Diagram Studio · Published

UI Evolution Flow

A self-documenting proof of how typed fixtures, reusable libraries, creative exploration, production pull, and data-model feedback strengthen one another.

Dogfood proof: the process is expressed through the same typed A&ID metadata and reusable renderer it promotes.

§01

Strength flows inside-out; learning returns inward

The forward path protects creative freedom. The return leg turns fixture discoveries into honest model and repository demand.

UI evolution: typed bricks, creative proof, real flow

dogfood-proof

Interface demand moves outward through fixtures, reusable libraries, intentional variants, evidence, and production pull. Fixture discoveries return inward as model and repository hypotheses that require a real producer.

Source: docs/apps/stackmates/stackmates-design-system/app-evolution/01-reality.md

UIE-R01Job + reference evidence
HADesigner / product explorer[L2]?
intent

Source provenance, target user, intended feeling, and constraints.

make demand executable
UIE-T02Typed fixtures + scenarios
OADesign-system steward[L3]
shape-proof

Executable demand hypotheses for states, relationships, commands, and scale.

freeze typed contract
UIE-C03Reusable library bricks
OADesign-system steward[L3]
inside-out

The narrowest correct libs/app-client owner defines the stable contract.

compose freely
UIE-S04Intentional compositions
HADesigner / product explorer[L2]?
creative-lab

Lock invariants; vary one or two dimensions across distinct expressions.

compare
UIE-V05Evidence + disposition
OADesign-system steward[L3]
proofUIE-PROOF-001

States, accessibility, responsive behavior, motion, performance, and portability.

[promote]pull proved capability
UIE-G06Production pull[FC]
HAProduction product team[L3]
real-consumermedium risk

Apps bind real auth, data, actions, persistence, navigation, and telemetry.

prove real data path
UIE-V07Model + repository flow proof
HAProduction product team[L3]
return-legTYPE-FLOW-001

Classify fixture demand and prove a real producer before claiming persistence.

Feedback controls

model-flow
[producer + repository evidence]align scenarios to reality
fixtures
evidence
[proof fails]revise or delete
variants
production
[usage or drift]renewed demand
demand
  • Typed fixtures prove shape and reveal data demand; they do not prove persistence flow.
  • Library rigor enables many expressions instead of prescribing one finished screen.
  • Production pull and repository evidence close the learning loop.