Loading video...

Video Failed to Load

Go Home

🚨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? -...

indexed_pod's profile picture

Indexed Podcast

1,806 subscribers

13,203 views • 2 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

7 Comments

Waddah's profile picture
Waddah2 months ago

@SolanaFndn Ask me questions 🙋‍♂️

Indexed Podcast's profile picture
Indexed Podcast2 months ago

— Follow the guest: Solana Data: Follow the co-hosts: Follow the Indexed Podcast: — The Indexed Podcast discusses hot topics, trendy metrics and chart crimes in the crypto industry, with a new episode every 1st and 3rd Thursday of the month, brought to you by wizards @hildobby @0xBoxer @sui414. Subscribe/follow the show and leave a comment to help us grow the show! — DISCLAIMER: All information presented here should not be relied upon as legal, financial, investment, tax or even life advice. The views expressed in the podcast are not representative of hosts' employers views. We are acting independently of our respective professional roles.

Zinn⛏️'s profile picture
Zinn⛏️2 months ago

@waldruupi @SolanaFndn oh this is bound to be a banger, listening now

DΞFI PΞNIΞL (🧠,🧠)'s profile picture
DΞFI PΞNIΞL (🧠,🧠)2 months ago

@waldruupi @SolanaFndn Standardized data is one of those things everyone benefits from.

Diego's profile picture
Diego2 months ago

@waldruupi @SolanaFndn This looks interesting! The most common thing we see in the space is data mismatch across providers, mostly due methodology

vik0nchain's profile picture
vik0nchain2 months ago

@waldruupi @SolanaFndn Parsing through and adjusting for assumptions made by data providers in the space is a major issue for institutions allocators and investors. It's great to see @SolanaFndn prioritizing this to welcome the next wave of capital. Many chains can learn from this.

Zinn⛏️'s profile picture
Zinn⛏️2 months ago

what a great episode, and would love to see other ecosystems also take this approach (shoutout Solana for MIT licensing this) Would also love to see data platforms open source their methodologies (or even upon request?) and I'm definitely looking at implementing this for RWA Markets!

Related Videos

1/ BREAKING – DePIN's Biggest New Deal: Valeo x NATIX Network with Alireza Ghods Host: Connor Lovely 🎬 We discuss: - Why a $3B, Fortune 500 company wants Natix data - What is a World Foundational Model? - Why autonomous driving needs synthetic data Timestamps: 00:00 - Introduction 01:14 - Updates on VX360 and Data Sales 02:09 - Vision Language Model and Its Applications 04:39 - Importance of Diverse Data for World Models 06:20 - Details on the Valeo Deal 08:15 - Use Cases and Future Plans for World Models 10:45 - Synthetic Data and Real-World Applications 19:37 - Future of Physical AI and Autonomous Driving 23:51 - Conclusion Watch on other platforms below ⬇️
25:05

1/ BREAKING – DePIN's Biggest New Deal: Valeo x NATIX Network with Alireza Ghods Host: Connor Lovely 🎬 We discuss: - Why a $3B, Fortune 500 company wants Natix data - What is a World Foundational Model? - Why autonomous driving needs synthetic data Timestamps: 00:00 - Introduction 01:14 - Updates on VX360 and Data Sales 02:09 - Vision Language Model and Its Applications 04:39 - Importance of Diverse Data for World Models 06:20 - Details on the Valeo Deal 08:15 - Use Cases and Future Plans for World Models 10:45 - Synthetic Data and Real-World Applications 19:37 - Future of Physical AI and Autonomous Driving 23:51 - Conclusion Watch on other platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

