in real time. If you've never seen that moment up close, you don't actually know what "trust" looks like at its most primal. Most of us only ever encounter trust after it's already established, already comfortable, already taken for granted. This is trust being built from absolute zero, live, on camera, with everything on the line. I've watched hundreds of these rescue videos over the years — the genuinely real ones, not the staged reels that flood every feed now with fake "before" shots and suspiciously perfect lighting. The real ones all share this same fingerprint: a pause. A held breath. A moment where absolutely nothing happens except two creatures deciding, silently, whether the next few seconds are going to be safe. This video has that pause. And it's the reason you need to actually watch it instead of just reading about it. Because here's the thing text can never replicate: body language. The tension in a spine before it releases. The exact angle of an ear that tells you, before anything else does, whether fear is winning or losing. None of that survives translation into words. You either watch it happen, or you miss it completely. Let's talk about what "broken" actually costs an animal, physically, because most people underestimate it wildly. Extended abandonment doesn't just mean hunger. It means the body starts making impossible trade-offs. Muscle gets sacrificed for basic organ function. Coat and skin — usually the first thing to show damage — become secondary priorities compared to keeping a heart beating and lungs working. By the time visible damage shows up on the outside, the inside has usually already been compromising for weeks, sometimes months. Vets who specialize in stray and feral rescue will tell you the same thing over and over: what you see on the surface is never the full story. The surface is the last thing the body protects. If the outside already looks that rough, you almost don't want to know what's happening underneath. That's the stakes this video is actually operating at, even if the softness of the moment makes it easy to forget. This isn't a feel-good clip about a slightly dirty dog getting a bath. This is triage. This is the line between "made it" and "didn't," captured completely by accident, because someone happened to have a phone out at exactly the right moment. And that's maybe the most unsettling part of all of this, if you actually think about it for more than five seconds: how much of survival — for an animal with zero ability to ask for help — comes down to pure, dumb chance. Right place. Right time. Right person, willing to stop instead of walk past. How many times has "walking past" been the actual ending to a story like this one? We'll never know, because those endings don't get filmed. They don't get posted. They don't get millions of views and thousands of comments. They just... end, quietly, in a field somewhere, with nobody ever finding out there was a story there at all. This time, someone stopped. I want to be straight with you about something else, because it's the part that actually separates a real rescue from a manufactured one, and it matters more than people think. You can tell — almost instantly — when a rescue video is authentic versus when it's been engineered for engagement. Authentic ones are messy. Uncomfortable. Slow in places where a scripted video would cut. The animal doesn't hit its emotional beats on cue. There's confusion, hesitation, sometimes a step backward before the step forward. Real fear doesn't resolve on a content creator's timeline. It resolves on the animal's timeline, whenever that ends up being — thirty seconds, three minutes, sometimes far longer than anyone filming has patience for. This one has that texture. That's what makes it worth your two minutes instead of just another manufactured "rescue" clip built for a fake reaction. There's a reason rescue footage — the real kind — keeps outperforming almost everything else on this platform, and it's not because people are shallow or looking for cheap emotion. It's because this content taps into something most of us don't get nearly enough exposure to anymore: raw, unscripted stakes. Something is genuinely at risk. Something genuinely uncertain is happening. In a feed built almost entirely out of performance, irony, and content calculated down to the frame, a moment like this lands like a gut punch precisely because nobody could have staged the way it actually plays out. You can't fake that pause. You can't script that flinch, or the moment right after it, when the flinch stops. That's why you need to watch this instead of scrolling past the description of it. Because I can tell you it happened. I can't make you feel it happening. That gap — between knowing about something and actually witnessing it — is the entire reason video exists as a medium in the first place. Let's talk for a second about what happens after a moment like this, because most people watch these videos and never think past the ending card. Rescue is not resolution. It's the very first data point in a much longer, much harder process that almost nobody films because it isn't visually dramatic enough to go viral. Quarantine periods. Vet visits. Bloodwork. The slow, exhausting process of convincing a nervous system that's been running on high alert for months that it's actually allowed to relax now. Some animals take days to decompress. Some take literal years. Trust, once it's been broken at the level this video hints at, doesn't rebuild on anyone's convenient schedule. That's the part that never gets the same reach. The three-months-later update, the "he finally slept through the night" post, the "he let a stranger pet him for the first time" milestone that would look like nothing to anyone who didn't know the whole story. Those posts get a fraction of the views the rescue moment gets, even though they're arguably the more important part of the story. The internet loves a beginning. It's much worse at sticking around for the middle. So if this video moves you — and it will, if you actually watch it instead of skimming past — do something with that feeling beyond just scrolling to the next post. Rescues like this don't happen because of luck alone. They happen because someone, somewhere, decided that stopping mattered more than being on time to wherever they were headed. They happen because someone funded a vet bill, fostered an animal mid-recovery, drove two hours to pick up a dog that wasn't even theirs yet. None of that is glamorous. None of it goes viral on its own. But all of it is the actual machinery behind every single one of these videos you've ever watched and felt something about. If there's one thing I'd want you to take from this before you hit play, it's this: pay attention to the exact moment things shift. Don't just watch for the "aww." Watch for the decision. Watch for the specific second an animal that has every biological reason to run instead chooses to stay. That's the whole story, compressed into a handful of frames. Everything else — the outcome, the relief, the ending — is just what happens after that decision gets made. Most people will watch this video for the ending. Watch it for the middle instead. That's where the real thing is happening. And once you've seen it — once you've actually watched that shift happen in real time instead of reading about it secondhand — you'll understand why videos like this stop people mid-scroll every single time, no matter how many of them they've already seen. Because no matter how many of these you watch, that exact moment never gets less powerful. It just reminds you, over and over, of how much is riding on someone simply choosing to stop. Let's go back to the beginning for a second, because there's a question almost nobody asks about videos like this, and it's the one that actually matters most. How long was he out there before anyone found him? Nobody in the video knows the answer. Nobody watching it will ever know the answer. That's the part that sits with you long after the clip ends — not the moment of rescue itself, but the enormous, silent, unfilmed stretch of time that came before it. Days? Weeks? However long it was, it was long enough to erase every part of him except the will to keep breathing. Long enough that hiding in overgrown grass, invisible to a world that had stopped looking for him, had become the only strategy left. That's the actual horror hiding underneath a video that, on the surface, looks gentle. Everything soft about the footage is only possible because of everything brutal that happened just outside the frame, in the hours and days the camera never captured. This is why I keep telling you not to treat this as passive scrolling material. There is an entire invisible story sitting behind every second of visible footage, and your brain fills in almost none of it unless you slow down and actually let the video play out in full, without skipping ahead, without half-watching while doing five other things. Now, let's talk about why content like this spreads the way it does — because understanding the mechanics actually makes the moment hit harder, not softer. Platforms reward exactly one thing above everything else: retention. Not likes. Not shares. Not comments. Time spent actually watching, second by second, without looking away. And there is almost nothing on this entire platform that earns retention like genuine, unscripted animal footage where the outcome isn't obvious from frame one. Think about why that is. A cooking video, you already know the dish is going to come out fine. A prank video, you already know it's a joke. A dance video, a fashion video, a "get ready with me" video — the outcome was never in question to begin with. But a video like this one? The outcome is not guaranteed. For the first several seconds, you genuinely do not know which way it's going to go. That uncertainty is exactly what makes your eyes stay locked on the screen instead of your thumb flicking to the next post. That's not manipulation. That's not a trick. That's just what happens when something real, with real stakes, ends up on a feed built almost entirely out of things that were never actually uncertain in the first place. Here's something worth sitting with: most people, if you stopped them and asked directly, would say they care about animal welfare. Genuinely, sincerely, most people mean it when they say that. And yet the actual scale of the stray and abandoned animal crisis worldwide remains something most of the same people have almost no real information about. Not because they don't care — because nobody ever hands them the numbers in a way that actually lands. So here they are, stated plainly, without softening them: hundreds of millions of dogs live as strays globally, a significant share of them in conditions of chronic hunger, untreated injury, and disease. Shelters in country after country report the same pattern every single year — intake numbers that outpace adoption numbers, resources that fall short of need, and volunteers stretched thin trying to hold together a system that was never built to handle the actual scale of the problem. None of that is abstract when you're watching one specific dog in one specific patch of grass. It becomes very real, very fast, once you understand that what you're looking at isn't an isolated incident. It's one visible thread pulled out of an enormous, mostly invisible pattern. That's part of why this particular video deserves more than a passive scroll-past. It's not just "a nice moment." It's a small, rare, filmed exception to something that is happening constantly, relentlessly, without cameras, without rescuers, without any kind of happy resolution at all. Let's talk about the rescuer for a second, because they never get enough credit in videos like this, and it's worth correcting that. Whoever is behind that camera — whoever made the decision to stop, to approach slowly, to read an animal's body language carefully enough to know when to move closer and when to hold back — did something that looks simple on video and is genuinely difficult in real life. Approaching a frightened, possibly injured, possibly defensive stray animal correctly is a skill. Get it wrong, and you can panic the animal into fleeing somewhere far worse, or hurting itself trying to escape, or in rare cases, defending itself the only way it knows how. The people who do this well, over and over, in situation after situation, are operating on a mix of instinct and hard-earned experience that most of us will never develop, because most of us will never need to. That instinct is exactly why this rescue plays out the way it does instead of ending in a chase, a panic, or worse. Give that person their due before you close this tab. What you're watching is competence dressed up as tenderness. It looks soft. It is not easy. Now — a quick myth-versus-reality breakdown, because misconceptions about rescue moments like this one are everywhere, and they change how people watch this kind of footage. Myth: a starving, frightened stray will always be aggressive toward humans. Reality: fear responses vary enormously animal to animal. Some go defensive. Many, especially those who were once someone's pet before being abandoned, retain a flicker of learned trust toward people, buried under the fear, waiting for a reason to resurface. Myth: rescue is basically instant — approach, comfort, done. Reality: what you see in a two-minute clip is often the result of a much slower, more careful approach happening just before the camera starts rolling, sometimes minutes, sometimes far longer. Myth: once rescued, an animal is "fine." Reality: rescue is step one of dozens. Medical evaluation, parasite treatment, nutritional rehabilitation, and behavioral decompression all come after, often over a period of weeks or months, invisible to anyone who only ever saw the viral clip. Keep those in mind while you watch. They completely change what you're actually looking at. One more thing before you go press play, because it's the detail that changes how the whole video reads. Location matters more than people think. An animal hidden deep in overgrown grass, off any visible path, away from foot traffic, isn't there by accident. Animals in that condition instinctively seek concealment — it's one of the last functioning defense mechanisms left once running is no longer a realistic option. Being found at all, in a spot chosen specifically to not be found, is already a low-probability event before a single second of footage even starts. That's the invisible math sitting underneath this entire video. The odds of this exact outcome happening were never good. And yet here we are. If you've read this far without watching the video yet, that's honestly a little bit funny, because everything above was building toward one single, simple, unavoidable conclusion: None of this — the psychology, the statistics, the survival instinct, the odds stacked against him — means anything close to what it means once you actually see it unfold in real time, on his face, in his body, in the exact second everything changes. Reading about a moment like this is the trailer. Watching it is the movie. Let me leave you with one last thing, because it's the part people usually forget by the time they close the app and move on with their day. Every single one of us has scrolled past a moment like this before. Not this exact video — but this exact shape of moment. The thumbnail that looked heavy. The caption that hinted at something hard. The three seconds of hesitation before deciding whether today was a day you had the emotional bandwidth to watch an animal suffer before things got better. Most days, most people, scroll past. That's not a character flaw. That's just what an endless feed trains you to do — protect your attention, protect your mood, keep moving. But here's the thing about this specific kind of video that makes stopping worth it, every single time: the discomfort at the start is never the point. It's the toll you pay to get to the part that actually matters — the shift, the decision, the moment fear loses. Skip the discomfort, and you skip the entire reason the video exists in the first place. You end up with secondhand information about a moment that was never designed to be understood secondhand. Think about the last time a video actually changed your mood for the rest of the day. Not entertained you — changed something. Made you call someone. Made you donate somewhere you'd never donated before. Made you look at your own dog curled up on the couch and feel something different for a second. Those moments are rare precisely because most content isn't built to carry that kind of weight. Most content is built to be forgotten by the next scroll. This isn't most content. There's a reason rescue footage keeps circulating years after it's first posted, resurfacing on different accounts, different platforms, different captions, over and over, long after the original context is gone. It's because moments like this don't expire. A joke gets old. A trend dies in a week. But the exact second an abandoned, broken animal decides to trust a stranger anyway — that doesn't age. It hits the same way in five years that it hits today, because the thing it's tapping into isn't a trend. It's something much older than any platform, any algorithm, any feed. It's the oldest story there is, really, just told without words: something was broken, someone showed up anyway, and against every reasonable expectation, that was enough. We don't get many unscripted, unforced examples of that anymore. Almost everything in front of us now is curated, edited, angled for a reaction. This isn't. This is just what actually happened, captured because a phone happened to be there, no different than it would have looked if no camera existed at all. That's rare. That's worth two minutes of your undivided attention instead of a half-watched scroll-by. So here's the actual ask, plain and simple: don't just watch this one and move on like it's any other post in your feed today. Watch it properly. Let it play all the way through without skipping ahead to see how it ends. Notice the exact second his body changes. Notice what happens right after that. And then, if it moves you even a fraction as much as it should, do one small thing with that feeling before it fades — share it with someone, support a rescue near you, or just remember, the next time you see a stray on the side of a road, that stopping is always an option. It's always been an option. Somebody just has to choose to take it. That choice is the entire video. Everything else is just what happens after it gets made. One last thought, and then I'll let you go watch it instead of reading about it any longer. Somewhere out there, right now, there's another version of this exact scene playing out with no camera anywhere near it. No rescuer. No hand reaching out slowly through tall grass. No moment where fear loses. Just an animal, alone, running out of time in silence. We can't fix that with a video. Nobody's pretending we can. But we can make sure the ones that do get found, the ones that do get a camera and a rescuer and a happy ending, actually get seen — properly seen, not scrolled past in half a second on the way to something louder and easier. This is one of the good ones. One of the rare stories where showing up actually happened in time. Don't waste that by treating it like background noise. He didn't run. Now go find out why.

