CR

CRD Wizard – A GUI for Kubernetes Custom Resource Definitions

Hacker News

CRD Wizard – A GUI for Kubernetes Custom Resource Definitions

Hey HN, I’ve been working with Kubernetes for a while now, and one thing that has always been a friction point for me is dealing with Custom Resource Definitions (CRDs). We use them for everything—monitoring, cert-manager, custom controllers—but the tooling around them always felt a bit raw. Dealing with them usually meant running `kubectl get crds`, piping output to grep, or staring at 5,000-line YAML files just to figure out what fields were available in the schema. It got frustratingly worse when I started managing multiple clusters. I found myself constantly context-switching just to check if a CRD was installed or to diff versions between environments. It felt like I was spending more time memorizing `kubectl` flags than actually working. So, I started building *CRD Wizard* to scratch my own itch. It started as a simple TUI to view resources, but I realized I needed more visual context. Now it’s a full desktop app (Go backend + Next.js frontend) that auto-discovers your local kubeconfigs and gives you a unified interface to explore CRDs across all your clusters. Over the last few weekends, I’ve added a few features that I really wanted: 1. *Multi-Cluster Support:* It loads all your contexts automatically. You can switch between clusters instantly without touching your terminal. 2. *Documentation Generator:* I realized I often needed to share CRD specs with devs who don't have cluster access. I added a generator that turns any CRD (from your cluster or a Git URL) into a clean, searchable static HTML or Markdown page. 3. *AI Integration:* I hooked it up to local LLMs (Ollama) and Google Gemini. You can click a button to have the AI explain a complex schema or generate a sample manifest for you. I wrote it in Go because I wanted it to be snappy and easy to distribute as a single binary. It’s open source, and I’d love to hear what you think or what other pain points you run into with CRDs. Repo: https://github.com/pehlicd/crd-wizard

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, context, visual · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, llama, 000 · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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