Hyperion – Deploy, manage and debug Helm apps across Multiple Clusters

Hacker News

Hi Everyone, Prashant here from Devtron. With the community’s help and feedback, we built Hyperion - An Opensource tool to observe, deploy, manage & debug Helm Application on Multiple Clusters. We developed Hyperion as a module that comes with Devtron preinstalled or it can be separately installed. While there are pre-existing tools like Kubernetes dashboard, Lens, Octant; Hyperion is focused on helm charts. Some of the features which sets it apart are: - Deploy, manage, observe and Debug Helm charts across multiple clusters. A. Install, Manage Helm charts, Applications across multiple Kubernetes clusters (hosted on multiple cloud/on-prem) from a single Hyperion setup. - Visualize resources deployed via helm charts contextually in a slick UI for easier monitoring and debugging. A. Hyperion groups your Kubernetes objects deployed via Helm charts and display them in a slick UI, for easier monitoring or debugging. Access pod logs and resource manifests right from the Hyperion UI and even edit them! - Centralized fine grained Access Management across multiple cluster. A. Control and give customizable view-only, edit access to users at Project, Environment and Application levels - View and Edit Kubernetes manifests, grep logs right from the Hyperion dashboard. A. View and Edit all the Kubernetes resources right from the Hyperion dashboard We have many more features planned for it in coming weeks. Here are some more links that might be helpful: Documentation: https://docs.devtron.ai/hyperion/hyperion Devtron Website: https://devtron.ai/ Please try it for yourself, hope it will be useful for you !! :-)

Visit product View on Hacker NewsMar 17, 2022

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

74%74% 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: para · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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: apps, visualize, users · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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