Op

Open-source tool for deploying applications to K8s/OpenShift

Hacker News

Open-source tool for deploying applications to K8s/OpenShift

Hey HN! We are back with new features in the open-source project, nxs-universal-chart. It's a Helm chart for deploying applications to Kubernetes/OpenShift. Instead of maintaining dozens of YAML files for each microservice, you define the configuration in a single values.yaml, and the chart renders the rest. In the new release, we expanded the chart ecosystem, reduced unnecessary synchronization, improved template rendering robustness, and increased efficiency and resilience of the tool. New features: • Added 11 OCI-hosted subcharts • Added MCP server for values.yaml generation and Helm chart validation • Added support for projected volumes and ServiceAccount imagePullSecrets • Adjusted autoRolloutChecksums behavior • Added GitHub Actions CI/CD out of the box • Fixed handling of null entries and rendering issues We’re looking forward to your feedback, contributions and stars! Feel free to join our Telegram community to share ideas, reports, or ask questions directly to the maintainers: https://t.me/nxs_universal_chart_chat

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, new, single · 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% 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
31%31% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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