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Decentralized AI Product Deployment with KIP Protocol Workshop Recap 🎦 Learn from top educators on creating your own Knowledge Base and deploying AI RAG dApps on KIP Protocol 💡 In this TinTinLand workshop, our Chief AI Officer, Jen D., showcases the power of KIP Developer Sandbox👩‍💻 Highlights: 00:00 Introduction... 00:40 KIP Ecosystem Overview 01:23 KIP Identification Contract 02:55 KIP Registration Contract 03:36 KIP Service Contract 06:31 AMA Summary and Clarification 07:50 Smart Contract Interaction Demo 19:36 Sandbox Site Introduction 21:12 Account Abstraction and Data Upload 24:17 Creating AI dApps 32:33 Prompt Settings and Model Selection 37:11 App Testing 45:01 Main Net Deployment and Testing Tokens 45:36 Community Engagement and Follow-Up 46:27 Q&A Session 50:21 Conclusion What will you build? 🔧show more

KIP Protocol

333,601 subscribers

181,848 views • 2 years ago •via X (Twitter)

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10 Comments

chuks2 years ago

@OurTinTinLand @HumanLevelJen Lets go $KIP and @KIPprotocol Supporting Everyone

Appie2 years ago

@OurTinTinLand @HumanLevelJen Who needs $KIP points?? 👇👇

Sarmad 🐐 ⚡️ 🟠 NFTARMY Lingo Clone2 years ago

@OurTinTinLand @HumanLevelJen Awesome 😎 $Kip

T.E.E🧞‍♀️DEFI. CLONE2 years ago

@OurTinTinLand @HumanLevelJen $KIP @KIPprotocol #Kipprotocol When every physical and mental resource is focused, one’s power to solve a problem multiplies tremendously. norman vincent peale $KIP @KIPprotocol 💯 💯 💯

Temidayo CLONE2 years ago

@OurTinTinLand @HumanLevelJen We mount again $KIP So bullish on this project @KIPprotocol

DEELOO - CLONE 👌 🐐2 years ago

@OurTinTinLand @HumanLevelJen LFG $KIP @KIPprotocol

Praise Ahmed Lingo (*,*)2 years ago

@OurTinTinLand @HumanLevelJen Well organized projects $KIP LFG @KIPprotocol

Lexybabs2 years ago

@OurTinTinLand @HumanLevelJen $KIP cooking

Tower2 years ago

@OurTinTinLand @HumanLevelJen Let’s farm together $KIP peeps!

Bliss2 years ago

@OurTinTinLand @HumanLevelJen $KIP building,we stand with you.

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2,033,123 views • 2 years ago

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NEARWEEK

25,584 views • 1 year ago

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University of Austin (UATX)

27,770 views • 6 months ago

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Nathan Lands

28,827 views • 3 months ago

2025 = the year of AI Agents x DAOS x Onchain Capital Allocation NEW greenpill.network podcast: Today, I'm joined by Shaw (spirit/acc) jin from ai16zdao to talk about AI Agents. How we use them to allocate capital in DAOs? To route information? To accelerate movements? To solve coordination failure? Timestamps for ez navigation 🫡 00:00 - Intro & focusing on DAOs and the intersection of AI agents. 01:00 - AI Agents and Their Impact 02:03 - The Evolution of the Internet 07:52 - Defining AI Agents 09:40 - Reducing Friction in Information Sharing 11:56 - Building a Movement, Not a Cult 14:15 - The Cathedral and the Bazaar Metaphor 16:04 - Using AI for Coordination 17:49 - Reducing Information Siloing 18:39 - Common Tools Across Organizations 19:31 - Open Sourcing Solutions 20:07 - Capital Allocation Challenges 20:55 - AI and Capital Allocation Intersection 21:50 - Human Oversight in Funding 22:48 - Continuous Retroactive Funding 24:42 - Creating a Supportive Culture 25:06 - Feedback Loops for Builders 26:09 - Reducing Overhead in Funding 26:36 - Acknowledging Non-Coding Contributions 27:58 - Chat Summarization Tool 28:34 - Profiles and Social Capital 29:00 - Marketplace of AI Agents 29:46 - Layered AI Systems 32:42 -Human Oversight in AI 34:39 - Feedback Loops in Development 37:01- Documentation and Community Engagement 38:12 - Self-Documenting Code 40:20 - Accelerated Idea Generation 45:03 - Circle of AI Champions 46:32 - Infinite Backrooms Concept 47:48 - Personal Infinite Backroom Ideas 49:23 - Concerns About Backrooms and Information Hiding 51:23 - Infinite Backrooms Concept 53:35 - Implementers and Skill Sharing 54:40 - Vision for the Future 56:39 - Concerns About UBI Implementation 57:56 - Community Income as a Solution 58:14 - A Hope for Self-Sufficiency and AI 59:36 - Outro

