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MERIDIAN: Memory-Encoded Rituals for Intimacy across Distance in an Interactive, Asynchronous Neighbourhood
arXiv cs.HC0 views
arXiv:2610.41776v1 Announce Type: new Abstract: How can shared memories become playable structures for partners who live in different time zones? We present MERIDIAN, a pre-deployment design case of Yi Town, a bilingual browser game developed by one member of a long-distance couple as a gift for the other. Nine levels translate personally meaningful events, places and cultural references into tasks within a navigable pixel town. A birthday prologue and a fictional wedding frame the intended experience. Drawing on research through design, we reconstruct four design changes from 109 development commits, 28 changelog versions and three design specifications. These records support six situated design commitments, including memory-derived mechanics, staged access, local progress storage and bilingual coverage. The implementation combines a union-based progress model with manually exchanged, checksummed codes and time-zone-dependent scenery. An implementation audit reproduced 153 passing unit tests and examined all 1,024 canonical progress codes, while identifying limits involving resets, unversioned level registries and residual transcription errors. The application has no author-operated backend for progress, but external media services and browser speech recognition limit its privacy and offline guarantees. No recipient evaluation or relationship outcomes are reported. The contribution is an inspectable account of design decisions and their trade-offs, including the tension between authoring a personal gift and preserving the recipient's freedom to interpret, decline or reshape it. The game is playable at https://jingyifan.love and its source is public at https://github.com/rayford295/yi-town.
Source:https://arxiv.org/abs/2610.41776PreviousDesigning wearable haptic cueing for neurological gait rehabilitation: user requirements across residential care and community settingsNextAutoAnalysis: An automated, self-updating meta-analysis of human-AI collaboration
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