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Compounding Success.

Bark

284,244 subscribers

23,501 просмотров • 1 год назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Ant

Ant1 год назад

Lock TF in 🚀

Фото профиля Dan Peña

Dan Peña3 лет назад

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!

Фото профиля Order

Order1 год назад

i see you 👁️

Фото профиля AK

AK1 год назад

It all starts with mind and body 🧘‍♀️

Фото профиля Vibes

Vibes1 год назад

This is the story of greatness!

Фото профиля Grami

Grami1 год назад

Planting the seeds and giving them the time to grow 🌱

Фото профиля JAE

JAE1 год назад

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

Фото профиля GOOD

GOOD1 год назад

We got no limit!!!

Фото профиля Regal

Regal1 год назад

poetry in motion. great work Bark! 👑💪

Фото профиля Kiney

Kiney1 год назад

This is the way! Each day compounds into greatness

Фото профиля Boku

Boku1 год назад

I needed this

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THE SKIN DOCTOR

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PrivateEquityGuy (Mikk Markus)

39,692 просмотров • 10 месяцев назад

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MR SHIFT 🦁

40,264 просмотров • 6 месяцев назад

Kobe Bryant on how he used the Compound Effect to inevitably become one of the best in the world at his craft: ✖️The Compound Interest equation is Reps multiplied by Time (Reps x Time = Growth). Improvement is quite early and obvious late— as you put in more time and reps. Most people quit in the silent phase, early on, not realizing the results were already building beneath the surface. The key is starting as early as possible and putting energy toward that improvement, because you can’t ever get TIME back, no matter how many reps you plan to do in the future. 🐍 Kobe didn’t just wake up at 4am for discipline, he did it to create separation between him and his opponent. The edge wasn’t intensity, it was timing. While others were still asleep, he was stacking deposits. By the time they caught up for their first session, he was already on his second, compounding effort into distance. ⛄️ Growth doesn’t just come from working harder, but can also come from working earlier AND more often. Kobe didn’t chase overnight success, he built a schedule that made exponential improvement inevitable and catching up to him nearly impossible. Design a system where TIME works for you, so every REP doesn’t just add up, it multiplies and makes it harder for others to close the gap.

Kobe Bryant on how he used the Compound Effect to inevitably become one of the best in the world at his craft: ✖️The Compound Interest equation is Reps multiplied by Time (Reps x Time = Growth). Improvement is quite early and obvious late— as you put in more time and reps. Most people quit in the silent phase, early on, not realizing the results were already building beneath the surface. The key is starting as early as possible and putting energy toward that improvement, because you can’t ever get TIME back, no matter how many reps you plan to do in the future. 🐍 Kobe didn’t just wake up at 4am for discipline, he did it to create separation between him and his opponent. The edge wasn’t intensity, it was timing. While others were still asleep, he was stacking deposits. By the time they caught up for their first session, he was already on his second, compounding effort into distance. ⛄️ Growth doesn’t just come from working harder, but can also come from working earlier AND more often. Kobe didn’t chase overnight success, he built a schedule that made exponential improvement inevitable and catching up to him nearly impossible. Design a system where TIME works for you, so every REP doesn’t just add up, it multiplies and makes it harder for others to close the gap.

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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.

Bob Knakal | NYC Investment Sales

11,198 просмотров • 11 месяцев назад

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?

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 просмотров • 5 месяцев назад

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

144,993 просмотров • 3 месяцев назад

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

80,288 просмотров • 6 месяцев назад

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 просмотров • 2 лет назад