Self-Hosted

TL;DR You have a 24GB VRAM GPU. You want a private, self-hosted AI assistant that rivals ChatGPT – no subscriptions, no data leaving your machine. This guide walks you through setting up Ollama (local model runtime) and OpenClaw (AI gateway with a web UI) on Windows using Docker Desktop. But the real value here is the model recommendations. I ran 5,475 evaluations across 21 prompt variants and 6 models on real trading data. The results contradicted almost everything the community recommends. Finance-tuned models performed worse than a coin flip. Chain-of-thought reasoning models were anti-patterns. The winners were general-purpose MoE (Mixture-of-Experts) models that nobody talks about for specialized tasks. ...

TL;DR AWS Lens is an open-source Electron desktop app for managing AWS resources — EC2, S3, Lambda, IAM, Cost Explorer, and more. I wanted it accessible from my browser without running a desktop app. I adapted it to run as a containerized Express server on k3s, fixed a class of runtime crashes from the Electron-to-web adapter, hardened it against three security issues, and deployed it behind Traefik and Let’s Encrypt. The changes are open-source in BoraKostem/AWS-Lens#21. ...

TL;DR Someone asked me on Reddit for a comprehensive guide to securing a public-facing Jellyfin instance, so here it is. The short answer I gave was: fail2ban, automate patching, implement OAuth, and download an IP block list. This post expands all four into actionable steps and adds a fifth option — IP whitelisting with a DDNS-aware Python cron job — plus the honest answer that a VPN eliminates most of this complexity entirely. ...

TL;DR If you run self-hosted GitHub Actions runners, every actions/cache step is round-tripping to GitHub’s cloud storage. For a homelab cluster with local runners, that means cache restores travel from GitHub’s CDN to your runner, through your ISP, and back – even though the runner is 10 feet from your NAS. I deployed falcondev-oss/github-actions-cache-server as a Kubernetes deployment, pointed it at NFS storage on my NAS, set one environment variable on my runners, and flushed all the GitHub-hosted caches. Zero workflow changes required. ...

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. ...