Show HN: Adaptive Recall, persistent memory for AI assistants over MCP (www.adaptiverecall.com)

Adaptive Recall has launched a new memory system designed for AI assistants, aimed at enhancing persistence and accuracy in recalling information. Unlike standard memory APIs that rely solely on vector search and cosine similarity, Adaptive Recall integrates six advanced capabilities, allowing for more nuanced interaction. This includes parallel search strategies—vector similarity, temporal recency, full-text keyword, and knowledge graph traversal—along with a cognitive scoring mechanism that prioritizes memories based on recency, access frequency, and entity connections. The significance of Adaptive Recall lies in its self-improving architecture; the system learns from user interactions to refine its retrieval performance over time. As memories evolve through a lifecycle—gaining or losing confidence and fading when not accessed—the implications for AI/ML applications are profound. This adaptive learning capability not only enhances user experience by surfacing relevant information more efficiently, but it also provides a deeper, cognitive approach to memory management, setting a new standard for AI assistants and influencing future development in memory APIs.