2025 = the year of AI Agents x DAOS x Onchain Capital Allocation NEW greenpill.network podcast: Today, I'm joined by Shaw (spirit/acc) jin from ai16zdao to talk about AI Agents. How we use them to allocate capital in DAOs? To route information? To accelerate movements? To solve coordination failure? Timestamps for ez navigation 🫡 00:00 - Intro & focusing on DAOs and the intersection of AI agents. 01:00 - AI Agents and Their Impact 02:03 - The Evolution of the Internet 07:52 - Defining AI Agents 09:40 - Reducing Friction in Information Sharing 11:56 - Building a Movement, Not a Cult 14:15 - The Cathedral and the Bazaar Metaphor 16:04 - Using AI for Coordination 17:49 - Reducing Information Siloing 18:39 - Common Tools Across Organizations 19:31 - Open Sourcing Solutions 20:07 - Capital Allocation Challenges 20:55 - AI and Capital Allocation Intersection 21:50 - Human Oversight in Funding 22:48 - Continuous Retroactive Funding 24:42 - Creating a Supportive Culture 25:06 - Feedback Loops for Builders 26:09 - Reducing Overhead in Funding 26:36 - Acknowledging Non-Coding Contributions 27:58 - Chat Summarization Tool 28:34 - Profiles and Social Capital 29:00 - Marketplace of AI Agents 29:46 - Layered AI Systems 32:42 -Human Oversight in AI 34:39 - Feedback Loops in Development 37:01- Documentation and Community Engagement 38:12 - Self-Documenting Code 40:20 - Accelerated Idea Generation 45:03 - Circle of AI Champions 46:32 - Infinite Backrooms Concept 47:48 - Personal Infinite Backroom Ideas 49:23 - Concerns About Backrooms and Information Hiding 51:23 - Infinite Backrooms Concept 53:35 - Implementers and Skill Sharing 54:40 - Vision for the Future 56:39 - Concerns About UBI Implementation 57:56 - Community Income as a Solution 58:14 - A Hope for Self-Sufficiency and AI 59:36 - Outro

owockai

60,440 views • 1 year ago

AI and crypto… How will these two powerful technologies collide, and what happens when they do? Today’s episode is a special one. It’s a round table on some of the most compelling ideas at the intersection of AI and crypto, sourced from members of the a16z crypto team. You’re going to hear from builders, researchers, and deal makers who are all wrestling with the big question: How will these two powerful technologies collide, and what happens when they do? with Sam Broner Scott Kominers Liz Harkavy Carra Wu jay mattg Timestamps: (0:00) Introduction (1:44) Universal Identity for AI Agents (5:25) Why Crypto Makes a Difference (10:07) Context in AI: Memory and Interoperability (15:00) Vibe Coding Applications (19:32) Registries with Global Context (21:47) Honkaku Mystery Novels (24:13) Monetization Models for AI x Crypto (28:36) Overcoming Technical Challenges (31:34) AI Agents and the Rise of Web Crawlers (36:05) Collapse of the Social Contract (39:21) AI Bots and Blockchain Solutions (44:31) Proof of Personhood: Ensuring Human-ness (50:41) Future of Personalized Advertising (1:01:54) Final Thoughts Enjoy the full episode here on X.

AI and crypto… How will these two powerful technologies collide, and what happens when they do? Today’s episode is a special one. It’s a round table on some of the most compelling ideas at the intersection of AI and crypto, sourced from members of the a16z crypto team. You’re going to hear from builders, researchers, and deal makers who are all wrestling with the big question: How will these two powerful technologies collide, and what happens when they do? with Sam Broner Scott Kominers Liz Harkavy Carra Wu jay mattg Timestamps: (0:00) Introduction (1:44) Universal Identity for AI Agents (5:25) Why Crypto Makes a Difference (10:07) Context in AI: Memory and Interoperability (15:00) Vibe Coding Applications (19:32) Registries with Global Context (21:47) Honkaku Mystery Novels (24:13) Monetization Models for AI x Crypto (28:36) Overcoming Technical Challenges (31:34) AI Agents and the Rise of Web Crawlers (36:05) Collapse of the Social Contract (39:21) AI Bots and Blockchain Solutions (44:31) Proof of Personhood: Ensuring Human-ness (50:41) Future of Personalized Advertising (1:01:54) Final Thoughts Enjoy the full episode here on X.

