Abstract Resource-constrained environments, such as edge devices and low-tier servers, require database systems that efficiently support both vector similarity search over embeddings and complex graph traversals over interconnected data. Current vector or graph databases and their hybrid integrations are often memory-intensive, precluding their deployment under strict RAM limits, specifically those below one hundred megabytes. This paper presents the architectural design and theoretical framework for garibiDB, a proposed vector-graph database explicitly optimized for operation within such tight memory constraints while maintaining transactional consistency.

Abstract Habits—automatic, cue‐triggered behaviors—constitute a substantial portion of daily actions and present powerful targets for interventions aimed at improving health, productivity, and well-being. However, extant habit-formation research is constrained by three key limitations: (1) reliance on self-report measures that conflate repetition with true automaticity, (2) short-term efficacy demonstrated primarily over 3–12-week intervals, and (3) siloed theoretical models that fail to integrate neural, contextual, and digital dimensions into a cohesive framework. To address these gaps, this paper (a) systematically reviews foundational and post-2020 empirical literature on habit formation and change, and (b) proposes the Integrated Multilevel Habit Change (IMHC) Model, a conceptual framework that (i) combines objective measurement with dual-process and COM-B theory, (ii) leverages micro-contextual “levers” and neurobiological insights, and (iii) employs adaptive algorithms to personalize cue timing and pacing.