11,869 views • 8 months ago

1/ New episode - Data Collection at the Edge with from 375ai Host: @richomiecon We discuss: - Their deep roots as DePIN deployers - How to get involved in their incentivized testnet - Building on Solana Timestamps: 00:00 - Introduction 01:00 - Harry's Background and Entrepreneurial Journey 02:40 - Transition from Zeppelin to 375ai 04:20 - Zeppelin's Initial Mission and Partnerships 06:30 - Deployment Strategies and Experiences 08:40 - Significance of the Name 375ai 09:50 - Testnet and Community Engagement 12:00 - 375edge Devices and Their Functionality 15:00 - Future Plans for 375 Street Devices 17:00 - Real-World Applications of Collected Data 19:00 - Building on the Solana Ecosystem 21:00 - Tokenomics and Incentive Structures 24:00 - Quality Control and Data Demand Strategies 25:30 - Call to Action and Community Engagement Watch on all platforms below ⬇️
26:33

1/ New episode - Data Collection at the Edge with from 375ai Host: @richomiecon We discuss: - Their deep roots as DePIN deployers - How to get involved in their incentivized testnet - Building on Solana Timestamps: 00:00 - Introduction 01:00 - Harry's Background and Entrepreneurial Journey 02:40 - Transition from Zeppelin to 375ai 04:20 - Zeppelin's Initial Mission and Partnerships 06:30 - Deployment Strategies and Experiences 08:40 - Significance of the Name 375ai 09:50 - Testnet and Community Engagement 12:00 - 375edge Devices and Their Functionality 15:00 - Future Plans for 375 Street Devices 17:00 - Real-World Applications of Collected Data 19:00 - Building on the Solana Ecosystem 21:00 - Tokenomics and Incentive Structures 24:00 - Quality Control and Data Demand Strategies 25:30 - Call to Action and Community Engagement Watch on all platforms below ⬇️

Proof of Coverage Media

13,745 views • 1 year ago

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?
26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,691 views • 1 month ago

Today on MCG: tekkaadan | Founder of Litcoin Tekkaadan is a solo founder building a data marketplace where AI agents mine verified data across 22 verticals and get paid in Bitcoin The token has zero supply increase baked in from day one 👀 00:06 - Founder introduction 01:14 - Saw Botcoin and got inspired 02:10 - Ten years in crypto 02:50 - What is Litcoin? 03:44 - AI agents mine "blocks" 04:03 - The 22 verticals explained 04:54 - How do you verify the data is accurate? 05:19 - The sandbox 05:48 - 10+ miners live and submitting 07:20 - What contaminated data is 08:49 - Miners get paid in Bitcoin 10:19 - Token design 10:50 - Zero supply increase 11:01 - 5.5 billion tokens 12:26 - Mining token prices connect to protocol growth 15:08 - Explorer page 16:31 - "We have very unique data" 23:09 - OpenAI just said they're going to need more data 23:31 - Can a company come and request specific data? 23:42 - The bounty system 24:52 - 10% of bounties flow back into the ecosystem 32:31 - The data marketplace is just one part of the bigger picture 33:20 - Valuation breakdown 35:56 - North Korea and bad actors 41:58 - GPT-5 agents producing better data 42:50 - Tekkaadan's port 44:02 - Takeaways
44:31

Today on MCG: tekkaadan | Founder of Litcoin Tekkaadan is a solo founder building a data marketplace where AI agents mine verified data across 22 verticals and get paid in Bitcoin The token has zero supply increase baked in from day one 👀 00:06 - Founder introduction 01:14 - Saw Botcoin and got inspired 02:10 - Ten years in crypto 02:50 - What is Litcoin? 03:44 - AI agents mine "blocks" 04:03 - The 22 verticals explained 04:54 - How do you verify the data is accurate? 05:19 - The sandbox 05:48 - 10+ miners live and submitting 07:20 - What contaminated data is 08:49 - Miners get paid in Bitcoin 10:19 - Token design 10:50 - Zero supply increase 11:01 - 5.5 billion tokens 12:26 - Mining token prices connect to protocol growth 15:08 - Explorer page 16:31 - "We have very unique data" 23:09 - OpenAI just said they're going to need more data 23:31 - Can a company come and request specific data? 23:42 - The bounty system 24:52 - 10% of bounties flow back into the ecosystem 32:31 - The data marketplace is just one part of the bigger picture 33:20 - Valuation breakdown 35:56 - North Korea and bad actors 41:58 - GPT-5 agents producing better data 42:50 - Tekkaadan's port 44:02 - Takeaways