Earth Unveiled

189,242 Aufrufe • vor 6 Tagen

MY GROK BOT CONTENT SYSTEM JUST WENT VIRAL ON X – SO I THINK YOU NEED TO SEE WHAT'S ACTUALLY IN IT It wasn't the visual that got people. It was realizing this is already possible, today, for anyone. That's the part worth saying out loud. AI agents used to be a developer thing. You needed an API key, a server, a framework, and a weekend you weren't getting back. Grok Bot deleted all of that. You describe the job in plain language, and you have an agent that does it – with its own computer, its own browser, its own files, running whether you're at the desk or not. Which means the question stopped being can I build this and became what do I point it at. People are pointing it at everything: sales outreach, research, SEO, bookkeeping, customer replies, personal admin. This one is aimed at the thing most people say they'll start and never do – publishing consistently. It's seven agents. One in the centre, six in orbit. Here's what each one actually does. CHIEF OF STAFF – the centre. Every request lands here first, gets broken into jobs, and gets routed to whoever owns that kind of work. It chases what's late, keeps the others from duplicating each other, and surfaces exactly one thing to me when a real decision is needed. Everything else it just handles. RESEARCHER – goes out and finds what's actually moving in the niche. Reads the sources, checks the claim, drops the duplicates, and hands over a brief with links attached. Not "here's what I think" – here's what's true and where it came from. WRITER – takes that brief and writes the finished piece in my voice. Hooks, threads, captions, long-form. It's had my old posts as reference, so what comes back isn't a draft I rewrite – it's a draft I approve. VISUALISER – I fed it my reference visuals once. Now every cover, image and dashboard comes back in that exact style, without a single new instruction. This is the agent that kills the "I have the post but no visual" excuse. ANALYST – reads what actually performed. Not vanity numbers: which hook shape landed, which format died, which time slot was dead air. Then it tells the Writer and the Scheduler what to do differently. This is the loop almost every setup is missing, and it's why most of them plateau. SCHEDULER – owns timing. Holds the queue, spaces things properly, protects the calendar, and makes sure nothing ships into an empty feed at 3am. PUBLISHER – ships it. Formats per platform, posts, confirms it went out, logs it back to the Chief. The reason it holds together instead of falling apart: they share memory. The research is already sitting inside the draft before the draft starts. No copy-pasting between tools, no re-explaining context, no approving every step. And you can teach any of them a repetitive task by recording yourself doing it – start recording, do the thing, stop. I wrote the whole thing up below – every agent, what each one is told, how they hand off, and how to wire it together. Read it and you can start building this today, not "eventually" ↓

MY GROK BOT CONTENT SYSTEM JUST WENT VIRAL ON X – SO I THINK YOU NEED TO SEE WHAT'S ACTUALLY IN IT It wasn't the visual that got people. It was realizing this is already possible, today, for anyone. That's the part worth saying out loud. AI agents used to be a developer thing. You needed an API key, a server, a framework, and a weekend you weren't getting back. Grok Bot deleted all of that. You describe the job in plain language, and you have an agent that does it – with its own computer, its own browser, its own files, running whether you're at the desk or not. Which means the question stopped being can I build this and became what do I point it at. People are pointing it at everything: sales outreach, research, SEO, bookkeeping, customer replies, personal admin. This one is aimed at the thing most people say they'll start and never do – publishing consistently. It's seven agents. One in the centre, six in orbit. Here's what each one actually does. CHIEF OF STAFF – the centre. Every request lands here first, gets broken into jobs, and gets routed to whoever owns that kind of work. It chases what's late, keeps the others from duplicating each other, and surfaces exactly one thing to me when a real decision is needed. Everything else it just handles. RESEARCHER – goes out and finds what's actually moving in the niche. Reads the sources, checks the claim, drops the duplicates, and hands over a brief with links attached. Not "here's what I think" – here's what's true and where it came from. WRITER – takes that brief and writes the finished piece in my voice. Hooks, threads, captions, long-form. It's had my old posts as reference, so what comes back isn't a draft I rewrite – it's a draft I approve. VISUALISER – I fed it my reference visuals once. Now every cover, image and dashboard comes back in that exact style, without a single new instruction. This is the agent that kills the "I have the post but no visual" excuse. ANALYST – reads what actually performed. Not vanity numbers: which hook shape landed, which format died, which time slot was dead air. Then it tells the Writer and the Scheduler what to do differently. This is the loop almost every setup is missing, and it's why most of them plateau. SCHEDULER – owns timing. Holds the queue, spaces things properly, protects the calendar, and makes sure nothing ships into an empty feed at 3am. PUBLISHER – ships it. Formats per platform, posts, confirms it went out, logs it back to the Chief. The reason it holds together instead of falling apart: they share memory. The research is already sitting inside the draft before the draft starts. No copy-pasting between tools, no re-explaining context, no approving every step. And you can teach any of them a repetitive task by recording yourself doing it – start recording, do the thing, stop. I wrote the whole thing up below – every agent, what each one is told, how they hand off, and how to wire it together. Read it and you can start building this today, not "eventually" ↓

SCOTTY BEAM

10,486 Aufrufe • vor 17 Tagen

🙌Meet Artifig: A Figma Plugin to Generate Figma Plugins Do you use Figma and ever feel like this: - Your mind is bursting with plugin ideas, but you can't bring them to life because you don't know how to code? - You want to focus on design, but repetitive tasks keep slowing you down? - You dream of creating custom tools for your team, but lack the time or resources? I’ve been there too. That’s why I created Artifig. ✨ What is Artifig? Artifig is an AI-powered Figma plugin that empowers anyone to build their own Figma plugins using just natural language. No coding needed—simply describe what you want, and watch as your idea transforms into a fully functional, real-time plugin. 🚀 Redefining Figma Plugin Development The core philosophy of Artifig is simple: Designers often have countless ideas and creative visions, but many of them remain unrealized due to a lack of technical skills. We believe designers shouldn’t be limited by their inability to code. You should focus on creating, not be held back by technical barriers or repetitive tasks. Artifig takes you directly from "description" to "implementation." 🛠️ How Does It Work? 1. Describe Your Needs: Tell Artifig what you want, like “Create a skew transformation tool for objects, supporting horizontal and vertical skew with real-time preview functionality.” 2. Generate and Run the Plugin: Artifig instantly generates the plugin and runs it right within Figma. For example, the generated plugin can apply skew transformations to objects, precisely controlled via matrix transformations, with an intuitive user experience. 3. Optimize and Iteration: Need adjustments? Simply describe them, and Artifig will Iterating the plugin step by step. 4. Share Your Creations: Publish your plugins to the Artifig community, or remix plugins shared by others to build on their ideas. No learning curve. No complex steps. It’s as simple as that. 🌟 Key Features - Zero Barrier to Entry: No coding experience needed—any Figma user can create plugins effortlessly. - Multilingual Support: Works in multiple languages, including English, Chinese, French, Japanese, and German. - What-You-See-Is-What-You-Get: Generated plugins run in real-time, so you can quickly validate and refine your ideas. - Open and Flexible: The generated plugin code is 100% yours—modify it, distribute it, even use it commercially. - Global Community: Share your plugins, explore others’ creations, and publish your plugins to the Figma community. 🎯 Why is Artifig a Game-Changer? 1. No More Repetitive Work Let AI handle the tedious, time-consuming tasks: batch renaming layers, auto-aligning elements, or applying styles in bulk. All you need to do is say, “Import a PDF and arrange each image on the canvas with 20px spacing.” 2. Quickly Bring Ideas to Life From color contrast checks to data imports and custom components, all your “what if we could” ideas can now become plugins. Just one natural language description, and Artifig makes it happen. 3. Custom Tools for Your Team Build tailored tools for your team, creating unique solutions to streamline your workflow. 4. Not Just a Tool, But a Learning Experience Artifig explains the logic behind the code it generates, helping you understand Figma APIs and JavaScript. Today, you’re a designer; tomorrow, you could also be a design engineer. 🧑‍🚀👩🏻‍💻🥷🏻 Who is Artifig For? - Beginners: No development experience needed—just describe your ideas and let Artifig do the rest. - Experts: Save time and focus on high-value tasks while Artifig handles the repetitive work. - Learners: Use Artifig as a bridge to deepen your understanding of development. - Teams: Build custom tools to enhance collaboration and efficiency. 🎉 Ready to Get Started? I believe designers’ time and focus should be spent on creating, not on wrestling with complex tools. Artifig is the first step toward realizing this vision. Try Artifig now and experience an unprecedented flow of creativity!

