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🚨NEW EP: Why Solana Data Doesn't Agree (And Why That Matters)🚨 Today we're joined by Waddah from the Solana Foundation to discuss Solana Data, an initiative tackling one of crypto's biggest hidden problems: why different data providers report completely different numbers. We discuss: - What is Solana Data? -...

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Indexed Podcast

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12,701 次观看 • 1 个月前 •via X (Twitter)

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Epicenter Podcast

28,507 次观看 • 4 个月前

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition
56:10

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition

papiofficial ᛤ

29,967 次观看 • 1 年前

New The Peel with toly 🇺🇸 at Solana 0:00 Trading SpaceX on Solana 3:02 Wall Street runs on 100+ year old tech 10:51 US dominance created demand for tokenization 13:58 Complexity reduces risk of the financial system 15:51 Do you need to use blockchain? 18:34 Privacy tradeoffs of public ledgers 23:45 Making a 10,000x faster blockchain 30:05 A new data structure based on time 32:54 Trading was Solana’s first use case 37:41 Advice from his wife that led to Solana 40:05 Why a16z passed (then paid up 1,000x) 43:50 Rejection and COVID led to Solana’s success 48:30 Best time to launch is the bottom of a market 52:56 Why Bitcoin and Ethereum were so slow 57:38 Rebuilding Solana with Alpenglow 1:01:23 35% of all stablecoin volume runs on Solana 1:04:26 AI will replace 100 billion jobs 1:08:17 Protesting data centers is selfish 1:10:11 Growing up in the USSR: one toilet, four families 1:12:27 Culture shock moving to the US 1:13:23 Government spending is fake GDP 1:15:49 Playing competitive underwater hockey 1:18:01 Armani at Backpack 1:19:23 How Solana survived the FTX collapse 1:22:47 There won’t be massive AI job loss
1:25:44

New The Peel with toly 🇺🇸 at Solana 0:00 Trading SpaceX on Solana 3:02 Wall Street runs on 100+ year old tech 10:51 US dominance created demand for tokenization 13:58 Complexity reduces risk of the financial system 15:51 Do you need to use blockchain? 18:34 Privacy tradeoffs of public ledgers 23:45 Making a 10,000x faster blockchain 30:05 A new data structure based on time 32:54 Trading was Solana’s first use case 37:41 Advice from his wife that led to Solana 40:05 Why a16z passed (then paid up 1,000x) 43:50 Rejection and COVID led to Solana’s success 48:30 Best time to launch is the bottom of a market 52:56 Why Bitcoin and Ethereum were so slow 57:38 Rebuilding Solana with Alpenglow 1:01:23 35% of all stablecoin volume runs on Solana 1:04:26 AI will replace 100 billion jobs 1:08:17 Protesting data centers is selfish 1:10:11 Growing up in the USSR: one toilet, four families 1:12:27 Culture shock moving to the US 1:13:23 Government spending is fake GDP 1:15:49 Playing competitive underwater hockey 1:18:01 Armani at Backpack 1:19:23 How Solana survived the FTX collapse 1:22:47 There won’t be massive AI job loss

Turner Novak 🍌🧢

55,018 次观看 • 19 天前

Data labelling is a fricking hard market and there are some core questions. 1. Why does no player want to be called a talent marketplace? 2. Is the proclaimed “revenue” real revenue or GMV? 3. What are the margins given the GMV structure of the business? 4. Are the players not concerned by the concentration of revenue with 2 customers being over 50% of revenue for every player in market? 5.If synthetic data continues to improve, WTF happens to their core business? We do not pull punches in this discussion with Top 5 takeaways below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 00:51 Redefining “Talent Marketplaces” Today 03:46 Data, Compute, Algorithms: What is Most Abundant? 16:59 The Biggest Challenges Enterprises Have with AI Adoption 20:57 Why Will 99% of Knowledge Work Will be Gone in 10 Years 28:53 How Will Data-Driven Feedback Loops Replace Technology as the Moat 34:20 Is Revenue BS in Data Labelling? Are Players Calling GMV Revenue? 43:43 Are We in an AI Bubble? 52:22 Why is SaaS Dead in a World of AI? 01:00:32 Will the Phone be the Primary User Interface to an AI World? 01:07:46 Quick-Fire Round
1:17:05

