AI World Gen

Describe a place. A language model invents what it is made of. A decision model places it, one cell at a time.

Describe the world

Where is it, when is it, and what is it like? Pick a suggestion or write your own. The model that invents the vocabulary reads exactly these three fields.

Suggestions

One model call per cell. A 12 × 12 map is 144 decisions.

Vocabulary (optional)

The list of element types the map is built from, as JSON. A suggestion fills it in for free. Leave it empty and the narrative model writes one for your setting (the one paid call). Edit it as you like: the same checks a model's answer must pass run on this box.

    How this works, and why two models

    One creative call, or none. A language model (Claude, GPT, Gemini, your pick) reads your three fields and writes the vocabulary of the world: 6 to 24 element types, each with a description, placement rules, whether you can walk on it, and the properties one instance of it would need later. That JSON is checked, and the model is asked to fix it if it is wrong. It is also the expensive part, so every suggestion ships with its vocabulary written already, and you can read, edit or replace it in the Vocabulary box above.

    Many fast decisions. Then, for every cell, a decision model (TypeSafe's Jev, by default) is given the neighbouring cells and asked one typed question: which type goes here? It answers with a choice and a probability per option, not with text. Cheap, fast, and exactly the kind of decision it is built for.

    Plain code checks the result. When the map is full, a flood fill finds walkable cells that cannot be reached from the largest open area, so a sealed-off room is reported rather than hidden.