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.

What if I told you the voice at the start of this video wasn’t actually me speaking live? In this video, I test Fish Audio’s brand new S2 Pro model and show you how to generate realistic AI voices, add emotion tags, create multi-character conversations, clone your own voice, and then plug everything into a real Next.js app using the Fish Audio API. We’ll go from testing voices inside the Fish Audio dashboard to building a full developer demo with text-to-speech, speech-to-text, voice search, voice cloning, and real-time streaming audio. We’ll cover: ✅ Testing Fish Audio’s new S2 Pro voice model ✅ Adding emotion tags like laughing, aggressive, cheerful, and more ✅ Creating multi-speaker AI conversations ✅ Cloning your own voice from a short audio sample ✅ Keeping cloned voices private ✅ Creating a Fish Audio API key ✅ Building a Next.js demo app with the JavaScript SDK ✅ Text-to-speech, speech-to-text, and voice search ✅ Real-time streaming audio with low latency ✅ How to keep API keys safely on the server side If you’re building AI agents, voice assistants, creator tools, customer support bots, or any app that needs realistic AI voice, this is one of the coolest tools to try right now.

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.

Most products have data, but no real insights. Modern dashboards should: → track real-time metrics (MRR, churn, activity) → detect issues automatically → help you decide what to do next I build these systems end-to-end — backend, data models, AI insights, and UI. If your analytics feels messy or unclear, I can help fix it. DMs open.

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.