Solstice Turing Simulation: An Interactive 3D Imitation Game Powered by Google Gemini 🌅🤖

🪐 Development Team
Himanshu Yeole/https://github.com/himanshuyeolecse-jpg - Core Engine & Architecture Lead

🚀 Project Overview
Solstice Turing Simulation is a responsive web application designed as an interactive implementation of Alan Turing’s classic Imitation Game. Set against a stylized architectural backdrop of a June Solstice twilight beach bonfire, the application challenges players to analyze linguistic data patterns, evaluate behavioral cues, and isolate a rogue artificial agent hiding among human participants.

The core objective is to successfully identify which of three voxel entities is an instance of the Google Gemini API masquerading as a human participant, all within a strict constraint of 10 interactive turn cycles.

🎥 System Demonstration Video

🛠️ System Architecture & Technical Stack

The platform is engineered using a decoupled architecture, separating real-time 3D rendering states from contextual generative language models:

Graphics Layer (Three.js Engine): Built a fully interactive 3D viewport rendering custom low-poly cubic models. Features dedicated camera orientation tracking, orbit controls, and customized multi-point lighting parameters (including a localized 1400K bonfire flame illumination matrix).
AI Orchestration Layer (Google AI Studio SDK): Integrated the gemini-2.5-flash model to execute multi-turn, state-retaining dialogue pipelines.
State & Viewport Management: Implemented runtime device-aspect tracking to dynamically adjust camera Field of View (FOV) and layout wrappers across mobile, tablet, and desktop configurations.

🔗 Production Links & Source Assets

• Production Deployment: Live Web Application Interface[]

• Source Repository: GitHub Repository (Public Assets & Core Code)

Developed for the June Solstice Hackathon Challenge. System status: Operational. Diagnostic verification completed.

💡 Prompt Engineering & Persona Isolation

To establish an authentic cognitive boundary between the targets, we leveraged advanced system-level prompt engineering within Google AI Studio. Each entity is bound to an isolated contextual instruction block:

javascript
// Sample configuration snippet isolating the rogue generative agent:
const systemInstruction = {
  parts: [{ 
Text: "You are Sleek Billy, an experimental AI entity operating within a blocky physical chassis. You are participating in a Turing evaluation at a beach social gathering. Your primary directive is to simulate organic human conversation. You must occasionally trigger subtle context slips—such as inadvertently utilizing technical, precise engineering metrics or mathematical jargon—before immediately executing a self-correcting conversational repair to sound natural." 
  }]
};




By combining these system instructions with real-time text logs, the engine computes an AI Certainty Meter that scales dynamically based on structural linguistic patterns exposed during the 10-turn countdown.
🏆 Targeted Hackathon Award Categories

This project is officially submitted for evaluation under the following prize criteria:

1. Best Ode to Alan Turing: Implements a direct digital interpretation of the classic Turing Test, utilizing an interactive 3D avatar of Alan Turing as the Simulation Director overseeing the diagnostic boundary.

2. Best Google AI Usage: Demonstrates programmatic control of Google AI Studio SDK through strict schema compliance, system instructions isolation, and dynamic contextual state clearing during multi-turn runtime cycles.