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The internet is getting agentified Winners make agents deployable. Routable. Trusted. New distribution is agent-first Your audience isn’t developers It’s their agents Dedalus Labs = Vercel for AI Agents What it does (so you ship): • Hosts your MCP servers on their cloud • Autoscaling + load balancing handled...

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David J Phillips

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27,396 görüntüleme • 7 ay önce •via X (Twitter)

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If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 görüntüleme • 11 ay önce

We just raised an $11M seed to help developers build complex agents across any model and any tool in 5 lines of code. Our round was led by Kindred Ventures (steve jang) and Saga (Max Altman, Ben Braverman, Thomson Nguyen). At Dedalus Labs, we believe the future of AI agents isn’t linear or deterministic. Agents should know when to stop, when to branch, when to reattempt a task, and when to pause and ask for context. As a developer, we make your job simple: define the prompt, select the right tools and guardrails, and let your agent run. We were tired of writing hundreds of lines of code and battling Dockerfiles and infra—all for a spaghetti agent that breaks under stress. With Dedalus, you don’t have to do things the hard way. Our open-source SDK supports vendor-agnostic model handoffs, chaining both local and hosted (MCP) tools, and streaming across any provider—all in 5 lines of code. Plus, our cloud infra lets you deploy an MCP server in 3 clicks so anyone can equip their agent with your tools. Let us do the heavy lifting so your agents can fly 🪽 Thank you to our incredible team and our early supporters: Calvin Chin, habib.hajloo from E14 Fund, Yazan El-Baba from Emergence Capital, don stalter from Sunshine Lake, jamestan from Transpose Platform, Liquid 2 Ventures, FPV Ventures, Umesh Khanna 🇨🇦🇺🇸, Ant from Supabase, Cal Henderson, Thomas Wolf, Thomas Dohmke, Elad Hazan, Tri Dao
1:47

We just raised an $11M seed to help developers build complex agents across any model and any tool in 5 lines of code. Our round was led by Kindred Ventures (steve jang) and Saga (Max Altman, Ben Braverman, Thomson Nguyen). At Dedalus Labs, we believe the future of AI agents isn’t linear or deterministic. Agents should know when to stop, when to branch, when to reattempt a task, and when to pause and ask for context. As a developer, we make your job simple: define the prompt, select the right tools and guardrails, and let your agent run. We were tired of writing hundreds of lines of code and battling Dockerfiles and infra—all for a spaghetti agent that breaks under stress. With Dedalus, you don’t have to do things the hard way. Our open-source SDK supports vendor-agnostic model handoffs, chaining both local and hosted (MCP) tools, and streaming across any provider—all in 5 lines of code. Plus, our cloud infra lets you deploy an MCP server in 3 clicks so anyone can equip their agent with your tools. Let us do the heavy lifting so your agents can fly 🪽 Thank you to our incredible team and our early supporters: Calvin Chin, habib.hajloo from E14 Fund, Yazan El-Baba from Emergence Capital, don stalter from Sunshine Lake, jamestan from Transpose Platform, Liquid 2 Ventures, FPV Ventures, Umesh Khanna 🇨🇦🇺🇸, Ant from Supabase, Cal Henderson, Thomas Wolf, Thomas Dohmke, Elad Hazan, Tri Dao

Cathy Di

1,053,201 görüntüleme • 11 ay önce

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,994 görüntüleme • 4 ay önce

Over the past few months at Dedalus Labs, we’ve noticed how many talented founders from around the world are being held back by one thing: location. There is no better place in the world to build an AI startup than San Francisco. SF is home to all the major AI labs, top talent, world-class investors, and it is where the future is being built every day. That’s why, this December, we’re giving builders everywhere a chance to break in. We are taking over and launching Break In—a month-long hacker house program in the heart of San Francisco. If you’re not from SF, and you’re building an AI startup, this is your chance to join us. Build your startup on top of Dedalus’ Agents SDK or MCP deployment infrastructure and we’ll cover your stay (housing, lunch, and co-working space). As a member of Break In, you’ll get access to all the best parts of SF. You’ll be introduced to top founders, mentors, and investors. You’ll get to skip the line at the biggest tech events. Plus, you’ll receive free API credits and expanded access to the best-in-class AI stack through our partner network of top AI infrastructure companies. Applications open October 27th and close November 10th. We will be accepting applications on a rolling basis. Final acceptances will go out November 14th to ensure international founders have enough time to secure visas. Ready to break in? Follow us on Twitter/X for more updates and visit the link in the comments to apply on October 27th!
2:39

