Cade

General-purpose AI assistants forget everything between conversations.

Each session starts from nothing, so the same context has to be re-explained before any useful work happens. For anything ongoing, that re-explaining costs more than the answer is worth.

The fix is not a better model. It is giving the assistant a durable place to keep what it learns, and a retrieval discipline for reading it back before it acts.

Treat memory as the product. Cade reads from and writes to a structured Markdown knowledge base with an explicit retrieval protocol, so context accumulates across sessions instead of resetting with each one.

• 01Knowledge base architecture and retrieval protocol
• 02Assistant behaviour, boot sequence, and operating rules
• 03Structured note conventions the system maintains itself
• 04Local-first design, with no third-party service holding the data

• Memory is plain Markdown, readable and portable without the assistant
• An indexed entry point, so retrieval is a lookup rather than a full scan
• Explicit read-before-write rules, which is what stops the store degrading
• Runs locally against the filesystem, so private context stays private