🙌Meet Artifig: A Figma Plugin to Generate Figma Plugins Do you use Figma and ever feel like this: - Your mind is bursting with plugin ideas, but you can't bring them to life because you don't know how to code? - You want to focus on design, but repetitive tasks keep slowing you down? - You dream of creating custom tools for your team, but lack the time or resources? I’ve been there too. That’s why I created Artifig. ✨ What is Artifig? Artifig is an AI-powered Figma plugin that empowers anyone to build their own Figma plugins using just natural language. No coding needed—simply describe what you want, and watch as your idea transforms into a fully functional, real-time plugin. 🚀 Redefining Figma Plugin Development The core philosophy of Artifig is simple: Designers often have countless ideas and creative visions, but many of them remain unrealized due to a lack of technical skills. We believe designers shouldn’t be limited by their inability to code. You should focus on creating, not be held back by technical barriers or repetitive tasks. Artifig takes you directly from "description" to "implementation." 🛠️ How Does It Work? 1. Describe Your Needs: Tell Artifig what you want, like “Create a skew transformation tool for objects, supporting horizontal and vertical skew with real-time preview functionality.” 2. Generate and Run the Plugin: Artifig instantly generates the plugin and runs it right within Figma. For example, the generated plugin can apply skew transformations to objects, precisely controlled via matrix transformations, with an intuitive user experience. 3. Optimize and Iteration: Need adjustments? Simply describe them, and Artifig will Iterating the plugin step by step. 4. Share Your Creations: Publish your plugins to the Artifig community, or remix plugins shared by others to build on their ideas. No learning curve. No complex steps. It’s as simple as that. 🌟 Key Features - Zero Barrier to Entry: No coding experience needed—any Figma user can create plugins effortlessly. - Multilingual Support: Works in multiple languages, including English, Chinese, French, Japanese, and German. - What-You-See-Is-What-You-Get: Generated plugins run in real-time, so you can quickly validate and refine your ideas. - Open and Flexible: The generated plugin code is 100% yours—modify it, distribute it, even use it commercially. - Global Community: Share your plugins, explore others’ creations, and publish your plugins to the Figma community. 🎯 Why is Artifig a Game-Changer? 1. No More Repetitive Work Let AI handle the tedious, time-consuming tasks: batch renaming layers, auto-aligning elements, or applying styles in bulk. All you need to do is say, “Import a PDF and arrange each image on the canvas with 20px spacing.” 2. Quickly Bring Ideas to Life From color contrast checks to data imports and custom components, all your “what if we could” ideas can now become plugins. Just one natural language description, and Artifig makes it happen. 3. Custom Tools for Your Team Build tailored tools for your team, creating unique solutions to streamline your workflow. 4. Not Just a Tool, But a Learning Experience Artifig explains the logic behind the code it generates, helping you understand Figma APIs and JavaScript. Today, you’re a designer; tomorrow, you could also be a design engineer. 🧑‍🚀👩🏻‍💻🥷🏻 Who is Artifig For? - Beginners: No development experience needed—just describe your ideas and let Artifig do the rest. - Experts: Save time and focus on high-value tasks while Artifig handles the repetitive work. - Learners: Use Artifig as a bridge to deepen your understanding of development. - Teams: Build custom tools to enhance collaboration and efficiency. 🎉 Ready to Get Started? I believe designers’ time and focus should be spent on creating, not on wrestling with complex tools. Artifig is the first step toward realizing this vision. Try Artifig now and experience an unprecedented flow of creativity!

