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 19 859 subscribers, ranking 6 605 in the Technologies & Applications category and 33 088 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 19 859 subscribers.
According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -20 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 13.32%. Within the first 24 hours after publication, content typically collects 6.63% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 645 views. Within the first day, a publication typically gains 1 317 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- 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 04 November, 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.
How Slack trains engineers in incident response by ordering lunch together.https://slack.engineering/theres-no-such-thing-as-a-free-lunch
Embed an SQLite database in your PostgreSQL table. AKA multitenancy has been solved.https://github.com/frectonz/pglite-fusion
The open source Zapier alternative. Build workflow automation without spending time and money.https://github.com/automatisch/automatisch
Save or export your private and public Slack messages, threads, files, and users locally without admin privileges.https://github.com/rusq/slackdump
About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.https://www.honeycomb.io/blog/against-incident-severities-favor-incident-types
Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, erid: 2VtzqwCYrt7According to a survey done by W3Techs, as of October 2024, Cloudflare is used as an authoritative DNS provider by 14.5% of all websites. As an authoritative DNS provider, we are responsible for managing and serving all the DNS records for our clients’ domains. This means we have an enormous responsibility to provide the best service possible, starting at the data plane. As such, we are constantly investing in our infrastructure to ensure the reliability and performance of our systems.https://blog.cloudflare.com/migrating-billions-of-records-moving-our-active-dns-database-while-in-use
mise is a polyglot tool version manager. It replaces tools like asdf, nvm, pyenv, rbenv, etc. mise allows you to switch sets of env vars in different project directories. It can replace direnv. mise is a task runner that can replace make, or npm scripts.https://github.com/jdx/mise
Netavark is a rust based network stack for containers.https://github.com/containers/netavark
As generative AI evolves, we're beginning to see the transformative potential it is having across industries and our lives. And as large language models (LLMs) increase in size — current models are reaching hundreds of billions of parameters, and the most advanced ones are approaching 2 trillion — the need for computational power will only intensify. In fact, training these large models on modern accelerators already requires clusters that exceed 10,000 nodes. With support for 15,000-node clusters — the world’s largest — Google Kubernetes Engine (GKE) has the capacity to handle these demanding training workloads. Today, in anticipation of even larger models, we are introducing support for 65,000-node clusters. With support for up to 65,000 nodes, we believe GKE offers more than 10X larger scale than the other two largest public cloud providers.https://cloud.google.com/blog/products/containers-kubernetes/gke-65k-nodes-and-counting
BemiDB is a Postgres read replica optimized for analytics. It consists of a single binary that seamlessly connects to a Postgres database, replicates the data in a compressed columnar format, and allows you to run complex queries using its Postgres-compatible analytical query engine.https://github.com/BemiHQ/BemiDB