Over the past few months at Dedalus Labs, we’ve noticed how many talented founders from around the world are being held back by one thing: location. There is no better place in the world to build an AI startup than San Francisco. SF is home to all the major AI labs, top talent, world-class investors, and it is where the future is being built every day. That’s why, this December, we’re giving builders everywhere a chance to break in. We are taking over and launching Break In—a month-long hacker house program in the heart of San Francisco. If you’re not from SF, and you’re building an AI startup, this is your chance to join us. Build your startup on top of Dedalus’ Agents SDK or MCP deployment infrastructure and we’ll cover your stay (housing, lunch, and co-working space). As a member of Break In, you’ll get access to all the best parts of SF. You’ll be introduced to top founders, mentors, and investors. You’ll get to skip the line at the biggest tech events. Plus, you’ll receive free API credits and expanded access to the best-in-class AI stack through our partner network of top AI infrastructure companies. Applications open October 27th and close November 10th. We will be accepting applications on a rolling basis. Final acceptances will go out November 14th to ensure international founders have enough time to secure visas. Ready to break in? Follow us on Twitter/X for more updates and visit the link in the comments to apply on October 27th!

Cathy Di

109,650 görüntüleme • 10 ay önce

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.
0:33

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 görüntüleme • 4 ay önce

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
1:45:40

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self

Matt Turck

86,153 görüntüleme • 1 yıl önce

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 görüntüleme • 9 ay önce

20 GitHub repos with 2.4M+ combined stars that replace tools costing $60,000+/year 1. public-apis ⭐456k - 1,500+ free APIs across every category, weather to finance to games, all documented. 2. awesome-selfhosted ⭐312k - self-hosted replacements for Notion, Google Photos, Zapier, and dozens more paid subscriptions. 3. hermes-agent ⭐230k - self-improving personal agent with persistent memory, cron scheduling, MCP built in. Free alternative to paid always-on agent platforms. 4. n8n ⭐200k - visual automation with native AI agents. Replaces Zapier/Make entirely, self-hosted. 5. ollama ⭐178k - run Llama, Mistral, DeepSeek locally with one command. No API bill, no rate limits. 6. dify ⭐152k - visual builder for AI agents and RAG pipelines. Skip the $500/mo no-code AI builder subscription. 7. free-for-dev ⭐132k - hundreds of services with permanent free tiers. No trials, no credit card. 8. awesome-llm-apps ⭐132k - 100+ ready AI agents and RAG apps with full code. 9. awesome-mcp-servers ⭐92k - thousands of MCP servers connecting your agent to browsers, databases, anything. 10. supabase ⭐108k - Firebase alternative that's actually free to start. Auth, DB, storage in one Claude Code prompt. 11. strapi ⭐73k - open-source headless CMS, generates a full API from your content model in minutes. No Contentful bill. 12. immich ⭐110k - self-hosted photo and video backup with face recognition. Cancel the Google Photos storage plan. 13. appwrite ⭐57k - complete backend-as-a-service, self-hosted. Auth, DB, functions, storage, one prompt away from Firebase money. 14. medusa ⭐36k - full ecommerce backend, open source. Skip Shopify Plus fees entirely on your next vibe-coded store. 15. novu ⭐39k - notification infrastructure for email, SMS, push, in-app, all in one API. Replaces OneSignal's paid tiers. 16. tooljet ⭐38k - drag-and-drop internal tool builder connected to any database or API. Retool's seat pricing gone. 17. mattermost ⭐38k - self-hosted team chat built for engineering orgs. Slack without the per-seat bill. 18. outline ⭐40k - fast, clean team wiki and docs. Replaces Confluence and Notion's team plan. 19. plausible ⭐28.5k - privacy-friendly analytics, lightweight script, real dashboards. No GA360 contract needed. 20. openwork ⭐22k - open-source Claude Cowork alternative. Share skills and MCPs across Claude Code, Cursor, Codex, one setup for every agent. Save this before you pay for another tool this list already replaces for free 👇
0:28