yancymin

21,222 Aufrufe • vor 1 Jahr

Nobody prepares you for the moment a wild animal decides to trust you. Not "gets used to you." Not "tolerates you." Trusts you — the way a mother trusts someone with the one thing she would die protecting. It doesn't happen because you're kind. Kindness is cheap. Every predator in the bush can fake calm long enough to get close. Trust is not given for kindness. It's given for consistency, for stillness, for the thousand tiny signals a mother reads before she decides you are not a threat to the only life she's carrying. And once you understand that, you understand why what's in this video is not "cute." It's rare. It's the kind of rare that biologists spend entire careers chasing and never fully catch on camera. Here's the part almost nobody knows about kangaroo mothers, and once you know it, you will never look at a pouch the same way again. A kangaroo joey is born the size of a jellybean. Blind. Hairless. Barely more developed than an embryo. It has exactly one job the second it's born: crawl, unassisted, through its mother's fur, into the pouch, and find a teat — with no help from her at all. She doesn't guide it. She doesn't touch it. It's on its own, immediately, in a climb that takes minutes but might as well be a lifetime. Most don't make it. The ones that do spend the next 190 days growing inside that pouch, permanently attached to a single teat, while their mother hops, forages, runs from threats, and lives an entire dangerous life with a developing creature sealed against her body the whole time. Now sit with that for a second. Everything you're about to feel watching this video is built on top of that fact. The pouch isn't a cute detail. It's the most extreme parental investment in the entire animal kingdom, running quietly, invisibly, every single second — and most people scroll past kangaroos their whole life without knowing any of it. This is why the moment in this video hits differently. It's not staged tenderness. It's not a trained animal doing a trick for treats. It's an instinct thousands of generations deep, deciding, in real time, whether the human in front of it gets access to the most guarded thing in its world. And it says yes. Watch what that "yes" actually looks like. Watch the exact second it happens. Because most people miss it — they're looking at the wrong thing, waiting for something big and dramatic, when the real moment is small, quiet, and over in under two seconds. Blink and it's gone. That's the whole point. Trust from a wild animal never announces itself. It just happens, and if you're not paying attention, you miss the most important part of the entire video. Here's something else that will change how you see this. Kangaroos are not naturally social with other species. They are not dogs. They did not evolve for ten thousand years alongside humans, selectively bred generation after generation for affection and eye contact and cooperation. A kangaroo owes you nothing. Its entire evolutionary history says: keep your distance, stay alert, trust nothing that isn't kangaroo. So when you see a wild-born kangaroo choosing proximity — choosing it, not tolerating it — you are watching something closer to a diplomatic breakthrough between two species than a "cute animal moment." It is the exception to millions of years of instinct. And exceptions like that don't happen because an animal is "friendly." They happen because someone, somewhere, earned it slowly, patiently, without ever forcing a single second of it. That's the part that should actually get your attention. Not the fur. Not the ears. The patience behind it that you never see, because patience isn't visual — it's just time, quietly spent, off camera, for weeks, maybe months, before the fifteen seconds you're about to watch were even possible. Think about how many hours of nothing happened before this. Think about how many times the answer was "not yet." Think about how many people would have given up. That's what's actually being shown to you here, hidden inside something that looks simple. The result. Not the process. Just the result — and the process is the part that would actually break most people's patience in half. There's a reason content like this stops people mid-scroll and doesn't let go. It's not really about kangaroos. It's about a feeling almost everyone is quietly starving for: proof that connection across an impossible gap is still possible. That something wild, wary, and instinct-driven can still choose closeness. In a world where even people struggle to trust each other, watching an animal that has every biological reason not to trust anyone — decide to trust anyway — hits something deeper than "aww." It hits the same nerve that made you stop and stare the last time you saw two strangers help each other for no reason. The same nerve that makes rescue videos, reunion videos, "coming home" videos rack up hundreds of millions of views no matter how many times the format repeats. We are wired to seek proof that softness survives in a world that keeps telling us it doesn't. This video is that proof, wearing fur. Now here's the thing that will actually make you want to see it for yourself instead of taking my word for it. There is a specific frame in this video where the entire dynamic flips. Where you stop watching "a kangaroo" and start watching "a mother." The difference is enormous and almost nobody notices it consciously the first time, they just feel it — a shift in the chest, a small catch in the breath, and they don't know why until they rewatch it and actually look for what changed. I'm not going to tell you which frame it is. You'll know it when you feel it. And once you've seen it, you'll understand why people who work with rescued or hand-raised kangaroos talk about them the way new parents talk about their kids — with this specific mixture of exhaustion and reverence, like they've been let in on a secret the rest of the world doesn't know is right there in the daylight the whole time, just going unnoticed by everyone in too much of a hurry to actually look. Here's a fact that will sit with you long after this post: joeys communicate with their mothers primarily through touch and proximity, not sound. A healthy joey barely vocalizes. Silence, for a joey, usually means safety. The moment a joey starts making noise is often the moment something is wrong — a signal, a distress call, a break in the norm. Which means every quiet second in this video isn't empty. It's the sound of everything being exactly as it should be. Contentment doesn't announce itself either. It just exists, unremarked, easy to miss if you're expecting drama. Most people are trained by algorithms to expect the loud moment. The jump-scare cut. The dramatic reveal. This isn't that kind of video, and that's exactly why it works on a level louder content can't touch. There is no trick here. No forced angle. No moment engineered for a thumbnail. Just an animal, in a state most people will never witness in their lifetime, on camera, being real. You will not get this from a zoo enclosure behind glass. You will not get this from a nature documentary narrated over stock drone footage. Documentary crews spend months, sometimes years, waiting for unscripted, undirected, genuine wild behavior like this — and even then, half the time the animal senses the camera and the moment never comes. What you're about to see is the version of nature that doesn't perform for anyone. It just happens to have been caught. That's worth more than people give it credit for. Let's talk about scale for a second, because it recalibrates everything. An adult kangaroo can deliver a kick strong enough to break bone. Their hind legs are built for sudden, explosive force — evolved for fighting off dingoes, for defending against threats many times more aggressive than a curious human. A kangaroo that feels cornered or threatened is not an animal you want anywhere near you. This is not a small, harmless creature performing tricks. It is a powerful wild animal capable of real damage, choosing, in this exact moment, not to use any of that power. That choice is the entire video. Every second of calm you see on screen is a second where a kick, a scratch, a bolt for distance was the more "natural" default option — and it didn't happen. Instead: stillness. Proximity. Something closer to peace than instinct usually allows. People underestimate how remarkable that restraint actually is because the animal looks soft. Fur does that. It disguises power as gentleness in our minds before we even process what we're looking at. But strip away the cuteness bias for one second and look at what's actually happening biologically: a wild animal engineered by evolution for wariness and self-defense is choosing trust over instinct, in real time, on camera, for you. That's not something you see every day. That's not something most people will ever see in person, not even once in an entire lifetime. And it's sitting right there, one tap away. There's a version of this story that plays out constantly in nature and almost never gets filmed: the version where the trust never comes. Where the animal stays wary its entire life, where every human interaction ends the second the animal has an exit route, where the gap between species never closes even an inch. That's actually the statistical default. That's what "wild animal" usually means in practice — permanent, unbridgeable distance, no matter how much time passes. This video is the exception you almost never get to see. And exceptions, by definition, don't repeat on command. You can't schedule this. You can't force it into existing on a Tuesday afternoon because you need content. It either happens or it doesn't, and when it does, someone lucky enough to be holding a camera captures something the rest of us don't get. You're one of the lucky ones today. You get to see it too. Here's something people don't think about: what happens to footage like this after the camera stops rolling? The relationship doesn't end when the clip does. Whatever trust was built to make this moment possible keeps existing, keeps deepening, long after anyone stopped filming. The video you're about to watch is a single frozen cross-section of something much longer, much slower, much more patient than fifteen or thirty seconds could ever fully capture. Which means what you're seeing isn't the whole story. It's a highlight pulled from something bigger — a relationship, a history, a slow-built trust that took real time to construct and will keep existing whether or not anyone's watching. That's actually the most reassuring part of all of this. It's not a one-off stunt for the algorithm. It's a snapshot of something ongoing, something real, something that existed before the phone came out and kept existing after it went back in someone's pocket. That's a very different feeling than most viral content gives you. Most viral content is a closed loop — what you see is all there is, manufactured specifically to be seen, with nothing behind it. This is the opposite. This is a small window into something much larger than the video itself, and once you sense that, you start watching it differently. You start looking for what it implies rather than just what it shows. Let's go back to the mother for a second, because everything in this video ultimately orbits around her. Maternal instinct in kangaroos is so strong it can override basic self-preservation. There are documented cases of kangaroo mothers refusing to abandon injured joeys even when fleeing would have meant their own survival. Refusing to leave, even when every other instinct screams to run. That's not a Disney embellishment. That's observed, recorded animal behavior, the kind field researchers write papers about because it defies simple explanations of self-interest. So when you watch a mother in this state — calm, present, unhurried — you're not just watching an animal relax. You're watching the single most protective creature in the scene voluntarily lower its guard. Not because it forgot to be careful. Because, in this specific moment, with this specific presence nearby, it decided carefulness wasn't necessary. That decision is not nothing. That decision is everything. If you've ever had a genuinely anxious animal — a rescue dog that flinched at every hand for months, a cat that hid for weeks before