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5 Teams, 1 Mission: The Institutional Restaking Stack Explained - The Cap Room EP #1 On the very first episode of The Cap Room, The Flâneur of Bushwick sits down with Benjamin (Cap Founder), Misha (Symbiotic Co-Founder), Showtime (EtherFi Head of Ecosystem), Thibault (M11 Credit), and Craig Birchall (FalconX...

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25,747 просмотров • 4 месяцев назад •via X (Twitter)

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19,273 просмотров • 1 год назад

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37,479 просмотров • 1 месяц назад

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New Episode: Bringing All Your Assets to Crypto? Chris Yin, CEO of Plume Network, joins seb3point0 to argue that the first wave of RWAs failed because it tried to bring blockchain to TradFi instead of bringing the real world to crypto. He discusses how Plume is building an L1 specifically for RWA composability, turning illiquid assets into yield-bearing DeFi. Finally, they tackle how to build a global market that maintains "ball control" for crypto natives as the institutions arrive. Topics 00:00 The RWA Buzzword 05:15 From VC to Founder 10:30 Why Most RWAs are Worse On-Chain 16:00 Stablecoin as the North Star 24:45 Why Build a Custom L1? 33:20 Tokenizing Brazilian Credit & Oil 42:15 The "Serial Ape" vs. The Institutional Era 51:40 Why TradFi Assets Aren't Loopable. 58:30 Preserving Crypto Principles in a Regulated World Episode Links👇

Epicenter Podcast

20,697 просмотров • 4 месяцев назад

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How To Buy A Farm As A “City Based Entrepreneur” (And Why You Should) (00:00) How to buy a farm as an entrepreneur (00:13) The benefits of living outside the city (00:48) Steps to buying a farm (01:36) Considerations for farm location (03:04) Understanding different types of soils (03:50) Amish community and limestone soils (04:18) Choosing the right farm location (04:41) Buying a farm with infrastructure (05:23) Advice for new farmers (05:58) Safety and caution on the farm (08:01) Best farmland in the world (09:57) Infrastructure and house (10:45) Buying a farm (11:06) Consulting services (13:27) Valleys and flat land (14:07) Bringing city amenities to the farm
15:38

How To Buy A Farm As A “City Based Entrepreneur” (And Why You Should) (00:00) How to buy a farm as an entrepreneur (00:13) The benefits of living outside the city (00:48) Steps to buying a farm (01:36) Considerations for farm location (03:04) Understanding different types of soils (03:50) Amish community and limestone soils (04:18) Choosing the right farm location (04:41) Buying a farm with infrastructure (05:23) Advice for new farmers (05:58) Safety and caution on the farm (08:01) Best farmland in the world (09:57) Infrastructure and house (10:45) Buying a farm (11:06) Consulting services (13:27) Valleys and flat land (14:07) Bringing city amenities to the farm

Tai Lopez

39,124 просмотров • 1 год назад

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future
2:29:40

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future

Juan Benet

49,297 просмотров • 24 дней назад