a16z crypto

20,772 views • 1 year ago

Vibe Coding with Codex - Complete Guide Build a Web App, Desktop App & iOS App with Codex + GPT‑5.5 (No Coding Needed, Beginner Friendly) In this video you will learn: > Vibe Coding Basics + Vocab > How to build a web app using Codex > Add db, auth + storage with Firebase > Github Basics > Add AI Features (API's) > Deploy to internet (Vercel) > Convert Web App into Desktop app & iOS App Chapters 00:00 Intro 01:12 Setting up Codex 01:57 The basics of Vibe Coding and Codex 02:20 Projects, Files, App 03:45 Example App - Microsoft Paint 04:25 Running app locally 06:39 Save My Code - Use Github 10:37 Quick Review before building app 12:24 Building a web app - The Prompt 15:32 Creating Web App Project 16:34 Explaining Firebase (Database, Storage, Auth) 18:42 Setting up Firebase Project 22:54 Prompting Codex to build our app 24:49 Inspect Element - Console 26:24 Verify Data being Stored in Database 27:51 Making Changes to App 31:29 Fixing Storage Permissions with Codex 32:20 GPT API - Adding AI to our app 36:20 Making more changes using screenshots 39:02 Queuing vs Steering 40:23 Deploying our app to Vercel (App on Internet) 43:09 Convert web app to desktop app and iOS app 47:15 Web App and Desktop App work Now 48:36 Now let's run the iOS app 50:27 All three apps work! 51:11 Making Changes to iOS app 52:51 Testing agent skill feature of our app 53:55 Summary of what we did

Vibe Coding with Codex - Complete Guide Build a Web App, Desktop App & iOS App with Codex + GPT‑5.5 (No Coding Needed, Beginner Friendly) In this video you will learn: > Vibe Coding Basics + Vocab > How to build a web app using Codex > Add db, auth + storage with Firebase > Github Basics > Add AI Features (API's) > Deploy to internet (Vercel) > Convert Web App into Desktop app & iOS App Chapters 00:00 Intro 01:12 Setting up Codex 01:57 The basics of Vibe Coding and Codex 02:20 Projects, Files, App 03:45 Example App - Microsoft Paint 04:25 Running app locally 06:39 Save My Code - Use Github 10:37 Quick Review before building app 12:24 Building a web app - The Prompt 15:32 Creating Web App Project 16:34 Explaining Firebase (Database, Storage, Auth) 18:42 Setting up Firebase Project 22:54 Prompting Codex to build our app 24:49 Inspect Element - Console 26:24 Verify Data being Stored in Database 27:51 Making Changes to App 31:29 Fixing Storage Permissions with Codex 32:20 GPT API - Adding AI to our app 36:20 Making more changes using screenshots 39:02 Queuing vs Steering 40:23 Deploying our app to Vercel (App on Internet) 43:09 Convert web app to desktop app and iOS app 47:15 Web App and Desktop App work Now 48:36 Now let's run the iOS app 50:27 All three apps work! 51:11 Making Changes to iOS app 52:51 Testing agent skill feature of our app 53:55 Summary of what we did

