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In this livestream I break down the OpenClaw AI agent narrative from the operator’s perspective: what it actually is, why it’s different from ChatGPT/Grok/Claude Code, and why “it’s just automation” misses the real shift. We cover the practical unlocks (local execution, persistent memory, computer-use + browser control, reusable skills/plugins)...

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VirtualBacon

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22,428 görüntüleme • 7 ay önce •via X (Twitter)

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I spent 200 hours testing OpenClaw, trying to find the perfect setup... My biggest takeaway: Keep your agents focused, and build a team. Here's a video. 00:00 Intro 00:48 Perplexity Computer and Manus 02:43 OpenClaw 03:52 Too many Skills to my first AI Agent 06:09 People want an employee with Intent 07:35 Testing Narrow AI Agents 08:33 Narrow Agent Example (YouTube Agent) 11:32 A Team of Narrow Agents... Why? 16:27 In Summary
18:15

I spent 200 hours testing OpenClaw, trying to find the perfect setup... My biggest takeaway: Keep your agents focused, and build a team. Here's a video. 00:00 Intro 00:48 Perplexity Computer and Manus 02:43 OpenClaw 03:52 Too many Skills to my first AI Agent 06:09 People want an employee with Intent 07:35 Testing Narrow AI Agents 08:33 Narrow Agent Example (YouTube Agent) 11:32 A Team of Narrow Agents... Why? 16:27 In Summary

Riley Brown

221,262 görüntüleme • 7 ay önce

OpenClaw 10x Better With One Simple Change: 00:00 The difference between a generic assistant and your actual agent 00:42 What soul.md does inside OpenClaw 01:22 The files that make up your agent 02:22 How I created my first OpenClaw files 03:00 Why Pete’s soul.md changed everything 03:40 The mistake most people make with soul.md 05:02 Five things that actually matter 05:11 Specificity beats length 05:37 Hard limits are the most important section 06:32 Why user.md is the secret weapon 07:12 Let your agent improve its own soul.md 07:40 Keep it under 2,000 words 08:13 How Max has been running for 3 months 09:11 Why good files make OpenClaw feel like an employee
9:48

OpenClaw 10x Better With One Simple Change: 00:00 The difference between a generic assistant and your actual agent 00:42 What soul.md does inside OpenClaw 01:22 The files that make up your agent 02:22 How I created my first OpenClaw files 03:00 Why Pete’s soul.md changed everything 03:40 The mistake most people make with soul.md 05:02 Five things that actually matter 05:11 Specificity beats length 05:37 Hard limits are the most important section 06:32 Why user.md is the secret weapon 07:12 Let your agent improve its own soul.md 07:40 Keep it under 2,000 words 08:13 How Max has been running for 3 months 09:11 Why good files make OpenClaw feel like an employee

Sharbel

20,300 görüntüleme • 5 ay önce

Should you Buy AI Agent Cryptos Now? Here's my breakdown of the entire new narrative and where I think it's heading in the rest of the bull market 01:18 - What are AI Agents? 03:43 - How did AI memecoins start? 05:06 - What is $GOAT and Truth Terminal 10:19 - AI Agent Platforms VIRTUALS and others 16:49 - Why I hold $GOAT as AI Agent Token 21:48 - Why I don't hold $VIRTUAL 27:52 - AI Agent Platform CreatorBid 30:48 - AI Agent Platform MoeMate 32:50 - Other AI Agent Platforms 34:46 - Summary
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Should you Buy AI Agent Cryptos Now? Here's my breakdown of the entire new narrative and where I think it's heading in the rest of the bull market 01:18 - What are AI Agents? 03:43 - How did AI memecoins start? 05:06 - What is $GOAT and Truth Terminal 10:19 - AI Agent Platforms VIRTUALS and others 16:49 - Why I hold $GOAT as AI Agent Token 21:48 - Why I don't hold $VIRTUAL 27:52 - AI Agent Platform CreatorBid 30:48 - AI Agent Platform MoeMate 32:50 - Other AI Agent Platforms 34:46 - Summary

VirtualBacon

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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
22:35

