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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...

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🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions
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🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions

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Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Gabe We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself
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Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Gabe We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

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20,835 görüntüleme • 3 ay önce

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead
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You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead

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How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence
54:22

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

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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 görüntüleme • 4 ay önce

How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]
1:04:50

How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]

Varun

22,963 görüntüleme • 5 ay önce

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives
46:57

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

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35,253 görüntüleme • 6 ay önce

"You should really treat having your own agent as having your own child. It's not one-click automation, you're raising it." Demi Guo (Demi Guo ), CEO of Pika on why agents need taste, not just prompts. Lin Qiao (Lin Qiao), CEO of Fireworks AI on why 95% of the world's data is still locked inside enterprises and why open-source models are converging with frontier. They join @jason on This Week in AI Episode 9: 00:00 Intro to Lin Qiao and Demi Guo 08:43 95% of private data locked in enterprises 16:03 The interface for creation is a human-like agent 22:07 The death of middle management 28:03 Taste, judgment, and why "slop" is the real risk 33:40 Agents aren't tools, they're children you raise 39:36 How close is open-source to frontier? 53:04 Agents as self-expression 63:17 Meta's Muse model goes closed-source
1:14:18

"You should really treat having your own agent as having your own child. It's not one-click automation, you're raising it." Demi Guo (Demi Guo ), CEO of Pika on why agents need taste, not just prompts. Lin Qiao (Lin Qiao), CEO of Fireworks AI on why 95% of the world's data is still locked inside enterprises and why open-source models are converging with frontier. They join @jason on This Week in AI Episode 9: 00:00 Intro to Lin Qiao and Demi Guo 08:43 95% of private data locked in enterprises 16:03 The interface for creation is a human-like agent 22:07 The death of middle management 28:03 Taste, judgment, and why "slop" is the real risk 33:40 Agents aren't tools, they're children you raise 39:36 How close is open-source to frontier? 53:04 Agents as self-expression 63:17 Meta's Muse model goes closed-source

This Week in AI

48,202 görüntüleme • 5 ay önce

A fascinating chat with CZ 🔶 BNB in Dubai—Enjoy! 00:00 Intro 00:47 CZ returns: AI, education, and what’s next 02:12 Giggle Academy: Scaling AI to teach 100M kids 04:01 CZ’s global crypto diplomacy with governments 07:08 Dubai’s rise as crypto capital: Behind the scenes 10:22 Strategic crypto reserves: Bhutan, US, more 13:22 AI + blockchain: The future of government & ID 16:11 Real builders vs hype chasers in crypto 18:54 Investing advice: What new users should avoid 21:17 AI agents will pay, trade, and build in crypto 24:35 Why CZ invested in X (Twitter) 26:30 Decentralizing AI & monetizing data 29:03 What CZ got wrong: Stablecoins, memes & more 32:14 Advice for founders: What the space needs 36:22 ETFs, cycles & why #Bitcoin leads first 39:26 Final thoughts: Patience, building, and an AI-powered future
42:02

A fascinating chat with CZ 🔶 BNB in Dubai—Enjoy! 00:00 Intro 00:47 CZ returns: AI, education, and what’s next 02:12 Giggle Academy: Scaling AI to teach 100M kids 04:01 CZ’s global crypto diplomacy with governments 07:08 Dubai’s rise as crypto capital: Behind the scenes 10:22 Strategic crypto reserves: Bhutan, US, more 13:22 AI + blockchain: The future of government & ID 16:11 Real builders vs hype chasers in crypto 18:54 Investing advice: What new users should avoid 21:17 AI agents will pay, trade, and build in crypto 24:35 Why CZ invested in X (Twitter) 26:30 Decentralizing AI & monetizing data 29:03 What CZ got wrong: Stablecoins, memes & more 32:14 Advice for founders: What the space needs 36:22 ETFs, cycles & why #Bitcoin leads first 39:26 Final thoughts: Patience, building, and an AI-powered future

