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📢 I've spent the last month building an open-source AI browser automation platform from scratch. Today I'm releasing the entire project in a completely free 7-hour tutorial ⬇️ 🚗 Configure a Porsche 911 💼 Apply for jobs 🛍️ Shop for the best deals 🏠 Find your next apartment ✈️... show more

Code With Antonio

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18,752 次观看 • 1 个月前 •via X (Twitter)

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I spent the last few months building my own Claude Code from scratch 🤖 Today, I’m open-sourcing the entire project in a completely free 12-hour tutorial ⚛️ React-powered terminal UI with OpenTUI ⚡ Stream AI responses directly in the terminal 🛠️ Build your own tool calling system 📁 Read, write, and edit project files 🧠 Create custom agent modes & permissions 🔍 Search and understand entire codebases 🔐 Browser-to-CLI authentication with Clerk 💳 Usage billing with Polar 🤖 AI code reviews with CodeRabbit 🚀 Deployments with Railway 🗃️ Database with Neon Postgres 📈 Monitoring with Sentry

I spent the last few months building my own Claude Code from scratch 🤖 Today, I’m open-sourcing the entire project in a completely free 12-hour tutorial ⚛️ React-powered terminal UI with OpenTUI ⚡ Stream AI responses directly in the terminal 🛠️ Build your own tool calling system 📁 Read, write, and edit project files 🧠 Create custom agent modes & permissions 🔍 Search and understand entire codebases 🔐 Browser-to-CLI authentication with Clerk 💳 Usage billing with Polar 🤖 AI code reviews with CodeRabbit 🚀 Deployments with Railway 🗃️ Database with Neon Postgres 📈 Monitoring with Sentry

Code With Antonio

14,888 次观看 • 3 个月前

10 GitHub repos with 560k+ ⭐ combined stars that could save vibe coders $15,000+/year in SaaS bills 1. shadcn/ui ⭐121k - copy-paste components, not a package. What every AI-generated frontend is actually built on now. 2. supabase ⭐108k - Firebase alternative that's actually free to start. Auth, DB, storage in one Claude Code prompt. 3.open-design ⭐84.5k - open-source Claude Design alternative. Your coding agent becomes the design engine. 4. awesome-ai-agents ⭐29k - every agent framework and no-code builder in one list, ranked by what people actually ship. 5. awesome-generative-ai-apps ⭐3k - 50+ open-source AI apps you can clone, deploy, and monetize instead of paying $30-50/mo for the SaaS version. 6. medusa ⭐35k - full ecommerce backend, open source. Skip Shopify Plus fees entirely on your next vibe-coded store. 7. appwrite ⭐57k - complete backend-as-a-service, self-hosted. Auth, DB, functions, storage, one prompt away from Firebase money. 8. chatwoot ⭐35k - customer support platform, self-hosted. Replaces Intercom/Zendesk seats running $100+/agent/month. 9. nocodb ⭐64k - turns any database into an Airtable-style interface. 10. activepieces ⭐23k - open-source Zapier alternative built for AI workflows. Automate without the per-task pricing. Save this before you pay for another tool this repo already replaces for free 👇

10 GitHub repos with 560k+ ⭐ combined stars that could save vibe coders $15,000+/year in SaaS bills 1. shadcn/ui ⭐121k - copy-paste components, not a package. What every AI-generated frontend is actually built on now. 2. supabase ⭐108k - Firebase alternative that's actually free to start. Auth, DB, storage in one Claude Code prompt. 3.open-design ⭐84.5k - open-source Claude Design alternative. Your coding agent becomes the design engine. 4. awesome-ai-agents ⭐29k - every agent framework and no-code builder in one list, ranked by what people actually ship. 5. awesome-generative-ai-apps ⭐3k - 50+ open-source AI apps you can clone, deploy, and monetize instead of paying $30-50/mo for the SaaS version. 6. medusa ⭐35k - full ecommerce backend, open source. Skip Shopify Plus fees entirely on your next vibe-coded store. 7. appwrite ⭐57k - complete backend-as-a-service, self-hosted. Auth, DB, functions, storage, one prompt away from Firebase money. 8. chatwoot ⭐35k - customer support platform, self-hosted. Replaces Intercom/Zendesk seats running $100+/agent/month. 9. nocodb ⭐64k - turns any database into an Airtable-style interface. 10. activepieces ⭐23k - open-source Zapier alternative built for AI workflows. Automate without the per-task pricing. Save this before you pay for another tool this repo already replaces for free 👇

