Why Now pve005 was an i5-7500 with 16GB of RAM. It ran the original Jellyfin LXC with 1.1TB of media on a local ZFS pool. Once I rebuilt the lab around a 3-node Ryzen 9 cluster with Ceph storage, pve005 became dead weight. The media was migrated to jellyfin01 on the new cluster months ago. The old LXC was stopped. pve005 was still drawing power, still in the Ceph quorum, and still showing up in every Ansible run. ...
The Plan Move pi-burg, my Raspberry Pi 3 running restic as an offsite backup target, from my house to my mom’s. Simple, right? It already had Tailscale, so once it was on her wifi it would just appear on the tailnet from wherever. Total estimated time: 15 minutes. Total actual time: ~6 hours. The Stack Raspberry Pi 3 running Raspberry Pi OS Lite 8TB USB drive with an existing restic repository Tailscale for tailnet connectivity (no static IPs, no port forwarding) A long drive to mom’s house What Was Supposed to Happen Configure wifi on the Pi for mom’s network before leaving home Drive to mom’s Plug in power, let Tailscale come up Profit What Actually Happened Chapter 1: Cloud-init Is a Liar The Pi was already provisioned, so I edited /boot/firmware/network-config from my Mac to add mom’s wifi. I even recomputed the WPA PSK hash because the stored one was for a different SSID. Saved, ejected, booted. ...
The Goal I wanted a persistent AI memory layer — something that stores context across conversations and tools, accessible from Claude Desktop, Claude Code, and eventually other MCP clients. The official mem0 platform exists, but I wanted to self-host it on my Proxmox cluster for control and privacy. The Stack The deployment runs on mem01 (LXC 3003, pve02) with three Docker containers: Ollama — LLM inference for embeddings (bge-m3) and chat (qwen3:8b) OpenMemory — the MCP server itself, using SQLite for vectors and metadata Open WebUI — optional web interface for testing I started with mem0-mcp-selfhosted (Neo4j + Qdrant + Python SDK) but it crashed repeatedly and had a painful dependency chain. OpenMemory — SQLite for everything, Node.js SDK — just worked. ...
The Goal The kids want Minecraft servers. Not one — six. Survival, Creative, Adventure, Minigames, Hardcore, and a modded Fabric server. I have three Ryzen 9 5900X Proxmox nodes with 64-128GB RAM each, so hardware isn’t the problem. The question was how to deploy and manage them without it becoming a second job. The Stack After researching management panels (Pterodactyl, Crafty, AMP, MCSManager), I landed on the simplest approach: Docker Compose with the itzg/minecraft-server image. It handles Paper builds, EULA acceptance, Aikar’s JVM flags, RCON, and graceful shutdown — all via environment variables. No panel needed. ...
The Problem My homelab has multiple VLANs, a Tailscale overlay network, and no ad blocking. DNS was handled entirely by the EdgeRouter X-SFP forwarding to Cloudflare. I wanted: Network-wide ad blocking without per-device configuration Conditional DNS forwarding so LAN clients can resolve Tailscale hostnames Non-Tailscale devices (Rokus, smart TVs) able to reach Tailscale services like Jellyfin Client names on the DNS dashboard instead of raw IPs The Architecture 1 2 3 4 5 6 7 8 9 10 11 12 13 LAN Clients (10.150.10.0/24) │ ├─ DNS ──→ dns01 (AdGuard Home, 10.150.60.11) │ ├─ Upstream: Cloudflare DoH, Google DoH │ ├─ Conditional: *.<tailnet>.ts.net → 100.100.100.100 │ └─ Reverse DNS: PTR queries → EdgeRouter (10.150.10.1) │ ├─ DHCP ──→ EdgeRouter X-SFP (dnsmasq) │ ├─ Primary DNS: 10.150.60.11 (AdGuard) │ ├─ Secondary DNS: 10.150.10.1 (router fallback) │ └─ Search domain: <tailnet>.ts.net │ └─ 100.x.x.x traffic ──→ static route → dns01 → MASQUERADE → tailscale0 dns01 is an LXC container on pve01 running AdGuard Home and acting as a Tailscale subnet router advertising all four LAN subnets. ...
The Problem My homelab had grown organically into a 9-node Proxmox 8 cluster on a flat 10.150.10.0/24 network. Six i5/i7 machines (pve001-006) with 16GB RAM each, plus three Ryzen 9 5900X machines (pve007-009) with 64-128GB RAM and dedicated GPUs. The old nodes were underpowered, the network was a mess, and managing it all was getting painful. It was time to consolidate. The Plan Rebuild the three Ryzen machines as a proper 3-node cluster with: ...
The Setup I have a 3-node Proxmox 9 cluster, each with a 4TB SSD and 2TB HDD dedicated to Ceph. The NVMe drives stay local for fast VM storage. The question was: how do I use the SSDs for performance-sensitive workloads and the HDDs for bulk storage? 1 2 3 4 Per Node: 2TB NVMe → nvme-local (LVM-thin, not Ceph) 4TB SSD → Ceph OSD (fast tier) 2TB HDD → Ceph OSD (bulk tier) CRUSH Rules: Telling Ceph Where to Put Data Ceph already knows which OSDs are SSDs and which are HDDs — it assigns device classes automatically. But by default, it’ll spread data across all OSDs regardless of type. CRUSH rules let you pin pools to specific device classes. ...
The Problem Job hunting is repetitive. Every application means re-reading your own resume, figuring out how to position your experience for this specific role, writing yet another cover letter, and hoping a recruiter connects the dots. Multiply that by dozens of applications and it gets exhausting fast. I wanted a system where I could drop in a job posting and get back an honest fit assessment, a tailored resume, and a cover letter without re-explaining my entire background every time. ...
I migrated Jellyfin from an AMD RX 570 to the Intel HD Graphics 630 integrated into the host CPU. The result: 4 simultaneous HEVC transcodes at ~10% CPU each, with a ~105-135W power reduction. Why Switch The AMD RX 570 was overkill for transcoding. It pulled 50-70W at idle and 120-150W under load — running 24/7 in a media server that mostly handles a few streams at a time. The Intel iGPU built into the i5-7500 has Quick Sync Video, which is purpose-built for exactly this workload. ...
The Problem My two boys each had their own gaming PC. Two towers, two sets of peripherals, two machines to maintain, update, and troubleshoot. When I started consolidating everything into a Proxmox homelab, it seemed wasteful to keep two standalone gaming rigs around. The plan: one host, two GPUs, two Windows VMs. Each boy gets a dedicated GPU, their own monitor, keyboard, and mouse. They can game simultaneously without knowing they’re on virtual machines. ...