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.
A rejection isn’t actionable data. So an n8n workflow now extracts skill demand from live job listings, diffs it against what I can prove, and renders the gap as a dashboard — deployed like everything else here: via git push.
A farm robot is replacing pesticides with UV light at night. The clever part isn’t the robot — it’s the darkness. Here’s the home version, and the honest scope of what it can and can’t do.
You don’t have to be job-hunting to want to know your market — what’s out there, what it pays, where you’d fit. So I built an n8n workflow: it polls the public ATS APIs (Greenhouse/Lever/Ashby) plus a broad remote-jobs feed, filters for remote-EU infra roles, scores each posting against my CV with an LLM, and emails me only the 80%+ matches. No database, no scraping.