Riley Brown

146,324 views • 2 months ago

BREAKING: Naveen Rao (Naveen Rao) CEO & Co-Founder of Unconventional AI on Brain-Inspired AI Chips ICYMI Rao Founded: - Nervana, acq. by Intel for $400M+ - MosaicML, acq. by Databricks for $1.3B (fmr Chief AI Officer, Databricks) Unconventional AI raised $475M Seed at $4.5B valuation to build biology-scale efficiency to artificial intelligence. Recorded & hosted Jan 20th in Palo Alto by Playground Global's (Playground Global) General Partner Peter Barrett, this Sourcery discussion features: - Naveen Rao, CEO & Co-Founder (Unconventional AI) - Konstantine Buhler (Konstantine Buhler), Partner at Sequoia Capital & Early Investor - Molly O’Shea, Sourcery (Moderator) Rao's 'Un-Seed' financing round was co-led by a16z & Lightspeed Venture Partners, w/ participation from Sequoia Capital, Lux Capital, DCVC, Databricks, Jeff Bezos, & more (Rao invested $10M of his own at the same terms) Highlights: 00:00 Introduction to Unconventional AI 01:01 Playground 02:57 The Future of Computation and Moore's Law 04:38 The Evolution of AI and Computation 05:59 Naveen's Journey: From Nervana, Mosaic ML, Databricks, to Unconventional AI 07:33 The AI Supercycle 12:31 Layers of the AI Stack 15:31 Conviction in Unconventional AI 31:01 Building a Team 36:01 Understanding the Scale of the Problem 37:11 Capital vs. Power Bottleneck 38:37 Founder Principles 41:33 Sequoia Investing Principles 45:00 Circular AI Deals and Market Dynamics 46:33 Professional Race Car Driving 50:58 Q&A Session: joules/token, neuromorphic, recurrence 01:08:33 Final Thoughts

BREAKING: Naveen Rao (Naveen Rao) CEO & Co-Founder of Unconventional AI on Brain-Inspired AI Chips ICYMI Rao Founded: - Nervana, acq. by Intel for $400M+ - MosaicML, acq. by Databricks for $1.3B (fmr Chief AI Officer, Databricks) Unconventional AI raised $475M Seed at $4.5B valuation to build biology-scale efficiency to artificial intelligence. Recorded & hosted Jan 20th in Palo Alto by Playground Global's (Playground Global) General Partner Peter Barrett, this Sourcery discussion features: - Naveen Rao, CEO & Co-Founder (Unconventional AI) - Konstantine Buhler (Konstantine Buhler), Partner at Sequoia Capital & Early Investor - Molly O’Shea, Sourcery (Moderator) Rao's 'Un-Seed' financing round was co-led by a16z & Lightspeed Venture Partners, w/ participation from Sequoia Capital, Lux Capital, DCVC, Databricks, Jeff Bezos, & more (Rao invested $10M of his own at the same terms) Highlights: 00:00 Introduction to Unconventional AI 01:01 Playground 02:57 The Future of Computation and Moore's Law 04:38 The Evolution of AI and Computation 05:59 Naveen's Journey: From Nervana, Mosaic ML, Databricks, to Unconventional AI 07:33 The AI Supercycle 12:31 Layers of the AI Stack 15:31 Conviction in Unconventional AI 31:01 Building a Team 36:01 Understanding the Scale of the Problem 37:11 Capital vs. Power Bottleneck 38:37 Founder Principles 41:33 Sequoia Investing Principles 45:00 Circular AI Deals and Market Dynamics 46:33 Professional Race Car Driving 50:58 Q&A Session: joules/token, neuromorphic, recurrence 01:08:33 Final Thoughts

Molly O’Shea

142,359 views • 5 months ago

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 views • 2 months ago

. OpenLedger x : The full episode Our core contributor Ram sat down with Sreeram Kannan (founder ) to discuss verifiable AI, crypto's app problem, AI's trust problem and much more Check out the video below to learn more Timestamps: 00:00 Sreeram's introduction 00:29 How to build verification for AI systems 01:27 The Crypto and AI problem 03:10 Unstoppable AGI 05:08 What is Verifiable AI 06:05 The Verifiability Problem with Centralized AI 07:15 The current state of AI and its future 08:48 How humans are getting better at intelligence extraction 10:31 What is OpenLedger doing 11:50 Where LLMs fall short and how co-ordination helps 12:50 SLMs are already there 13:29 Crypto as an incentivisation mechanism 15:38 Apps with commitments, achieving product market fit 16:50 Existing examples of commitments in society 18:24 Investable AI 19:00 Data contributors as co-owner 21:30 The solution for the incentivisation problem 22:25 Explaining explainable AI 23:58 Trusting the trustless way 25:07 Data is the new oil 25:40 The idea of openledger is very crypto native 26:50 David v Goliath (in the data perspective) 28:03 What Eigen is working on 29:06 Making unstoppable verifiable AI practically possible 32:20 What Sreeram uses for productivity 33:30 Make Eigen Labs agentic 34:16 (community question) How Sreeram frames his prompts 36:08 (community question) What's the momentum driver for blockchain apps and what is Sreeram most excited about? (ZkTLS) 38:54 (community question) Where is attribution and verification really needed in AI?