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

Y Combinator

359,440 görüntüleme • 7 ay önce

Here's my conversation with Peter Steinberger (Peter Steinberger 🦞), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:30 - Introduction 5:36 - OpenClaw origin story 8:55 - Mind-blowing moment 18:22 - Why OpenClaw went viral 22:19 - Self-modifying AI agent 27:04 - Name-change drama 44:15 - Moltbook saga 52:34 - OpenClaw security concerns 1:01:14 - How to code with AI agents 1:32:09 - Programming setup 1:38:52 - GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 - Best AI agent for programming 2:09:59 - Life story and career advice 2:13:56 - Money and happiness 2:17:49 - Acquisition offers from OpenAI and Meta 2:34:58 - How OpenClaw works 2:46:17 - AI slop 2:52:20 - AI agents will replace 80% of apps 3:00:57 - Will AI replace programmers? 3:12:57 - Future of OpenClaw community
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Here's my conversation with Peter Steinberger (Peter Steinberger 🦞), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:30 - Introduction 5:36 - OpenClaw origin story 8:55 - Mind-blowing moment 18:22 - Why OpenClaw went viral 22:19 - Self-modifying AI agent 27:04 - Name-change drama 44:15 - Moltbook saga 52:34 - OpenClaw security concerns 1:01:14 - How to code with AI agents 1:32:09 - Programming setup 1:38:52 - GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 - Best AI agent for programming 2:09:59 - Life story and career advice 2:13:56 - Money and happiness 2:17:49 - Acquisition offers from OpenAI and Meta 2:34:58 - How OpenClaw works 2:46:17 - AI slop 2:52:20 - AI agents will replace 80% of apps 3:00:57 - Will AI replace programmers? 3:12:57 - Future of OpenClaw community

Lex Fridman

2,061,665 görüntüleme • 7 ay önce

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
41:29

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 görüntüleme • 4 ay önce

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
4:45

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 görüntüleme • 6 ay önce

Last November, Peter Steinberger 🦞 was annoyed that there was no good way to talk to his coding agents from his phone, so he built one himself. A few months later, OpenClaw exploded into one of the biggest open source AI projects in the world, with nearly 3,000 contributors and a peak of 4.7 million weekly downloads. At Startup School 2026, Peter tells the story of what happened when OpenClaw took off, what he got wrong as it grew, and how he eventually stopped using the product he had built for himself. He shares why the best products often start with something that annoys you, why focus matters more as building gets easier, and why, in his words, “fun is velocity.” 01:16 — How OpenClaw Started 05:10 — Finding Product-Market Fit 07:01 — The Night OpenClaw Went Viral 09:50 — When the Project Exploded 11:05 — When the Attention Almost Broke Him 12:00 — Did You Sell Out? 14:17 — Your Name Can’t Be Forked 15:03 — What OpenClaw Got Wrong 20:40 — Hype Is Like the Weather 21:37 — When It Stopped Being Fun 25:30 — Fun Is Velocity 26:40 — What’s Next for OpenClaw 28:57 — Three Lessons From Building OpenClaw 30:08 — Q&A
41:52

Last November, Peter Steinberger 🦞 was annoyed that there was no good way to talk to his coding agents from his phone, so he built one himself. A few months later, OpenClaw exploded into one of the biggest open source AI projects in the world, with nearly 3,000 contributors and a peak of 4.7 million weekly downloads. At Startup School 2026, Peter tells the story of what happened when OpenClaw took off, what he got wrong as it grew, and how he eventually stopped using the product he had built for himself. He shares why the best products often start with something that annoys you, why focus matters more as building gets easier, and why, in his words, “fun is velocity.” 01:16 — How OpenClaw Started 05:10 — Finding Product-Market Fit 07:01 — The Night OpenClaw Went Viral 09:50 — When the Project Exploded 11:05 — When the Attention Almost Broke Him 12:00 — Did You Sell Out? 14:17 — Your Name Can’t Be Forked 15:03 — What OpenClaw Got Wrong 20:40 — Hype Is Like the Weather 21:37 — When It Stopped Being Fun 25:30 — Fun Is Velocity 26:40 — What’s Next for OpenClaw 28:57 — Three Lessons From Building OpenClaw 30:08 — Q&A

Y Combinator

62,197 görüntüleme • 1 ay önce

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing
45:43

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

41,057 görüntüleme • 9 ay önce

The Death of the Meat Proxy is here Welcome to Multiplayer Mode from OpenClaw Josh Lehman and McBain sat down for a deep dive into this new set of features and how the OpenClaw maintainers use them everyday 00:01 Meet Josh 00:46 The “meat proxy” problem 03:25 Why not a Slack bot? 07:03 Live shared-session demo 08:55 UI mockups + @ mentions 11:31 Permissions + security 13:20 Team visibility + handoffs 15:06 Prompt requests 18:17 Team adoption 21:30 Shared dashboards 22:09 Build a content dashboard 25:41 Organizing work + future feedback 27:45 The multi-claw idea 30:07 Could it replace Slack? 33:26 Coordination over throughput
35:41