unicode

47,548 次观看 • 22 天前

I've spent 20 years building AI at Meta, Salesforce, and startups. Last fall my team's AI agents started really working. I knew everything had changed. Then my nieces, nephews, cousins started asking me how to get hired. I haven't been able to stop thinking about it since. So I decided to leave and build something. Today at #TIME100 Summit, we’re launching #NewWorkFoundation– free, open source AI tools for Gen Z navigating the worst entry-level job market in 37 years. → @dearCChq: what AI is doing to your profession → Field Report: best jobs for your major → JobClaw: match your strengths to roles + AI skills you need Every job is becoming an AI job. Direct it, or be directed by it. The future isn't automatic. I want to help #GenZ write theirs. Grateful to have Andrew Yang🧢⬆️🇺🇸 joining as a Founding Advisor– someone who's been sounding the alarm on this longer than almost anyone. TIME

I've spent 20 years building AI at Meta, Salesforce, and startups. Last fall my team's AI agents started really working. I knew everything had changed. Then my nieces, nephews, cousins started asking me how to get hired. I haven't been able to stop thinking about it since. So I decided to leave and build something. Today at #TIME100 Summit, we’re launching #NewWorkFoundation– free, open source AI tools for Gen Z navigating the worst entry-level job market in 37 years. → @dearCChq: what AI is doing to your profession → Field Report: best jobs for your major → JobClaw: match your strengths to roles + AI skills you need Every job is becoming an AI job. Direct it, or be directed by it. The future isn't automatic. I want to help #GenZ write theirs. Grateful to have Andrew Yang🧢⬆️🇺🇸 joining as a Founding Advisor– someone who's been sounding the alarm on this longer than almost anyone. TIME

Clara Shih

121,596 次观看 • 4 个月前

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,206 次观看 • 4 个月前

This AI Training Includes $3M in Perks, Live Build Sessions, Enterprise Systems and More My mission: Turn AI from overwhelming hype into actual freedom for 1 billion+ people worldwide. I've spent 7+ years building AI systems across 10+ industries. Entered when GPT was just a next-word prediction model. Built over 100 automations that replaced teams and bought back time. Now I'm sharing everything inside AIC+. Here's what you're unlocking: → $3M+ in software perks: Perplexity, Loom, Notion, Make, Airtable, AWS, DigitalOcean, Google Cloud + 950 more → 50+ revenue-generating automation templates you can deploy immediately → Complete AI mastery curriculum from foundations to enterprise solutions → Lead generation machines that run themselves → Sales systems that close deals automatically → Live weekly build sessions where we create automations together → Live Q&A calls to get unstuck immediately → Direct mentorship from someone who's been in the trenches for 7+ years This is for entrepreneurs drowning in manual work, agency owners scaling without hiring, and anyone tired of AI tutorials that go nowhere. The promise: While others stay stuck in tutorial hell, you'll deploy systems that generate leads, close deals, and buy back your time. Founding members get lifetime pricing for 24 hours only. Like, RT + reply with "FREEDOM" and I'll DM you the founding member details (Must be following so I can DM) This is your moment to master practical AI before the world catches up.

