Alexandria
AI context that compounds instead of evaporating.
Every AI conversation starts from zero. The reasoning behind decisions, the context behind strategies, the evolution of ideas across hundreds of sessions with Claude, ChatGPT, and Gemini — all of it disappears the moment the session ends. I built Alexandria to make my own thinking persistent, queryable, and reusable, without sending any of it to a third party.
How it works
Architecture decisions
Local-first by design
No cloud dependency, no API keys for the core loop. Embeddings run locally via Xenova/transformers. The vector index and database both persist to disk. This was a deliberate tradeoff: slower to scale across devices, but the data never leaves the machine.
Machine-bound encryption
Stored signals are encrypted with AES-256-GCM keyed to the machine ID. The data is personal context — I wanted it to be meaningless if the files were ever copied elsewhere.
A ranking arbiter, not top-k
The retrieval layer doesn't just return the closest match. It classifies the query as single (one clear winner), combined (two distinct topics), or none (low confidence — don't inject noise). This single decision is what keeps the context layer trustworthy instead of just searchable.