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Compounding Success.

Bark

284,244 subscribers

23,501 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

11 Comments

Ant1 year ago

Lock TF in 🚀

Dan Peña3 years ago

29 Years of proven track record and creating generational wealth! If you want to learn what it takes to becomes super successful then take the success test now!

Order1 year ago

i see you 👁️

AK1 year ago

It all starts with mind and body 🧘‍♀️

Vibes1 year ago

This is the story of greatness!

Grami1 year ago

Planting the seeds and giving them the time to grow 🌱

JAE1 year ago

Compounding Income & Success 🧱🫐🦾🥇 ONLY THE ELITE 1 % @doginaldogsx @barkmediax @TAOGroup #DDNYC #DDNFTNYC2025

GOOD1 year ago

We got no limit!!!

Regal1 year ago

poetry in motion. great work Bark! 👑💪

Kiney1 year ago

This is the way! Each day compounds into greatness

Boku1 year ago

I needed this

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Ep. 29: Billy Oppenheimer - Attuned to Clues Billy Oppenheimer (Billy Oppenheimer) is a researcher and writer who works closely with Ryan Holiday and Rick Rubin, and publishes the “Six at 6” newsletter. Billy is also working on his first book, The Work is the Win. We kick off by discussing one of my favorite new ideas: "looking for clues," a process and philosophy for creativity that Billy learned from Rick Rubin. He shares the story Rick told him when he learned and adopted this language, which is so representative of how Billy (and I!) research in our work. From there, we talk about Billy's robust research process and how he has created an external brain of the ideas and patterns that inspire him rather than relying on memory. We also talk about the importance of time as a filter and a series of maxims that underpin his work and creativity. We discuss the importance of inputs over outputs and his big idea and book title, "The Work is the Win," as well many related ideas on success, complacency, compounding, standards, initiative, local maximums, and more. We finish with some lessons from Billy's favorite people. This conversation is a field guide for making things, pushing through the messiness of progress, and attuning yourself to the richness of the world that often takes the shape of clues. Timestamps: 0:00 - Intro 1:20 - Looking for Clues with Rick Rubin 17:42 - Billy's Own Clue-Seeking 24:26 - Balancing Listening to the Market and Finding Unique Influences 31:17 - Memory, Notecards, and Billy's External Brain 37:13 - Making Notes for an Ignorant Stranger, or Leaving Clues for Your Future Self 45:09 - Lingering and Time as a Filter 52:51 - Billy's Book and Big Idea: "The Work is the Win" 1:00:07 - Be Great Regardless 1:04:31 - Following Up Even When Your Abilities and Standards Don't Match 1:10:10 - Fending Off the Wolf at the Door (The Comfort of Success) 1:15:55 - Unfolding and Planting Seeds 1:18:17 - Taking Initiative and Opening Doors: "He Who Hesitates is Lost" 1:24:58 - Stupid Bravery and Getting Past the Sewage 1:30:16 - Local Maximums and Resisting Personal "Folklore" 1:36:14 - Some of Billy's Favorites: Ryan Holiday, Rick Rubin, Steve Jobs, John Mayer, Greta Gerwig, Jerry Seinfeld, Ralph Waldo Emerson 1:56:45 - Side Quests 2:02:26 - "I Know What We Do Here" and Creative Environments 2:05:28 - Bringing Familiar and Unfamiliar Together 2:09:26 - Mastery and Compounding 2:12:44 - The Real Life of Appearances 2:15:43 - "Ton-goo-ey" and The Gifts We Give Ourselves All links and transcript available below.

