Not all AI memories should be treated equally. Here is why that matters.

When your AI remembers something, it treats every piece of context with the same weight. A decision you made six months ago sits alongside a rough idea you were brainstorming yesterday. There is no way to say "this is settled" versus "this was just thinking out loud."

That matters more than it sounds.

If you changed your mind about an architecture decision, the old reasoning should not carry the same authority as the new one. If you deprecated a workflow, your AI should not keep suggesting it.

This is something we spent a lot of time thinking about with Second Brain. Every memory can be marked canonical (this is settled), draft (still thinking), or deprecated (no longer relevant). When your AI recalls context, canonical memories are protected. Drafts are surfaced but clearly marked. Deprecated entries stay in your history for reference but drop out of active recall.

The result is that your AI's confidence in what it tells you is grounded in how settled the underlying information actually is.

We launched the desktop app for Second Brain today, making all of this available without any terminal or git setup: https://www.thesecondbrain.dev

We are live on Product Hunt: https://www.producthunt.com/posts/second-brain-for-mac-and-windows

How do you currently handle outdated context in your AI workflows? Do you just hope the model figures it out?