This course shows the full OpenClaw path from one Telegram request to a finished AI video. Watch the build, then try to map the workflow you would reproduce first in your own setup. After watching, answer one question: Which part would you rebuild first — the Telegram control flow, the Veo 3.1 step, the D-ID step, or the free webterminal practice setup at https://sell.systems/mod/assign/view.php?id=215

This is the first full business-use-case build in the lab. The important part is not just that a Telegram pipeline works at the end. The important part is that the AI agent builds the first working version inside a real environment, and the same system can keep evolving afterwards in the same context. After watching, answer this: If you had this workflow in your own environment, what would you change first — sources, formatting, approval logic, or delivery path?

This lesson is where the environment stops feeling basic and starts becoming a real operator workspace. We switch from Gemini to Codex, connect Dropbox, and make the agent more useful inside the working context. After watching, tell me this: What matters more for your work right now — stronger coding, native tools, or a better multi-agent flow?

This is the best place to start if you want to stop collecting AI content and start building. In this lesson we bring the environment to life: - terminal setup - Gemini onboarding - first simple Node-RED success After watching, answer this: What is the first system you want to bring to life in your own lab?