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🟢 Let's Vibe Code an Airbnb Clone with AI! | Beginner Series Ep #7 (Cursor, Clerk, Stripe Connect)
Episode 7 of our new Series 'Code with AI the Right Way' is here! — and this time, we're vibe coding an Airbnb clone LIVE from scratch! This is a LIVE build — mistakes, debugging, and all. That's the point. You learn more watching someone solve real problems in real-time than from a polished, pre-recorded tutorial.
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🔴 Your AI Agents Aren’t Secure Until You Do This!
Most AI agents today are still authenticating with a raw API key copied into an env file months ago — which means no real identity, no proper audit trail, and no clean way to revoke access for one service without breaking everything else. This video shows you how to fix that with Clerk’s Machine-to-Machine (M2M) tokens. Clerk M2M gives each service its own machine identity, so your AI agents, dashboards, cron jobs, webhook workers, and backend services can securely talk to each other without relying on one shared secret. That means better security, better visibility, and way more control as your systems start running autonomously. In this video, I will build two separate apps in one repo — a Next.js dashboard with a protected API and a separate AI agent service that authenticates itself using Clerk M2M tokens. You’ll see the full lifecycle: setup, token creation, verification, protected API access, and revocation. We’ll cover: ✅ Why API keys break down for AI agents and multi-service systems ✅ What Clerk Machine-to-Machine tokens are and why they matter ✅ Setting up machine identities inside Clerk ✅ Connecting a Next.js dashboard app and a Node.js agent service ✅ Generating M2M tokens and passing them in API requests ✅ Verifying tokens on a protected backend route ✅ Revoking compromised tokens instantly ✅ Opaque tokens vs JWTs and when to use each one ✅ Real-world use cases for AI agents, cron jobs, webhook workers, billing services, and more If you’re building AI agents that call your APIs, this is one of those security layers you really don’t want to skip.
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Building a high-end landing page shouldn't take days.
Just integrated the Overlay Nav + Overlay Menu from React Vibe into a new build. Smooth, cinematic, and production-ready in minutes. This is how we build the future of the web. The April Update drops in < 24h!
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OpenClaw vs Hermes Agent — Which AI Agent Should You Use? (Full Setup Guide for Beginners)
Most AI agents don’t actually improve over time — they just repeat the same workflows, accumulate noisy memory, and slowly get worse unless you constantly step in to refine them. This video breaks down the real differences between OpenClaw and Hermes Agent, so you can decide whether it’s worth switching entirely, sticking with OpenClaw, or running both side by side. Hermes Agent is getting attention because of its built-in self-learning loop — it can observe repeated workflows, turn them into reusable skills, and iteratively improve them over time. OpenClaw, on the other hand, shines with its rich ecosystem, multi-agent communication, and more flexible orchestration setup. I’ll show you exactly how both systems differ when it comes to self-learning, memory, skills, tool usage, and multi-agent architecture — and then I’ll walk you step-by-step through setting up your own Hermes Agent on a VPS with Hostinger, including Telegram bot setup, running the gateway in the background, and testing the self-learning workflow for yourself. We’ll cover: ✅ The real difference between OpenClaw and Hermes Agent ✅ Why Hermes Agent's self-learning loop is the biggest differentiator ✅ How memory works differently in both systems ✅ Why OpenClaw still wins for true multi-agent communication ✅ How I’m personally using both agents together ✅ One-click Hermes Agent deployment with a Hostinger VPS ✅ Telegram bot setup, gateway config, and background execution ✅ Why running AI agents on a VPS is safer than your local machine If you’re serious about building AI agents that are always on, safer to run, and actually useful in the real world, this video will save you a ton of trial and error.
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Elevenlabs build in Go GRPC Microservices
Whatsup chat Are you ready to see how to cook real and robust microservices applications with GO + GRPC + Nextjs In this micriservice everything you see is custom built from auth to AI Pipeline and there is no third party fancy warppers Auth is built with jwt tokens and redis tokens blacklist tracking Audio generation is built on Websockets and Redis Pub/Subs And AWS S3 for File storage These days I am loving Go its gigachad programing language. Right now its fully working if you wanna run in your local machine but I cant deploy it due to its architecture. https://github.com/Ajay01103/Go-GRPC-Microservices
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