Operations

TL;DR My Mac Studio M3 Ultra runs Ollama with 70B+ models but isn’t a k3s node. I needed it to show up in Grafana next to the cluster workloads. The solution: node_exporter for system metrics, a Go reverse proxy for per-model inference metrics, a custom Python exporter for model inventory and VRAM tracking, and Grafana Alloy for shipping logs to Loki. All four services managed by Ansible, all metrics scraped by the cluster’s Prometheus. ...

TL;DR OpenClaw, my self-hosted AI trading agent, was running in a fat container with 46 Critical CVEs, no network restrictions, and no automated vulnerability scanning. I fixed all three: multi-stage Dockerfile dropped the CVE count to single digits, default-deny NetworkPolicies locked down traffic, and a daily CronJob triages Trivy scan results via local LLM and posts a digest to Slack. Total cost of the automated triage: $0/day. The problem with AI agent containers AI agent containers are uniquely bad from a security perspective. They need: ...

TL;DR I built an “Ops Dream Worker” — a Kubernetes CronJob that runs at 3 AM, inspects the cluster, identifies improvements, and files GitHub issues with specific fixes. It runs entirely on local models (Mac Studio M3 Ultra), costs $0 per run, and went through 240 A/B test iterations to optimize the prompts. The anti-hallucination patterns were harder to get right than the analysis itself. The idea I have a k3s cluster with ~40 deployed services. I maintain it solo. There’s always something that could be better — a deployment missing resource limits, a CronJob that’s been failing silently, an ingress without SSO protection, a container image with known CVEs. These improvements pile up because I’m usually focused on building features, not auditing infrastructure. ...

TL;DR I deployed Whisper (speech-to-text), Piper (text-to-speech), and openWakeWord (wake word detection) as Kubernetes workloads on my k3s cluster. Home Assistant connects to them over the Wyoming protocol for fully local voice pipelines. Total resource cost: ~1 CPU core and 1.75GB RAM. Total cloud cost: $0. Why run voice services in Kubernetes Home Assistant’s voice pipeline needs three things: something to listen for a wake word, something to transcribe speech, and something to speak back. The usual approach is running these on the same box as HA, or on a dedicated Pi. Both work fine until you want the services to survive node failures, be independently upgradeable, or share resources with other workloads. ...

TL;DR Goss tests the image, not the running container. Use dgoss run --env-file .env to inject your environment and then assert in three layers: does the var exist, is it non-empty, and does it actually authenticate? That layering tells you exactly where the chain breaks instead of just “MySQL connection failed.” The problem I saw someone in a DevOps forum wrestling with this exact thing. They were manually debugging why their .env values weren’t translating properly into MySQL credentials, and had turned to Goss to automate sanity checks. Two questions tripped them up: ...

TL;DR Someone leaked the Claude Code source on GitHub. OpenClaw, the open-source AI coding agent with 346k stars, solves the same problem with a completely different architecture. I compared both codebases at the structural level. The verdict: these are independent implementations that converge on the same tool-use patterns because that is what the problem demands — not because one copied the other. Background In late March 2026, a repository appeared on GitHub containing what appears to be the full source code for Anthropic’s Claude Code — the terminal-based AI coding agent I wrote about switching to last month. The repo has two commits (“init” and “add readme”), 1,932 files, and weighs 43MB. ...

TL;DR Busy week. Three CVE patches shipped on the same day. OpenClaw stabilized with OpenRouter support and a cost exporter. The Wiki.js fork with Mermaid 11 went live after clearing a Trivy scan. PiKey — a Raspberry Pi that pretends to be a Bluetooth keyboard — shipped as a side project. A self-hosted GitHub Actions cache server cut CI restore times from minutes to seconds. And a Reddit comment defending “I use Claude to manage my infrastructure” turned into five new blog posts and a documentation sprint. ...

TL;DR Wiki.js 2.x ships Mermaid 8.8.2, released in 2020. Mermaid 11 — the current stable release — adds timeline diagrams, improved gitGraph, better theming, and fixes years of rendering bugs. The upstream project defers this upgrade to Wiki.js v3, which has no release date. The PR queue has sat idle for over a year. I forked requarks/wiki at tag v2.5.312, upgraded Mermaid in-place, patched 8 CVEs including one Critical SAML authentication bypass, reduced the vulnerability count from 8 Critical / 48 High to 3 Critical / 42 High, and deployed it to the cluster. The fork stays close to upstream — Vue 2 and Webpack 4 are untouched. Only the Mermaid surface and security dependencies are modified. ...

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 The Linux kernel is 36 million lines of C. Rust has been slowly entering the kernel since Linux 6.1, but progress is measured in individual drivers and abstractions – a few thousand lines per release cycle. What if you skipped the incremental approach and asked Claude to rewrite major subsystems wholesale? I spent a weekend scoping this as a serious project plan: breaking the kernel into translatable units, estimating token costs, identifying the hard walls, and testing Claude’s ability to produce correct Rust translations of real kernel C. The conclusion: Claude can translate isolated, well-bounded kernel modules with surprising accuracy. It cannot translate the kernel. The difference between those two statements is the entire lesson. ...