Kubernetes

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 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 Over the past two months, I have made a series of stability improvements to my k3s homelab cluster. The biggest wins: migrating from AWS ECR to self-hosted Harbor (eliminating 12-hour token expiry), fixing recurring Grafana crashes caused by SQLite corruption on Longhorn, recovering pve4 after a failed LXC experiment, hardening NetworkPolicies to close gaps in pod-to-host traffic rules, and patching multiple CVEs across the media stack. The cluster now runs 7/7 nodes on k3s v1.34.4, all services monitored, all images pulled from Harbor with static credentials that never expire. ...

TL;DR I am deploying Authentik as a centralized identity provider for my k3s cluster. It replaces the current OAuth2 Proxy setup with proper SSO, federates Google as a social login source, and introduces group-based RBAC (admins, writers, readers) across all services. The migration is phased – public services first via Traefik forwardAuth, then internal services via native OIDC, then proxy-protected apps that have no OIDC support. OAuth2 Proxy stays in git for instant rollback. This post covers the architecture, the user model, the edge security design, and the gotchas I expect to hit. ...

TL;DR JF_hw_stress is a headless transcoding stress tester that answers one question: how many concurrent transcode streams can your GPU actually handle before quality degrades? It runs escalating FFmpeg transcodes against real media files using VAAPI hardware acceleration, measures FPS ratios, and outputs a JSON report. I run it as a Kubernetes Job on the same k3s cluster from Cluster Genesis, scheduled exclusively on the GPU node (Intel UHD 630). The job auto-deletes after 10 minutes so it does not accumulate stale pods. ...

TL;DR Multi-user AI chat with privacy guarantees, dual model providers (Anthropic direct API + AWS Bedrock via LiteLLM), and per-user cost tracking via Prometheus and Grafana. The admin cannot read other users’ conversations. Three family members authenticate via Google OAuth, each getting isolated chat sessions. Anthropic serves as the primary model provider with lower latency, and Bedrock via LiteLLM acts as a fallback. Per-user spend is tracked through LiteLLM’s Prometheus metrics without any surveillance of conversation content. This is a follow-up to the OpenClaw on k3s setup post. ...

TL;DR I spent time evaluating Linkerd — the CNCF graduated service mesh — for my homelab k3s cluster. The conclusion: it’s an impressive piece of engineering with genuinely useful features like automatic mTLS, post-quantum cryptography, and per-service observability. But for a cluster with ~20 workloads and a single operator, the operational overhead outweighs the benefits today. I’ve written a complete deployment plan so I can adopt it quickly when the cluster grows to the point where it makes sense. ...

TL;DR I replaced Open WebUI with OpenClaw – a lighter, WebSocket-based AI assistant gateway that installs from npm, supports multiple chat channels (web, Telegram, Discord, WhatsApp), and deploys on k3s as a single Deployment with a custom Docker image. The primary model provider is Anthropic’s direct API (Claude Sonnet 4.5), with LiteLLM/Bedrock as a fallback. The biggest deployment lesson: OpenClaw binds to loopback by default, which makes it invisible to Kubernetes Services and health probes. The fix is --bind lan, which requires a gateway token for authentication. ...