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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)
A structured reflection surfaces what was liked, learned, lacked, and longed for. It is a conversation format and does not validate actions automatically.
Agile
4Ls Retrospective
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.When a sprint should not only be assessed but also learned from, it captures experience in clear learning categories. It records what worked, what's missing, and what should run differently next time.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 additional45–75 min45-90 min25-40 min
Participantsdifferent
Designers, developers as users, and facilitationTeam members involved4-121-5
Formatdifferent
Workshop + asyncWorkshopWorkshopWorkshop + async
Outputdifferent
Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisions4Ls Board, Learning Themes, Improvement ActionsStarfish Board, Practice Changes, ExperimentsLearning Card with evidence and next action
Tagsno overlap
ArchitectureEngineeringFeedbackValidationTasks
RetrospectiveLearningReflectionTeam
RetrospectiveImprovementExperimentsTeam
ExperimentsValidationDiscoveryLearning
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