Applications

TL;DR The media stack is now fully automated: content gets sourced, synced from a remote seedbox to the local NAS via an rclone CronJob, organized by Radarr/Sonarr, and served by Jellyfin with Intel iGPU hardware transcoding. I also deployed a Media Controller for lifecycle management and a Media Profiler for content analysis. This post covers the full pipeline from acquisition to playback. The Media Pipeline Jellyseerr (request) │ ▼ Radarr/Sonarr (search + organize) │ ▼ Prowlarr (indexer management) │ ▼ Seedbox (remote acquisition) │ ▼ rclone-sync CronJob (seedbox → NAS) │ ▼ NAS (TrueNAS, VLAN 30) │ ▼ Jellyfin (playback + GPU transcode) Each component runs as a Kubernetes deployment in the media namespace. ...

TL;DR Today I deployed two significant additions to the cluster: an AI-powered Alert Responder that uses AWS Bedrock (Amazon Nova Micro) to analyze Prometheus alerts and post remediation suggestions to Slack, and a multi-user dev workspace with per-user environments. I also hardened the cluster by constraining all workloads to the correct architecture nodes and fixing arm64 scheduling issues. The Alert Responder Running 13+ applications on a homelab cluster means alerts fire regularly. Most are straightforward — high memory, restart loops, certificate expiry warnings — but analyzing each one, determining root cause, and knowing the right remediation command gets tedious, especially at 2 AM. ...

TL;DR Today I deployed the most architecturally complex application on the cluster: a video service platform with a Vue.js frontend, 7 FastAPI backend microservices, NATS for messaging, PostgreSQL for persistence, and Redis for caching. This post covers the deployment patterns for NATS-based microservices on k3s and the RBAC fixes needed for Helm-based deployments. The Application Architecture The video service platform is a full microservices stack: ┌──────────────┐ │ Vue.js │ Frontend SPA │ Frontend │ └──────┬───────┘ │ HTTP/REST ┌──────┴───────────────────────────────────────┐ │ API Gateway │ └──────┬───────────────────────────────────────┘ │ ┌──────┴───────────────────────────────────────┐ │ FastAPI Microservices │ │ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │ │ │Auth │ │Video│ │Media│ │Queue│ │ │ └─────┘ └─────┘ └─────┘ └─────┘ │ │ ┌─────┐ ┌─────┐ ┌─────┐ │ │ │Stats│ │User │ │Notif│ │ │ └─────┘ └─────┘ └─────┘ │ └──────────────────────────────────────────────┘ │ │ │ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ │PostgreSQL│ │ NATS │ │ Redis │ └─────────┘ └─────────┘ └─────────┘ Seven FastAPI services communicate via NATS for asynchronous messaging and Redis for shared state. PostgreSQL handles persistent data. ...

TL;DR Today I migrated Digital Signage — an Angular SPA backed by 7 Flask microservices, an MQTT broker, and PostgreSQL — from a development environment to the k3s cluster. This is the most complex application on the cluster so far, and deploying it taught me a lot about managing multi-service applications in Kubernetes. The Application Digital Signage started as a side project back in May 2025, designed to drive informational displays on Raspberry Pi kiosk devices. It evolved over the months into a surprisingly complex system: ...

TL;DR Home Assistant runs on k3s using hostNetwork: true for mDNS/SSDP device discovery. I implemented split DNS routing so it is accessible both externally via Traefik and internally via its host IP. Then I built a Proxmox Watchdog — a custom service that monitors all Proxmox hosts via their API and automatically power-cycles unresponsive nodes using TP-Link Kasa HS300 smart power strips. Home Assistant on Kubernetes Home Assistant is one of those applications that does not play well with Kubernetes out of the box. It needs to discover devices on the local network via mDNS, SSDP, and other broadcast protocols. Put it in a regular Kubernetes pod with cluster networking and it cannot see any of your smart home devices. ...

TL;DR Day two of the cluster was a marathon. I deployed two full-stack applications (Cardboard TCG tracker and Trade Bot), set up PostgreSQL with Longhorn persistent storage, created a cluster dashboard, configured Prometheus service monitors, built a dev workspace for remote SSH, and scaled the ARC runners. By the end, the cluster was running real workloads and I had a proper development workflow. The Deployment Pattern Before diving into the applications, I established a consistent deployment pattern that every service follows: ...