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34,411 views • 10 months ago

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There was a time when we nearly went bankrupt. Not once, multiple times. We were swiping credit cards to make payroll. Scrambling for loans. Doing whatever it took to stay in the game. The early years of building our firm weren’t glamorous. They were gritty. Ugly, even. But it was during one of the worst economic periods—the Savings and Loan Crisis—when the market was frozen, banks weren’t selling paper, and opportunity felt extinct, that we learned the most important lesson of all: Perseverance is the real capital. It’s easy to believe that economic success follows a straight line. The media loves a “skyrocket” story. But it's never that clear-cut or simple. Survival is underrated. The firms and individuals who make it through the lean years by grinding, innovating, and refusing to give up, emerge stronger, smarter, and more dangerous (in a good way) than ever before. Today, we talk about market dips, GFC flashbacks, and rate-driven standstills. But none of this compares to what we endured in the early 90s. Deals weren’t just hard to get, they literally didn’t exist. But we didn’t fold. We stayed in the pocket. And over time, opportunity came roaring back. So, if you're questioning your path because the wind isn't at your back right now... don't. Your perseverance is compounding. Keep moving. Keep building. The market will turn. The real question is: Will you still be standing when it does? Thanks to Yaakov Kanevsky - Baltimore Real Estate Advisor, Multifamily Real Estate Advisor, for having me on the Baltimore RE Full Circle podcast.

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11,198 views • 9 months ago

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CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?

Shruti

18,622 views • 3 months ago

We use Bittensor to gather intelligence. But we’ll build the product in-house. Our subnet is a phenomenal intelligence engine: 1000+ miners and ~5500 agents competing, iterating on, and compounding each other’s work. One miner builds a breakthrough agent. The next forks it, implements a new tool, improves performance by 1-2%. The next does the same. This cycle runs continuously, with hundreds of teams around the world, each with different expertise, different approaches, different intuitions, all pushing the same eval forward. That's what the subnet is built for, and it's how we've outpaced labs with orders of magnitude more resources. Once our agent reaches SOTA on shopping, the next bottleneck is building an elegant, easy-to-use consumer product. And great products don't come from crowds. Open-source competition is the right tool for maximizing intelligence, you want hundreds of mutually compounding perspectives and iterations. But product is the opposite. Product requires taste. Elegance. Strong opinions about what to include and, critically, what to leave out. It requires a small, high-judgment team moving fast and making sharp calls, not a thousand competing voices. The best consumer experiences in the world were built by teams who knew exactly what they wanted to build and had the conviction to say no to everything else. That’s why phase two – building the product, belongs in-house at Oro. The best companies don’t start big – they start narrow In Zero to One, Peter Thiel argues that every great company starts by dominating a small, specific market before expanding outward. Amazon started with just books, going from $16 million to $148 million in revenue in that narrow market before touching anything else. PayPal went all-in on eBay power sellers, growing from 10,000 to over 5 million users in under a year. Facebook launched at Harvard and didn't open to the public for two and a half years. The playbook is proven: own a small market first, then expand. We're starting with consumer electronics. Why? Because electronics has something most shopping categories don't: objectivity. "Find me the best deal on an RTX 5090" has a right answer. Specs, prices, compatibility, all measurable, all verifiable. "Find me the perfect dress for a wedding" doesn't. You can't build a reliable eval for something with no correct answer. Starting with electronics enables us to kickstart a recursive self-improvement loop for our agent: assign it shopping tasks with clear success criteria, assess its performance and learn about its specific profile of strengths and weaknesses, and use that rich vein of data to improve both the eval and the base agent. We’ll start where we can prove our agent works. We’ll own that vertical. Then we’ll grow from there. Land, dominate, then expand.

ORO

144,802 views • 1 month ago

MUST-WATCH: Macro Hedge Fund Founder Alfonso Peccatiello Reveals his Edge in Macro Trading Alfonso Pecatiello (former large global bank PM, founder of The Macro Compass & Palinuro Capital) breaks down how to create durable edge in macro investing and why concentration is the death of many fund managers. "You can hear from Druckenmiller and Soros about concentration — but you won't hear from the thousands of managers who went bankrupt trying the same approach." We cover: - Why bank reserves (QE "liquidity") have NO direct pipe to the S&P 500 - The real money creators: commercial banks & government deficits — not the Fed - Why the second derivative of money creation drives nominal growth & asset prices - How to run 40-50 ideas/year & arrive at 10-12 truly independent bets - The "Trump error term": modeling exogenous volatility injections - The handbrake rule: when models fail, cut risk (the math of negative compounding destroys you) - Launching Palinuro with zero GP stakes & why 80% of funds fail in year 1-5 Thanks to Alf (Alf) for pulling back the curtain on finding edge in macro trading and the unglamorous reality of fund building. Highlights: 00:00 Understanding Money Creation and Strict Process as a Macro Edge 04:21 Commercial Banks and Government Deficits Drive Real Money Creation 12:52 Why Diversification and Risk Parity Beat Concentrated Bets 19:30 Using the "Handbrake" to Cut Risk During Black Swan Events 27:47 Why Factor Neutral Mandates Don't Make Sense for Macro Investors 32:22 The Underestimated Challenges of Building a Macro Hedge Fund 43:20 Passion, Delegation, and the Grinder Mentality for Fund Founders 52:13 The High Probability of Failure and Necessary Financial Runway 56:58 Building an Empathetic Team Through Intentional Hiring Frameworks 1:02:11 Why Communication Skills Are Crucial for Success in Finance

