Applications

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. ...

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. ...

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. ...

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: ...