finally sitting on your lap — you already know this feeling in your body even if you've never thought about it in these words. You know the specific, quiet triumph of the first time a scared creature decides you're safe. It's not loud. It's not dramatic. It's just a small shift, and it means more than almost anything else that animal could do. Now imagine that same feeling, except the animal isn't a domesticated pet bred for six thousand years to eventually trust a human. Imagine it's a wild mother kangaroo, entirely unindebted to your species, deciding the exact same thing. That's what you're about to watch. Here's the last thing I'll say before you go see it for yourself, because I think it matters more than any single fact I've already given you. We spend so much time online watching things designed to make us feel something — outrage engineered for clicks, drama engineered for watch time, conflict engineered for engagement. Almost none of it is real in the way this is real. Almost none of it exists independent of the platform that's showing it to you. This does. This kangaroo, this mother, this joey, this quiet unscripted trust — none of it needed you to exist. It was happening whether or not it ever made it to a screen. You're not watching content that was built for you. You're watching a moment of the actual world that someone happened to catch and decided to share. That distinction matters more than people realize. It's the difference between watching a performance and witnessing something true. Everyone can sense that difference on some level, even if they can't articulate it — it's why certain videos feel hollow the second they end, and others sit with you for the rest of the day. This is the second kind. Go watch it. Not for the aww, though you'll get that too. Watch it for the two seconds where instinct loses to trust, where a powerful, wary, wild animal decides a human is safe, where something ancient and untamed chooses softness anyway. Watch it for the frame you won't notice until the second time through. Watch it because moments like this are rarer than almost anything else the internet shows you, and rare things deserve more than a passive scroll past. You'll know exactly which second I meant the moment you see it. And once you do, you'll probably watch it again — not because you missed something the first time, but because now you know what you're actually looking at, and it hits completely differently the second time around. That's the real test of whether something is genuinely special or just briefly novel: does it get better once you understand it, or does it lose its shine the moment the surprise wears off? This gets better. Every rewatch reveals one more tiny detail you missed under the weight of the first, bigger emotional hit — the exact angle of an ear, the precise moment a body goes still, the breath held right before everything relaxes. Small things. Human-scale things, if kangaroos had a human scale. The kind of details that only reveal themselves once the initial "aww" has worn off enough for you to actually look. That's the mark of something worth your attention in a feed built almost entirely around things that aren't. So here's the only ask: don't just let this autoplay past you like everything else today. Actually watch it. Full attention, even if just for the length of a single held breath. It costs you nothing and it's one of maybe a handful of genuinely rare things you'll come across this week, buried in an endless river of things that are anything but. Somewhere out there, right now, a wild kangaroo mother is calm around a human she has every biological reason to fear. That single fact alone should be enough to make you want to see how it happened. Let's talk about something people almost never mention when they talk about kangaroos: how absurdly efficient their bodies actually are, and why that matters for understanding what restraint really costs a wild animal like this. A kangaroo's hop isn't just a way of moving. It's one of the most energy-efficient forms of locomotion in the entire animal kingdom. Their tendons act like giant rubber bands, storing and releasing elastic energy with almost no metabolic cost, which is why a kangaroo can cover huge distances at high speed while barely raising its heart rate. Past a certain speed, it actually becomes more efficient for a kangaroo to hop faster, not slower — the opposite of almost every other animal on earth. Why does that matter here? Because an animal built like that has almost nothing to lose by fleeing. Running isn't costly for a kangaroo the way it is for, say, a heavier, slower prey animal. There's no real biological argument for "staying is easier than leaving." Fleeing is cheap. Fleeing is basically free. Which means every second a kangaroo chooses to stay instead of bolt is a second it actively decided against the easiest possible option. That's not an animal that "couldn't be bothered to run." That's an animal that had every reason to leave, at almost no cost to itself, and chose not to. Sit with that for a second, because it reframes the entire video. This isn't laziness or indifference dressed up as trust. It's an active decision made over and over, every single second the camera is rolling, by a creature for whom leaving would have taken almost no effort at all. Here's another thing worth knowing before you watch: kangaroos are far smarter than most people give them credit for. Studies on kangaroo cognition have found they're capable of intentional communication with humans — deliberately looking at a person, then looking at an object, then back at the person again, the same gaze-alternation behavior researchers use as a gold-standard test for interspecies communication in dogs. Wild kangaroos aren't supposed to do that. Domesticated animals do that. Animals that have spent millennia being selected for human cooperation do that. Kangaroos were never bred for any of it. And yet, under the right conditions, some of them do it anyway. Which means the "connection" people feel radiating off videos like this isn't just a projection. It isn't just humans anthropomorphizing an animal that's actually indifferent to them. There's a real, measurable capacity for intentional engagement sitting underneath the fur — a capacity most people assume only exists in dogs, elephants, dolphins, the "smart" animals we've decided deserve credit for having inner lives. Kangaroos rarely make that list. Maybe they should. Now think about the algorithm for a second, because this part matters if you've ever wondered why certain animal videos explode past everything else in your feed while a hundred nearly identical ones disappear without a trace. Platforms don't reward "cute." Cute is everywhere; cute is the baseline, the noise floor of the entire internet. What actually stops a scroll and holds it is specificity — a moment that couldn't have happened just anywhere, to just anyone, with just any animal. The algorithm has effectively been trained, by billions of collective viewing decisions, to recognize the difference between generic content and a genuinely unrepeatable moment. And it rewards the second kind disproportionately, because it knows — better than we consciously do — that unrepeatable is the one thing viewers actually crave and almost never get. That's the quiet reason something like this travels the way it does. It's not marketing. It's not a trick. It's the platform correctly identifying that you're looking at something it can't manufacture on demand, and putting it in front of as many people as possible before the window closes. Here's a myth worth busting while we're at it, because it changes how you'll watch every future animal video, not just this one. People assume wild animals that appear "comfortable" around humans have been habituated through food — that somewhere off camera, there's a hand full of treats explaining all of it away. Sometimes that's true. Often it isn't. Genuine trust-based bonds, the kind built through slow, repeated, low-pressure exposure rather than bribery, tend to look calmer, quieter, and far less food-motivated than the habituated version. An animal trained through treats orients toward the treat. An animal that trusts you orients toward you. Watch for that distinction. It's subtle, but once you know to look for it, you'll never unsee it — in this video, and in every animal video you watch after this one. There's also a slower, sadder truth sitting underneath all of this that's worth acknowledging honestly, because it's part of why moments like this hit as hard as they do: most wild animals never get the chance to reach this state with a human at all. Not because they're incapable of it, but because most human contact with wildlife is fear, distance, or conflict. Roadways. Habitat loss. Encounters that end badly for one side or the other. The overwhelming majority of interactions between wild kangaroos and humans are neutral at best and dangerous at worst — for the kangaroo far more often than for the person. Which is exactly why the rare exception matters as much as it does. It's not just heartwarming in isolation. It's heartwarming because it's fighting the statistical odds of what usually happens when these two worlds intersect. Every calm, trusting moment like this is quietly making the case that the outcome doesn't have to be conflict by default — that with enough patience, enough respect for the animal's pace, enough restraint from forcing anything, something gentler is possible instead. That's a bigger idea than one video can carry on its own, but this video is a small, real piece of evidence for it. And evidence like that is worth more than another opinion piece ever could be, because you can't argue with something you're watching happen in real time in front of you. One more angle before you go find it and watch it yourself. Think about how many hours of your own life you've spent watching things that gave you nothing back — content built purely to fill fifteen seconds of dead time between one task and the next, forgotten before you've even finished swallowing whatever you were drinking while you watched it. That's most of what a feed is. Filler. Noise dressed up as content, optimized for exactly long enough to keep you from closing the app, and not one second longer. This isn't that. This is the rare kind of thing that actually leaves a residue — a specific image that resurfaces uninvited hours later, while you're doing something completely unrelated, because some part of your brain flagged it as meaningful and refused to fully let it go. Most content doesn't earn that. Most content isn't built to earn that. It's built to be replaced by the next thing thirty seconds later, forever, in an endless chain that leaves nothing behind. You'll know within the first few seconds whether this is one of the rare exceptions. Trust that instinct. It's the same instinct that made you stop scrolling long enough to read all the way down to this line instead of bouncing off after the first sentence like you do with almost everything else that crosses your screen today. That instinct is rarely wrong. One last thought, and then I'll stop talking and let the thing speak for itself. Somewhere between the jellybean-sized joey that shouldn't have survived its own birth, the mother who could kill with one kick and chose stillness instead, and the two-second frame you won't notice until you watch it twice — there's a version of this video that never existed. The version where the timing was off by a week, where the trust hadn't built far enough yet, where the camera wasn't rolling, where the moment happened anyway but nobody was there to see it and it simply evaporated into the ordinary, unwitnessed life of a wild animal in the bush. That version happens constantly. It's the default. It's what "wild animal" statistically means, every single day, all over the world, completely unseen. This is the version that didn't evaporate. This is the one that got caught. Not manufactured. Not staged. Not built for you in a studio, in a script, in an editing room designed to make you feel something on command. Just real, and rare, and lucky enough to have a lens pointed at it at the exact right second. You don't get many of those in a lifetime, let alone in a single scroll through your phone on an otherwise ordinary day. This is one of them.

