WildQuest: Offline Nature Quests with On-Device Qwen AI

说明与用途

WildQuest 是一款完全离线的移动游戏，使用本地 Qwen 模型生成自然探索任务并处理照片，所有数据保存在设备上，确保隐私。

问题

Users want structured nature exploration without relying on internet or cloud APIs, preserving privacy and enabling offline use.

结果

WildQuest enables structured, privacy-preserving nature walks entirely offline, with on-device AI mission generation and photo analysis, eliminating the need for cloud APIs and ensuring data stays on the device.

工作流

• Select walk duration (15, 30, or 60 minutes) and theme (plants, birds, textures, general nature).
• Generate 3–5 missions using the on-device Qwen2.5-1.5B-Instruct Q4_K_M model via llamadart.
• Follow missions outdoors, taking photos and writing brief descriptions.
• Submit photo and description to the same on-device model to receive structured JSON (label, confidence, key features).
• Save notes and mission completions locally in SQLite; drafts survive app restarts.
• Review field journal summarizing completions, observations, and next walk questions.

证据

WildQuest is a mobile-only, offline-first nature exploration game. You pick a 15-, 30-, or 60-minute walk and a theme (plants, birds, textures, general nature), and the app generates 3–5 short missions from an on-device open-weight LLM — with a bundled curated mission library as fallback, so it works with zero connectivity.

来源

The point is the screen stays secondary: you read one mission, put the phone away, and go outside. When you come back you photograph what you found, describe it in a sentence or two, and the same on-device model structures it into an ID suggestion (label, confidence, key features).

来源

Everything — quests, photos, notes, AI text — lives in on-device SQLite, including half-typed drafts that survive app restarts.

来源

The open-source AI is the core, in three places: Quest generation — Qwen2.5-1.5B-Instruct Q4_K_M (official Qwen GGUF on Hugging Face, ungated, Apache-2.0), run on-device through llamadart 0.11 (llama.cpp), CPU, 2048 context.

来源

No server, no account, no API keys. A Python/FastAPI + Ollama backend exists in the repo purely as a dev reference with the same prompt shapes and rules.

来源