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Criterion
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration of a review with planned work, actual event sequence, comparison, and assigned improvement actions.
Operations
After-Action Review
Paper illustration for Starfish Retrospective
Agile
Starfish Retrospective
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Purposedifferent
ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.After missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement.When improvement options need to be sorted more finely than just start and stop, it brings observations into clear kinds of action. This makes visible what should be done more, less, newly, or continued.The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible.
Complexitydifferent
MediumLowLowLow
Timedifferent
2–4 h review as a planning range; preparation and evaluation additional20-45 min45-90 min25-40 min
Participantsdifferent
Designers, developers as users, and facilitation3-124-121-5
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsLessons learned, Action items, Event summaryStarfish Board, Practice Changes, ExperimentsLearning Card with evidence and next action
Tagsno overlap
ArchitectureEngineeringFeedbackValidationTasks
LearningOperationsImprovement
RetrospectiveImprovementExperimentsTeam
ExperimentsValidationDiscoveryLearning
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