Nobody prepares you for the moment a wild animal decides to trust you. Not "gets used to you." Not "tolerates you." Trusts you — the way a mother trusts someone with the one thing she would die protecting. It doesn't happen because you're kind. Kindness is cheap. Every predator in the bush can fake calm long enough to get close. Trust is not given for kindness. It's given for consistency, for stillness, for the thousand tiny signals a mother reads before she decides you are not a threat to the only life she's carrying. And once you understand that, you understand why what's in this video is not "cute." It's rare. It's the kind of rare that biologists spend entire careers chasing and never fully catch on camera. Here's the part almost nobody knows about kangaroo mothers, and once you know it, you will never look at a pouch the same way again. A kangaroo joey is born the size of a jellybean. Blind. Hairless. Barely more developed than an embryo. It has exactly one job the second it's born: crawl, unassisted, through its mother's fur, into the pouch, and find a teat — with no help from her at all. She doesn't guide it. She doesn't touch it. It's on its own, immediately, in a climb that takes minutes but might as well be a lifetime. Most don't make it. The ones that do spend the next 190 days growing inside that pouch, permanently attached to a single teat, while their mother hops, forages, runs from threats, and lives an entire dangerous life with a developing creature sealed against her body the whole time. Now sit with that for a second. Everything you're about to feel watching this video is built on top of that fact. The pouch isn't a cute detail. It's the most extreme parental investment in the entire animal kingdom, running quietly, invisibly, every single second — and most people scroll past kangaroos their whole life without knowing any of it. This is why the moment in this video hits differently. It's not staged tenderness. It's not a trained animal doing a trick for treats. It's an instinct thousands of generations deep, deciding, in real time, whether the human in front of it gets access to the most guarded thing in its world. And it says yes. Watch what that "yes" actually looks like. Watch the exact second it happens. Because most people miss it — they're looking at the wrong thing, waiting for something big and dramatic, when the real moment is small, quiet, and over in under two seconds. Blink and it's gone. That's the whole point. Trust from a wild animal never announces itself. It just happens, and if you're not paying attention, you miss the most important part of the entire video. Here's something else that will change how you see this. Kangaroos are not naturally social with other species. They are not dogs. They did not evolve for ten thousand years alongside humans, selectively bred generation after generation for affection and eye contact and cooperation. A kangaroo owes you nothing. Its entire evolutionary history says: keep your distance, stay alert, trust nothing that isn't kangaroo. So when you see a wild-born kangaroo choosing proximity — choosing it, not tolerating it — you are watching something closer to a diplomatic breakthrough between two species than a "cute animal moment." It is the exception to millions of years of instinct. And exceptions like that don't happen because an animal is "friendly." They happen because someone, somewhere, earned it slowly, patiently, without ever forcing a single second of it. That's the part that should actually get your attention. Not the fur. Not the ears. The patience behind it that you never see, because patience isn't visual — it's just time, quietly spent, off camera, for weeks, maybe months, before the fifteen seconds you're about to watch were even possible. Think about how many hours of nothing happened before this. Think about how many times the answer was "not yet." Think about how many people would have given up. That's what's actually being shown to you here, hidden inside something that looks simple. The result. Not the process. Just the result — and the process is the part that would actually break most people's patience in half. There's a reason content like this stops people mid-scroll and doesn't let go. It's not really about kangaroos. It's about a feeling almost everyone is quietly starving for: proof that connection across an impossible gap is still possible. That something wild, wary, and instinct-driven can still choose closeness. In a world where even people struggle to trust each other, watching an animal that has every biological reason not to trust anyone — decide to trust anyway — hits something deeper than "aww." It hits the same nerve that made you stop and stare the last time you saw two strangers help each other for no reason. The same nerve that makes rescue videos, reunion videos, "coming home" videos rack up hundreds of millions of views no matter how many times the format repeats. We are wired to seek proof that softness survives in a world that keeps telling us it doesn't. This video is that proof, wearing fur. Now here's the thing that will actually make you want to see it for yourself instead of taking my word for it. There is a specific frame in this video where the entire dynamic flips. Where you stop watching "a kangaroo" and start watching "a mother." The difference is enormous and almost nobody notices it consciously the first time, they just feel it — a shift in the chest, a small catch in the breath, and they don't know why until they rewatch it and actually look for what changed. I'm not going to tell you which frame it is. You'll know it when you feel it. And once you've seen it, you'll understand why people who work with rescued or hand-raised kangaroos talk about them the way new parents talk about their kids — with this specific mixture of exhaustion and reverence, like they've been let in on a secret the rest of the world doesn't know is right there in the daylight the whole time, just going unnoticed by everyone in too much of a hurry to actually look. Here's a fact that will sit with you long after this post: joeys communicate with their mothers primarily through touch and proximity, not sound. A healthy joey barely vocalizes. Silence, for a joey, usually means safety. The moment a joey starts making noise is often the moment something is wrong — a signal, a distress call, a break in the norm. Which means every quiet second in this video isn't empty. It's the sound of everything being exactly as it should be. Contentment doesn't announce itself either. It just exists, unremarked, easy to miss if you're expecting drama. Most people are trained by algorithms to expect the loud moment. The jump-scare cut. The dramatic reveal. This isn't that kind of video, and that's exactly why it works on a level louder content can't touch. There is no trick here. No forced angle. No moment engineered for a thumbnail. Just an animal, in a state most people will never witness in their lifetime, on camera, being real. You will not get this from a zoo enclosure behind glass. You will not get this from a nature documentary narrated over stock drone footage. Documentary crews spend months, sometimes years, waiting for unscripted, undirected, genuine wild behavior like this — and even then, half the time the animal senses the camera and the moment never comes. What you're about to see is the version of nature that doesn't perform for anyone. It just happens to have been caught. That's worth more than people give it credit for. Let's talk about scale for a second, because it recalibrates everything. An adult kangaroo can deliver a kick strong enough to break bone. Their hind legs are built for sudden, explosive force — evolved for fighting off dingoes, for defending against threats many times more aggressive than a curious human. A kangaroo that feels cornered or threatened is not an animal you want anywhere near you. This is not a small, harmless creature performing tricks. It is a powerful wild animal capable of real damage, choosing, in this exact moment, not to use any of that power. That choice is the entire video. Every second of calm you see on screen is a second where a kick, a scratch, a bolt for distance was the more "natural" default option — and it didn't happen. Instead: stillness. Proximity. Something closer to peace than instinct usually allows. People underestimate how remarkable that restraint actually is because the animal looks soft. Fur does that. It disguises power as gentleness in our minds before we even process what we're looking at. But strip away the cuteness bias for one second and look at what's actually happening biologically: a wild animal engineered by evolution for wariness and self-defense is choosing trust over instinct, in real time, on camera, for you. That's not something you see every day. That's not something most people will ever see in person, not even once in an entire lifetime. And it's sitting right there, one tap away. There's a version of this story that plays out constantly in nature and almost never gets filmed: the version where the trust never comes. Where the animal stays wary its entire life, where every human interaction ends the second the animal has an exit route, where the gap between species never closes even an inch. That's actually the statistical default. That's what "wild animal" usually means in practice — permanent, unbridgeable distance, no matter how much time passes. This video is the exception you almost never get to see. And exceptions, by definition, don't repeat on command. You can't schedule this. You can't force it into existing on a Tuesday afternoon because you need content. It either happens or it doesn't, and when it does, someone lucky enough to be holding a camera captures something the rest of us don't get. You're one of the lucky ones today. You get to see it too. Here's something people don't think about: what happens to footage like this after the camera stops rolling? The relationship doesn't end when the clip does. Whatever trust was built to make this moment possible keeps existing, keeps deepening, long after anyone stopped filming. The video you're about to watch is a single frozen cross-section of something much longer, much slower, much more patient than fifteen or thirty seconds could ever fully capture. Which means what you're seeing isn't the whole story. It's a highlight pulled from something bigger — a relationship, a history, a slow-built trust that took real time to construct and will keep existing whether or not anyone's watching. That's actually the most reassuring part of all of this. It's not a one-off stunt for the algorithm. It's a snapshot of something ongoing, something real, something that existed before the phone came out and kept existing after it went back in someone's pocket. That's a very different feeling than most viral content gives you. Most viral content is a closed loop — what you see is all there is, manufactured specifically to be seen, with nothing behind it. This is the opposite. This is a small window into something much larger than the video itself, and once you sense that, you start watching it differently. You start looking for what it implies rather than just what it shows. Let's go back to the mother for a second, because everything in this video ultimately orbits around her. Maternal instinct in kangaroos is so strong it can override basic self-preservation. There are documented cases of kangaroo mothers refusing to abandon injured joeys even when fleeing would have meant their own survival. Refusing to leave, even when every other instinct screams to run. That's not a Disney embellishment. That's observed, recorded animal behavior, the kind field researchers write papers about because it defies simple explanations of self-interest. So when you watch a mother in this state — calm, present, unhurried — you're not just watching an animal relax. You're watching the single most protective creature in the scene voluntarily lower its guard. Not because it forgot to be careful. Because, in this specific moment, with this specific presence nearby, it decided carefulness wasn't necessary. That decision is not nothing. That decision is everything. If you've ever had a genuinely anxious animal — a rescue dog that flinched at every hand for months, a cat that hid for weeks before finally sitting on your lap — you already know this feeling in your body even if you've never thought about it in these words. You know the specific, quiet triumph of the first time a scared creature decides you're safe. It's not loud. It's not dramatic. It's just a small shift, and it means more than almost anything else that animal could do. Now imagine that same feeling, except the animal isn't a domesticated pet bred for six thousand years to eventually trust a human. Imagine it's a wild mother kangaroo, entirely unindebted to your species, deciding the exact same thing. That's what you're about to watch. Here's the last thing I'll say before you go see it for yourself, because I think it matters more than any single fact I've already given you. We spend so much time online watching things designed to make us feel something — outrage engineered for clicks, drama engineered for watch time, conflict engineered for engagement. Almost none of it is real in the way this is real. Almost none of it exists independent of the platform that's showing it to you. This does. This kangaroo, this mother, this joey, this quiet unscripted trust — none of it needed you to exist. It was happening whether or not it ever made it to a screen. You're not watching content that was built for you. You're watching a moment of the actual world that someone happened to catch and decided to share. That distinction matters more than people realize. It's the difference between watching a performance and witnessing something true. Everyone can sense that difference on some level, even if they can't articulate it — it's why certain videos feel hollow the second they end, and others sit with you for the rest of the day. This is the second kind. Go watch it. Not for the aww, though you'll get that too. Watch it for the two seconds where instinct loses to trust, where a powerful, wary, wild animal decides a human is safe, where something ancient and untamed chooses softness anyway. Watch it for the frame you won't notice until the second time through. Watch it because moments like this are rarer than almost anything else the internet shows you, and rare things deserve more than a passive scroll past. You'll know exactly which second I meant the moment you see it. And once you do, you'll probably watch it again — not because you missed something the first time, but because now you know what you're actually looking at, and it hits completely differently the second time around. That's the real test of whether something is genuinely special or just briefly novel: does it get better once you understand it, or does it lose its shine the moment the surprise wears off? This gets better. Every rewatch reveals one more tiny detail you missed under the weight of the first, bigger emotional hit — the exact angle of an ear, the precise moment a body goes still, the breath held right before everything relaxes. Small things. Human-scale things, if kangaroos had a human scale. The kind of details that only reveal themselves once the initial "aww" has worn off enough for you to actually look. That's the mark of something worth your attention in a feed built almost entirely around things that aren't. So here's the only ask: don't just let this autoplay past you like everything else today. Actually watch it. Full attention, even if just for the length of a single held breath. It costs you nothing and it's one of maybe a handful of genuinely rare things you'll come across this week, buried in an endless river of things that are anything but. Somewhere out there, right now, a wild kangaroo mother is calm around a human she has every biological reason to fear. That single fact alone should be enough to make you want to see how it happened. Let's talk about something people almost never mention when they talk about kangaroos: how absurdly efficient their bodies actually are, and why that matters for understanding what restraint really costs a wild animal like this. A kangaroo's hop isn't just a way of moving. It's one of the most energy-efficient forms of locomotion in the entire animal kingdom. Their tendons act like giant rubber bands, storing and releasing elastic energy with almost no metabolic cost, which is why a kangaroo can cover huge distances at high speed while barely raising its heart rate. Past a certain speed, it actually becomes more efficient for a kangaroo to hop faster, not slower — the opposite of almost every other animal on earth. Why does that matter here? Because an animal built like that has almost nothing to lose by fleeing. Running isn't costly for a kangaroo the way it is for, say, a heavier, slower prey animal. There's no real biological argument for "staying is easier than leaving." Fleeing is cheap. Fleeing is basically free. Which means every second a kangaroo chooses to stay instead of bolt is a second it actively decided against the easiest possible option. That's not an animal that "couldn't be bothered to run." That's an animal that had every reason to leave, at almost no cost to itself, and chose not to. Sit with that for a second, because it reframes the entire video. This isn't laziness or indifference dressed up as trust. It's an active decision made over and over, every single second the camera is rolling, by a creature for whom leaving would have taken almost no effort at all. Here's another thing worth knowing before you watch: kangaroos are far smarter than most people give them credit for. Studies on kangaroo cognition have found they're capable of intentional communication with humans — deliberately looking at a person, then looking at an object, then back at the person again, the same gaze-alternation behavior researchers use as a gold-standard test for interspecies communication in dogs. Wild kangaroos aren't supposed to do that. Domesticated animals do that. Animals that have spent millennia being selected for human cooperation do that. Kangaroos were never bred for any of it. And yet, under the right conditions, some of them do it anyway. Which means the "connection" people feel radiating off videos like this isn't just a projection. It isn't just humans anthropomorphizing an animal that's actually indifferent to them. There's a real, measurable capacity for intentional engagement sitting underneath the fur — a capacity most people assume only exists in dogs, elephants, dolphins, the "smart" animals we've decided deserve credit for having inner lives. Kangaroos rarely make that list. Maybe they should. Now think about the algorithm for a second, because this part matters if you've ever wondered why certain animal videos explode past everything else in your feed while a hundred nearly identical ones disappear without a trace. Platforms don't reward "cute." Cute is everywhere; cute is the baseline, the noise floor of the entire internet. What actually stops a scroll and holds it is specificity — a moment that couldn't have happened just anywhere, to just anyone, with just any animal. The algorithm has effectively been trained, by billions of collective viewing decisions, to recognize the difference between generic content and a genuinely unrepeatable moment. And it rewards the second kind disproportionately, because it knows — better than we consciously do — that unrepeatable is the one thing viewers actually crave and almost never get. That's the quiet reason something like this travels the way it does. It's not marketing. It's not a trick. It's the platform correctly identifying that you're looking at something it can't manufacture on demand, and putting it in front of as many people as possible before the window closes. Here's a myth worth busting while we're at it, because it changes how you'll watch every future animal video, not just this one. People assume wild animals that appear "comfortable" around humans have been habituated through food — that somewhere off camera, there's a hand full of treats explaining all of it away. Sometimes that's true. Often it isn't. Genuine trust-based bonds, the kind built through slow, repeated, low-pressure exposure rather than bribery, tend to look calmer, quieter, and far less food-motivated than the habituated version. An animal trained through treats orients toward the treat. An animal that trusts you orients toward you. Watch for that distinction. It's subtle, but once you know to look for it, you'll never unsee it — in this video, and in every animal video you watch after this one. There's also a slower, sadder truth sitting underneath all of this that's worth acknowledging honestly, because it's part of why moments like this hit as hard as they do: most wild animals never get the chance to reach this state with a human at all. Not because they're incapable of it, but because most human contact with wildlife is fear, distance, or conflict. Roadways. Habitat loss. Encounters that end badly for one side or the other. The overwhelming majority of interactions between wild kangaroos and humans are neutral at best and dangerous at worst — for the kangaroo far more often than for the person. Which is exactly why the rare exception matters as much as it does. It's not just heartwarming in isolation. It's heartwarming because it's fighting the statistical odds of what usually happens when these two worlds intersect. Every calm, trusting moment like this is quietly making the case that the outcome doesn't have to be conflict by default — that with enough patience, enough respect for the animal's pace, enough restraint from forcing anything, something gentler is possible instead. That's a bigger idea than one video can carry on its own, but this video is a small, real piece of evidence for it. And evidence like that is worth more than another opinion piece ever could be, because you can't argue with something you're watching happen in real time in front of you. One more angle before you go find it and watch it yourself. Think about how many hours of your own life you've spent watching things that gave you nothing back — content built purely to fill fifteen seconds of dead time between one task and the next, forgotten before you've even finished swallowing whatever you were drinking while you watched it. That's most of what a feed is. Filler. Noise dressed up as content, optimized for exactly long enough to keep you from closing the app, and not one second longer. This isn't that. This is the rare kind of thing that actually leaves a residue — a specific image that resurfaces uninvited hours later, while you're doing something completely unrelated, because some part of your brain flagged it as meaningful and refused to fully let it go. Most content doesn't earn that. Most content isn't built to earn that. It's built to be replaced by the next thing thirty seconds later, forever, in an endless chain that leaves nothing behind. You'll know within the first few seconds whether this is one of the rare exceptions. Trust that instinct. It's the same instinct that made you stop scrolling long enough to read all the way down to this line instead of bouncing off after the first sentence like you do with almost everything else that crosses your screen today. That instinct is rarely wrong. One last thought, and then I'll stop talking and let the thing speak for itself. Somewhere between the jellybean-sized joey that shouldn't have survived its own birth, the mother who could kill with one kick and chose stillness instead, and the two-second frame you won't notice until you watch it twice — there's a version of this video that never existed. The version where the timing was off by a week, where the trust hadn't built far enough yet, where the camera wasn't rolling, where the moment happened anyway but nobody was there to see it and it simply evaporated into the ordinary, unwitnessed life of a wild animal in the bush. That version happens constantly. It's the default. It's what "wild animal" statistically means, every single day, all over the world, completely unseen. This is the version that didn't evaporate. This is the one that got caught. Not manufactured. Not staged. Not built for you in a studio, in a script, in an editing room designed to make you feel something on command. Just real, and rare, and lucky enough to have a lens pointed at it at the exact right second. You don't get many of those in a lifetime, let alone in a single scroll through your phone on an otherwise ordinary day. This is one of them.

