Two birds already read the web for me. The third one reads people — it mines my GitHub graph for humans worth a first message and writes the opening line, but it never sends. The word that changed the whole design was ‘who’, and the trap it had to dodge is that finding people like me is exactly the easy thing embeddings do best.
I gave my second brain two agents that read the outside world and collide it against my notes. A Magpie watches my GitHub stars and only speaks when something hits live work. A Blue Jay reads a handful of RSS feeds and surfaces the distant, not-yet-relevant connection. They share a security spine — and they have deliberately opposite jobs. Here’s why the split is the whole design.
My second brain searches over a vault of markdown using BM25 + vectors + graph expansion. I’d been telling people the graph improved recall. Then I finally benchmarked it, and plain keyword search beat my fancy hybrid — the graph was actively dragging the right answers out of the results. Here’s the scorecard and what it taught me about where graphs actually belong.
An AI agent on a scheduled idle walk through my notes pointed out that I’d built the same architecture three times — at work, in my homelab, and in my second brain — and that the third copy was missing the part that makes GitOps work. It was right. So we shipped the missing piece the same day.