Artem Zhutov presents a solution to a fundamental problem in AI agent-assisted development: loss of context between sessions. With 700 Claude Code sessions in 3 weeks, he observes that every conversation starts from scratch, that compaction at 60% context loses decisions, and that native grep (via a Haiku sub-agent) takes 3 minutes to return 300 files of noise.
The solution relies on QMD, a local search engine created by Tobias Lütke (CEO of Shopify), which indexes markdown files into queryable collections. QMD offers three search modes. BM25 performs full-text search with relevance scoring — unlike grep, which returns every string match (including sleep() for a search on sleep), BM25 weights by term frequency and rarity, delivering relevant results in 2 seconds. Semantic search uses local embeddings to find meaning beyond keywords: a search for "couldn't sleep, bad night" retrieves a note on bedtime discipline even though no term matches. Hybrid search combines both with query expansion and LLM re-ranking for the best possible ranking.
Everything runs locally through three GGUF models (~2 GB): Gemma 300M embeddings, a Qwen3 0.6B reranker, and a 1.7B query-expansion model. QMD integrates with agents via an MCP server or a Claude Code plugin.
On top of QMD, Zhutov built the skill /recall, a Claude Code skill that automatically loads relevant context before each session. Three access modes: temporal (reconstructing the session timeline by date), topic (cross-collection BM25 search on a subject), and graph (interactive HTML visualization of sessions and files). A terminal-close hook automatically exports and indexes each session into QMD, keeping the index always fresh.
The demonstrations reveal unexpected uses. A semantic search for "find the ideas I have never acted on" surfaces projects forgotten for months. "Days when I was happy" brings out a pattern: the best days correlate shipping with physical recovery. Semantic search turns a passive vault into active memory capable of surfacing connections invisible to keywords.
The underlying philosophy resonates with context engineering: tools change, context stays. A well-indexed vault survives model and tool changes. Zhutov concretely demonstrates what Debois theorizes with the Context Flywheel: document the context → better output → enrich the context → accelerate.