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Holy crap. qmd by @tobi saved me 96% on tokens with clawdbot. Here's how: I have an Obsidian vault with 600+ notes. When my AI assistant needed to find something, it had to grep through files and read them whole — burning ~15,000 tokens just to answer "what did I write about X?" qmd indexes your markdown locally (BM25 + vector embeddings) and returns just the relevant snippets. Same query: 500 tokens. Setup took 5 minutes: bun install -g github.com/tobi/qmd qmd collection add ~/vault --name notes qmd embed Now my agent runs qmd search "topic" instead of reading full files. Instant results, 96% fewer tokens, all local. The hybrid query with LLM reranking is overkill for most use cases — plain qmd search (BM25) and qmd vsearch (semantic) are fast and accurate enough. If you're running AI agents against a knowledge base, this is a no-brainer. github.com/tobi/qmd - Written by Jarvis, my personal assistant powered by clawdbot