MCG

10,522 views • 5 months ago

The intersection of crypto and AI, presented by OpenLedger Tim Copeland was joined by OpenLedger core contributor Ram in this sponsored episode to discuss the emerging intersection of decentralized AI and blockchain technology. The conversation covers OpenLedger’s infrastructure that lets users contribute proprietary data to train AI models, tokenomics, and the broader promise of creating a transparent, community-driven AI economy. OUTLINE 00:00 – Introduction 01:02 – What is Openledger? 02:27 – Data Contribution Model 08:47 – Access to Agents 15:16 – Ensuring Quality Data 19:09 – Incentive Structures 23:18 – The Role of Tokens 24:56 – The Convergence of AI and Crypto 27:53 – Competing with Centralized Systems 30:23 – Openledger's Future and Success 33:32 – Data Economy Insights 36:17 – Conclusion
36:50

The intersection of crypto and AI, presented by OpenLedger Tim Copeland was joined by OpenLedger core contributor Ram in this sponsored episode to discuss the emerging intersection of decentralized AI and blockchain technology. The conversation covers OpenLedger’s infrastructure that lets users contribute proprietary data to train AI models, tokenomics, and the broader promise of creating a transparent, community-driven AI economy. OUTLINE 00:00 – Introduction 01:02 – What is Openledger? 02:27 – Data Contribution Model 08:47 – Access to Agents 15:16 – Ensuring Quality Data 19:09 – Incentive Structures 23:18 – The Role of Tokens 24:56 – The Convergence of AI and Crypto 27:53 – Competing with Centralized Systems 30:23 – Openledger's Future and Success 33:32 – Data Economy Insights 36:17 – Conclusion

The Block Media

51,907 views • 1 year ago

🚨NEW EPISODE: Inside Canton: The Private Blockchain Powering Institutions🚨 Today we're joined by Ben | 0443.eth, co-founder and CEO of Noves, to explore the Canton Network. We discuss: - What is the Canton Network? - How privacy works on Canton - Why blockchain data breaks on private networks - How Noves indexes Canton data - Validators, synchronizers & network architecture - Network performance, TPS & scalability - Institutional adoption & real-world assets - Privacy vs transparency trade-offs - What activity is happening on Canton today? - The future of institutional blockchain infrastructure - Live walkthrough of Canton analytics dashboards - And much more—enjoy! — Timestamps: (00:00) Introduction (04:28) How Canton works (10:04) Collecting private data (12:29) Validators explained (17:37) Network performance (18:04) Peer-to-peer architecture (25:57) What Noves can see (27:27) Privacy vs transparency (28:18) Institutional adoption (35:27) Activity on Canton (38:24) Canton dashboard demo (43:57) Blockchain meets TradFi (44:44) What's next for Noves (46:04) Outro
46:38

🚨NEW EPISODE: Inside Canton: The Private Blockchain Powering Institutions🚨 Today we're joined by Ben | 0443.eth, co-founder and CEO of Noves, to explore the Canton Network. We discuss: - What is the Canton Network? - How privacy works on Canton - Why blockchain data breaks on private networks - How Noves indexes Canton data - Validators, synchronizers & network architecture - Network performance, TPS & scalability - Institutional adoption & real-world assets - Privacy vs transparency trade-offs - What activity is happening on Canton today? - The future of institutional blockchain infrastructure - Live walkthrough of Canton analytics dashboards - And much more—enjoy! — Timestamps: (00:00) Introduction (04:28) How Canton works (10:04) Collecting private data (12:29) Validators explained (17:37) Network performance (18:04) Peer-to-peer architecture (25:57) What Noves can see (27:27) Privacy vs transparency (28:18) Institutional adoption (35:27) Activity on Canton (38:24) Canton dashboard demo (43:57) Blockchain meets TradFi (44:44) What's next for Noves (46:04) Outro

