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How do we find and help passionate founders build successful projects on Supra? James Bondsama caught up with Teagan, Ecosystem Lead at Supra, to get the answer and understand how DeFi, gaming, and more are coming together across the Supra Ecosystem 🚀 00:00 Introduction 02:05 Finding Web3 through CSGO...

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In the world of tech and finance, openness breeds innovation ⚡ “The bottom line is, I think open networks always win... you can be a random dude in your garage and build on top of Lightning and run your own node. I think that has immense value.” - David Marcus, Lightspark CEO Watch the rest of his appearance on the @unchainedcom podcast here:

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We're developing and using AI to revolutionize scientific discovery - from predicting protein structures with #AlphaFold, to materials discovery with GNoME. Join our VP Science, Pushmeet Kohli, and Professor Hannah Fry as they explore how AI could lead to a new era of breakthroughs that could benefit humanity in countless ways, on Google DeepMind: The Podcast. Watch now ↓ Timestamps: 00:00 Intro 01:13 AlphaFold 06:13 AlphaFold Database 08:14 Weather forecasting 11:24 Creating new materials with deep learning 25:10 Imposter syndrome, being a generalist, and Nobel prize winners 31:21 Choosing the right projects 32:07 Root node problems 34:32 Large language models for science 36:06 Function search and algorithmic discovery 42:10 Math Olympiad 46:26 What’s coming next 48:35 Reflections from Hannah
50:05

We're developing and using AI to revolutionize scientific discovery - from predicting protein structures with #AlphaFold, to materials discovery with GNoME. Join our VP Science, Pushmeet Kohli, and Professor Hannah Fry as they explore how AI could lead to a new era of breakthroughs that could benefit humanity in countless ways, on Google DeepMind: The Podcast. Watch now ↓ Timestamps: 00:00 Intro 01:13 AlphaFold 06:13 AlphaFold Database 08:14 Weather forecasting 11:24 Creating new materials with deep learning 25:10 Imposter syndrome, being a generalist, and Nobel prize winners 31:21 Choosing the right projects 32:07 Root node problems 34:32 Large language models for science 36:06 Function search and algorithmic discovery 42:10 Math Olympiad 46:26 What’s coming next 48:35 Reflections from Hannah

Google DeepMind

197,357 Aufrufe • vor 1 Jahr

DROPS E29: Ivo Grigorov, CEO at Real - The Trust Layer That Traditional Finance Has Been Waiting For From managing $1.2B in traditional finance to building an institutional-grade L1, Ivo is on a mission to bring real-world assets on-chain and rebuild trust between banks and crypto. We talk about: - Why trust is the missing link in tokenized real-world assets - How Real Finance is designing an L1 for banks and institutions - Turning a $100M guarantee scheme into smart contracts - Why crypto misunderstands sustainability and compounding - Bridging TradFi and Web3 with transparency, validators & on-chain metadata - Raising $29M and onboarding banks before mainnet launch And much more... Timestamps: 0:00 - Introduction 1:35 - Founder Life 2:37 - The Mission & Sacrifices 4:10 - Building globally before launch 5:00 - Background & Qualifications 6:23 - Childhood Experiences 09:58 - Co-founders & Partnership 12:55 - Credibility 15:16 - What Crypto misunderstood about TradFi? 19:39 - What TradFi misunderstood about Crypto? 24:26 - The Real Turning Point 26:46 - Credefi to Real Finance 32:00 - What’s RWA? 34:40 - What’s broken with RWAs? 36:58 - Real Finance in simple terms 38:09 - Institutional interest in Real Finance 41:28 - Success & Failures 41:58 - How Global Finance could change 43:02 - Crypto isn’t a slow process 45:35 - Conclusion
47:16

DROPS E29: Ivo Grigorov, CEO at Real - The Trust Layer That Traditional Finance Has Been Waiting For From managing $1.2B in traditional finance to building an institutional-grade L1, Ivo is on a mission to bring real-world assets on-chain and rebuild trust between banks and crypto. We talk about: - Why trust is the missing link in tokenized real-world assets - How Real Finance is designing an L1 for banks and institutions - Turning a $100M guarantee scheme into smart contracts - Why crypto misunderstands sustainability and compounding - Bridging TradFi and Web3 with transparency, validators & on-chain metadata - Raising $29M and onboarding banks before mainnet launch And much more... Timestamps: 0:00 - Introduction 1:35 - Founder Life 2:37 - The Mission & Sacrifices 4:10 - Building globally before launch 5:00 - Background & Qualifications 6:23 - Childhood Experiences 09:58 - Co-founders & Partnership 12:55 - Credibility 15:16 - What Crypto misunderstood about TradFi? 19:39 - What TradFi misunderstood about Crypto? 24:26 - The Real Turning Point 26:46 - Credefi to Real Finance 32:00 - What’s RWA? 34:40 - What’s broken with RWAs? 36:58 - Real Finance in simple terms 38:09 - Institutional interest in Real Finance 41:28 - Success & Failures 41:58 - How Global Finance could change 43:02 - Crypto isn’t a slow process 45:35 - Conclusion

