Media

TL;DR Jellyfin is a fantastic open-source media server. It is also, architecturally, a single-process application that assumes it’s the only instance running. SQLite as the database. Eleven ConcurrentDictionary caches holding sessions, users, devices, and task queues in memory. A file-based config directory that gets written to at runtime. None of this survives a second pod. This is the first post in a seven-part series documenting how I converted Jellyfin into a highly available, multi-replica deployment on my home k3s cluster. The project spans two repositories, four phases, ~20 GitHub Issues executed by AI agents, and a live failover demo where I killed a pod and the service continued with zero downtime — users on the surviving replica never saw an interruption. ...

TL;DR Jellyseerr already knows what I have. Radarr and Sonarr already know how to find things. The missing piece was a front door that understood intent instead of requiring me to search for specific titles. I wired Jellyseerr’s REST API to Claude and gave it a system prompt that knows my taste profile. Now I can say “download 100GB of family-friendly anime I might like” and get a queue of requests back. A Kubernetes CronJob runs the same prompt on a schedule so the library grows without me thinking about it. ...

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. ...