A couple of days ago I saw a post here where someone built a similar “add things into a room” demo.

I wondered how hard it would be to recreate it without touching code.

As an experiment, I used Codex with GPT‑5, gave it GitHub, Vercel, and GCP access, and asked it to build and deploy a minimal version.

How it works

Web: Next.js on Vercel. Users pick a room and type what to add (e.g., “add a lamp”).

State: Firestore stores room docs (paths, status, version).

Images: Google Cloud Storage holds original/current images.

Queue: API publishes edit requests to Pub/Sub.

Worker: Cloud Run worker fetches the current image, calls Gemini to add the requested object, writes back to GCS, and updates Firestore.

Live updates: The UI listens via SSE and refreshes when the version changes.

Admin: Panic switch (disable edits just in case if i go out of api credit or something), per‑room reset, “auto‑reset” with a countdown broadcast to rooms, and a banned‑words filter. Rate limiting per IP to avoid spam.

Infra odds‑and‑ends: simple presence via Firebase RTDB; GCS URLs as CDN.

I have some free Gemini credits from Google, so I’m keeping it open to see how it behaves in the wild: latency, error modes, costs, moderation edge cases, etc.

This is my first time watching a fully AI‑written codebase under real usage and load. Happy to answer questions or share more if people are curious.