Yichus

Minimal state updates with deterministic event-to-output behavior.

List state operations with predictable add/remove update paths.

Condition-driven views with explicit state transitions.

Controlled inputs and state synchronization without hidden effects.

Pointer-driven interactions backed by explicit IO event flow.

Dynamic list composition with stable rendering paths.

Canvas animation loop expressed through Yichus IO updates.

Inspect end-to-end provenance across UI bindings and derived values.

Visualize IO boundaries and state mutation timing in the runtime.

Detect when AI-generated code fakes outputs instead of using real inputs.

Low-latency keyboard interactions with explicit effect handling.

Large animated state updates using incremental runtime updates.

Change rates and terms, then inspect calculation provenance per field.

Explore brackets and assumptions with explainable, traceable outputs.

Model emissions scenarios and inspect the chain behind each total.

Run portfolio assumptions and compare outcomes across scenarios.

Evaluate policy branches and inspect why a path was selected.

Test strategic allocations and compare simulated performance.

Evaluate tier transitions and inspect traceable decision points.

Compare branch outcomes side-by-side with full provenance context.

Program a move loop, predict future-self collisions, and verify the replay on the full game surface.

Step the deterministic rules fixture tick by tick to inspect collisions, rewinds, and win conditions.

Validate the reusable grid renderer, tile palette, and entity drawing primitives that power the game.