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.