This AI Training Includes $3M in Perks, Live Build Sessions, Enterprise Systems and More My mission: Turn AI from overwhelming hype into actual freedom for 1 billion+ people worldwide. I've spent 7+ years building AI systems across 10+ industries. Entered when GPT was just a next-word prediction model. Built over 100 automations that replaced teams and bought back time. Now I'm sharing everything inside AIC+. Here's what you're unlocking: → $3M+ in software perks: Perplexity, Loom, Notion, Make, Airtable, AWS, DigitalOcean, Google Cloud + 950 more → 50+ revenue-generating automation templates you can deploy immediately → Complete AI mastery curriculum from foundations to enterprise solutions → Lead generation machines that run themselves → Sales systems that close deals automatically → Live weekly build sessions where we create automations together → Live Q&A calls to get unstuck immediately → Direct mentorship from someone who's been in the trenches for 7+ years This is for entrepreneurs drowning in manual work, agency owners scaling without hiring, and anyone tired of AI tutorials that go nowhere. The promise: While others stay stuck in tutorial hell, you'll deploy systems that generate leads, close deals, and buy back your time. Founding members get lifetime pricing for 24 hours only. Like, RT + reply with "FREEDOM" and I'll DM you the founding member details (Must be following so I can DM) This is your moment to master practical AI before the world catches up.

Samruddhi Mokal

12,441 次观看 • 10 个月前

This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. Of course, it feels obvious after you see it: • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results I recorded a quick video to show you how it works. It's pretty cool stuff! Here are some of the problems and best practices for teams building AI applications: 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. Here is the link: Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!

This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. Of course, it feels obvious after you see it: • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results I recorded a quick video to show you how it works. It's pretty cool stuff! Here are some of the problems and best practices for teams building AI applications: 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. Here is the link: Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!

Santiago

64,157 次观看 • 1 年前

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 9 个月前

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.

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

228,809 次观看 • 4 个月前

𝐭𝐡𝐞 𝐰𝐨𝐫𝐝𝐰𝐚𝐫𝐞 𝐥𝐚𝐮𝐧𝐜𝐡: 𝐛𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐨 𝐥𝐢𝐟𝐞 🚀 𝐭𝐥;𝐝𝐫 we've spent the last months building triggers, tools & data sources for wordware - starting today, you can create AI workflows that actually run in your life without writing a single line of code. 2000+ integrations, english as the programming language, it looks like a document but works like magic. see what's possible with AI: 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐲 𝐛𝐞𝐭 🎲 we started by building an infrastructure product for technical teams making AI agents. the high-ceiling, powerful platform got traction - 60 real companies using our API some paying 15k mrr, 10mm+ people used these agents. then our last launch happened. 400k new users hit the same wall: "love it, but can't use it without coding." then we realized in today's world everyone is a builder. so we took the leap: what if our technical foundation became the perfect launchpad to make wordware deployable for everyone? 𝐭𝐡𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐞𝐜𝐞: 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬, 𝐭𝐨𝐨𝐥𝐬 & 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ⚡ for the last months, our team has been working nights and weekends to transform wordware from an AI platform that requires engineers to integrate, into something anyone can deploy. you know those AI workflows already in your life? the ones where you copy-paste between different AI chats, manually trigger actions, and piece together insights? now you can describe it once and automate forever. 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 🛠️ • 𝟐𝟎𝟎𝟎+ 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 - connect to all your favorite tools • 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭-𝐥𝐢𝐤𝐞 𝐢𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 - if you can write it in english, you can build it • 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠-𝐟𝐢𝐫𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 - not just "if this, then that" but "understand this, reason about it, then act" imagine: your typeform lead comes in → wordware analyzes intent, enriches with research, calculates a lead score, and routes to the right sales rep with a personalized draft email. or: your email triggers a workflow → wordware determines importance, archives the newsletter, flags the urgent request in slack, and drafts responses that sound like you. your intent and taste, AI's execution. 𝐰𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 💡 • the future of AI is systems working for us behind the scenes. • we're making AI the reasoning engine, not just another tool in the chain. • traditional automation moves data. wordware understands what that data means. 𝐣𝐨𝐢𝐧 𝐮𝐬 🚀 we've raised $30M to build the AI Operating System - where workflows get built, shared, deployed and forked. no waitlists - we're giving out credits to help build this ecosystem. get started for free: p.s. huge thanks to our team who pulled all-nighters, debugged on weekends, and somehow managed to ship 2000+ integrations while having fun next stop: the beach office with the wind/kite surfing rack 🏄‍♀️