Data labelling is a fricking hard market and there are some core questions. 1. Why does no player want to be called a talent marketplace? 2. Is the proclaimed “revenue” real revenue or GMV? 3. What are the margins given the GMV structure of the business? 4. Are the players not concerned by the concentration of revenue with 2 customers being over 50% of revenue for every player in market? 5.If synthetic data continues to improve, WTF happens to their core business? We do not pull punches in this discussion with Top 5 takeaways below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 00:51 Redefining “Talent Marketplaces” Today 03:46 Data, Compute, Algorithms: What is Most Abundant? 16:59 The Biggest Challenges Enterprises Have with AI Adoption 20:57 Why Will 99% of Knowledge Work Will be Gone in 10 Years 28:53 How Will Data-Driven Feedback Loops Replace Technology as the Moat 34:20 Is Revenue BS in Data Labelling? Are Players Calling GMV Revenue? 43:43 Are We in an AI Bubble? 52:22 Why is SaaS Dead in a World of AI? 01:00:32 Will the Phone be the Primary User Interface to an AI World? 01:07:46 Quick-Fire Round

Harry Stebbings

36,985 次观看 • 8 个月前

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products
29:20

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

Pat Grady

107,734 次观看 • 15 小时前

🚨NEW EPISODE🚨 The Data Behind DeFi’s Biggest Upgrade In this episode, we’re joined by Jackie (jackie | agaperste), Head of Data Science at Uniswap Foundation, and Grace (@grace_lily), part of the Data team at Uniswap 🦄. Together we explore: * Uniswap V4 and the broader innovations shaping the Uniswap ecosystem * Unique mechanics of Bunni * Challenges of liquidity management * New TVL metrics, and arbitrage flows * We also explore Unichain * Spam mitigation on L2s * The evolution of smart wallets in the Uniswap Wallet, And much more—enjoy! — Timestamps: (00:00) Introduction (01:42) Uniswap V4 and Ecosystem Growth (05:35) Understanding Uniswap V4's Unique Features (10:32) Bunni and Liquidity Management (15:59) Challenges and Innovations in Uniswap V4 (22:36) Data Analysis and Adoption of Uniswap V4 (37:11) Debating New Metrics for TVL (38:24) Challenges with TVL and Liquidity (41:26) Uniswap V4 and Liquidity Incentives (45:13) Arbitrage Opportunities in V4 (48:57) Introduction to Unichain (51:34) Adoption and Use Cases of Unichain (53:31) Mitigating Spam on L2 Chains (1:01:22) Uniswap Wallet and Smart Wallets (1:07:59) Closing Thoughts and Future Prospects (1:08:23) Outro — Links: Uniswap: Uniswap Twitter: Uniswap V4: — Follow the guests: Grace: Jackie:
1:10:47

🚨NEW EPISODE🚨 The Data Behind DeFi’s Biggest Upgrade In this episode, we’re joined by Jackie (jackie | agaperste), Head of Data Science at Uniswap Foundation, and Grace (@grace_lily), part of the Data team at Uniswap 🦄. Together we explore: * Uniswap V4 and the broader innovations shaping the Uniswap ecosystem * Unique mechanics of Bunni * Challenges of liquidity management * New TVL metrics, and arbitrage flows * We also explore Unichain * Spam mitigation on L2s * The evolution of smart wallets in the Uniswap Wallet, And much more—enjoy! — Timestamps: (00:00) Introduction (01:42) Uniswap V4 and Ecosystem Growth (05:35) Understanding Uniswap V4's Unique Features (10:32) Bunni and Liquidity Management (15:59) Challenges and Innovations in Uniswap V4 (22:36) Data Analysis and Adoption of Uniswap V4 (37:11) Debating New Metrics for TVL (38:24) Challenges with TVL and Liquidity (41:26) Uniswap V4 and Liquidity Incentives (45:13) Arbitrage Opportunities in V4 (48:57) Introduction to Unichain (51:34) Adoption and Use Cases of Unichain (53:31) Mitigating Spam on L2 Chains (1:01:22) Uniswap Wallet and Smart Wallets (1:07:59) Closing Thoughts and Future Prospects (1:08:23) Outro — Links: Uniswap: Uniswap Twitter: Uniswap V4: — Follow the guests: Grace: Jackie:

Indexed Podcast

12,866 次观看 • 1 年前