20 GitHub repos with 2.4M+ combined stars that replace tools costing $60,000+/year 1. public-apis ⭐456k - 1,500+ free APIs across every category, weather to finance to games, all documented. 2. awesome-selfhosted ⭐312k - self-hosted replacements for Notion, Google Photos, Zapier, and dozens more paid subscriptions. 3. hermes-agent ⭐230k - self-improving personal agent with persistent memory, cron scheduling, MCP built in. Free alternative to paid always-on agent platforms. 4. n8n ⭐200k - visual automation with native AI agents. Replaces Zapier/Make entirely, self-hosted. 5. ollama ⭐178k - run Llama, Mistral, DeepSeek locally with one command. No API bill, no rate limits. 6. dify ⭐152k - visual builder for AI agents and RAG pipelines. Skip the $500/mo no-code AI builder subscription. 7. free-for-dev ⭐132k - hundreds of services with permanent free tiers. No trials, no credit card. 8. awesome-llm-apps ⭐132k - 100+ ready AI agents and RAG apps with full code. 9. awesome-mcp-servers ⭐92k - thousands of MCP servers connecting your agent to browsers, databases, anything. 10. supabase ⭐108k - Firebase alternative that's actually free to start. Auth, DB, storage in one Claude Code prompt. 11. strapi ⭐73k - open-source headless CMS, generates a full API from your content model in minutes. No Contentful bill. 12. immich ⭐110k - self-hosted photo and video backup with face recognition. Cancel the Google Photos storage plan. 13. appwrite ⭐57k - complete backend-as-a-service, self-hosted. Auth, DB, functions, storage, one prompt away from Firebase money. 14. medusa ⭐36k - full ecommerce backend, open source. Skip Shopify Plus fees entirely on your next vibe-coded store. 15. novu ⭐39k - notification infrastructure for email, SMS, push, in-app, all in one API. Replaces OneSignal's paid tiers. 16. tooljet ⭐38k - drag-and-drop internal tool builder connected to any database or API. Retool's seat pricing gone. 17. mattermost ⭐38k - self-hosted team chat built for engineering orgs. Slack without the per-seat bill. 18. outline ⭐40k - fast, clean team wiki and docs. Replaces Confluence and Notion's team plan. 19. plausible ⭐28.5k - privacy-friendly analytics, lightweight script, real dashboards. No GA360 contract needed. 20. openwork ⭐22k - open-source Claude Cowork alternative. Share skills and MCPs across Claude Code, Cursor, Codex, one setup for every agent. Save this before you pay for another tool this list already replaces for free 👇

unicode

34,424 görüntüleme • 1 ay önce

⚫UNCANNY VALLEY: PERPLEXITY VS GOOGLE — THE WAR FOR THE FUTURE OF SEARCH BEGINS Special Guest: Aravind Srinivas Host: Dr Danish Search is dead. Agents are rising. Phones are getting smarter—without Apple or Google. Aravind Srinivas, CEO of Perplexity, lays out the future: AI-native assistants, ambient search, agent browsers, and a war for the next-gen OS. If Google is the old internet—Perplexity wants to be the new one. Welcome to The Uncanny Valley Weekly Series, Fridays at 4:20 PM ET, ONLY on 𝕏. Episode 5: THE AGENT ERA IS HERE — AND GOOGLE CAN’T STOP IT 01:05 – “We started as an answer engine.” How Perplexity went from RAG to research to real-world action. 03:03 – “Tell me what I should think about NVIDIA.” Agents as consultants, not just search engines. 05:07 – Buy buttons, autofill, AI shopping carts—how agents are already doing tasks. 07:05 – “You don’t have to see the tabs.” What the new browser will look like. 10:45 – “Warren Buffett doesn’t use apps—why should you?” The inspiration behind the AI-native phone. 12:36 – Why Apple, Google, and even OpenAI won’t build an AI-native OS. 17:28 – “Search is the most important tool in the agent era.” And Perplexity owns it. 21:33 – Mukesh Ambani’s advice: “Content is king, but distribution is GOD.” 27:13 – “Retrofit AI into iOS? It’ll never work.” Why the future needs a native platform. 30:26 – Humane failed, but the idea didn’t. Why assistants—not apps—are the next big UX shift. 33:38 – “We want Perplexity to feel like Apple.” Design still matters—even with agents. 37:11 – Why the real moat in AI isn’t models—it’s trust, taste, and vibe. 42:07 – 80M impressions in 6 weeks: How “Ask Perplexity” is going viral on 𝕏. 47:52 – “We’ve survived the internet—AI’s not scarier.” Aravind pushes back on the doomerism. 50:00 – Healthcare, finance, travel—Perplexity wants to power the vertical agent revolution. 54:24 – “LLMs can reason. But only agents can do.” Why execution is the next AI frontier. 56:34 – COMET browser drops in 3 weeks.
57:05