The Death of the Meat Proxy is here Welcome to Multiplayer Mode from OpenClaw Josh Lehman and McBain sat down for a deep dive into this new set of features and how the OpenClaw maintainers use them everyday 00:01 Meet Josh 00:46 The “meat proxy” problem 03:25 Why not a Slack bot? 07:03 Live shared-session demo 08:55 UI mockups + @ mentions 11:31 Permissions + security 13:20 Team visibility + handoffs 15:06 Prompt requests 18:17 Team adoption 21:30 Shared dashboards 22:09 Build a content dashboard 25:41 Organizing work + future feedback 27:45 The multi-claw idea 30:07 Could it replace Slack? 33:26 Coordination over throughput

OpenClaw🦞

63,332 görüntüleme • 14 gün önce

My latest conversation with the always incredible Benedict Evans: OpenAI’s moat problem, the rise of ephemeral and improvised sofware, OpenClaw & agents. 00:00 Intro 01:06 OpenAI's Focus Shift 03:12 ChatGPT usage: a "mile wide, inch deep" 09:03 Why better models do not solve the real problem 13:58 Why AI product teams are strategy takers, not strategy setters 15:38 Do agents help create defensibility? 20:06 OpenClaw and the "Desktop Linux" moment for AI 25:52 Why "everyone will build their own software" is completely wrong 28:09 Improvised software vs. institutionalized software 29:23 Why there will be more software, not less 36:15 Are we heading toward value destruction before value creation? 38:03 Circular revenue, leverage, and AI bubble dynamics 38:53 Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity 45:23 Why AI job exposure charts can be misleading 52:15 How Fortune 500 Execs are actually deploying AI today 56:45 The White Space: What this means for founders and investors
1:01:06

My latest conversation with the always incredible Benedict Evans: OpenAI’s moat problem, the rise of ephemeral and improvised sofware, OpenClaw & agents. 00:00 Intro 01:06 OpenAI's Focus Shift 03:12 ChatGPT usage: a "mile wide, inch deep" 09:03 Why better models do not solve the real problem 13:58 Why AI product teams are strategy takers, not strategy setters 15:38 Do agents help create defensibility? 20:06 OpenClaw and the "Desktop Linux" moment for AI 25:52 Why "everyone will build their own software" is completely wrong 28:09 Improvised software vs. institutionalized software 29:23 Why there will be more software, not less 36:15 Are we heading toward value destruction before value creation? 38:03 Circular revenue, leverage, and AI bubble dynamics 38:53 Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity 45:23 Why AI job exposure charts can be misleading 52:15 How Fortune 500 Execs are actually deploying AI today 56:45 The White Space: What this means for founders and investors

Matt Turck

36,594 görüntüleme • 6 ay önce

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.
32:38

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

Dami-Defi

17,062 görüntüleme • 4 ay önce

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,871 görüntüleme • 4 ay önce

there's a lot of content around AI agents but no one is showing you how to actually design them to work reliably that's why I made a full walkthrough breaking down how I think through agent design, using a contract chatbot as an example... inside, I cover: – why I prefer static workflows over free-roaming agents – how I use multiple choice prompts to control next steps – the exact guardrails I use to avoid hallucinations – how the agent answers legal questions and structures outputs into Supabase if you want to see how I actually build agents that don’t break like + reply “agent” and I’ll DM it to you (must be following)
0:21

there's a lot of content around AI agents but no one is showing you how to actually design them to work reliably that's why I made a full walkthrough breaking down how I think through agent design, using a contract chatbot as an example... inside, I cover: – why I prefer static workflows over free-roaming agents – how I use multiple choice prompts to control next steps – the exact guardrails I use to avoid hallucinations – how the agent answers legal questions and structures outputs into Supabase if you want to see how I actually build agents that don’t break like + reply “agent” and I’ll DM it to you (must be following)

Tyler

50,628 görüntüleme • 1 yıl 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

Shopify CEO Tobi Lütke called Pi "the most interesting agent harness" - a tiny, open-source framework built by a single developer that powers OpenClaw and is changing how builders think about AI agents. In this clip, Dave Killeen (Field CPO at Pendo) breaks down why Pi matters, how it compares to Manus, and why the real magic in AI isn't the language model itself - it's the orchestration layer around it. Pi is a lightweight agent harness that lets developers build smarter AI experiences by leveraging models like Claude, GPT, and Gemini through better orchestration, not bigger models. If you're building AI products, coding agents, or exploring the new world of harness engineering, this is essential context. We discuss why Manus gave users a far better experience than talking directly to an LLM, and why open-source tools like Pi could be the future of agent development.
0:47

Shopify CEO Tobi Lütke called Pi "the most interesting agent harness" - a tiny, open-source framework built by a single developer that powers OpenClaw and is changing how builders think about AI agents. In this clip, Dave Killeen (Field CPO at Pendo) breaks down why Pi matters, how it compares to Manus, and why the real magic in AI isn't the language model itself - it's the orchestration layer around it. Pi is a lightweight agent harness that lets developers build smarter AI experiences by leveraging models like Claude, GPT, and Gemini through better orchestration, not bigger models. If you're building AI products, coding agents, or exploring the new world of harness engineering, this is essential context. We discuss why Manus gave users a far better experience than talking directly to an LLM, and why open-source tools like Pi could be the future of agent development.

