--- title: "Omniscient Reader — Scenario Simulator" emoji: 📖 colorFrom: indigo colorTo: purple sdk: gradio sdk_version: "6.18.0" app_file: app.py hardware: cpu pinned: true license: mit tags: - game - ai - interactive-fiction - omniscient-reader - build-small - thousand-token-wood - best-use-of-modal - off-brand - llama-champion - custom-ui - llama-cpp - track:wood - sponsor:modal - achievement:offbrand - achievement:llama short_description: "Play as Kim Dokja. The Dokkaebi knows you know everything." --- --- title: "Omniscient Reader — Scenario Simulator" emoji: 📖 colorFrom: indigo colorTo: purple sdk: gradio sdk_version: "6.18.0" app_file: app.py hardware: cpu pinned: true license: mit tags: - game - ai - interactive-fiction - omniscient-reader - build-small - thousand-token-wood - best-use-of-modal - best-agent - off-brand - llama-champion - custom-ui - llama-cpp short_description: "Play as Kim Dokja. The Dokkaebi knows you know everything." ---

📖 Omniscient Reader — Scenario Simulator

"The AI isn't a narrator — it's the antagonist."

An AI-powered scenario simulator based on the legendary Korean web novel and manhwa **Omniscient Reader's Viewpoint (ORV)**. You play as Kim Dokja — a man who has read the apocalypse novel exactly 3,149 times. The twist? The Dokkaebi (the AI Game Master) **knows you know everything** — and has been redesigning reality specifically to break your meta-knowledge. Built for the **Build Small Hackathon 2026**. --- ## 🏆 Hackathon Categories & Justifications ### 🍄 Track 2: Thousand Token Wood We built a complex, immersive narrative game utilizing a dual-model architecture to stay strictly under the 32B parameter limit (8B + 14B = 22B total). ### đŸŸĸ Best Use of Modal We utilize Modal to host our "Cinematic Engine". While our fast gameplay loop uses an 8B model on Groq, high-stakes cinematic moments are dynamically routed to a **Qwen 2.5 14B GGUF** running via `llama.cpp` on a **Modal A10G GPU**. We use a persistent volume cache on Modal to keep the model warm, preventing cold starts during active gameplay sessions. ### 🤖 Best Agent The Dokkaebi operates as an **autonomous adversarial agent**. Instead of simply generating text, it continuously runs an internal planning loop evaluating the player's *intent*. It autonomously decides when to trigger "Phase Shifts" (Combat/Exploration), when to deploy "Probability Storms", and manipulates internal state tools (Meta Exposure, Probability Stability) to actively work against the player's meta-knowledge. ### 🎨 Off-Brand Badge 100% custom UI overriding Gradio defaults. We injected 14KB of custom CSS and 21KB of JS to create glassmorphism panels, glitch shaders, and particle effects without utilizing a single default Gradio visual component. ### đŸĻ™ Llama Champion Badge Heavy narrative lifting and agentic planning are powered by `llama.cpp` serving the Qwen 2.5 14B model on our Modal backend. --- ## 🕹ī¸ The Rules of the Star Stream 1. **Begin the Scenario**: Initiate the apocalypse. 2. **Declare Your Action**: Type freely or select from contextual suggestions. 3. **Choose Your Stance**: *Aggressive / Deceptive / Empathetic / Observant / Neutral*. Your stance dictates how the Dokkaebi interprets your intent. 4. **Beware the Dokkaebi**: The AI evaluates your *intent*. If you use out-of-universe novel knowledge to solve problems too easily, reality fights back. 5. **The Constellations are Watching**: A live cosmic audience reacts to your every move. 6. **Monitor Your Gauges**: Watch your HP, Probability Stability, and most importantly... **Meta Exposure**. 7. **Do Not Get Noticed**: Push Meta Exposure to 100% and reality breaks. --- ## ⚙ī¸ Dual-Model Architecture Built strictly under the 32B parameter hackathon cap. **Total Parameters: 22B ✅** | Backend | Model | Size | Usage / Latency | | :--- | :--- | :--- | :--- | | **Groq LPU** | Llama 3.1 8B Instant | 8B | Gameplay Loop, State Tracking & Intent Parsing (~2s) | | **Modal (A10G)** | Qwen 2.5 14B (llama.cpp) | 14B | Cinematic Scenario Generation & Deep Agentic Planning | --- ## 🛠ī¸ Local Setup ```bash # 1. Clone the repository git clone https://huggingface.co/spaces/build-small-hackathon/omniscient-reader # 2. Install dependencies pip install -r requirements.txt # 3. Set your Groq API Key export GROQ_API_KEY="your_groq_key_here" # 4. Launch the Scenario python app.py ``` *Note: On HuggingFace Spaces, ensure `GROQ_API_KEY` is added to the Space Secrets.* --- ## 🔗 Links & Media - đŸŽŦ **Demo Video:** [Watch the Gameplay on YouTube](https://youtu.be/x6-GxICsk2I) - đŸ’ŧ **Social Proof:** [Read the LinkedIn Architecture Breakdown](https://www.linkedin.com/posts/aswini-kumar-yanamadala_llm-aiengineering-gradio-share-7472424679501103104-NhwT/)