⚫UNCANNY VALLEY: PERPLEXITY VS GOOGLE — THE WAR FOR THE FUTURE OF SEARCH BEGINS Special Guest: Aravind Srinivas Host: Dr Danish Search is dead. Agents are rising. Phones are getting smarter—without Apple or Google. Aravind Srinivas, CEO of Perplexity, lays out the future: AI-native assistants, ambient search, agent browsers, and a war for the next-gen OS. If Google is the old internet—Perplexity wants to be the new one. Welcome to The Uncanny Valley Weekly Series, Fridays at 4:20 PM ET, ONLY on 𝕏. Episode 5: THE AGENT ERA IS HERE — AND GOOGLE CAN’T STOP IT 01:05 – “We started as an answer engine.” How Perplexity went from RAG to research to real-world action. 03:03 – “Tell me what I should think about NVIDIA.” Agents as consultants, not just search engines. 05:07 – Buy buttons, autofill, AI shopping carts—how agents are already doing tasks. 07:05 – “You don’t have to see the tabs.” What the new browser will look like. 10:45 – “Warren Buffett doesn’t use apps—why should you?” The inspiration behind the AI-native phone. 12:36 – Why Apple, Google, and even OpenAI won’t build an AI-native OS. 17:28 – “Search is the most important tool in the agent era.” And Perplexity owns it. 21:33 – Mukesh Ambani’s advice: “Content is king, but distribution is GOD.” 27:13 – “Retrofit AI into iOS? It’ll never work.” Why the future needs a native platform. 30:26 – Humane failed, but the idea didn’t. Why assistants—not apps—are the next big UX shift. 33:38 – “We want Perplexity to feel like Apple.” Design still matters—even with agents. 37:11 – Why the real moat in AI isn’t models—it’s trust, taste, and vibe. 42:07 – 80M impressions in 6 weeks: How “Ask Perplexity” is going viral on 𝕏. 47:52 – “We’ve survived the internet—AI’s not scarier.” Aravind pushes back on the doomerism. 50:00 – Healthcare, finance, travel—Perplexity wants to power the vertical agent revolution. 54:24 – “LLMs can reason. But only agents can do.” Why execution is the next AI frontier. 56:34 – COMET browser drops in 3 weeks.

Mario Nawfal

1,748,592 görüntüleme • 1 yıl önce

Supabase (YC S20) is one of the fastest-growing developer platforms in the world, with more than 10 million developers using it. As AI coding agents like Claude Code, Lovable, Bolt, and Codex have taken off, they've become the backend powering millions of AI-generated apps. At Startup School Paris, Supabase co-founder and CEO Paul Copplestone (Paul Copplestone - e/postgres) sat down with YC's Nicolas Dessaigne to discuss why AI coding agents have changed the way developers build software, how open source helped Supabase compete with the cloud giants, and why the next frontier is self-driving databases. 00:56 — Paul's Origin Story 02:13 — Two Failed Startups First 04:12 — Why Postgres? Why Open Source? 07:11 — The Pivot That Actually Worked 08:03 — Targeting YC Startups from Day One 09:45 — Fully Open Source — The Risky Choice 12:04 — Winning Developer Trust 13:33 — Time to Value: 8 Minutes → 5 Seconds 15:45 — The AI Second Life Begins 17:48 — Supabase for Platforms 19:09 — Everything Going Up Into the Right 20:44 — When Agents Became the Users 22:26 — Advice for Founders Building Dev Tools Today 23:47 — Why Open Source Won the LLM Era 25:32 — $500M Round, $10B Valuation 26:01 — What Keeps a Founder Grounded 28:31 — 360 People, 60 Countries, Zero Offices
31:20

Supabase (YC S20) is one of the fastest-growing developer platforms in the world, with more than 10 million developers using it. As AI coding agents like Claude Code, Lovable, Bolt, and Codex have taken off, they've become the backend powering millions of AI-generated apps. At Startup School Paris, Supabase co-founder and CEO Paul Copplestone (Paul Copplestone - e/postgres) sat down with YC's Nicolas Dessaigne to discuss why AI coding agents have changed the way developers build software, how open source helped Supabase compete with the cloud giants, and why the next frontier is self-driving databases. 00:56 — Paul's Origin Story 02:13 — Two Failed Startups First 04:12 — Why Postgres? Why Open Source? 07:11 — The Pivot That Actually Worked 08:03 — Targeting YC Startups from Day One 09:45 — Fully Open Source — The Risky Choice 12:04 — Winning Developer Trust 13:33 — Time to Value: 8 Minutes → 5 Seconds 15:45 — The AI Second Life Begins 17:48 — Supabase for Platforms 19:09 — Everything Going Up Into the Right 20:44 — When Agents Became the Users 22:26 — Advice for Founders Building Dev Tools Today 23:47 — Why Open Source Won the LLM Era 25:32 — $500M Round, $10B Valuation 26:01 — What Keeps a Founder Grounded 28:31 — 360 People, 60 Countries, Zero Offices

Y Combinator

56,760 görüntüleme • 1 ay önce

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.
1:15

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.

