Nautik – A native Kubernetes client written in SwiftUI and Rust

Hacker News

Nautik is an accessible, concurrent Kubernetes client that is native to Apple devices. Its features include: - Multi-cluster mode (viewing the resources of multiple clusters at once). - Viewing built-in resources and custom resources in gorgeous native detail views. - Automatic resource watching with realtime updates. - Watching and searching logs of multiple pods and containers in one combined stream. - Attaching to running pods with the integrated terminal. - Attaching shells (exec) to running pods with the integrated terminal. - Editing resources as YAML source with the integrated, customizable text editor. - Deleting resources. - Cordoning and uncordoning nodes. - Evicting pods. - Restarting deployments, daemonsets, statefulsets and replicasets. - Accessing clusters stored on a local kubeconfig file. - Accessing clusters stored on the local system keychain. - Optionally syncing stored clusters between devices via iCloud Keychain. - Related events overview on resource detail views. - Related pods overview on workload detail views. - Handy overview graphs for workloads and compute resource statuses. - Slideover, split view, Stage Manager and multi-window support. - Performant, truly native implementation in Swift and Rust. - Multi-tab support (on macOS). - Multiple app icons. - Handoff support. - Integration with Siri and Shortcuts. - Integration with Google Cloud Platform. - Integration with Amazon Web Services. - Integration with Microsoft Azure. - Integration with DigitalOcean. - Optional list view caching to speed up loading. - Optional support for gzipped Kubernetes API requests. - Optional base64-decoding of secrets on the secrets UI. - Customizable list sorting.

Visit product View on Hacker NewsApr 16, 2024

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

81%81% 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

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

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% 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

18%18% 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.

Correct prediction on native model

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