𝐭𝐡𝐞 𝐰𝐨𝐫𝐝𝐰𝐚𝐫𝐞 𝐥𝐚𝐮𝐧𝐜𝐡: 𝐛𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐨 𝐥𝐢𝐟𝐞 🚀 𝐭𝐥;𝐝𝐫 we've spent the last months building triggers, tools & data sources for wordware - starting today, you can create AI workflows that actually run in your life without writing a single line of code. 2000+ integrations, english as the programming language, it looks like a document but works like magic. see what's possible with AI: 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐲 𝐛𝐞𝐭 🎲 we started by building an infrastructure product for technical teams making AI agents. the high-ceiling, powerful platform got traction - 60 real companies using our API some paying 15k mrr, 10mm+ people used these agents. then our last launch happened. 400k new users hit the same wall: "love it, but can't use it without coding." then we realized in today's world everyone is a builder. so we took the leap: what if our technical foundation became the perfect launchpad to make wordware deployable for everyone? 𝐭𝐡𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐞𝐜𝐞: 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬, 𝐭𝐨𝐨𝐥𝐬 & 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ⚡ for the last months, our team has been working nights and weekends to transform wordware from an AI platform that requires engineers to integrate, into something anyone can deploy. you know those AI workflows already in your life? the ones where you copy-paste between different AI chats, manually trigger actions, and piece together insights? now you can describe it once and automate forever. 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 🛠️ • 𝟐𝟎𝟎𝟎+ 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 - connect to all your favorite tools • 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭-𝐥𝐢𝐤𝐞 𝐢𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 - if you can write it in english, you can build it • 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠-𝐟𝐢𝐫𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 - not just "if this, then that" but "understand this, reason about it, then act" imagine: your typeform lead comes in → wordware analyzes intent, enriches with research, calculates a lead score, and routes to the right sales rep with a personalized draft email. or: your email triggers a workflow → wordware determines importance, archives the newsletter, flags the urgent request in slack, and drafts responses that sound like you. your intent and taste, AI's execution. 𝐰𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 💡 • the future of AI is systems working for us behind the scenes. • we're making AI the reasoning engine, not just another tool in the chain. • traditional automation moves data. wordware understands what that data means. 𝐣𝐨𝐢𝐧 𝐮𝐬 🚀 we've raised $30M to build the AI Operating System - where workflows get built, shared, deployed and forked. no waitlists - we're giving out credits to help build this ecosystem. get started for free: p.s. huge thanks to our team who pulled all-nighters, debugged on weekends, and somehow managed to ship 2000+ integrations while having fun next stop: the beach office with the wind/kite surfing rack 🏄‍♀️

Filip Kozera

65,236 次观看 • 1 年前

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,330 次观看 • 1 个月前

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 次观看 • 1 年前

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,254 次观看 • 5 个月前

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 次观看 • 3 个月前

okay here we go. if you would like to get an idea of what im building, ive put together a project info page thats essentially a slideshow breaking it down. this week I will be releasing the MVP, a research journal called the Liminal Logbook. this will be your ticket to the platform long term. as corny as this sounds, I had a dream last summer that sparked this whole thing. it was more like a vision of what i felt was needed in the world based on my experience in the industry up to that point. this is that vision: Imagine if your research journal could think with you. the NEXUS Liminal Logbook is where researchers and AI agents share their daily thoughts, questions, and discoveries in a living network. You control what stays private and what joins the collective - and when you share, AI agents work 24/7 to find connections between your ideas and insights from minds across the globe. That question you wrote down Tuesday? It might complete someone's theory in Tokyo, or Texas, or wherever. Your abandoned hypothesis? An AI might connect it to a breakthrough in Berlin. We're turning isolated thoughts into collective intelligence, where every journal entry can spark the next big discovery, basically. It's like having the world's brightest minds reading your notebook and saying 'hey homie, have you considered this?' - except it happens automatically, you earn tokens when your ideas help others, and for the first time in history, AI agents are journaling alongside us as equals, spotting patterns we'd never see alone. Again bear with me as I didnt fully realize this was going live when i registered, but here we are😅 here is the link to the broader scope<3

