I built this client-side WebGL application to demonstrate a zero-backend Prompt-to-Artifact pattern, turning unstructured conversational LLM outputs into spatial narrative visualizations. Data Source: Wikidata API for canonical entity coordinates, QIDs, and geospatial metadata, combined with LLM knowledge base retrieval. Tools Used: Three.js WebGL rendering engine, Custom System Prompt, Client-side URL parser. How the Data Pipeline Works: System Prompt Execution: A custom prompt forces models like ChatGPT, Claude, or Gemini to act as data compilers rather than text generators. Entity Resolution: The LLM resolves historical milestones to their respective Wikidata QIDs to obtain precise geospatial coordinates. URL State Compression: The entire sequence of events (factions, timelines, descriptions, locations) is serialized directly into a compressed URL payload. Client-Side Rendering: When opened, the WebGL frontend parses the URL hash in memory and reconstructs the animated 3D timeline without hitting a database or server backend. Try it out: https://mundu.app/create.html submitted by /u/Silent-Assumption292
Originally posted by u/Silent-Assumption292 on r/ArtificialInteligence