YanXbt

17,312 görüntüleme • 1 ay önce

The first company I ever joined was Rubrik, Arvind Jain's previous company. It's where I learned how to build systems and engineering teams. Years later, when we started Composio, Glean became one of our first customers, one of the first to believe in what we were building. So sitting down with Arvind felt like closing a loop. In this episode: •How Glean brought transformers to enterprise search before "semantic search" had a name •Glean's partner-first strategy and why they chose Composio for actions •Why Arvind has never worried about competing with OpenAI or Anthropic •GLM 5.2 as the open-source inflection: 90%+ of enterprise AI tasks, majority of inference within 12–18 months •Measuring real ROI: the US telco that cut case-resolution time by 48% •Why agents built on MCP alone act like day-one employees and how context graphs make them tenured CHAPTERS: (00:00) – Rubrik days: how Arvind and Karan met (01:05) – Glean's origin story: transformers before "generative AI" existed (03:04) – What Glean is today: superset of ChatGPT and Claude (05:39) – From embedding search to agents: was it a pivot? (11:39) – The Composio partnership and why actions are hard (14:43) – Why competition doesn't matter yet (20:11) – The open-source inflection point (GLM 5.2) (24:41) – Contracts and model risk: who absorbs the volatility? (26:57) – Enterprise ROI: cost, tokens, and what to measure (39:57) – Measuring value team by team (44:11) – Day-one employees vs. tenured agents (46:09) – Closing thoughts
47:31

The first company I ever joined was Rubrik, Arvind Jain's previous company. It's where I learned how to build systems and engineering teams. Years later, when we started Composio, Glean became one of our first customers, one of the first to believe in what we were building. So sitting down with Arvind felt like closing a loop. In this episode: •How Glean brought transformers to enterprise search before "semantic search" had a name •Glean's partner-first strategy and why they chose Composio for actions •Why Arvind has never worried about competing with OpenAI or Anthropic •GLM 5.2 as the open-source inflection: 90%+ of enterprise AI tasks, majority of inference within 12–18 months •Measuring real ROI: the US telco that cut case-resolution time by 48% •Why agents built on MCP alone act like day-one employees and how context graphs make them tenured CHAPTERS: (00:00) – Rubrik days: how Arvind and Karan met (01:05) – Glean's origin story: transformers before "generative AI" existed (03:04) – What Glean is today: superset of ChatGPT and Claude (05:39) – From embedding search to agents: was it a pivot? (11:39) – The Composio partnership and why actions are hard (14:43) – Why competition doesn't matter yet (20:11) – The open-source inflection point (GLM 5.2) (24:41) – Contracts and model risk: who absorbs the volatility? (26:57) – Enterprise ROI: cost, tokens, and what to measure (39:57) – Measuring value team by team (44:11) – Day-one employees vs. tenured agents (46:09) – Closing thoughts

Karan Vaidya

62,170 görüntüleme • 1 ay önce

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." MongoDB CEO CJ Desai (CJ Desai) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?
43:11

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." MongoDB CEO CJ Desai (CJ Desai) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?

Molly O’Shea

170,730 görüntüleme • 1 ay önce

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

307,869 görüntüleme • 8 gün 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

Today on MCG $MADE | SP3ND 🛍️ w/ Kenton | SP3ND 🛍️ Key chapters include: 00:00 – Intro: Sp3nd reveals the new product built overnight for the Ansem hackathon 01:16 – The AI agent uses trading fees to buy GPUs, build its own body 03:17 – Sp3nd background: stablecoin checkout platform, no KYC needed 05:22 – Origin story: idea sparked at a birthday dinner, launched overnight 07:20 – First milestone updates: agent raised $10K, picked its own Nvidia rig 08:52 – The guardrail: human approval needed before purchases go through 09:37 – The bigger vision: fully autonomous agentic finance 13:11 – Early copycats: other builders spinning up similar agents 19:33 – Token vs. product: "the token is a tool, not the product" 21:47 – The ClawPump connection: fast launch, pure hackathon speed-run 22:44 – 60-day roadmap: build the physical rig, run the revenue loop 26:34 – The chaos: token bonded in 3 minutes, founders ran on no sleep 27:57 – Guardrails Q&A: separate wallet, approval required per purchase 29:05 – Long-term plan: core to Spend's revenue now that Spend is fee-free
31:46

