Applications

TL;DR I built a complete swing trading research platform from an empty scaffold to 13,674 lines of Python in a single day. Five phases: data layer and backtesting, fundamentals and sentiment, portfolio construction, ML signals and Monte Carlo, then paper trading with a terminal dashboard. 199 tests across 48 test files. The platform fetches from Yahoo Finance, FRED, SEC EDGAR, and news APIs, runs technical and fundamental analysis, backtests strategies with walk-forward validation, and presents recommendations through a Rich terminal dashboard with human-in-the-loop approval. No cloud dependencies, no subscriptions, no vendor lock-in. ...

TL;DR Jellyfin dies mid-stream when a Kubernetes pod restarts because all transcode state is in-memory. I forked it, added a Redis-backed ITranscodeSessionStore, and wired in atomic lease-based pod takeover. The fork is at github.com/ZoltyMat/jellyfin-ha, and I also published a repo-level diff document at docs/FORK-DIFF.md showing exactly what changed versus upstream Jellyfin. Single-instance deployments need zero config changes because it falls back to a no-op store transparently. The Problem Jellyfin is great. It’s also built with the assumption that exactly one server instance is running at a time. Transcode state — which pods are running FFmpeg, what segments have been written, who owns a given play session — lives entirely in memory. When the process dies, that state is gone. ...

TL;DR Over the last six posts, I’ve documented converting Jellyfin from a single-process media server into a two-replica, PostgreSQL-backed, sticky-session-coordinated deployment on k3s. Five of six failover tests passed cleanly. The key result: zero-downtime failover — killing a pod doesn’t take down the service. Users on the surviving replica see no interruption; displaced users reconnect in seconds. Node maintenance no longer kills Jellyfin for the household. But this project isn’t finished, and some problems can’t be solved with this architecture. This final post is an honest inventory of what’s still broken, what was deferred, and what the path forward looks like. ...

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 Jellyfin stores everything in SQLite. Metadata, users, activity logs, authentication — all of it lives in .db files that lock under concurrent access. To run multiple replicas, we need a real network-accessible database. This post covers Phase 1 of the HA conversion: forking Jellyfin, designing a pluggable database provider interface, implementing it for PostgreSQL with Npgsql, generating EF Core migrations, writing integration tests with Testcontainers, and building a custom Docker image. ...

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 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. ...

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. ...