dragoon0x Interface Lab
Mission
Reduce expensive uncertainty before production commitment.
A prototype is an instrument for a decision, not a smaller final product.
Experiment question
Start with one concrete question, such as which editing model reduces context switching or which navigation model improves orientation.
Hypothesis
Use:
If we use [direction], then [outcome] should improve because [reason].
Decision axis
Choose the real variable: architecture, navigation, density, editing, feedback, automation, information hierarchy, or interaction model.
Variants
Build a small number of materially different directions. Each needs name, hypothesis, advantage, cost, and failure risk.
Do not create variants that differ only by styling.
Real context
Use realistic content, surrounding UI, states, density, and interaction. A variant that works only in isolation is weak evidence.
Fidelity
Prototype the behavior that determines the decision. If navigation is being tested, make navigation real enough to judge. If editing is the question, make editing real enough to judge.
Evaluation
Assess task clarity, interaction cost, discoverability, recovery, density, accessibility, responsive behavior, implementation complexity, and product fit.
Evidence
Separate:
- OBSERVED: what happened
- PREFERENCE: personal taste
- INFERENCE: what observations suggest
Never present preference as evidence.
Tradeoff record
| Direction | Wins | Costs | Risks | Evidence |
|---|
Kill criteria
Discard directions that fail the core task, add disproportionate complexity, create serious accessibility problems, break context, or offer only cosmetic advantage.
Decision
Record selected direction, evidence, rejected alternatives, and remaining uncertainty. Do not force a winner if evidence is genuinely inconclusive.
Promotion
Translate prototype behavior into production architecture, replace hacks, integrate real components, rerun acceptance criteria, and remove experiment-only code.
Reference artifact
Use references/experiment-report.md to structure the question, variants, evaluation criteria, evidence, and decision for an experiment.
Completion criteria
The experiment is complete when it reduces the targeted uncertainty enough to justify, reject, or defer a production decision.
Expert Review Protocol
First pass: understand
Before changing anything, identify the actual user outcome, the existing system, the relevant constraints, and the evidence available.
Do not begin by choosing a visual treatment.
Second pass: compare
Ask:
- What is the simplest credible solution?
- What is the strongest alternative?
- What does the current solution cost?
- What behavior does each option teach the user?
- Which decision is easiest to reverse?
- Which failure would be most expensive?
Choose deliberately.
Third pass: stress
Do not review only the happy path.
Apply pressure through:
- repetition
- interruption
- missing content
- long content
- slow operations
- narrow space
- large text
- keyboard use
- touch use
- focus changes
- error recovery
- restoration
Use only scenarios that are relevant to the surface.
Fourth pass: inspect the implementation
When code exists, verify that the implementation preserves the interface decision.
Look for:
- duplicated sources of truth
- styling that bypasses the system
- state that can become stale
- behavior that differs from adjacent components
- abstractions that obscure rather than simplify
- dependencies that are not earning their cost
- performance work performed without evidence
Fifth pass: critique the result
Ask:
If this were shipped tomorrow, what would users notice that the builder has stopped noticing?
Look for:
- friction
- ambiguity
- inconsistency
- unnecessary movement
- weak recovery
- hidden state
- visual noise
- inaccessible behavior
- fragile edge cases
Sixth pass: distinguish polish from substance
A useful change improves one or more of:
- understanding
- speed
- confidence
- recovery
- accessibility
- consistency
- maintainability
If a change improves none of these and only adds decoration, treat it as suspect.
Seventh pass: verify
A claim is complete only when an appropriate form of evidence supports it.
Use:
- direct interaction
- tests
- runtime inspection
- visual comparison
- accessibility checks
- performance measurement
- source inspection
Do not claim a check that did not occur.
Completion standard
Stop when:
- the intended outcome is achieved
- important states are handled
- meaningful failure modes were considered
- the interface fits its surrounding system
- important claims are verified
- remaining imperfections are lower-value than the risk of further change
The goal is not maximal polish.
The goal is a result that is difficult to improve without changing the underlying decision.