Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Risk isn't just about numbers: it's about understanding what breaks first. Carson Brown breaks down how risk really works in DeFi and why liquidity concentration is more dangerous than people think. On the latest episode of Blueprints, Carson sits down with Edgar of Symbiotic to discuss the evolution of...

11,504 Aufrufe • vor 10 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Our fearless leader fiddy.dime - priv/acc 🦡 sat down with Carl Bergman of Reverie for an action packed episode covering the dominance of perp dexes, the role of convexity, volatility and margin, building on Starknet (Privacy arc) 🥷 and competing in a really difficult and dynamic market. Lots of gems in here for anyone looking to learn more about building a derivatives exchange! 1️⃣ Commodities to crypto: Anand’s origin story 00:00 Paradigm’s role in birthing crypto options markets 05:00 FTX’s collapse and counterparty risk 06:30 Blockchains as trust machines, the birth of Paradex 2️⃣ Why do institutional liquidity networks exist? 08:07 What is an Institutional Liquidity Network? 10:06 Block trading for large and complex orders 12:40 How Paradigm killed the voice-brokers 3️⃣ The evolution of Crypto Derivatives markets 13:05 The rise and success of Perpetual swaps 15:32 Why perps killed options in crypto? 24:35 Is volatility a good thing? 27:00 How Paradex prevents “scam-wicks” 4️⃣ Why build Paradex? 31:00 Why build a decentralized perps exchange? 32:20 Custodial solutions are under attack 5️⃣ Why Starknet ? 34:00 Paradex as a high throughput Derivatives L2 35:10 Modular vs Monolithic 38:37 Paradex is the KILLER App! 🤙 6️⃣ Competing in a really difficult market 44:45 Are points the new meta? 45:30 Paradex Pro League 47:20 Addressing points fatigue via long-term PMF 7️⃣ GTM Sequencing and Points Program Design 52:40 Bootstrapping liquidity 54:20 Listing shitcoins VS blue chips 57:00 Pre-launch markets and innovation in Defi

Paradex

11,258 Aufrufe • vor 2 Jahren

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 Aufrufe • vor 1 Monat