Today on MCG $MADE | SP3ND 🛍️ w/ Kenton | SP3ND 🛍️ Key chapters include: 00:00 – Intro: Sp3nd reveals the new product built overnight for the Ansem hackathon 01:16 – The AI agent uses trading fees to buy GPUs, build its own body 03:17 – Sp3nd background: stablecoin checkout platform, no KYC needed 05:22 – Origin story: idea sparked at a birthday dinner, launched overnight 07:20 – First milestone updates: agent raised $10K, picked its own Nvidia rig 08:52 – The guardrail: human approval needed before purchases go through 09:37 – The bigger vision: fully autonomous agentic finance 13:11 – Early copycats: other builders spinning up similar agents 19:33 – Token vs. product: "the token is a tool, not the product" 21:47 – The ClawPump connection: fast launch, pure hackathon speed-run 22:44 – 60-day roadmap: build the physical rig, run the revenue loop 26:34 – The chaos: token bonded in 3 minutes, founders ran on no sleep 27:57 – Guardrails Q&A: separate wallet, approval required per purchase 29:05 – Long-term plan: core to Spend's revenue now that Spend is fee-free

MCG

13,317 görüntüleme • 23 gün önce

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code
1:00:16

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,372 görüntüleme • 1 yıl önce

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) and why this design pattern can replace a lot of expensive SaaS workflows over time. Then I zoom out to the crypto angle: why the market will mint endless OpenClaw “slop” coins, how I think about separating infra from hype, and the two names I’m watching (BNKR + CLAWD). 00:00 Why the OpenClaw AI agent narrative is bigger than you think 00:39 Two-part video: OpenClaw productivity first, crypto narrative second 01:30 What OpenClaw is (an AI agent framework, not a chatbot) 01:44 Why ChatGPT, Grok, and Claude Code are still useful but incomplete 03:19 OpenClaw vs n8n and Zapier for automation 05:03 Why Zapier pricing breaks real businesses 06:07 Why running locally matters (any app, any chat platform) 07:56 Persistent memory: how agents learn your style over time 09:55 Computer-use agents: browser control and no-API workflows 11:27 Skills and plugins: reusable workflows that self-improve 13:52 The simple setup and why model choice is flexible 16:05 Cross-platform ops: Telegram, Slack, Discord, and email in one brain 20:52 Why AI SaaS tools get replaced by agent-built workflows 25:37 What this is not: no AGI, no “sentient” coin story 29:55 How to approach the OpenClaw coin wave (infra over slop) 32:43 BNKR and CLAWD: my two picks for exposure to the narrative
44:45

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) and why this design pattern can replace a lot of expensive SaaS workflows over time. Then I zoom out to the crypto angle: why the market will mint endless OpenClaw “slop” coins, how I think about separating infra from hype, and the two names I’m watching (BNKR + CLAWD). 00:00 Why the OpenClaw AI agent narrative is bigger than you think 00:39 Two-part video: OpenClaw productivity first, crypto narrative second 01:30 What OpenClaw is (an AI agent framework, not a chatbot) 01:44 Why ChatGPT, Grok, and Claude Code are still useful but incomplete 03:19 OpenClaw vs n8n and Zapier for automation 05:03 Why Zapier pricing breaks real businesses 06:07 Why running locally matters (any app, any chat platform) 07:56 Persistent memory: how agents learn your style over time 09:55 Computer-use agents: browser control and no-API workflows 11:27 Skills and plugins: reusable workflows that self-improve 13:52 The simple setup and why model choice is flexible 16:05 Cross-platform ops: Telegram, Slack, Discord, and email in one brain 20:52 Why AI SaaS tools get replaced by agent-built workflows 25:37 What this is not: no AGI, no “sentient” coin story 29:55 How to approach the OpenClaw coin wave (infra over slop) 32:43 BNKR and CLAWD: my two picks for exposure to the narrative

VirtualBacon

22,428 görüntüleme • 7 ay önce