Homelab

The ATS job poller workflow in n8n: schedule and manual triggers feeding config, fetch & normalize, filter, dedup, per-job LLM scoring via NVIDIA, then digest, email, and mark-seen

🎯 Know the Market Without Job-Hunting: An LLM-Scored Job Poller in n8n

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.

The exocortex knowledge base rendered as a 3D force-directed graph — 36 notes, 165 edges

🧠 A Second Brain You Can `git clone`

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.

Brew Buddy batch detail with fermentation log

🫙 I Built a Tracker for My Kombucha. The Data Model Was the Hard Part.

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.

n8n workflow canvas

🍵 I A/B-Tested Cloud vs Local LLMs in One n8n Agent. The Local One Faked It.

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.

cartoon cover for: Five Ways to Manage Kubernetes Manifests (and Why They're Not All Equal)

📦 Five Ways to Manage Kubernetes Manifests (and Why They're Not All Equal)

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.

the little robot stands guard at a doorway like a friendly bouncer, holding up a hand to check a stack of papers, while the boy in the cap watches; a shield symbol floats above them, protective and watchful

🔒 Building a PII Guardrail Proxy for Cloud LLM Calls

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.

cartoon cover for: Privacy-Preserving LLM Pipelines: Anonymize Before You Send

🕵️ Privacy-Preserving LLM Pipelines: Anonymize Before You Send

Replace PII with semantically realistic fakes before sending to a cloud LLM, then restore the originals from the response. Started with a general model and prompt engineering — then upgraded to a purpose-built 1.7B fine-tune via Ollama.

cartoon cover for: Observing Local LLM Inference: llama.cpp's Built-in Prometheus Metrics

📈 Observing Local LLM Inference: llama.cpp's Built-in Prometheus Metrics

llama.cpp’s inference server ships a /metrics endpoint. One flag, Prometheus scraping, a Grafana dashboard loaded via ConfigMap sidecar — AI observability without a proxy layer.

cartoon cover for: Local LLM Inference on Kubernetes, No GPU Required

🤖 Local LLM Inference on Kubernetes, No GPU Required

A CPU-only self-hosted LLM stack running on k3s: llama.cpp as the inference server, Open WebUI as the chat interface, deployed as a single Git push.

cartoon cover for: Don't Restart the Node. Quarantine It First.

🚨 Don't Restart the Node. Quarantine It First.

Rebooting a misbehaving node feels productive. It isn’t. You’re erasing your evidence and skipping the lesson.