MUST-WATCH: Macro Hedge Fund Founder Alfonso Peccatiello Reveals his Edge in Macro Trading Alfonso Pecatiello (former large global bank PM, founder of The Macro Compass & Palinuro Capital) breaks down how to create durable edge in macro investing and why concentration is the death of many fund managers. "You can hear from Druckenmiller and Soros about concentration — but you won't hear from the thousands of managers who went bankrupt trying the same approach." We cover: - Why bank reserves (QE "liquidity") have NO direct pipe to the S&P 500 - The real money creators: commercial banks & government deficits — not the Fed - Why the second derivative of money creation drives nominal growth & asset prices - How to run 40-50 ideas/year & arrive at 10-12 truly independent bets - The "Trump error term": modeling exogenous volatility injections - The handbrake rule: when models fail, cut risk (the math of negative compounding destroys you) - Launching Palinuro with zero GP stakes & why 80% of funds fail in year 1-5 Thanks to Alf (Alf) for pulling back the curtain on finding edge in macro trading and the unglamorous reality of fund building. Highlights: 00:00 Understanding Money Creation and Strict Process as a Macro Edge 04:21 Commercial Banks and Government Deficits Drive Real Money Creation 12:52 Why Diversification and Risk Parity Beat Concentrated Bets 19:30 Using the "Handbrake" to Cut Risk During Black Swan Events 27:47 Why Factor Neutral Mandates Don't Make Sense for Macro Investors 32:22 The Underestimated Challenges of Building a Macro Hedge Fund 43:20 Passion, Delegation, and the Grinder Mentality for Fund Founders 52:13 The High Probability of Failure and Necessary Financial Runway 56:58 Building an Empathetic Team Through Intentional Hiring Frameworks 1:02:11 Why Communication Skills Are Crucial for Success in Finance

Ethan Kho

77,233 views • 5 months ago

Q: Why is it easier to start a hard company than an easy company? In the clip below, Sam Altman tells the class at Stanford: “It’s easier to start a hard company than an easy company. Most people—especially young people—want to pick something that doesn’t sound too ambitious. They say to themselves: ‘starting a company sounds really hard. I better pick the easiest possible company.’” But as Sam explains: “Starting a company is always hard and it’s about equally hard no matter what you do. If you start a hard company though and you inspire passionate people—for example, if you are working on general AI or supersonic airplanes or nuclear power—you’ll find a lot more people who are excited about that than another derivative idea.” He elaborates on this idea even further in a blog post from four years ago: “The most precious commodity in the startup ecosystem right now is talented people, and for the most part talented people want to work on something they find meaningful… An easy startup is a headwind; a hard startup is a tailwind. If people care about your success because you seem committed to doing something significant, it’s a background force helping you with hiring, advice, partnerships, fundraising, etc.” He continues: “Let yourself become more ambitious—figure out the most interesting version of where what you’re working on could go. Then talk about that big vision and work relentlessly towards it, but always have a reasonable next step. You don’t want step one to be incorporating the company and step two to be going to Mars. Be willing to make a very long-term commitment to what you’re doing. Most people aren’t, which is part of the reason they pick ‘easy’ startups. In a world of compounding advantages where most people are operating on a 3 year timeframe and you’re operating on a 10 year timeframe, you’ll have a very large edge.”