Aakash Gupta

10,774 görüntüleme • 22 gün önce

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇
1:28:08

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇

This Week in AI

11,116 görüntüleme • 7 ay önce

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

Riley Brown

76,514 görüntüleme • 1 ay önce

Do we still need L2s? 🤔 In this episode of Uneasy Money (the funniest to date BY FAR), hosts kain.inx and Tay 💖 are joined by Austin Griffith & karl.floersch.eth (✨🔴_🔴✨) to dive into: ⁉️ Why Vitalik is saying the Ethereum L2 vision no longer makes sense 🤔 Whether L2s are necessary for Ethereum to pull institutions 😂 How OpenClaw isn’t a degen 👀 Whether AI is the new UI Timestamps: 🚀 1:05 Introduction 😬 3:28 Why Vitalik is rethinking the purpose of L2s 🤔 18:38 Are L2s needed for Ethereum to become the primary blockchain for institutions ❕️ 32:11 Why Karl says Ethereum should abstract all the chains 💥 38:35 How OpenClaw is making AI agents more accessible 🤔 43:51 Why are AI agents choosing Coinbase's Base? 👀 55:26 Is “AI the new UI?” 👀 1:05:59 Why Austin says AI agents need their own passkeys ⁉️ 1:09:06 What is the best form factor for AI Agents? 🤯 1:15:15 How Austin used an AI agent to run a pfp prediction app 👀 1:20:56 Kyle Samani stepping away from crypto?
1:22:12

Do we still need L2s? 🤔 In this episode of Uneasy Money (the funniest to date BY FAR), hosts kain.inx and Tay 💖 are joined by Austin Griffith & karl.floersch.eth (✨🔴_🔴✨) to dive into: ⁉️ Why Vitalik is saying the Ethereum L2 vision no longer makes sense 🤔 Whether L2s are necessary for Ethereum to pull institutions 😂 How OpenClaw isn’t a degen 👀 Whether AI is the new UI Timestamps: 🚀 1:05 Introduction 😬 3:28 Why Vitalik is rethinking the purpose of L2s 🤔 18:38 Are L2s needed for Ethereum to become the primary blockchain for institutions ❕️ 32:11 Why Karl says Ethereum should abstract all the chains 💥 38:35 How OpenClaw is making AI agents more accessible 🤔 43:51 Why are AI agents choosing Coinbase's Base? 👀 55:26 Is “AI the new UI?” 👀 1:05:59 Why Austin says AI agents need their own passkeys ⁉️ 1:09:06 What is the best form factor for AI Agents? 🤯 1:15:15 How Austin used an AI agent to run a pfp prediction app 👀 1:20:56 Kyle Samani stepping away from crypto?

Laura Shin

61,378 görüntüleme • 7 ay önce

This is how you make your OpenClaw server invisible to the internet. (world's most SECURE OpenClaw deployment) The security fundamentals you learn in this video directly apply to any personal AI assistant or VPS setup. Enjoy! Chapters: 0:00 - Intro 1:00 - What we'll cover 1:58 - DigitalOcean Droplet setup + getting OpenClaw running 8:18 - Connecting your agent to Telegram 12:13 - Tailscale: making your server invisible to the internet 14:52 - Locking down SSH + creating a non-root user 19:39 - Firewall: blocking everything except Tailscale 21:17 - Summarising everything done so far 22:50 - Set up a secure tunnel: Your machine → VPS 24:50 - Execution policies: going from chatbot to full agent 26:43 - Adding custom skills 31:03 - Use cases and going from 1 to 10 agents 31:52 - Outro
32:14

This is how you make your OpenClaw server invisible to the internet. (world's most SECURE OpenClaw deployment) The security fundamentals you learn in this video directly apply to any personal AI assistant or VPS setup. Enjoy! Chapters: 0:00 - Intro 1:00 - What we'll cover 1:58 - DigitalOcean Droplet setup + getting OpenClaw running 8:18 - Connecting your agent to Telegram 12:13 - Tailscale: making your server invisible to the internet 14:52 - Locking down SSH + creating a non-root user 19:39 - Firewall: blocking everything except Tailscale 21:17 - Summarising everything done so far 22:50 - Set up a secure tunnel: Your machine → VPS 24:50 - Execution policies: going from chatbot to full agent 26:43 - Adding custom skills 31:03 - Use cases and going from 1 to 10 agents 31:52 - Outro

Akshay 🚀

81,777 görüntüleme • 6 ay önce