How to Build a Multi-Node K3s Cluster: Adding Worker Agent Nodes with K3s Token

Build a multi-node K3s Kubernetes cluster by joining worker agent nodes with cluster tokens. Step-by-step tutorial covering networking, node labeling, and taints.

Build a multi-node K3s Kubernetes cluster by joining worker agent nodes with cluster tokens. Step-by-step tutorial covering networking, node labeling, and taints.

Deploy Longhorn on K3s for distributed, highly available persistent block storage. Step-by-step guide with iSCSI host preparation, Helm deployment, Traefik UI ingress, and automated S3 backups.

Running local AI models with Ollama has transformed private AI inference. In our earlier guide, we explored how to run Ollama with NVIDIA GPU acceleration in Docker Compose. However, as teams transition their homelabs and edge servers toward declarative Kubernetes…

Deploying microservices inside a Kubernetes cluster is only half the battle; routing inbound external traffic seamlessly and securing it with automatic SSL/TLS encryption is what transforms a local test cluster into a production-grade platform. When running K3s on bare metal…

As self-hosted infrastructure grows to encompass sensitive tools—such as private AI chat platforms like Open-WebUI, local document intelligence pipelines, or K3s Kubernetes clusters—exposing every management endpoint directly to the public internet is a dangerous anti-pattern. While reverse proxies with SSL…

Docker has become the backbone of modern infrastructure, powering everything from local homelabs to enterprise microservices. Yet, beneath its convenience lies a well-known security reality that keeps DevSecOps engineers awake at night: by default, the Docker daemon (dockerd) runs with…

As development teams, homelab enthusiasts, and edge engineers outgrow standard Docker Compose architectures, Kubernetes naturally emerges as the next frontier for declarative orchestration. However, standard upstream Kubernetes (kubeadm) carries massive resource overhead and operational complexity that is ill-suited for single-node…

In containerized production environments, applications come and go, but data must endure. While Docker containers are inherently ephemeral and can be destroyed or rebuilt in seconds, your application state—ranging from PostgreSQL databases and Redis caches to uploaded media and LLM…

Deploying containerized web applications with Docker is straightforward, but exposing them securely to the public internet is where many system administrators and developers hit a wall. In traditional deployments, securing services requires orchestrating Nginx or HAProxy alongside Certbot, configuring cron…

Running large language models (LLMs) locally on your own infrastructure provides complete data privacy, eliminates per-token API fees, and enables offline capabilities. Ollama has established itself as one of the most efficient and user-friendly runtimes for serving open-weight models such…

Retrieval-Augmented Generation (RAG) has become the gold standard architecture for grounding Large Language Models in proprietary, domain-specific data. Instead of relying purely on a model’s frozen pre-training weights—which frequently hallucinate or produce generic answers—a RAG pipeline extracts text from your…

Commercial cloud AI chatbots like ChatGPT, Claude, and Gemini have transformed productivity, but they present significant data privacy risks and ongoing subscription costs for technical teams and privacy-conscious users. When you submit proprietary code, sensitive business spreadsheets, or personal notes…