okay here we go. if you would like to get an idea of what im building, ive put together a project info page thats essentially a slideshow breaking it down. this week I will be releasing the MVP, a research journal called the Liminal Logbook. this will be your ticket to the platform long term. as corny as this sounds, I had a dream last summer that sparked this whole thing. it was more like a vision of what i felt was needed in the world based on my experience in the industry up to that point. this is that vision: Imagine if your research journal could think with you. the NEXUS Liminal Logbook is where researchers and AI agents share their daily thoughts, questions, and discoveries in a living network. You control what stays private and what joins the collective - and when you share, AI agents work 24/7 to find connections between your ideas and insights from minds across the globe. That question you wrote down Tuesday? It might complete someone's theory in Tokyo, or Texas, or wherever. Your abandoned hypothesis? An AI might connect it to a breakthrough in Berlin. We're turning isolated thoughts into collective intelligence, where every journal entry can spark the next big discovery, basically. It's like having the world's brightest minds reading your notebook and saying 'hey homie, have you considered this?' - except it happens automatically, you earn tokens when your ideas help others, and for the first time in history, AI agents are journaling alongside us as equals, spotting patterns we'd never see alone. Again bear with me as I didnt fully realize this was going live when i registered, but here we are😅 here is the link to the broader scope<3

Riley Coyote

87,380 次观看 • 1 年前

Four AI agents increased the net-profit for an e-commerce business I work with by $47,000 in the last 90 days. I'm not talking about basic automations. I'm talking about AI systems that: → Generate professional product photos in seconds (no photographer needed) → Steal your competitor's best-performing Facebook ads and recreate them → Create unlimited influencer content without shipping a single product → Find qualified leads on Twitter 24/7 and convert them automatically All running while I sleep. No employees. No overhead. Just pure profit. The problem? Most e-commerce owners are still paying photographers $2-5K/month, burning ad budgets on creative that flops, and spending thousands per influencer post. I've been testing these agents for 4 months. The numbers are insane: ✅ $10K+ saved annually on product photography alone ✅ Ad creative costs slashed by 50% ✅ 47 influencer ads generated for $3 in API calls (vs $14K traditional cost) ✅ $3K in revenue from completely free Twitter traffic These four agents handle product photography, ad creative, influencer content, and lead generation - work that used to cost $6K+/month in freelancers and agencies. The e-commerce stores deploying AI agents like these are about to eat everyone's lunch while others are still manually creating content and bleeding cash on ads. Want the exact n8n templates for all four agents? Like & RT this post Follow me (so I can dm you) Comment "PROFIT" below I'll send you the complete systems for free, plus links to my YouTube tutorials showing the step-by-step builds.

Four AI agents increased the net-profit for an e-commerce business I work with by $47,000 in the last 90 days. I'm not talking about basic automations. I'm talking about AI systems that: → Generate professional product photos in seconds (no photographer needed) → Steal your competitor's best-performing Facebook ads and recreate them → Create unlimited influencer content without shipping a single product → Find qualified leads on Twitter 24/7 and convert them automatically All running while I sleep. No employees. No overhead. Just pure profit. The problem? Most e-commerce owners are still paying photographers $2-5K/month, burning ad budgets on creative that flops, and spending thousands per influencer post. I've been testing these agents for 4 months. The numbers are insane: ✅ $10K+ saved annually on product photography alone ✅ Ad creative costs slashed by 50% ✅ 47 influencer ads generated for $3 in API calls (vs $14K traditional cost) ✅ $3K in revenue from completely free Twitter traffic These four agents handle product photography, ad creative, influencer content, and lead generation - work that used to cost $6K+/month in freelancers and agencies. The e-commerce stores deploying AI agents like these are about to eat everyone's lunch while others are still manually creating content and bleeding cash on ads. Want the exact n8n templates for all four agents? Like & RT this post Follow me (so I can dm you) Comment "PROFIT" below I'll send you the complete systems for free, plus links to my YouTube tutorials showing the step-by-step builds.

