If you’ve ever built a B2B SaaS, you already know the messy part starts when you need organization-specific features like seat limits, team members, subscriptions, invites, roles, checkout flows, emails, and keeping everything in sync. This video is all about how to set up Clerk’s new seat-limited organization plans so you can enforce member limits without building all the custom logic yourself. Clerk now lets you define organization plans with specific seat limits, connect them to checkout flows, manage subscriptions, and control how many members each organization can invite — all without manually syncing Stripe, webhooks, organization data, and your own database logic. I’ll show you how the full flow works inside a real B2B demo app: creating organizations, switching between Growth and Pro plans, inviting members, enforcing seat limits, setting up plans in the Clerk dashboard, and using Clerk’s new components to build both default and custom pricing experiences. We’ll cover: ✅ Why seat limits are painful to build manually in B2B SaaS apps ✅ How Clerk handles organization members, invites, roles, and subscriptions ✅ Creating a demo organization and testing member limits in real time ✅ Using Growth and Pro plans with limited and unlimited seats ✅ How invites count toward available seats before users accept ✅ Setting up organization membership limits inside Clerk ✅ Creating seat-based organization plans in the Clerk dashboard ✅ Using Clerk’s PricingTable, CheckoutButton, PlanDetailsButton, and SubscriptionDetailsButton components ✅ Building a custom pricing page without writing your own checkout or webhook sync logic This is one of those features that removes a huge amount of backend complexity. Fewer custom systems, fewer edge cases, fewer places for your SaaS app to break.

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.

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.

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.

Episode 6 of our new Series 'Code with AI the Right Way' is here! — and this time, we're vibe coding a Image & Video Studio 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.