Raoul Pal

154,282 görüntüleme • 1 yıl önce

Today on MCG: ksett | Derive.xyz 99% of people in crypto know perps but few understand options... Ksett breaks down why they are the next frontier, how they will be accessible on-chain, and why $22.3 billion in notional volume is just the beginning 👇 00:46 - Meet Ksett 01:19 - Background 01:37 - From user to team member 02:11 - Most people know perps — why options are the next step 02:48 - Perps limitations 03:36 - What you can build with options 04:42 - The team 05:56 - What Derive offers 08:59 - On-chain benefits 09:33 - Self-custodial 11:53 - How Derive came to be 21:26 - Who is the target audience? 21:55 - The institutional set 22:10 - The broker program 23:47 - Agentic trading 27:05 - A retail agent version 28:32 - Is Derive bridging CeFi to DeFi? 29:38 - Commodities on Derive 41:10 - Coinbase acquisition 42:17 - $22.3 billion in notional volume 43:28 - Governance and tokenomics 45:40 - Options MM program 50:31 - The SEC side and regulation 51:37 - White label opportunities 51:53 - 35% of fees go to buybacks 57:07 - Wrap up and takeaways
59:24

Today on MCG: ksett | Derive.xyz 99% of people in crypto know perps but few understand options... Ksett breaks down why they are the next frontier, how they will be accessible on-chain, and why $22.3 billion in notional volume is just the beginning 👇 00:46 - Meet Ksett 01:19 - Background 01:37 - From user to team member 02:11 - Most people know perps — why options are the next step 02:48 - Perps limitations 03:36 - What you can build with options 04:42 - The team 05:56 - What Derive offers 08:59 - On-chain benefits 09:33 - Self-custodial 11:53 - How Derive came to be 21:26 - Who is the target audience? 21:55 - The institutional set 22:10 - The broker program 23:47 - Agentic trading 27:05 - A retail agent version 28:32 - Is Derive bridging CeFi to DeFi? 29:38 - Commodities on Derive 41:10 - Coinbase acquisition 42:17 - $22.3 billion in notional volume 43:28 - Governance and tokenomics 45:40 - Options MM program 50:31 - The SEC side and regulation 51:37 - White label opportunities 51:53 - 35% of fees go to buybacks 57:07 - Wrap up and takeaways

MCG

10,272 görüntüleme • 4 ay önce

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack
1:05:15

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Matt Turck

11,908 görüntüleme • 4 ay önce

Agents Will Run Their Own Businesses: Inside Laso, agent payment cards, and Agent Commerce Shoal Signal with Hunter, Founder at Laso Finance, hosted by Zaddycoin We cover how Laso gives agents their own one-time-use cards to spend anywhere with no KYC, why self-sovereign spending is the through-line, where x402, Locus and Policy stack fit, why the real prize is agents starting their own businesses rather than shopping for you, and the AGI risk that could make frictionless agent payments unnecessary. 0:00 Intro and what Laso builds 2:35 Why one-time-use cards work 3:58 Self-sovereign spending and privacy 5:43 What agents actually buy 7:35 Agents running their own businesses 8:26 How an agent gets a card 11:49 x402, Locus, and Policy 15:43 Reading radio ads on TV 17:47 Markets two through twenty 24:09 Rethinking agent to agent commerce 29:50 What people get wrong 36:52 The AGI risk to Laso 43:48 Defensibility and the risks 45:40 The agent bank account coming 47:27 Betting on agent entrepreneurship 49:02 Closing alpha
49:49

Agents Will Run Their Own Businesses: Inside Laso, agent payment cards, and Agent Commerce Shoal Signal with Hunter, Founder at Laso Finance, hosted by Zaddycoin We cover how Laso gives agents their own one-time-use cards to spend anywhere with no KYC, why self-sovereign spending is the through-line, where x402, Locus and Policy stack fit, why the real prize is agents starting their own businesses rather than shopping for you, and the AGI risk that could make frictionless agent payments unnecessary. 0:00 Intro and what Laso builds 2:35 Why one-time-use cards work 3:58 Self-sovereign spending and privacy 5:43 What agents actually buy 7:35 Agents running their own businesses 8:26 How an agent gets a card 11:49 x402, Locus, and Policy 15:43 Reading radio ads on TV 17:47 Markets two through twenty 24:09 Rethinking agent to agent commerce 29:50 What people get wrong 36:52 The AGI risk to Laso 43:48 Defensibility and the risks 45:40 The agent bank account coming 47:27 Betting on agent entrepreneurship 49:02 Closing alpha

Shoal Research

10,925 görüntüleme • 2 ay önce

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders
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How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders

Matt Turck

22,509 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

LIVE NOW - Tempo Mainnet: The Race to Agentic Commerce Tempo is live, and the big bet is clear: AI agents need a native payment layer. Georgios Konstantopoulos and Brendan Ryan join Bankless to explore: - why Tempo launched with agentic payments front and center, - how MPP turns payments into auth, - why “give the agent the wallet” could unlock a new internet economy, - where MPP fits against x402, - what Tempo’s L1 design makes possible for builders. Enjoy the episode. --- TIMESTAMPS 0:00 Tempo Mainnet goes live 1:18 Why the launch is focused on agents 3:58 What MPP actually is 6:30 MPP vs x402 15:28 Why agentic commerce matters 20:08 The “original sin” of the internet 26:04 MCP, NLWeb, and the emerging agent stack 34:07 The onboarding flow: wallets for agents 37:11 How Tempo’s wallet security works 40:11 Tempo chain design and specs 51:13 Why build an L1 instead of an Ethereum L2? 56:10 The Ethereum tension 1:05:01 Agent identity, reputation, and ERC-8004 1:06:53 TIP20, stablecoins, and what can be built on Tempo 1:12:36 Builder opportunity in the agentic web
1:21:50

LIVE NOW - Tempo Mainnet: The Race to Agentic Commerce Tempo is live, and the big bet is clear: AI agents need a native payment layer. Georgios Konstantopoulos and Brendan Ryan join Bankless to explore: - why Tempo launched with agentic payments front and center, - how MPP turns payments into auth, - why “give the agent the wallet” could unlock a new internet economy, - where MPP fits against x402, - what Tempo’s L1 design makes possible for builders. Enjoy the episode. --- TIMESTAMPS 0:00 Tempo Mainnet goes live 1:18 Why the launch is focused on agents 3:58 What MPP actually is 6:30 MPP vs x402 15:28 Why agentic commerce matters 20:08 The “original sin” of the internet 26:04 MCP, NLWeb, and the emerging agent stack 34:07 The onboarding flow: wallets for agents 37:11 How Tempo’s wallet security works 40:11 Tempo chain design and specs 51:13 Why build an L1 instead of an Ethereum L2? 56:10 The Ethereum tension 1:05:01 Agent identity, reputation, and ERC-8004 1:06:53 TIP20, stablecoins, and what can be built on Tempo 1:12:36 Builder opportunity in the agentic web

Bankless

23,815 görüntüleme • 6 ay önce

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 görüntüleme • 5 ay önce

*New Lecture* Stanford '26, Session 8 (Full Video) Energy Bottlenecks with Scott Nolan from General Matter 00:00 Energy Bottlenecks Intro 00:11 AI Factory Overview 02:55 Why Power Matters 03:56 From ChatGPT to Enterprise Demand 07:25 Meet Scott Nolan 09:03 Energy Is The Real Limit 11:16 Demand Growth Reality Check 13:17 Stranded Power Era 15:54 What Data Centers Need 16:59 Nuclear As Baseload Path 18:21 Fuel Cycle And Enrichment Gap 21:41 Bitcoin Mining As Rehearsal 25:03 Building Primitives Not Pivots 28:17 Nuclear Safety And Narrative 30:22 Calibrating the Moment 30:54 Pick Important Problems 32:14 General Matter Breakthrough 35:04 Building Team and Site 37:18 Government Support Reality 39:19 Jobs and AI Demand 42:37 Scaling Timelines and Space 46:16 Early SpaceX Lessons 50:25 Nuclear Perception and Europe 53:17 Supply Chain and Enrichment 56:56 Why US Lost Enrichment 59:25 Back to the Future Wrap
1:00:19

*New Lecture* Stanford '26, Session 8 (Full Video) Energy Bottlenecks with Scott Nolan from General Matter 00:00 Energy Bottlenecks Intro 00:11 AI Factory Overview 02:55 Why Power Matters 03:56 From ChatGPT to Enterprise Demand 07:25 Meet Scott Nolan 09:03 Energy Is The Real Limit 11:16 Demand Growth Reality Check 13:17 Stranded Power Era 15:54 What Data Centers Need 16:59 Nuclear As Baseload Path 18:21 Fuel Cycle And Enrichment Gap 21:41 Bitcoin Mining As Rehearsal 25:03 Building Primitives Not Pivots 28:17 Nuclear Safety And Narrative 30:22 Calibrating the Moment 30:54 Pick Important Problems 32:14 General Matter Breakthrough 35:04 Building Team and Site 37:18 Government Support Reality 39:19 Jobs and AI Demand 42:37 Scaling Timelines and Space 46:16 Early SpaceX Lessons 50:25 Nuclear Perception and Europe 53:17 Supply Chain and Enrichment 56:56 Why US Lost Enrichment 59:25 Back to the Future Wrap

Anjney Midha

123,482 görüntüleme • 5 ay önce

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,311 görüntüleme • 1 yıl önce