Q: Why is it easier to start a hard company than an easy company? In the clip below, Sam Altman tells the class at Stanford: “It’s easier to start a hard company than an easy company. Most people—especially young people—want to pick something that doesn’t sound too ambitious. They say to themselves: ‘starting a company sounds really hard. I better pick the easiest possible company.’” But as Sam explains: “Starting a company is always hard and it’s about equally hard no matter what you do. If you start a hard company though and you inspire passionate people—for example, if you are working on general AI or supersonic airplanes or nuclear power—you’ll find a lot more people who are excited about that than another derivative idea.” He elaborates on this idea even further in a blog post from four years ago: “The most precious commodity in the startup ecosystem right now is talented people, and for the most part talented people want to work on something they find meaningful… An easy startup is a headwind; a hard startup is a tailwind. If people care about your success because you seem committed to doing something significant, it’s a background force helping you with hiring, advice, partnerships, fundraising, etc.” He continues: “Let yourself become more ambitious—figure out the most interesting version of where what you’re working on could go. Then talk about that big vision and work relentlessly towards it, but always have a reasonable next step. You don’t want step one to be incorporating the company and step two to be going to Mars. Be willing to make a very long-term commitment to what you’re doing. Most people aren’t, which is part of the reason they pick ‘easy’ startups. In a world of compounding advantages where most people are operating on a 3 year timeframe and you’re operating on a 10 year timeframe, you’ll have a very large edge.”

Michael McGuiness

504,007 views • 2 years ago

part 4 Gsenti Sentient Sentient Chat Explaining ROMA – Recursive Open Meta-Agent 1. What is ROMA? ROMA is an open-source framework for building meta-agents — systems that can orchestrate multiple smaller agents and tools to solve complex tasks. Instead of letting one AI model handle an entire large problem (which often fails due to complexity), ROMA applies a recursive approach: Parent nodes break a big goal into subtasks. Child nodes handle those subtasks and return results. All results are then combined into a final solution. 2. How does ROMA work? The architecture has four main components: Atomizer – decides whether a task is simple or requires decomposition. Planner – splits the complex task into smaller subtasks. Executor – runs the right tools/agents to complete each subtask. Aggregator – collects and synthesizes all results into one coherent answer. Because each node follows the same recursive logic, ROMA naturally scales as tasks become more complex. 3. Example Use Case – Deep Research Question: “Who are the top 5 NBA players by PPG in a season that have won both an NCAA and an NBA championship?” How ROMA handles it: Atomizer → identifies it as complex → needs decomposition. Planner → breaks it down: (1) find top NBA PPG seasons, (2) check NCAA champions, (3) check NBA champions, (4) combine filters. Executor → runs each search/tool for data. Aggregator → merges the results into the final answer. This creates a transparent, step-by-step reasoning process, unlike “black box” answers. 4. What Problem Does ROMA Solve? In long-horizon tasks, errors accumulate. A model may be 99% accurate at one step. But over 10 steps, success rates collapse due to compounding errors. Current agents are often opaque — hard to see where and why they failed. ROMA solves this by: Breaking tasks into clear logic chains, Making every step traceable, verifiable, and fixable. 5. Why ROMA Matters Open-source → available for anyone to build upon. Community-driven → empowering developers to create advanced multi-agent systems. Scalable → recursive logic adapts naturally to any task complexity. This makes ROMA not just a framework, but a foundation for the next wave of decentralized, transparent AI systems. 6. Learn More 📖 Technical blog: 💻 GitHub repo:

part 4 Gsenti Sentient Sentient Chat Explaining ROMA – Recursive Open Meta-Agent 1. What is ROMA? ROMA is an open-source framework for building meta-agents — systems that can orchestrate multiple smaller agents and tools to solve complex tasks. Instead of letting one AI model handle an entire large problem (which often fails due to complexity), ROMA applies a recursive approach: Parent nodes break a big goal into subtasks. Child nodes handle those subtasks and return results. All results are then combined into a final solution. 2. How does ROMA work? The architecture has four main components: Atomizer – decides whether a task is simple or requires decomposition. Planner – splits the complex task into smaller subtasks. Executor – runs the right tools/agents to complete each subtask. Aggregator – collects and synthesizes all results into one coherent answer. Because each node follows the same recursive logic, ROMA naturally scales as tasks become more complex. 3. Example Use Case – Deep Research Question: “Who are the top 5 NBA players by PPG in a season that have won both an NCAA and an NBA championship?” How ROMA handles it: Atomizer → identifies it as complex → needs decomposition. Planner → breaks it down: (1) find top NBA PPG seasons, (2) check NCAA champions, (3) check NBA champions, (4) combine filters. Executor → runs each search/tool for data. Aggregator → merges the results into the final answer. This creates a transparent, step-by-step reasoning process, unlike “black box” answers. 4. What Problem Does ROMA Solve? In long-horizon tasks, errors accumulate. A model may be 99% accurate at one step. But over 10 steps, success rates collapse due to compounding errors. Current agents are often opaque — hard to see where and why they failed. ROMA solves this by: Breaking tasks into clear logic chains, Making every step traceable, verifiable, and fixable. 5. Why ROMA Matters Open-source → available for anyone to build upon. Community-driven → empowering developers to create advanced multi-agent systems. Scalable → recursive logic adapts naturally to any task complexity. This makes ROMA not just a framework, but a foundation for the next wave of decentralized, transparent AI systems. 6. Learn More 📖 Technical blog: 💻 GitHub repo:

thoai66.ip

16,298 views • 10 months ago

Brian Tracy increased his income 100x in 12 years using this formula: "Once upon a time, I sat down at the end of the year and my tax returns were $14,400. Twelve years later, my tax returns were $1,440,000. I'd increased my income by 100 times." Brian explains the math behind it: "It's based on the law of incremental improvement. The Japanese call it Kaizen, the principle of continuous betterment. If you could increase your productivity by one-tenth of 1% per day, could you do that? Of course. If you did that every day for a week, you'd be half a percent more productive. Do that every week for a month, 2% more productive. Do that for a year, 26% more productive. Exposed to compounding, that's 1000% in 10 years." He shares the daily formula: Step 1: The Golden Hour. "Get up at least two hours before you have to be somewhere. Invest the first hour reading something uplifting, educational, or motivational. Reading is to the mind as exercise is to the body. The first hour is the rudder of the day." Step 2: Make a list. "Write down everything you have to do that day. Plan your day in advance." Step 3: Prioritize."Determine what's most important. Put a number next to each task." Step 4: Start on your most important task. "Work on it single-mindedly with concentration, focus, and discipline until it's done. Then go to task number two." Step 5: Turn your car into a university. "A study at USC concluded that if you listen to educational audio programs instead of music, you'll get the equivalent of full-time university attendance, except you only select things valuable to you in the moment." Step 6: Ask two questions after every interaction. "What did I do right? And what would I do differently next time?" Brian concludes: "This formula has been the key to success throughout history. You don't make quantum leaps. You don't go from zero to rich. You go to work on yourself bit by bit, day by day, week by week, month by month and your results are virtually guaranteed."

Brian Tracy increased his income 100x in 12 years using this formula: "Once upon a time, I sat down at the end of the year and my tax returns were $14,400. Twelve years later, my tax returns were $1,440,000. I'd increased my income by 100 times." Brian explains the math behind it: "It's based on the law of incremental improvement. The Japanese call it Kaizen, the principle of continuous betterment. If you could increase your productivity by one-tenth of 1% per day, could you do that? Of course. If you did that every day for a week, you'd be half a percent more productive. Do that every week for a month, 2% more productive. Do that for a year, 26% more productive. Exposed to compounding, that's 1000% in 10 years." He shares the daily formula: Step 1: The Golden Hour. "Get up at least two hours before you have to be somewhere. Invest the first hour reading something uplifting, educational, or motivational. Reading is to the mind as exercise is to the body. The first hour is the rudder of the day." Step 2: Make a list. "Write down everything you have to do that day. Plan your day in advance." Step 3: Prioritize."Determine what's most important. Put a number next to each task." Step 4: Start on your most important task. "Work on it single-mindedly with concentration, focus, and discipline until it's done. Then go to task number two." Step 5: Turn your car into a university. "A study at USC concluded that if you listen to educational audio programs instead of music, you'll get the equivalent of full-time university attendance, except you only select things valuable to you in the moment." Step 6: Ask two questions after every interaction. "What did I do right? And what would I do differently next time?" Brian concludes: "This formula has been the key to success throughout history. You don't make quantum leaps. You don't go from zero to rich. You go to work on yourself bit by bit, day by day, week by week, month by month and your results are virtually guaranteed."

