Grogg – Manage Multiple Kubernetes Clusters in VSCode

Hacker News

Hi HN, I’m Michael and I’m here to introduce you to Grogg. I got frustrated over the years with the endless chain of kubectl commands and disjointed dashboards, so I built Grogg, a VSCode extension that delivers a modern, slick interface for Kubernetes management. Check Grogg at https://grogg.app It’s designed for tasks like quick scaling, multi-log viewing, and handling multiple clusters, all from your editor. What Grogg Does: - Multi cluster, manage multiple clusters and namespaces without leaving your editor. - Built as a vscode extension, Stay in your editor. - Quick actions, scale deployments, view logs, and delete pods without memorizing long commands. - Secure integration, connects directly to your Kubernetes API with support for air-gapped environments. It runs on macOS, Linux, and Windows (both arm64 and x64) with zero installation required on your clusters. I’m offering a 14-day free trial (no credit card required) for anyone interested in giving it a spin. I’d love to hear your feedback and suggestions on how to make Grogg even better. You can also earn with Grogg affiliate program at https://grogg.app/affiliates Planning to also distribute as a standalone app later on if context switching does not bother you. Thanks for checking it out!

Visit product View on Hacker NewsFeb 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, context · Missing: agents, agent, cursor

79%79% 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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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