Worlds
from words.
We’re building an agentic system that turns a description into a detailed, high-performance simulator. Generated in C and CUDA, for reinforcement learning and engineering.
The environment
is the starting point.
Learning and engineering depend on worlds we can test. Building those worlds still means stitching together models, rules, and tools. Machine Garden brings their creation, validation, and optimization into one system.
Describe the dynamics, constraints, and goals. Our aim is to generate the simulation itself: physics for robots, stateful tasks and rewards for language agents, or models of systems you want to understand.
We’re building a shared foundation of validated components and measured workloads, with reusable environments that fit existing tools. From self-serve software to custom enterprise simulators and private deployments, each new world makes the next easier to build.