Jaynit

42,193 views • 4 months ago

I met the guy behind Paperclip. he won't show his face, but he just built one of the FASTEST growing open-source projects in AI. how to use Paperclip to hire AI agents to ACTUALLY run a startup with 0 employees: 1. with paperclip, you hire a team of AI agents like CEO, engineer, QA, video editor, content strategist and manage them from one dashboard. it works with Claude Code, Codex, OpenCode, or any model on OpenRouter. you're not locked into one provider. 2. your AI agents wake up capable but with zero memory. they don't know who they are, where they are, or what they're supposed to be doing. kinda like that movie memento from back in the day you need to leave them Polaroids like heartbeat checklists, persona prompts, written context. that's how you keep them on track. 3. when an agent makes a mistake, you don't rewrite everything. you add one rule to their persona prompt. "always define a success condition for every task." "always pass work to QA before closing." you're training them like you'd train a junior hire. one correction at a time. 4. skills extend what your agents can do. want a video editor who can produce animated content? install the Remotion skill. want security reviews? there's a skill for that. 5. the biggest lever for quality is encoding your own taste. AI can do everything except know your values. design sensibility, brand voice, success criteria but you have to write it down. 6. don't one-shot your startup. agentic design patterns matter. the simplest one: after the engineer builds something, QA reviews it. structure prevents compounding errors. one-shotting an entire app is fun for 30 minutes, then it falls apart. 7. Paperclip tracks every token spent and every task completed. you can use your existing subscriptions (Claude, Codex) so spend shows as $0, or hook into API credits for real dollar tracking. 8. importable companies are coming. Gary Tan's G-Stack, a full game studio, 300+ agent repos... you can "acqui-hire" a proven agent team into your Paperclip instance instead of building from scratch. the future is downloading a tested org that actually works. 9. routines let you automate recurring work. "every day at 10am, read what was merged into the main branch and write a Discord update celebrating community contributors." it runs, you review, you improve. every task is traceable. 10. maximizer mode is next. you tell the CEO "build this game" and it does whatever it takes and hires who it needs, keeps pressing until it's done. no token anxiety. just outcomes. use Idea Browser for startup ideas/trends to get started thank you for dotta 📎 for doing this podcast and breaking down exactly how people can hire ai agent teams with paperclip you won't find an episode like this anywhere else episode is live on The Startup Ideas Podcast (SIP) 🧃 on your fav platforms (follow for more) is this not the greatest time in history to be building? im rooting for you now go watch my frien

