Dennis Kappel

IT Operations • Homelab • Learning in Public

I build, break, and learn. Step by step.

This site is a realistic snapshot of where I am right now: solid daily IT practice, hands-on homelab work, and early Kubernetes learning. I am not an expert yet, but I am consistent, curious, and improving every week.

See my current level View projects

Progress Snapshot (Honest)

Live lab tracking
Kubernetes fundamentalsBeginner
TroubleshootingIntermediate
Automation & scriptingLearning
Production-style operationsGetting there

Current Kubernetes Knowledge State

This snapshot mirrors my live CKA knowledge tracker repo. I keep it brutally honest: solid fundamentals in progress, clear weak spots marked, and a direct link to the source.

Source of truth: github.com/ffworker/cka-qa

Strong Base

Foundations, workloads, basic services/networking, scaling basics, and core observability topics are already practiced regularly.

Improving Zone

Topics like taints/tolerations, node selectors, configmaps, service accounts, and RBAC are currently active improvement targets.

Not Reviewed Yet

Ingress, network policies, CoreDNS/CNI, TLS/certificates, kubeconfig, and several security internals are still explicitly unreviewed.

Practice Workflow

Theory and recall are tracked in cka-qa, then handoff data drives practical lab priorities so weak areas get deliberate repetition.

Current Stack (What I actually use)

This is the real stack I work with in my homelab today. I am listing key IT elements explicitly so it is clear what I built and operated hands-on.

Platform & Runtime

  • • Raspberry Pi hosts (Pi 4, Pi 3, Pi Zero W) running Linux-based services.
  • • Docker Engine + Docker Compose as the primary deployment model.
  • • Portainer for day-to-day container lifecycle management.

Networking & Access

  • • Segmented internal Docker bridge networks between service groups.
  • • VPN-routed container namespace with kill-switch behavior for specific workloads.
  • • Controlled UI access via local proxy endpoints and restricted LAN/VPN reachability.

Observability & Data

  • • Grafana dashboards for uptime and service visibility.
  • • InfluxDB 1.8 as a lightweight metrics/time-series backend.
  • • Log-based troubleshooting and service dependency checks during incidents.

Automation & Workloads

  • • Node-RED flows for practical automation and signal/control workflows.
  • • Python and PowerShell scripts for maintenance tasks and repetitive operations.
  • • Utility services including Paperless-ngx, Samba/FTP, and sensor-driven automation.

What I can do today

No buzzwords, just what I really do in practice.

Homelab Operations

  • • Run multi-service Raspberry Pi setups and keep them stable.
  • • Use Docker Compose for practical deployments and updates.
  • • Maintain backups and recover services when something breaks.

Troubleshooting

  • • Isolate issues using logs, connectivity checks, and service dependencies.
  • • Document fixes so recurring problems are faster to solve.
  • • Ask better questions when I am blocked and iterate quickly.

Automation & Tools

  • • Build small automations with Python, PowerShell, and Node-RED.
  • • Connect monitoring and notifications for daily visibility.
  • • Work with OpenClaw on a Raspberry Pi 4 sandbox by giving clear, technically accurate natural-language prompts to drive agent tasks.
  • • Improve scripts over time instead of chasing perfect v1.

Mindset

  • • I share what I know and I clearly label what I am still learning.
  • • I prefer honest documentation over polished claims.
  • • I am comfortable saying "I don’t know yet" and then figuring it out.

Selected Work

Raspberry Pi 4

Mixed Service Host

Runs utility services plus a segmented download-and-index workflow: one core client is forced through a dedicated VPN network namespace with kill-switch failover, management services communicate over an internal bridge network, and UI access is exposed only through a local proxy endpoint for controlled LAN/VPN reachability.

Raspberry Pi 3

Watering Automation

Automated watering with DHT22 + hygrometer sensor readings and control logic for my own small environmental setup.

Kubernetes Learning

CKA-QA Knowledge Tracker

Public map of my CKA learning state with good/meh/needs-improvement markers and explicit "not reviewed yet" sections to keep progress measurable.

Open repository →

Pi Zero W

Public Camera Node

A lightweight surveillance camera node exposed intentionally as a simple public-facing lab project.

In Planning...

Next major step: convert my current Docker Compose-based stack (single docker-compose.yml approach) into a Kubernetes cluster. I am intentionally transparent about the process.

  • • I built and refined parts of the Compose stack together with support from different LLMs.
  • • For the Kubernetes translation, I will rely only on what I personally learned through hands-on practice and lab work.
  • • I will document gaps openly and avoid claiming production-level Kubernetes experience too early.

Let’s connect

If you want to talk about beginner Kubernetes learning, homelab projects, or practical IT operations, feel free to reach out.

Email me GitHub