. OpenLedger x : The full episode Our core contributor Ram sat down with Sreeram Kannan (founder ) to discuss verifiable AI, crypto's app problem, AI's trust problem and much more Check out the video below to learn more Timestamps: 00:00 Sreeram's introduction 00:29 How to build verification for AI systems 01:27 The Crypto and AI problem 03:10 Unstoppable AGI 05:08 What is Verifiable AI 06:05 The Verifiability Problem with Centralized AI 07:15 The current state of AI and its future 08:48 How humans are getting better at intelligence extraction 10:31 What is OpenLedger doing 11:50 Where LLMs fall short and how co-ordination helps 12:50 SLMs are already there 13:29 Crypto as an incentivisation mechanism 15:38 Apps with commitments, achieving product market fit 16:50 Existing examples of commitments in society 18:24 Investable AI 19:00 Data contributors as co-owner 21:30 The solution for the incentivisation problem 22:25 Explaining explainable AI 23:58 Trusting the trustless way 25:07 Data is the new oil 25:40 The idea of openledger is very crypto native 26:50 David v Goliath (in the data perspective) 28:03 What Eigen is working on 29:06 Making unstoppable verifiable AI practically possible 32:20 What Sreeram uses for productivity 33:30 Make Eigen Labs agentic 34:16 (community question) How Sreeram frames his prompts 36:08 (community question) What's the momentum driver for blockchain apps and what is Sreeram most excited about? (ZkTLS) 38:54 (community question) Where is attribution and verification really needed in AI?

OpenLedger

274,765 views • 1 year ago

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

166,795 views • 1 year ago

1/ New episode - DePIN Roundtable Ep. 2 with Helium🎈 Hosts: Santiago R Santos Jason Badeaux MoneroMahesh Connor Lovely We discuss: - DePIN Sector Update - Will crypto projects merge their equity and tokens? - Helium highlights: $32M ARR, 1M DAU, 100k hotspots Timestamps: 00:00 - Introduction 02:25 - Microstrategy and Digital Asset Accumulation 03:42 - Market Trends: Crypto and Wall Street 05:03 - Santi's Perspective on Market Efficiency 06:19 - DePIN Projects and Public Market Strategies 06:41 - Helium's Potential for Going Public 08:50 - Cash Flow and Tokenomics in DePIN 12:07 - Helium's Recent Revenue Growth 12:55 - PMF for DePIN Networks 18:03 - User Engagement and Helium's Growth 19:05 - Helium's Revenue Sources Explained 21:06 - Off-Chain and On-Chain Revenue 24:12 - Learning from Helium's Evolution 25:03 - Focus on Distribution Over Product 27:27 - Daily Active Users and Their Interaction 31:26 - Valuable Users and Helium's Ecosystem 33:45 - Cloud Points and User Experience 36:44 - Retention Curves: Crypto vs. Traditional Users 39:59 - Aligning Token and Equity Interests Watch on all platforms below ⬇️

1/ New episode - DePIN Roundtable Ep. 2 with Helium🎈 Hosts: Santiago R Santos Jason Badeaux MoneroMahesh Connor Lovely We discuss: - DePIN Sector Update - Will crypto projects merge their equity and tokens? - Helium highlights: $32M ARR, 1M DAU, 100k hotspots Timestamps: 00:00 - Introduction 02:25 - Microstrategy and Digital Asset Accumulation 03:42 - Market Trends: Crypto and Wall Street 05:03 - Santi's Perspective on Market Efficiency 06:19 - DePIN Projects and Public Market Strategies 06:41 - Helium's Potential for Going Public 08:50 - Cash Flow and Tokenomics in DePIN 12:07 - Helium's Recent Revenue Growth 12:55 - PMF for DePIN Networks 18:03 - User Engagement and Helium's Growth 19:05 - Helium's Revenue Sources Explained 21:06 - Off-Chain and On-Chain Revenue 24:12 - Learning from Helium's Evolution 25:03 - Focus on Distribution Over Product 27:27 - Daily Active Users and Their Interaction 31:26 - Valuable Users and Helium's Ecosystem 33:45 - Cloud Points and User Experience 36:44 - Retention Curves: Crypto vs. Traditional Users 39:59 - Aligning Token and Equity Interests Watch on all platforms below ⬇️

Proof of Coverage Media

17,697 views • 1 year ago