I wanted to test a practical idea: AI should help us make things people can use, not just give us impressive answers. The workflow worth copying 1. Define the experience: a small, gentle 3D café, not an endless feature list. 2. Write constraints: local play, no account required by the game, readable touch controls and a finite first release. 3. Give the assistant appropriate tools and review their actual outputs. A named plugin is not proof it executed a task. 4. Implement a playable loop before polishing the promotional materials. 5. Test the real menus, style changes, recipes and recovery paths. 6. Keep the working version when a newer build regresses. We used JavaScript/WebGL for the game and Python/Playwright for browser checks. Build Web Apps and Test Android Apps workflow instructions informed the work; browser tests did not substitute for Android-device testing. This is a chat-led development case, not evidence that every app can be built, fully tested and approved by a store in a few minutes. Which would you like me to unpack next: the prompt, the tool setup, or the testing process? Play the game: https://sellsystems.itch.io/capy-boba-club Join the discussion: https://www.producthunt.com/posts/capy-boba

The fragile part of an AI website workflow is usually not generation. It is the handoff between the domain, the correct runtime, the brief and the public release. This mission makes the boundary visible: managed domain -> selected instance -> agent handoff -> Codex execution -> HTTPS verification -> mentor evidence Direct Codex is the simplest first route. With an active ChatGPT Plus subscription and the separate setup mission completed, OpenClaw in Telegram becomes an optional second control surface for the same managed instance. Which handoff in your current automation stack fails most often? Public workflow: https://sell.systems/localdocs/seo/mission-1-live-ai-website/en.html?utm_source=linkedin&utm_medium=group&utm_campaign=mission1_build_live_v2_20260826&utm_content=li_group_integration_boundary