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punq – open-source Kubernetes manager with team collaboration

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punq – open-source Kubernetes manager with team collaboration

At mogenius we’re working with multiple Kubernetes clusters across clouds with shared responsibilities on the team. For managing workloads on the clusters we’ve been trying out several tools, but something was missing. With probably the most popular tools, Lens and K9s, the free options only allowed local access which required each team member to set up all clusters over and over again. We were looking for something cloud-based, where we set up the clusters once and the whole team can use them. Plus, it should be lightweight and fast (we love that in K9s). At some point during research we realised that we had the foundation in our hands with the technology of our mogenius platform. So we decided to built the tool ourselves and today we’re introducing punq. punq is a lightweight, open-source Kubernetes manager with built-in team collaboration. It is designed for DevOps teams to manage workloads across multiple clusters without the need to share kubeconfigs among team members. The most important features in version 1 include: Web app and CLI, adding clusters via kubeconfig, YAML editor, Terminal access to pods, and RBAC. We wrote punq in Go and used to Angular for the web application. It can be hosted as a single container on a Kubernetes cluster by installing it via the punq CLI. By default, the user interface shows the cluster where punq is installed. You can add more clusters by uploading your kubeconfig; the data will be extracted once and stored as secrets on your Kubernetes cluster. After adding a cluster, each team member on your punq instance can manage it without access to the kubeconfig itself. This way, you're making it easy for the team to collaborate across clusters. On top of that, we've put a lot of effort into making punq very fast to keep you productive :) Check out punq on Github, we’re excited to hear your feedback! https://punq.dev

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, single, open · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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