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.
Zero-shot voice cloning with XTTS-v2 on a CPU-only k3s node: 26 seconds of phone audio in, a cloned-voice audiobook out — and an honest verdict from the bedtime jury. Every manual step, including the ones that went wrong.
I gave my markdown knowledge base a nightly gardener — an AI that finds orphan notes and missing links and fixes them, every change a reviewable git commit. The fun part was the Kubernetes wall I hit on the way.
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.
My first second brain died the way most do — on multi-device sync. The rebuild: plain markdown as the source of truth, every clever layer derived and disposable, and an AI that tends it through reviewable git diffs.
Brewing kombucha looks simple until you try to model it: one batch splits into many flavored bottles, every jar generates a stream of pH and taste readings, and a SCOBY has a lineage. Here’s the little app I built to keep track — and why the schema, not the code, was the real work.
I built an AI agent in self-hosted n8n over my kombucha-tracking app, then gave it two brains — NVIDIA’s 70B and a local Phi-3.5 — sharing the same tools. The cloud model called the tools and answered from real data. The local one couldn’t, so it made things up.
Raw YAML, Kustomize, Helm, Jsonnet — there’s more than one way to describe what you want running in a cluster. Here’s what each actually looks like in practice and where each one breaks.
A local model classifies every prompt before it leaves the cluster. If it’s sensitive, it’s blocked. If it’s clean, it goes to NVIDIA NIM. 150 lines of FastAPI, deployed on k3s.