Kubernetes

TL;DR This is the moment everything was built for. Three phases of preparation — PostgreSQL provider (Day 3), storage migration (Day 4), state externalization (Day 5) — all leading to a single kubectl scale command. This post covers Phase 4: scaling the Jellyfin StatefulSet to 2 replicas, configuring anti-affinity to spread pods across nodes, running six structured failover tests, building Prometheus alerts, and one test that only partially passed. The headline result: killing a pod causes zero service downtime — users on the surviving replica experience no interruption at all, and displaced users reconnect within seconds. ...

TL;DR Phase 3 is where the rubber meets the road. We have PostgreSQL for persistent data (Day 4) and NFS for shared config. But Jellyfin still holds critical runtime state — sessions, users, devices, tasks — in 11 ConcurrentDictionary instances scattered across singleton managers. Two pods with independent memory spaces means two independent views of reality. This post covers the state externalization decision: what got moved to Redis, what got solved by sticky sessions, what got disabled entirely, and why pragmatism beat perfection for a homelab media server. ...

TL;DR Phase 2 is the scariest phase. It’s where we take a running Jellyfin instance with years of playback history, user preferences, and media metadata — then swap the database from SQLite to PostgreSQL and restructure every volume. One wrong move and the family discovers their “Continue Watching” list is gone. This post covers deploying PostgreSQL as a k3s StatefulSet, restructuring Jellyfin’s volume layout from a monolithic RWO PVC to NFS shared config + Longhorn per-pod storage, and building a SQLite-to-PostgreSQL migration tool. ...

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 A routine cluster health check surfaced seven simultaneous issues. Most were transient — Longhorn self-healed its replica fault, Prometheus recovered behind it, a stale manually-created Job was deleted in one command, and a liveness probe blip fixed itself. The real work was dnd-backend, which had been in CrashLoopBackOff and turned out to contain three separate bugs layered on top of each other. The AI identified all three during a single debugging session, authored the fixes across three PRs, and the service came up 1/1 Running with all 18 database tables created on the first boot after the final merge. ...

TL;DR I deployed a private, self-hosted ChatGPT alternative on the homelab k3s cluster. Open WebUI provides a polished chat interface. LiteLLM acts as a proxy that translates the OpenAI API into AWS Bedrock’s Converse API. Four models are available: Claude Sonnet 4, Claude Haiku 4.5, Amazon Nova Micro, and Amazon Nova Lite. Authentication is handled by the existing OAuth2 Proxy – no additional SSO configuration needed. The whole stack runs in three pods consuming under 500MB of RAM, and the only ongoing cost is per-request Bedrock pricing. No API keys from OpenAI or Anthropic required. ...

TL;DR I’m building a multiplayer D&D platform where an AI powered by AWS Bedrock Claude runs the game. Players connect via a Next.js web app or Discord. A 5-tier lore context system gives the AI persistent memory across sessions. A background world simulation engine tracks NPC positions, inventory, faction standings, and in-game time so the AI can focus on storytelling instead of bookkeeping. The foundation is fully deployed on my home k3s cluster. The current work is turning a working tech demo into a game people actually want to sit down and play. ...

Context: This is the live docs/ai-lessons.md from the home k3s cluster repository, referenced extensively across posts on this blog — starting with AI Memory System and GitHub Copilot Setup Guide. Every entry exists because its absence caused a production incident. Personal identifiers and internal domains have been replaced with generic placeholders. Updated: 2026-03-03 Rules discovered through production breakage. Each entry prevents recurrence of a specific failure. Update this file whenever a new non-obvious failure pattern is discovered. ...

TL;DR Grafana went completely dark for about 26 hours on my home k3s cluster. Two things broke simultaneously: Loki entered CrashLoopBackOff, and Prometheus silently stopped ingesting metrics — its pods showed as healthy and 2/2 Running the whole time. The actual cause was Longhorn’s auto-balancer migrating replicas onto a freshly-added cluster node (k3s-agent-4) that had unstable storage during its first 48 hours. The replica I/O errors propagated directly into the workloads, corrupting mid-write files: a Prometheus WAL segment and a Loki TSDB index file. Both required offline surgery via a busybox pod to delete the corrupted files before the services could recover. ...

TL;DR I run Wiki.js on k3s as the cluster’s internal knowledge base. It is not a place I write documentation — it is a place the AI writes documentation after completing work. When Claude finishes deploying a service, debugging an incident, or refactoring infrastructure, it commits the results to the wiki with architecture diagrams, decision rationale, and operational notes. I am the primary reader. When I want to understand how something works, or why a specific decision was made three weeks ago, I go to the wiki instead of digging through git history or re-reading code. ...