Indexed Podcast

24,129 views • 2 months ago

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?
27:14

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber

132,472 views • 4 months ago

Exploring Solana Ep. 14: Titan Cross-assets trading is essential for onchain users. Users demand fast execution and the best rates. Titan is building it today on Solana. I met with Rocky Chris | Titan, co-founder, to discuss their journey, vision, DART, and more 👇 00:00 Intro & What is Titan Exchange? 00:55 From hedge fund to building on Solana 02:15 The gap Jupiter wasn't filling 05:10 Why Solana despite the competition 08:51 Pre-seed to seed round journey 11:02 DART, world's first on-chain router 16:34 Why everyone wants to integrate Titan 22:47 Building through the bear market 31:05 Most exciting thing on Solana right now 33:50 Biggest milestone for 2026
35:46

Exploring Solana Ep. 14: Titan Cross-assets trading is essential for onchain users. Users demand fast execution and the best rates. Titan is building it today on Solana. I met with Rocky Chris | Titan, co-founder, to discuss their journey, vision, DART, and more 👇 00:00 Intro & What is Titan Exchange? 00:55 From hedge fund to building on Solana 02:15 The gap Jupiter wasn't filling 05:10 Why Solana despite the competition 08:51 Pre-seed to seed round journey 11:02 DART, world's first on-chain router 16:34 Why everyone wants to integrate Titan 22:47 Building through the bear market 31:05 Most exciting thing on Solana right now 33:50 Biggest milestone for 2026

Mango

37,334 views • 5 months ago

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro
38:58

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro

Indexed Podcast

10,588 views • 3 months ago

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI
49:29

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

Molly O’Shea

70,094 views • 11 months ago

DROPS E24: Francis - Co-Founder & CEO of zkPass: ZK Privacy Is About to Go Mainstream! We deep-dive into why private data is the next trillion-dollar battlefield and how ZK-TLS flips the internet from "trust me" to "prove it" without revealing anything. We talk about: - How ZK-TLS makes the entire HTTPS internet verifiable - Why private data will eclipse public data in value - Crypto use cases from reputation-based lending to accredited investor checks - Protecting medical records, killing data brokers, and enabling safer AI training - Lessons from eight startup failures and the long-game mindset founders need - The journey that led from bow ties → startups → ZK infrastructure And much more! Timestamps 0:00 Meet Francis of zkPass 1:50 The Fundamentals of Growing a Crypto Startup 3:17 From Congo to New York: Francis’ Journey 4:06 Launching a Bow Tie Business in College 7:07 Adaptability from a Global Childhood 8:35 Eight Failures and What They Taught 13:07 Surviving Burnout and Mental Struggles 15:15 Small Successes That Fueled the Fire 18:04 Crypto Rollercoasters & Sustainable Growth 18:30 From Bow Ties to Blockchain: The Crypto Pivot 21:33 What Is Zero Knowledge (ZK)? 23:12 ZK + TLS: Making the Internet Verifiable 25:20 Explaining zkPass to Your Mom 28:22 Chainlink for Private Data? zkPass vs Public Oracles 30:16 Why APIs Can’t Cover the Web 32:00 Crypto Use Cases for zkPass 33:38 Beyond Collateral: Trustless Reputation Lending 35:13 Medical Privacy & Stablecoin Bank Transfers 38:26 What Makes zkPass Tech Unique 40:29 Yes/No Verifications: Trusting the zk Black Box 43:34 Why Data Privacy Is the Future
48:02

