DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
Show more📈 Analytical overview of Telegram channel DevOps&SRE Library
Channel DevOps&SRE Library (@devopslibrary) in the English language segment is an active participant. Currently, the community unites 19 395 subscribers, ranking 6 952 in the Technologies & Applications category and 34 902 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 19 395 subscribers.
According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 154 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 15.22%. Within the first 24 hours after publication, content typically collects 7.12% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 949 views. Within the first day, a publication typically gains 1 380 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as kubernete, cluster, infrastructure, storage, configuration.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Thanks to the high frequency of updates (latest data received on 11 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
Oh My Zsh is still getting recommended a lot. The main problem with Oh My Zsh is that it adds a lot of unnecessary bloat that affects shell startup time. Since OMZ is written in shell scripts, every time you open a new terminal tab, it has to interpret all those scripts. Most likely, you don't need OMZ at all.https://rushter.com/blog/zsh-shell
kaniko is a tool to build container images from a Dockerfile, inside a container or Kubernetes cluster. This is a supported replacement of the original GoogleContainerTools/kaniko repository, which was archived in June of 2025.https://github.com/chainguard-forks/kaniko
Kubernetes Operator to automate Helm, DaemonSet, StatefulSet & Deployment updateshttps://github.com/keel-hq/keel
Write Kubernetes manifests in TypeScript.https://github.com/konfjs/k8skonf
Unit test for helm chart in YAML to keep your chart consistent and robust!https://github.com/helm-unittest/helm-unittest
This repo aims to provide a comprehensive test suite that goes far beyond the conformance tests, to help users better understand the real-world behavior of implementations.https://github.com/howardjohn/gateway-api-bench
Think building a SaaS platform is out of reach? With Kamaji, GitOps, and Kubernetes, it’s simpler — and more powerful — than it seems.https://itnext.io/build-your-own-saas-cloud-platform-with-kamaji-and-gitops-aeec1b5f17fd
Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting.https://github.com/Arize-ai/phoenix
A set of Grafana dashboards and Prometheus alerts for Kubernetes Autoscaling using the metrics from Kube-state-metrics, Karpenter, and Cluster-autoscaler.https://github.com/adinhodovic/kubernetes-autoscaling-mixin
JuiceFS is a high-performance POSIX file system released under Apache License 2.0, particularly designed for the cloud-native environment. The data, stored via JuiceFS, will be persisted in Object Storage (e.g. Amazon S3), and the corresponding metadata can be persisted in various compatible database engines such as Redis, MySQL, and TiKV based on the scenarios and requirements. With JuiceFS, massive cloud storage can be directly connected to big data, machine learning, artificial intelligence, and various application platforms in production environments. Without modifying code, the massive cloud storage can be used as efficiently as local storage.https://github.com/juicedata/juicefs
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