I've just finished my latest video: a three and a half hour zero-to-hero Claude Code explainer. I've included the most recent improvements on Claude to move as fast as possible and get a product which ACTUALLY makes money: https://youtu.be/roYQt9rL8Kc This is the actual method I'm using right now to generate solutions which people are genuinely excited to pay me! A real win-win... If you guys can drop some love on the video, I'd be SUPER grateful to try and help others with this... 🙏🏼

🔴 Live Stream – Tuesday, February 3rd at 11 AM >> CALENDAR << I'll be covering four topics I've been digging into lately: 1. OpenClaw (aka Maltbot/Clawdbot) Going beyond the surface on this one — I'll break down the lesser-known underlying framework that powers OpenClaw and what makes it interesting. 2. Remotion.dev + AI Video Orchestration I've been building an MCP that connects to various AI APIs and Airtable to manage assets and orchestrate video creation — scenes, segments, faceless videos, demo videos, etc. I'll walk through what's possible with Remotion as a web-based video editor paired with Claude Code. 3. Running Your Own Local LLM If you're going to have an AI agent/assistant running 24/7, paying for expensive models like Claude around the clock isn't practical. I'll share my thinking on how to set up a local machine running models like DeepSeek or Kimi for always-on tasks, while reserving premium models for when you actually need them. 4. AI Chat Interfaces as a SaaS Framework I built a YouTube Strategist SaaS (thumbnail help, research, etc.) on top of a framework I created for launching chat-based SaaS apps quickly. The idea: instead of designing complex UI for every feature, give an LLM the right tools and let the chat interface handle it. I'll demo the app and walk through the framework. Hope to see you there! 🎙️

Hey Academy! Think about it—Claude Code, WhatsApp bots, Slack agents, every new AI product. They're all chat interfaces. Why? Because it's just easier. Building custom UIs takes forever. But chat? It's universal. It works. And when you DO need a specialized view (like a video player), you're still doing the actual work in the chat. The UI is just for viewing. So here's the problem I ran into... I built some really powerful Claude projects. YouTube title research agents, tools that hit APIs, stuff that actually saves me hours. But I can't sell my Claude project. I can't put a paywall in front of it. Same with GPTs. So I built my own framework. It looks like ChatGPT, but it's not. It's a SaaS shell that lets you: → Build your own agents (like GPTs or Claude projects) → Connect them to backend automations (n8n, APIs, whatever) → Add billing and credits → Actually charge people to use them The agents live in the chat. Users subscribe. You get paid. Whether you're building AI tools for YouTubers, chefs, marketers—doesn't matter. You define the agents, hook up the backend, and monetize. I'm still building this out. If you're interested in something like this, drop your feedback below—I'd love to hear what you'd build with it.

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Want a REAL production-ready n8n deployment? In this video we break down the n8n-aiwithapex infrastructure stack and why it’s a massive upgrade over a “basic docker-compose n8n” setup. You’ll see how this project implements a full queue-mode architecture with: - n8n-main (Editor/API) separated from execution - Redis as the queue broker - Multiple n8n workers for horizontal scaling - External task runners (isolated JS/Python execution) for safer Code node workloads - PostgreSQL persistence with tuning + initialization - ngrok for quick secure access in WSL2/local dev We’ll also cover the “Ops” side that most tutorials ignore: - Comprehensive backups (Postgres + Redis + n8n exports + env backups) - Offsite sync + optional GPG encryption - Health checks, monitoring, queue depth, and log management scripts - Restore + disaster recovery testing so you can recover fast - Dual deployment paths: WSL2 for local + Coolify for cloud/production If you’re building automations for clients, running n8n for a team, or scaling AI workflows, this architecture is the blueprint: separation of concerns, isolation, scaling, and recoverability. Youtube video: https://youtu.be/HzgrId0kgfw?si=0bzdvDgJW4dLApfi Repo: https://github.com/moshehbenavraham/n8n-aiwithapex