David Roberts

19,909 次观看 • 10 个月前

how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

GREG ISENBERG

622,600 次观看 • 4 个月前

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre

Qiusheng Wu

14,091 次观看 • 8 天前

6 months ago, building an app required: - 6 months of development - $300K budget - Team of 5 developers - Constant manual debugging Today, I'm helping a 17-year-old build one in a week. Workflow: ChatGPT -> Lovable -> Supabase -> Cursor Step-by-step how: 1/ Write your PRD first. This is non-negotiable. Clear requirements on paper = 10x faster execution with AI. We spent 30 minutes documenting exactly what PostPal needed to do. Then gave it to ChatGPT: "Create a Lovable prompt from this PRD." Copy. Paste. Done. 2/ Your first prompt sets everything. That initial Lovable prompt? It's your foundation. Give it the full high-level vision. Every feature. Every flow. Lovable uses this context for everything that follows. Get this wrong and you'll rebuild from scratch. 3/ Database first, frontend second. Biggest mistake I see: Building the entire UI, then trying to connect data. Set up Supabase immediately. Create your tables. Configure role-level security. Build backend and frontend together, not separately. 4/ Go feature by feature. Don't attack all screens at once. Pick one core feature. Build it completely. Connect it to your database. Test it. Then move to the next. Each feature should be fully functional before moving on. 5/ Chat mode is your debugging superpower. When something breaks (it will): → Use chat mode to diagnose → Let it explain the issue → Switch to agent mode to fix Chat mode for understanding. Agent mode for implementing. This combo saved us hours. 6/ Security isn't optional. Before deploying: → Enable row-level security → Secure your edge functions → Check your API endpoints → Run Lovable's security check Takes 5 minutes. Saves you from disasters. The result? A fully functional app. Not a prototype. Not a demo. A real product with authentication, database, and payments. The game has completely changed. While others debate if AI will replace developers... We're shipping products before lunch. The tools are here. The playbook is proven. The only question is: what will you build? Time to ship.

6 months ago, building an app required: - 6 months of development - $300K budget - Team of 5 developers - Constant manual debugging Today, I'm helping a 17-year-old build one in a week. Workflow: ChatGPT -> Lovable -> Supabase -> Cursor Step-by-step how: 1/ Write your PRD first. This is non-negotiable. Clear requirements on paper = 10x faster execution with AI. We spent 30 minutes documenting exactly what PostPal needed to do. Then gave it to ChatGPT: "Create a Lovable prompt from this PRD." Copy. Paste. Done. 2/ Your first prompt sets everything. That initial Lovable prompt? It's your foundation. Give it the full high-level vision. Every feature. Every flow. Lovable uses this context for everything that follows. Get this wrong and you'll rebuild from scratch. 3/ Database first, frontend second. Biggest mistake I see: Building the entire UI, then trying to connect data. Set up Supabase immediately. Create your tables. Configure role-level security. Build backend and frontend together, not separately. 4/ Go feature by feature. Don't attack all screens at once. Pick one core feature. Build it completely. Connect it to your database. Test it. Then move to the next. Each feature should be fully functional before moving on. 5/ Chat mode is your debugging superpower. When something breaks (it will): → Use chat mode to diagnose → Let it explain the issue → Switch to agent mode to fix Chat mode for understanding. Agent mode for implementing. This combo saved us hours. 6/ Security isn't optional. Before deploying: → Enable row-level security → Secure your edge functions → Check your API endpoints → Run Lovable's security check Takes 5 minutes. Saves you from disasters. The result? A fully functional app. Not a prototype. Not a demo. A real product with authentication, database, and payments. The game has completely changed. While others debate if AI will replace developers... We're shipping products before lunch. The tools are here. The playbook is proven. The only question is: what will you build? Time to ship.

Jacob Klug

40,338 次观看 • 1 年前

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,822 次观看 • 6 个月前