cityscape with flying cars

A developer built Aether Grid, an open-source real-time 3D futuristic cityscape in which a locally run Llama 3.2 3B model, served through Ollama, acts as the traffic controller instead of hardcoded rules or a physics engine. The model returns structured JSON — spawn, reroute and atmosphere fields — that a parser maps to Three.js scene actions, letting users type natural-language commands like "a storm is rolling in from the east" to make vehicles scatter and the sky darken. The project runs entirely on-device at roughly 15 tokens per second, with no API or cloud calls, and is MIT-licensed under the Hacktoberfest 2026 tag.

I Built a Living Futuristic City Where Open-Source AI Drives the Traffic Tags: hf26challenge hacktoberfest2026 ai webdev open-source What I Built Aether Grid is a real-time, procedurally generated futuristic cityscape where every flying car, drone taxi, and sky-rail is orchestrated by a local open-weight language model. No API calls. No cloud. The city thinks for itself. The core idea: instead of hand-coding traffic patterns or using a physics engine, a small open-weight model running via Ollama acts as the city's nervous system. It decides: You get a 3D city rendered with Three.js that responds to natural-language commands. Type "a storm is rolling in from the east" and the vehicles scatter, neon signs flicker, and the sky darkens — all decided by the model, not by hardcoded rules. Demo Video / Live Demo Link Here Code GitHub Repository Link Here Repo structure: aether-grid/ ├── src/ │ ├── city/ Three.js scene, buildings, roads │ ├── traffic/ Vehicle spawning, lane logic │ ├── ai/ │ │ ├── prompt.js System prompt for the city model │ │ ├── parse.js JSON response → scene actions │ │ └── client.js Ollama local inference wrapper │ └── ui/ Prompt input, stats overlay ├── models/ Local model config llama3.2:3b └── README.md How I Built It The AI layer. I used Llama 3.2 3B via Ollama, running entirely on my machine. The system prompt instructs the model to act as a city traffic controller and return structured JSON: { "spawn": { "type": "taxi", "lane": 3, "speed": 1.2 } , "reroute": { "vehicle id": "v-42", "new lane": 7 } , "atmosphere": { "sky": "storm", "lighting": "neon-amber" } } The response parser maps each field to a Three.js action. No external API — the model runs at ~15 tokens/sec on my laptop, which is plenty for a city that updates every 2 seconds. The 3D scene. Buildings are instanced meshes with emissive window textures. Flying cars are simple box-and-wing geometries on spline paths. The trick that made it feel alive: each vehicle has a slight sinusoidal bob, and the camera slowly orbits the city center unless the user takes control. The prompt interface. A single text input at the bottom. You describe what's happening in the city, the model decides how the city reacts. This is where the open-weight model does the heavy lifting — it's not generating text for a user, it's generating actions for a simulation. Why Open Innovation Matters This is the part I care about most. I could have called a hosted API to do this. I didn't, and here's why: That last point is what excites me about open-weight models. The barrier between "I have an idea for how a city should feel" and "here's a city that feels that way" is a text file. What's Next Built during Hacktoberfest 2026. All code is MIT-licensed. Go build a city.