How to Build an AI Second Brain That Preserves Evidence and Context
I started with a practical problem: the periods when I most needed a record of my decisions were often the periods when I had the least time to write one. Meetings, email and messages already left traces. I wanted those traces to remain usable after a conversation with an AI ended.
In my earlier description, I called the input REAL and drew a path through collection, memory and ontology to a single source of truth, or SSOT. That explained the idea. It left the difficult part inside the arrows.
Here is a narrower account of how my meeting pipeline handles those arrows, including the boundary between receiving evidence and deciding what it means.
What the layers actually do
Concrete responsibility in this example
REAL / source
A Google Meet transcript-generation event and the original utterance records it identifies.
An existing Workspace/Pub/Sub receiver validates and stores the event. A Node.js worker...
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