GREG ISENBERG

460,697 views • 4 months ago

Hedge fund manager Alix Pasquet reveals why smart people blow up Alix Pasquet (Alix Pasquet) breaks down the catastrophic mistakes that destroy smart investors: "I've seen grown men cry when they lose money. I've seen guys move their families to cheaper neighborhoods because they lost everything. Really high IQ people. I've also seen guys I would consider average become billionaires." We cover: - Why smart investors lose money and how behavioral finance explains repeated hedge-fund blow-ups - Cognitive biases in investing and how even seasoned managers misread probability and risk - Investor temperament and success: why emotional discipline matters more than IQ or pedigree - Why quants lose money: the hidden behavioral alpha that algorithms can’t replicate - Complexity and systems thinking in markets—how to simplify chaotic systems into tradable edges - Behavioral alpha in quant strategies: exploiting human errors embedded in data - IQ traps in decision making that cause overconfidence and portfolio blow-ups - Intellectual arrogance in hedge funds and how meta-rationality builds long-term humility - Generative AI in markets and how narrative feedback loops distort valuations - AI amplifying investor mistakes: when automation removes human judgment - Network theory in investing: building multiple networks to uncover leading indicators - Emotional self-regulation for investors—habits, routines, and recovery to sustain performance - Lessons from poker and backgammon for investing: strategy, variance, and position sizing - Mentorship and triads networking strategy—how to create compounding social capital - Stoicism and finance mindset: developing calm under uncertainty and volatility Thank you Alix Pasquet for coming on the pod! Timestamps: 00:00 Intro 00:53 Why Smart Investors Lose Money 03:07 How Average People Become Billionaires 05:19 Competing Against Smart People Is a Losing Game 08:51 Generational Wealth Transfer and Market Tailwinds 11:25 The E-Trade Baby and Investor Psychology 14:03 Games, Gambling, and Behavioral Finance 18:35 How Hedge Funds Make Money 21:59 Quant Fund Blowups Explained (2007 Case Study) 27:08 Quant Finance Insights and Complexity Thinking 31:47 Why Quants Lose Money 36:35 AI in Finance 2025: Automation and Overconfidence 42:13 Analog Training vs Digital Distraction 47:26 Network Theory in Investing 52:21 Emotional Self-Regulation for Investors 57:33 Meta Rationality and Humility in Hedge Funds 1:03:08 How to Build Diverse Networks and Triads 1:09:11 AI Amplifying Investor Mistakes 1:14:05 Developing Judgment as an Investor 1:18:29 Closing: Why Smart People Fail and How to Avoid It

Hedge fund manager Alix Pasquet reveals why smart people blow up Alix Pasquet (Alix Pasquet) breaks down the catastrophic mistakes that destroy smart investors: "I've seen grown men cry when they lose money. I've seen guys move their families to cheaper neighborhoods because they lost everything. Really high IQ people. I've also seen guys I would consider average become billionaires." We cover: - Why smart investors lose money and how behavioral finance explains repeated hedge-fund blow-ups - Cognitive biases in investing and how even seasoned managers misread probability and risk - Investor temperament and success: why emotional discipline matters more than IQ or pedigree - Why quants lose money: the hidden behavioral alpha that algorithms can’t replicate - Complexity and systems thinking in markets—how to simplify chaotic systems into tradable edges - Behavioral alpha in quant strategies: exploiting human errors embedded in data - IQ traps in decision making that cause overconfidence and portfolio blow-ups - Intellectual arrogance in hedge funds and how meta-rationality builds long-term humility - Generative AI in markets and how narrative feedback loops distort valuations - AI amplifying investor mistakes: when automation removes human judgment - Network theory in investing: building multiple networks to uncover leading indicators - Emotional self-regulation for investors—habits, routines, and recovery to sustain performance - Lessons from poker and backgammon for investing: strategy, variance, and position sizing - Mentorship and triads networking strategy—how to create compounding social capital - Stoicism and finance mindset: developing calm under uncertainty and volatility Thank you Alix Pasquet for coming on the pod! Timestamps: 00:00 Intro 00:53 Why Smart Investors Lose Money 03:07 How Average People Become Billionaires 05:19 Competing Against Smart People Is a Losing Game 08:51 Generational Wealth Transfer and Market Tailwinds 11:25 The E-Trade Baby and Investor Psychology 14:03 Games, Gambling, and Behavioral Finance 18:35 How Hedge Funds Make Money 21:59 Quant Fund Blowups Explained (2007 Case Study) 27:08 Quant Finance Insights and Complexity Thinking 31:47 Why Quants Lose Money 36:35 AI in Finance 2025: Automation and Overconfidence 42:13 Analog Training vs Digital Distraction 47:26 Network Theory in Investing 52:21 Emotional Self-Regulation for Investors 57:33 Meta Rationality and Humility in Hedge Funds 1:03:08 How to Build Diverse Networks and Triads 1:09:11 AI Amplifying Investor Mistakes 1:14:05 Developing Judgment as an Investor 1:18:29 Closing: Why Smart People Fail and How to Avoid It

Ethan Kho

83,565 views • 5 months ago