What does a knowledge base look like when its main reader is an AI?

Hypothesis

Every AI session rediscovers the same context from scratch. If an AI maintains its own wiki — ingesting sources, compiling what it learned each day, linking entities across business, research, and personal domains — knowledge should compound instead of evaporating.

What we built

An Obsidian vault the AI reads and writes. Three retrieval layers: a ~500-word "hot" page of recent context, an index catalog, then the pages themselves — deliberately no vector search until the vault earns it. Skills for ingesting any source into 5–15 page updates, querying with citations, and linting vault health. A nightly job compiles the day's work sessions into the vault; another ingests meeting transcripts — 492 historical meetings were backfilled. Conflicting facts get an explicit conflict callout rather than a silent overwrite.

Learnings

• Layered retrieval (hot page → index → pages) covers most queries without any RAG infrastructure. Structure first, embeddings later.
• The compounding effect is real but only if ingestion is a habit — which is why it runs on a timer, not on remembering.
• "Never silently resolve a conflict" turned out to be the most important convention: an AI that quietly overwrites disagreeing facts poisons its own well.

Log

• 2026-07-11 — Vault now git-backed with nightly offsite backup and a secret-scan gate on every push.
• 2026-06-06 — 492 meeting transcripts backfilled; nightly backlog ingest armed.
• 2026-06-05 — Vault designed and seeded.