I reverse-engineered Instinct's memory. Here's exactly how it works

An author describes a speculative reverse-engineering of Instinct’s iMessage assistant memory system based on interface probing rather than source-code access, concluding that it likely combines a persistent user profile, a daily updated memory summary, compacted conversation context, a todo index, and git-tracked Markdown records. The reported file structure organizes people, organizations, facts, preferences, decisions, communications, timelines, and workstreams using structured metadata, aliases, and cross-links, while background ingestion appears to reconcile, compress, correct, and occasionally prune information roughly every 24 hours. Instinct reportedly retrieves records through grep-like keyword matching rather than vector or BM25 search, making aliases important, and keeps memory read-only for the main agent while separate processes update it. The assessment finds it strong at explicit factual recall, temporal updates, contradiction handling, and in-session learning, but weaker in multi-hop retrieval, natural personalization, multimodal memory, procedural knowledge, and automatic forgetting. The author then argues that the same design could be reproduced with Supermemory, promoting its profiles, dynamic memory buckets, search tools, automated reconciliation and forgetting, multimodal ingestion, and on-demand versioned record construction as alternatives to Instinct’s file-based approach.

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