Projectsveltos: managing Kubernetes add-ons in tens of clusters

Hacker News

Sveltos is a lightweight application designed to manage Kubernetes add-ons hundreds of clusters. Features List includes: - Kubernetes add-on distribution across multiple clusters; - configuration drift detection: when sveltos detects a configuration drift, it re-syncs the cluster state back to the state described in the management cluster; - Templates instantiated reading values from management cluster; - Dry run to preview effect of a change; - Kubernetes cluster classification and automatic label management based on cluster runtime states; - Snapshot and Rollback. I would love to get feedbacks (positive or negative both will help me improve). If you try it out and see any issue or would like to suggest a new feature, I would love to hear.

Visit product View on Hacker NewsJan 16, 2023

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

71%71% 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

62%62% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

32%32% 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

16%16% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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