MiniDevOps – a toolbox that runs in Alpine

Hacker News

This is a small docker environment based on Alpine that contains several devops tools like helm, terraform, kubectl and Kind (kubernetes-in-docker). I am using it for quick experiments with k8s on my local Docker instance. One can use Kind from within Alpine to run k8s in the local Docker instance. There is also a script available that installs a k8s with NGINX IngressController enabled. Docker images are available at: https://hub.docker.com/repository/docker/brakmic/devops Maybe it can be of some use to others. Regards,

Visit product View on Hacker NewsApr 13, 2023

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

66%66% 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.

Hacker NewsStrong engagement from HN community · Strong signals: nginx, ide · Missing: https docs, excited, just released

57%57% 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 · Missing: plus, platform, intuitive

44%44% 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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

28%28% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

15%15% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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