MR SHIFT 🦁

40,246 Aufrufe • vor 6 Monaten

NEW Solana Sessions dives into Harmonic’s plan to bring Nasdaq onchain Its cofounder Ben ⌛ explains how it’s creating an open, competitive marketplace on Solana with plans to scale to 1M TPS Timestamps 00:00 – Intro: Solana’s progress & how Temporal fits in 01:03 – Creating an “onchain Nasdaq” for Solana 02:15 – Ben’s background: from math & CS student to interning at Citadel 03:19 – What drew him from traditional finance to crypto 04:11 – First impressions of Solana’s speed and performance 05:25 – Why Ben left HFT firms to build in decentralized finance 06:20 – Problems with centralized markets and the appeal of DeFi transparency 07:04 – How Solana achieved real efficiency and low fees 08:50 – Understanding more about Harmonic 10:15 – Why sequencing and inclusion latency matter for DeFi apps 11:35 – Standardizing Solana’s transaction rules with ACE (Application Controlled Execution) 13:13 – Why open sequencing helps apps like DEXs and liquidators 14:04 – Bringing predictability and competition to Solana’s block production 15:32 – How multiple builders competing improves network throughput 16:51 – The goal: Solana reaching 10 to 100x throughput with stability 18:06 – Long-term potential can be 1 million TPS and sustainable decentralization 19:22 – How open sequencing improves inclusion, latency and efficiency 20:57 – Building trust between validators, builders, and applications 22:13 – Preventing toxic block-building practices 23:46 – Why transparency and fairness increase validator revenue 25:04 – What “success” looks like for Solana developers and apps 26:33 – Why Solana’s user and institutional adoption is accelerating 28:10 – The role of liquidity providers and market competition 29:12 – Institutional capital, DeFi adoption & network maturity 31:04 – Advice from Ben: ignore short-term noise, keep building 32:33 – Closing thoughts & future of open blockchain design Watch on X:
32:44

NEW Solana Sessions dives into Harmonic’s plan to bring Nasdaq onchain Its cofounder Ben ⌛ explains how it’s creating an open, competitive marketplace on Solana with plans to scale to 1M TPS Timestamps 00:00 – Intro: Solana’s progress & how Temporal fits in 01:03 – Creating an “onchain Nasdaq” for Solana 02:15 – Ben’s background: from math & CS student to interning at Citadel 03:19 – What drew him from traditional finance to crypto 04:11 – First impressions of Solana’s speed and performance 05:25 – Why Ben left HFT firms to build in decentralized finance 06:20 – Problems with centralized markets and the appeal of DeFi transparency 07:04 – How Solana achieved real efficiency and low fees 08:50 – Understanding more about Harmonic 10:15 – Why sequencing and inclusion latency matter for DeFi apps 11:35 – Standardizing Solana’s transaction rules with ACE (Application Controlled Execution) 13:13 – Why open sequencing helps apps like DEXs and liquidators 14:04 – Bringing predictability and competition to Solana’s block production 15:32 – How multiple builders competing improves network throughput 16:51 – The goal: Solana reaching 10 to 100x throughput with stability 18:06 – Long-term potential can be 1 million TPS and sustainable decentralization 19:22 – How open sequencing improves inclusion, latency and efficiency 20:57 – Building trust between validators, builders, and applications 22:13 – Preventing toxic block-building practices 23:46 – Why transparency and fairness increase validator revenue 25:04 – What “success” looks like for Solana developers and apps 26:33 – Why Solana’s user and institutional adoption is accelerating 28:10 – The role of liquidity providers and market competition 29:12 – Institutional capital, DeFi adoption & network maturity 31:04 – Advice from Ben: ignore short-term noise, keep building 32:33 – Closing thoughts & future of open blockchain design Watch on X:

Talking Tokens Podcast

23,219 Aufrufe • vor 9 Monaten

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):
17:04

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):

Matt Dancho (Business Science)

77,293 Aufrufe • vor 1 Jahr

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):
15:38

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 Aufrufe • vor 1 Jahr