Earth Unveiled

37,997 Aufrufe • vor 9 Tagen

I found God on 300mg of DMT at DreamMind. . . At the core of a human being is a divine spark of light. Some call it their spirit or their soul. That divine spark of light drives our physical human bodies. Our physical bodies operate on the 3rd dimensional plane, but our souls operate on a higher plane. Breathe work, meditation, sound, etc. allow humans to disconnect from their physical bodies and operate on this “soul level” of reality. You know that feeling when you’re thinking of someone and they call you? That is your soul operating on a higher plane. You know the feeling when you’re day dreaming and visualize a different time in a different place? Or that feeling when you’re in a dream and you just know it’s real? It’s because it is real, in a different time and place, on a higher plane of reality where 3rd dimensional rules don’t apply. Psychedelics like DMT (ayahuasca), Psilocybin (mushrooms), mescaline (peyote), etc give us the ability to interact with these higher realms. You could say they “thin the veil” between levels of reality. Native people have been using meditation, breath, sound, and psychedelics for tens of thousands of years. Modern businessmen like Steve Jobs have credited meditation and psychedelics as one of the most impactful experiences of their lives. Why? Because it allows them to connect, or “tune in” with the spirit realm/higher dimension/whatever you want to call it. Think of it like a video game…once you tap in to this higher consciousness level, you gain access to knowledge, understanding, etc. It’s a level up so to speak. BUT… It’s not that easy. If you have negative things you have buried deep, they will be exposed if you want to ascend. You will be forced to be the most honest with yourself that you have ever been. Some aren’t ready for that in this modern society we live in. But others are ready. You will be forced to face the feeling of dying. For some it takes traumatic life experiences to understand that feeling. For others it could take eating mushrooms with your buddies in the backyard. Sounds scary, but there are some positives to experiencing the feeling of death. You realize that death of your physical body is just a transition of your soul from the 3rd dimensional plane of existence to a higher realm. Once you realize that your soul never dies, you lose that “fear of death” so to speak. Going into this 300mg extended state DMTx journey, I already knew what death felt like, and made peace with that before going in. I’ve learned there is knowledge found on the edge of death that only comes to those who have felt it. I’ve learned that speaking an intention can help unlock things you want to figure out as well. My intention going in was to bring back knowledge to help humanity “wake up” so to speak. Now that you have a baseline understanding, let me tell you what happened. This is how I would describe it. “I felt the weight of humanity resting on my heart, like it is my job to carry the torch for all of mankind. And that feeling was one of pure love, gratitude, and understanding of the gravity of what that meant. But I realized that feeling isn’t just in me, that feeling is inside all of us, waiting to be unlocked. That pure fire of all knowing belief that no matter what evil and darkness throws at us, NOTHING can extinguish the flame, it’s NOT possible. That understanding, if felt by all, could free a people from the chains of slavery they don’t even know exist. That flame is the divine spark of existence that is humanity. That divine spark is GOD.” You see…it took me traveling to the edge of death to understand that good ALWAYS defeats evil. The flame of life cannot be extinguished. It’s impossible. They call that faith. Your divine soul never dies. Once you realize it, you have a duty to tell others. All it takes is one person. One thought. One step. And the world is changed forever. That’s how easy love conquers fear. That’s how fast good defeats evil. Imagine an army of humans they are truly awake. Truly aware to the scam that we call this modern society. They would move mountains and part seas. They would see that we are just ONE STEP away. One step away from transcending the fear. One step from overcoming the negativity. One step from freedom. You have that power inside you, waiting to be unlocked. You are God expressing himself in human form. You have an eternal flame burning inside you. A divine spark that CANNOT be extinguished. The power to overcome any and all. That power is the divine spark of existence. That power is GOD. You are a super natural being… Wake up and start acting like it.

I found God on 300mg of DMT at DreamMind. . . At the core of a human being is a divine spark of light. Some call it their spirit or their soul. That divine spark of light drives our physical human bodies. Our physical bodies operate on the 3rd dimensional plane, but our souls operate on a higher plane. Breathe work, meditation, sound, etc. allow humans to disconnect from their physical bodies and operate on this “soul level” of reality. You know that feeling when you’re thinking of someone and they call you? That is your soul operating on a higher plane. You know the feeling when you’re day dreaming and visualize a different time in a different place? Or that feeling when you’re in a dream and you just know it’s real? It’s because it is real, in a different time and place, on a higher plane of reality where 3rd dimensional rules don’t apply. Psychedelics like DMT (ayahuasca), Psilocybin (mushrooms), mescaline (peyote), etc give us the ability to interact with these higher realms. You could say they “thin the veil” between levels of reality. Native people have been using meditation, breath, sound, and psychedelics for tens of thousands of years. Modern businessmen like Steve Jobs have credited meditation and psychedelics as one of the most impactful experiences of their lives. Why? Because it allows them to connect, or “tune in” with the spirit realm/higher dimension/whatever you want to call it. Think of it like a video game…once you tap in to this higher consciousness level, you gain access to knowledge, understanding, etc. It’s a level up so to speak. BUT… It’s not that easy. If you have negative things you have buried deep, they will be exposed if you want to ascend. You will be forced to be the most honest with yourself that you have ever been. Some aren’t ready for that in this modern society we live in. But others are ready. You will be forced to face the feeling of dying. For some it takes traumatic life experiences to understand that feeling. For others it could take eating mushrooms with your buddies in the backyard. Sounds scary, but there are some positives to experiencing the feeling of death. You realize that death of your physical body is just a transition of your soul from the 3rd dimensional plane of existence to a higher realm. Once you realize that your soul never dies, you lose that “fear of death” so to speak. Going into this 300mg extended state DMTx journey, I already knew what death felt like, and made peace with that before going in. I’ve learned there is knowledge found on the edge of death that only comes to those who have felt it. I’ve learned that speaking an intention can help unlock things you want to figure out as well. My intention going in was to bring back knowledge to help humanity “wake up” so to speak. Now that you have a baseline understanding, let me tell you what happened. This is how I would describe it. “I felt the weight of humanity resting on my heart, like it is my job to carry the torch for all of mankind. And that feeling was one of pure love, gratitude, and understanding of the gravity of what that meant. But I realized that feeling isn’t just in me, that feeling is inside all of us, waiting to be unlocked. That pure fire of all knowing belief that no matter what evil and darkness throws at us, NOTHING can extinguish the flame, it’s NOT possible. That understanding, if felt by all, could free a people from the chains of slavery they don’t even know exist. That flame is the divine spark of existence that is humanity. That divine spark is GOD.” You see…it took me traveling to the edge of death to understand that good ALWAYS defeats evil. The flame of life cannot be extinguished. It’s impossible. They call that faith. Your divine soul never dies. Once you realize it, you have a duty to tell others. All it takes is one person. One thought. One step. And the world is changed forever. That’s how easy love conquers fear. That’s how fast good defeats evil. Imagine an army of humans they are truly awake. Truly aware to the scam that we call this modern society. They would move mountains and part seas. They would see that we are just ONE STEP away. One step away from transcending the fear. One step from overcoming the negativity. One step from freedom. You have that power inside you, waiting to be unlocked. You are God expressing himself in human form. You have an eternal flame burning inside you. A divine spark that CANNOT be extinguished. The power to overcome any and all. That power is the divine spark of existence. That power is GOD. You are a super natural being… Wake up and start acting like it.

Nathan Hughes

58,877 Aufrufe • vor 7 Monaten

Most $TAO holders staking right now are trusting the wrong validators. Not because they are careless. Because nobody explained what the numbers on the Validators page actually mean. There is a tool inside Taostats that shows you exactly which validators are genuinely working and which ones are collecting your emissions without contributing anything to the network. It is free. It is live. And almost nobody is using it correctly. Here is exactly how to read it. Step 1: Understand what Dominance actually measures. Dominance is not popularity. It is not a ranking of which validator is best. It describes a validator's Stake Weight as a percentage of all validator stake weights combined across the network. Stake Weight is calculated as: root stake multiplied by 0.18, plus all alpha staked across subnets converted into TAO. Root stake is deliberately discounted at 18 percent of its face value. Alpha stake carries the full weight. This means a validator with deep subnet-level staking is structurally more powerful than one sitting purely on root, even if their raw TAO numbers look similar on the surface. When you see a validator with rising Dominance over time, it is not just getting more popular. It is getting more alpha stake directed toward it across active subnets. That is a meaningful signal about where serious capital is moving inside the network. Step 2: Check the Take percentage before you delegate anything. Take is the percentage of emissions the validator keeps for itself. Everything above that number flows to you as a nominator. A validator with a 18 percent Take keeps 18 percent of the emissions their position generates and distributes the remainder to stakeholders. A validator with a 50 percent Take is keeping half of what your stake earns. Most people never look at this number before delegating. It is the first number you should check. A high Take is not automatically a red flag if the validator is genuinely performing well and contributing to the network. But a high Take combined with low VTrust in their subnet performance page is the exact combination that should make you move your stake immediately. Step 3: Open the Validator Performance page and find the VTrust score. This is the number most holders never see. VTrust measures how closely a validator's weight assignments align with the honest stake-weighted majority across the network inside each subnet they operate in. Validators are responsible for evaluating miner output and assigning scores. Those scores go into Yuma Consensus and determine which miners earn emissions. A validator doing genuine evaluation work will have weights that align closely with the honest consensus. High VTrust. Consistent emissions. Reliable nominator returns. A validator that is weight copying, meaning they are simply copying the Yuma consensus scores back onto themselves rather than doing real evaluation, will show a flagged return on Taostats. Their nom/24hr/1k TAO score appears in red. This is Taostats telling you directly: this validator is extracting value from the network without contributing to it. When you stake to a weight copying validator, you are funding a free rider. Step 4: Watch the 24hr Nominator Change column. This number moves fast and it tells you something before any other signal does. A validator losing nominators over consecutive days is a validator that informed stakers are quietly leaving. A validator gaining nominators rapidly while their VTrust is healthy is a validator attracting attention for the right reasons. The 24hr column is the on-chain version of sentiment before sentiment becomes a narrative on social media. Step 5: Check Active subnets alongside Total Weight. Active tells you the number of subnets where the validator has a parent or child hotkey running. A validator with high Total Weight but low Active subnets is concentrated. They are running a specific strategy in specific markets. A validator with broad Active coverage across many subnets is building a wider surface area for emissions and is more exposed to the overall network performance rather than any single subnet cycle. Neither is inherently better. But knowing which type of validator you are delegating to tells you what you are actually betting on when you stake. Step 6: Check the Weight Change column over time. Total Weight is a snapshot. Weight Change is momentum. A validator with stable or growing Total Weight over consecutive days is attracting net new stake consistently. A validator with declining Weight Change is losing stake faster than it is gaining it. Most people look at the current number. The people positioning correctly are watching which direction the number is moving and how fast. The difference between a good validator and a dangerous one is not obvious from the outside. It is not the name. It is not the size. It is the VTrust score, the Take percentage, the nominator trend, and whether Taostats is showing their return in red or not. Every one of those signals is sitting on the Validators page right now. Free. Live. Updated every block. The investors who read the data layer before the narrative layer will not need to explain their staking decisions later. Open Taostats tonight. You will want to find this post when you do.