DROPS E24: Francis - Co-Founder & CEO of zkPass: ZK Privacy Is About to Go Mainstream! We deep-dive into why private data is the next trillion-dollar battlefield and how ZK-TLS flips the internet from "trust me" to "prove it" without revealing anything. We talk about: - How ZK-TLS makes the entire HTTPS internet verifiable - Why private data will eclipse public data in value - Crypto use cases from reputation-based lending to accredited investor checks - Protecting medical records, killing data brokers, and enabling safer AI training - Lessons from eight startup failures and the long-game mindset founders need - The journey that led from bow ties → startups → ZK infrastructure And much more! Timestamps 0:00 Meet Francis of zkPass 1:50 The Fundamentals of Growing a Crypto Startup 3:17 From Congo to New York: Francis’ Journey 4:06 Launching a Bow Tie Business in College 7:07 Adaptability from a Global Childhood 8:35 Eight Failures and What They Taught 13:07 Surviving Burnout and Mental Struggles 15:15 Small Successes That Fueled the Fire 18:04 Crypto Rollercoasters & Sustainable Growth 18:30 From Bow Ties to Blockchain: The Crypto Pivot 21:33 What Is Zero Knowledge (ZK)? 23:12 ZK + TLS: Making the Internet Verifiable 25:20 Explaining zkPass to Your Mom 28:22 Chainlink for Private Data? zkPass vs Public Oracles 30:16 Why APIs Can’t Cover the Web 32:00 Crypto Use Cases for zkPass 33:38 Beyond Collateral: Trustless Reputation Lending 35:13 Medical Privacy & Stablecoin Bank Transfers 38:26 What Makes zkPass Tech Unique 40:29 Yes/No Verifications: Trusting the zk Black Box 43:34 Why Data Privacy Is the Future

MR SHIFT 🦁

48,994 views • 10 months ago

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved
50:36

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved

Acc Ventures

43,976 views • 9 months ago

New Episode: Inside Nansen's AI Trading Agent Platform Alex Svanevik 🐧, CEO of Nansen, joins Friederike Ernst 🦉 to discuss Nansen's massive evolution from an on-chain analytics platform to a full-stack AI trading powerhouse. Alex explains why raw blockchain data is now a commodity and how Nansen maintains its edge through deterministic and inferred address labeling. They dive into why vanilla LLMs break on crypto data, the upcoming launch of predictive Smart Money 2.0, and how Nansen Gym allows AI agents to backtest trading strategies by time-traveling through historical on-chain events. Topics 00:00 Nansen's Pivot to Agentic Trading 04:15 On-Chain Data Processing 10:30 Inferred Address Labeling 16:00 Immutable Nature of ENS 24:45 Ultimate On-Chain Trading OS 33:20 Why LLMs Fail at Blockchain 42:15 Smart Money 2.0 51:40 Nansen Gym 58:30 The Open Source AI Boom Episode Links👇
55:51

New Episode: Inside Nansen's AI Trading Agent Platform Alex Svanevik 🐧, CEO of Nansen, joins Friederike Ernst 🦉 to discuss Nansen's massive evolution from an on-chain analytics platform to a full-stack AI trading powerhouse. Alex explains why raw blockchain data is now a commodity and how Nansen maintains its edge through deterministic and inferred address labeling. They dive into why vanilla LLMs break on crypto data, the upcoming launch of predictive Smart Money 2.0, and how Nansen Gym allows AI agents to backtest trading strategies by time-traveling through historical on-chain events. Topics 00:00 Nansen's Pivot to Agentic Trading 04:15 On-Chain Data Processing 10:30 Inferred Address Labeling 16:00 Immutable Nature of ENS 24:45 Ultimate On-Chain Trading OS 33:20 Why LLMs Fail at Blockchain 42:15 Smart Money 2.0 51:40 Nansen Gym 58:30 The Open Source AI Boom Episode Links👇

Epicenter Podcast

28,677 views • 6 months ago

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!
1:13:04

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!

Logan Jastremski

132,617 views • 1 month ago