Zonghao Yuan d1f13d51a3 feat: scene/beat architecture — decouple dialogue from image generation (#2)
Replace the one-image-per-interaction model with scenes that hold multiple
dialogue beats. The image regenerates only on scene-change actions; tapping
through beats and in-scene choices are instant and zero-network.

Squashed from #2:
- feat: scene/beat architecture — decouple dialogue from image generation
- fix: harden LLM-output parsing, prefetch lifecycle, and typewriter (PR review)
- fix: dedupe beat ids; fallback narration on empty insert-beat (PR review #2)

🤖 Generated with [Claude Code](https://claude.com/claude-code)
2026-05-28 15:20:12 +08:00

云梦

An AI-driven visual novel painted by an AI, one scene at a time. You talk and explore within a scene; when the story turns a corner, it paints the next. You click. It paints. The story unfolds.


How it works

The story unfolds as a sequence of scenes. Each scene is one AI-painted background plus a short tree of beats — moments of narration, dialogue, and the occasional choice. You tap through a scene's beats and the image stays put; only when a choice leads somewhere genuinely new — another place, a new point of view, a jump in time — does the AI paint the next scene.

entering a scene
        │
        ▼
1. Text LLM     directs the whole scene at once — a background prompt
                plus a tree of beats (narration / dialogue / choices)
        │
        ▼
2. Image model  paints the background once, 16:9, no UI baked in
        │
        ▼
[ tap through beats — no model calls, instant ]
        │
        ├─ in-scene choice ──────▶ jump to another beat (instant)
        │
        └─ scene-change choice ──▶ the next scene
                                   (usually pre-generated — see below)

While you're reading one scene, the engine speculatively generates the scenes your choices could lead to — and, for unavoidable next steps, the scene after that. By the time you pick a direction, its image is usually already painted, so the cut feels instant.

Clicking the background itself (not a button) routes through a vision model: it reads where you tapped and decides whether you're exploring the current scene (it inserts a beat — no new image) or moving on (a new scene).

There is no traditional game UI baked into the art. The AI paints the world in whatever style you pick — "stick figure on grid paper" or "cyberpunk noir" — and the dialogue panel and choice buttons are a light HTML layer drawn on top, tuned to sit over the scene.


One-click deploy

Deploy with Vercel

After deploy, set the nine environment variables (see below) in your Vercel project. That's it.


Environment variables

Three providers, all independently configurable. Any OpenAI-compatible chat / image endpoint works (OpenAI, Anthropic via OpenAI-compat proxy, Gemini, OpenRouter, DeepSeek, local Ollama, …).

Provider Variables Recommended
Text · story director TEXT_BASE_URL TEXT_API_KEY TEXT_MODEL claude-opus-4-7 via Anthropic
Image · UI renderer IMAGE_BASE_URL IMAGE_API_KEY IMAGE_MODEL gpt-image-2 via OpenAI
Vision · click reader VISION_BASE_URL VISION_API_KEY VISION_MODEL gemini-3-flash via Google

See apps/web/.env.example for the exact shape.


Local development

Requires Node 20+ and pnpm 9+.

pnpm install
cp apps/web/.env.example apps/web/.env.local
# fill in the nine env vars
pnpm dev
# open http://localhost:3000

Project layout

yume/
├── apps/web/              Next.js 16 app — pages + API routes
└── packages/
    ├── types/             shared TypeScript types
    ├── ai-client/         unified OpenAI-compatible clients
    └── engine/            three-stage AI orchestration (open core)

packages/engine is the open core — pure TS, no Next.js or browser dependency. Import it directly to build your own visual-novel front-end (Tauri, Electron, CLI, anywhere).


Cost & limits

Each scene costs roughly $0.150.25 in API fees with the recommended model trio (one text + one image call); tapping through a scene's beats is free. To keep transitions instant, the engine also pre-generates scenes you might pick but don't — so real spend runs somewhat higher than the scenes you actually see. There is no rate limiting or auth out of the box — if you make your deployment public, your bill will reflect that. Add limits (and consider lowering the prefetch depth) before sharing widely.

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