Most $TAO holders staking right now are trusting the wrong validators. Not because they are careless. Because nobody explained what the numbers on the Validators page actually mean. There is a tool inside Taostats that shows you exactly which validators are genuinely working and which ones are collecting your emissions without contributing anything to the network. It is free. It is live. And almost nobody is using it correctly. Here is exactly how to read it. Step 1: Understand what Dominance actually measures. Dominance is not popularity. It is not a ranking of which validator is best. It describes a validator's Stake Weight as a percentage of all validator stake weights combined across the network. Stake Weight is calculated as: root stake multiplied by 0.18, plus all alpha staked across subnets converted into TAO. Root stake is deliberately discounted at 18 percent of its face value. Alpha stake carries the full weight. This means a validator with deep subnet-level staking is structurally more powerful than one sitting purely on root, even if their raw TAO numbers look similar on the surface. When you see a validator with rising Dominance over time, it is not just getting more popular. It is getting more alpha stake directed toward it across active subnets. That is a meaningful signal about where serious capital is moving inside the network. Step 2: Check the Take percentage before you delegate anything. Take is the percentage of emissions the validator keeps for itself. Everything above that number flows to you as a nominator. A validator with a 18 percent Take keeps 18 percent of the emissions their position generates and distributes the remainder to stakeholders. A validator with a 50 percent Take is keeping half of what your stake earns. Most people never look at this number before delegating. It is the first number you should check. A high Take is not automatically a red flag if the validator is genuinely performing well and contributing to the network. But a high Take combined with low VTrust in their subnet performance page is the exact combination that should make you move your stake immediately. Step 3: Open the Validator Performance page and find the VTrust score. This is the number most holders never see. VTrust measures how closely a validator's weight assignments align with the honest stake-weighted majority across the network inside each subnet they operate in. Validators are responsible for evaluating miner output and assigning scores. Those scores go into Yuma Consensus and determine which miners earn emissions. A validator doing genuine evaluation work will have weights that align closely with the honest consensus. High VTrust. Consistent emissions. Reliable nominator returns. A validator that is weight copying, meaning they are simply copying the Yuma consensus scores back onto themselves rather than doing real evaluation, will show a flagged return on Taostats. Their nom/24hr/1k TAO score appears in red. This is Taostats telling you directly: this validator is extracting value from the network without contributing to it. When you stake to a weight copying validator, you are funding a free rider. Step 4: Watch the 24hr Nominator Change column. This number moves fast and it tells you something before any other signal does. A validator losing nominators over consecutive days is a validator that informed stakers are quietly leaving. A validator gaining nominators rapidly while their VTrust is healthy is a validator attracting attention for the right reasons. The 24hr column is the on-chain version of sentiment before sentiment becomes a narrative on social media. Step 5: Check Active subnets alongside Total Weight. Active tells you the number of subnets where the validator has a parent or child hotkey running. A validator with high Total Weight but low Active subnets is concentrated. They are running a specific strategy in specific markets. A validator with broad Active coverage across many subnets is building a wider surface area for emissions and is more exposed to the overall network performance rather than any single subnet cycle. Neither is inherently better. But knowing which type of validator you are delegating to tells you what you are actually betting on when you stake. Step 6: Check the Weight Change column over time. Total Weight is a snapshot. Weight Change is momentum. A validator with stable or growing Total Weight over consecutive days is attracting net new stake consistently. A validator with declining Weight Change is losing stake faster than it is gaining it. Most people look at the current number. The people positioning correctly are watching which direction the number is moving and how fast. The difference between a good validator and a dangerous one is not obvious from the outside. It is not the name. It is not the size. It is the VTrust score, the Take percentage, the nominator trend, and whether Taostats is showing their return in red or not. Every one of those signals is sitting on the Validators page right now. Free. Live. Updated every block. The investors who read the data layer before the narrative layer will not need to explain their staking decisions later. Open Taostats tonight. You will want to find this post when you do.

2xnmore

11,771 Aufrufe • vor 3 Monaten

Steve Jobs literally gave a 10-minute masterclass on building a company (better than $100K MBA): 1. Someone has to be the keeper of the vision. There is a staggering amount of work in building anything, and when you have to walk a thousand miles, the first step looks impossibly far. Jobs saw his job as the person who constantly reminds everyone that the goal is real, not a mirage, and that each step gets you closer. In a thousand small and occasionally large ways, the vision has to be repeated, or people lose faith it exists. 2. The honeymoon ends fast, and then the world only cares what you produce. After six months, Jobs told his team that all the goodwill they got just for existing was old news. You can point to the lawsuit, the setbacks, the this and that, but the bottom line is the world does not care. It cares about what you ship and how timely you bring it to market. From here on out you get judged like every other startup, by your product, not your story. 3. The most important job of a leader is recruiting. Jobs says it plainly: hiring is the thing he considers most important about a role like his. He didn't want seasoned professionals so much as people who were insanely great at what they did, who had the latest understanding of the technology at their fingertips and the passion to bring it to millions. 4. A great team becomes self-policing about who it lets in. Once you assemble a core group of ten genuinely great people, something powerful happens: they start guarding the bar themselves. They will not tolerate letting mediocre people into the group. The quality defends itself, which is why getting those first ten right matters more than almost anything. 5. Start with the customer experience and work backwards to the technology. This is the mistake Jobs says he made more than anyone in the room, and he has the scar tissue to prove it. You cannot start with a piece of awesome technology and then hunt for where to sell it. You start with what incredible benefit you can give the customer and where you can take them, and only then figure out the technology to get there. 6. You have to find what you love, and not settle. Your work fills a huge part of your life, and the only way to be truly satisfied is to do what you believe is great work. The only way to do great work is to love what you do. If you haven't found it, keep looking, and don't settle. Like any matter of the heart, you'll know when you find it, and it only gets better over the years. 7. They didn't set out to start a company. They just wanted the thing to exist. There was no personal computer in 1975, so Jobs and Wozniak built one because they wanted one and none existed. They made it for themselves, showed it to friends, and the friends all wanted one. They started building by hand until it consumed all their spare time, so they decided to manufacture a hundred just to stop. That is how Apple began. Not a plan to start a company, a circle that kept getting bigger. 8. "I want to make lots of money" is not a good enough reason. When people tell Jobs they want to start a company, he asks why. If the answer is to make lots of money, he tells them to forget it. He has barely seen anyone succeed that way. The ones who succeed often didn't even want to start a company. They just had an idea they needed to get out into the world, and had to build a company because no one else would listen. 9. You lead by example, because everyone is watching. You can say anything you want, but people watch extremely carefully what senior management actually does. When something isn't quite good enough, do you stop and make it great, or do you just ship it? Everyone sees which choice you make in the hard moments, what decisions you make and what values you hold, and that, not your speeches, is what permeates a company of thousands. 10. Focus doesn't mean saying yes. It means saying no. Jobs kept the organization simple and did very few things well. And focusing is hard precisely because it means deciding not to do a huge number of things so you can pour everything into a handful. Most people think focus is about what you take on. He understood it's about what you refuse. 11. Let people do the best work of their lives. Everyone wants to do something great, to be excited about what they're building, and to be recognized when they nail it. So Jobs tried to build a place where people could do their best work and then get it in front of 25 million customers. Knowing millions of people will use the thing you're making, and that the whole industry will copy it if it works, is enormously motivating. 12. Don't lose the war because you won a few battles. Late in the clip Jobs gets blunt about a danger he sees creeping in: losing the startup hustle. If you zoom out, it would be a shame to lose the war because you were busy winning small battles. He felt people were concentrating too hard on minor fights and losing perspective on the real one. And the war, he says, is survival, not running out of money before you get your product to market.

Steve Jobs literally gave a 10-minute masterclass on building a company (better than $100K MBA): 1. Someone has to be the keeper of the vision. There is a staggering amount of work in building anything, and when you have to walk a thousand miles, the first step looks impossibly far. Jobs saw his job as the person who constantly reminds everyone that the goal is real, not a mirage, and that each step gets you closer. In a thousand small and occasionally large ways, the vision has to be repeated, or people lose faith it exists. 2. The honeymoon ends fast, and then the world only cares what you produce. After six months, Jobs told his team that all the goodwill they got just for existing was old news. You can point to the lawsuit, the setbacks, the this and that, but the bottom line is the world does not care. It cares about what you ship and how timely you bring it to market. From here on out you get judged like every other startup, by your product, not your story. 3. The most important job of a leader is recruiting. Jobs says it plainly: hiring is the thing he considers most important about a role like his. He didn't want seasoned professionals so much as people who were insanely great at what they did, who had the latest understanding of the technology at their fingertips and the passion to bring it to millions. 4. A great team becomes self-policing about who it lets in. Once you assemble a core group of ten genuinely great people, something powerful happens: they start guarding the bar themselves. They will not tolerate letting mediocre people into the group. The quality defends itself, which is why getting those first ten right matters more than almost anything. 5. Start with the customer experience and work backwards to the technology. This is the mistake Jobs says he made more than anyone in the room, and he has the scar tissue to prove it. You cannot start with a piece of awesome technology and then hunt for where to sell it. You start with what incredible benefit you can give the customer and where you can take them, and only then figure out the technology to get there. 6. You have to find what you love, and not settle. Your work fills a huge part of your life, and the only way to be truly satisfied is to do what you believe is great work. The only way to do great work is to love what you do. If you haven't found it, keep looking, and don't settle. Like any matter of the heart, you'll know when you find it, and it only gets better over the years. 7. They didn't set out to start a company. They just wanted the thing to exist. There was no personal computer in 1975, so Jobs and Wozniak built one because they wanted one and none existed. They made it for themselves, showed it to friends, and the friends all wanted one. They started building by hand until it consumed all their spare time, so they decided to manufacture a hundred just to stop. That is how Apple began. Not a plan to start a company, a circle that kept getting bigger. 8. "I want to make lots of money" is not a good enough reason. When people tell Jobs they want to start a company, he asks why. If the answer is to make lots of money, he tells them to forget it. He has barely seen anyone succeed that way. The ones who succeed often didn't even want to start a company. They just had an idea they needed to get out into the world, and had to build a company because no one else would listen. 9. You lead by example, because everyone is watching. You can say anything you want, but people watch extremely carefully what senior management actually does. When something isn't quite good enough, do you stop and make it great, or do you just ship it? Everyone sees which choice you make in the hard moments, what decisions you make and what values you hold, and that, not your speeches, is what permeates a company of thousands. 10. Focus doesn't mean saying yes. It means saying no. Jobs kept the organization simple and did very few things well. And focusing is hard precisely because it means deciding not to do a huge number of things so you can pour everything into a handful. Most people think focus is about what you take on. He understood it's about what you refuse. 11. Let people do the best work of their lives. Everyone wants to do something great, to be excited about what they're building, and to be recognized when they nail it. So Jobs tried to build a place where people could do their best work and then get it in front of 25 million customers. Knowing millions of people will use the thing you're making, and that the whole industry will copy it if it works, is enormously motivating. 12. Don't lose the war because you won a few battles. Late in the clip Jobs gets blunt about a danger he sees creeping in: losing the startup hustle. If you zoom out, it would be a shame to lose the war because you were busy winning small battles. He felt people were concentrating too hard on minor fights and losing perspective on the real one. And the war, he says, is survival, not running out of money before you get your product to market.

Jaynit

27,088 Aufrufe • vor 1 Monat