State machine patterns and concepts
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Autonomous systems are getting more capable and less legible. Machine learning models can perceive, classify, and optimize, but as capability grows, interpretability shrinks. In safety-critical environments, systems act faster than humans can understand why. Trait Machines address this by making autonomous behavior explicitly readable, auditable, and constrainable without discarding machine learning.
Ever opened a modal and could not close it? That is a broken circuit. The .orb compiler makes it impossible to build one.
Five boolean flags create 32 possible state combinations. Most are invalid. A state machine with five named states has exactly five valid states. That is the core argument for modeling UI behavior in .orb.
Authorization logic is usually scattered across components, API routes, and middleware. In .orb, guards are part of the state machine definition. One declaration, enforced everywhere.
In most games, enemy AI is a black box of nested conditionals and random weights. The player reacts to opaque decisions they cannot predict or outthink. In .orb, enemy behavior is a state machine the player can read, reason about, and exploit.
Programming a robot today means writing thousands of lines of tangled imperative code where every new condition doubles the complexity, hidden state transitions breed bugs, and documentation drifts from reality within weeks. .orb takes a different approach: declare machine behavior as composable traits with explicit states, guarded transitions, and structured effects.