DevOps & SRE notes
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Больше📈 Аналитический обзор Telegram-канала DevOps & SRE notes
Канал DevOps & SRE notes (@devops_sre_notes) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 13 264 подписчиков, занимая 9 302 место в категории Технологии и приложения и 2 759 место в регионе США.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 13 264 подписчиков.
Согласно последним данным от 26 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 248, а за последние 24 часа — 5, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 17.01%. В первые 24 часа после публикации контент обычно набирает 4.41% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 256 просмотров. В течение первых суток публикация набирает 585 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как kubernete, cluster, author, engineering, monitoring.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Helpful articles and tools for DevOps&SRE
WhatsApp: https://whatsapp.com/channel/0029Vb79nmmHVvTUnc4tfp2F
For paid consultation (RU/EN), contact: @tutunak
All ways to support https://telegra.ph/How-support-the-channel-02-19”
Благодаря высокой частоте обновлений (последние данные получены 27 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
13 264
Подписчики
+524 часа
+237 дней
+24830 день
Архив постов
13 264
The new DNSTracking feature in the Red Hat network observability operator 1.11, which now captures DNS query names directly via eBPF without additional configuration.
https://developers.redhat.com/articles/2026/04/09/how-dns-name-tracking-enhances-network-observability#
13 264
When you have a special math to calculate your uptime, you always have 100%.
13 264
Repost from N/a
kagent runs your agents where your workloads already live — on Kubernetes. Deploy, observe, and govern AI agents with the tools your platform team already trusts. Open source. Production grade. Built by the founders of Istio.
https://github.com/kagent-dev/kagent
13 264
I found a good example of why autoscaling based only on CPU utilization can cause an outage.
About a week ago, Twingate had an incident that affected us as a client. They've published a postmortem, and it's a good example of why CPU isn't a good metric to rely on when autoscaling your services.
The incident was triggered by elevated network latency affecting communication paths used by the Authorization service. As requests took longer to complete, individual service instances were able to process fewer requests than normal. This reduction in throughput exposed a limitation in our auto-scaling configuration, which primarily relied on CPU utilization to determine service capacity requirements.So, from the CPU utilization perspective, everything was OK, but the number of processed requests decreased. https://status.twingate.com/incidents/49qvqk7swjpq
13 264
Networking within container orchestration can often seem like a black box to developers. This explanation aims to demystify Kubernetes CNI providers and how they manage connectivity.
https://medium.com/@csinclair11/demystifying-kubernetes-cni-providers-5ed79569c797
13 264
The article details how to implement production-grade distributed tracing for complex multi-agent AI workflows using OpenTelemetry.
https://developers.redhat.com/articles/2026/04/06/distributed-tracing-agentic-workflows-opentelemetry#
13 264
Many organizations are looking for more efficient logging solutions than the traditional stack. This comparison highlights a modern alternative to ELK that aims to reduce complexity and resource usage.
https://osuite.io/articles/modern-alternative-to-elk
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This informative post details a clever method for securing Grafana dashboards when using Google Cloud Identity-Aware Proxy. You will learn how to seamlessly integrate these two powerful technologies for enhanced access control.
https://www.vidbregar.com/blog/grafana-gcp-iap
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Managing expenses in the cloud requires a strategic approach beyond just looking at bills. A senior engineer shares valuable insight into optimizing costs effectively in this detailed read.
https://medium.com/@razkevich8/cloud-cost-optimization-a-senior-engineers-guide-d49ed4606de1
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A popular & widely deployed Open Source Container Native Storage platform for Stateful Persistent Applications on Kubernetes.
https://github.com/openebs/openebs
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The observability market is shifting from volume-based data ingestion to a value-driven model due to the unsustainable costs of scaling cloud-native and AI workloads. Driven by innovations like Chronosphere’s "Logs 2.0" and its subsequent acquisition by Palo Alto Networks, the industry is prioritizing "signal discipline"—retaining only actionable telemetry—and integrating observability directly into broader AI and security platforms.
https://siliconangle.com/2026/02/05/observability-cost-ai-scale-chronosphere-opensourcesummit/
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Claude Code gave me three "tickets" for a free week. You can grab them using this link: https://claude.ai/referral/NXtyf-cgbQ13 264
Uber engineered an automated approach to migrate its massive Java monorepo (over 600,000 tests, 15 million lines of code) from the deprecated JUnit 4 to JUnit 5. Facing challenges like the lack of native JUnit 5 support in their Bazel build system and custom test configurations, they successfully migrated over 75,000 test classes and 1.25 million lines of code in just four months without disrupting developer workflows.
https://www.uber.com/us/en/blog/junit-migration/
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The article explains that while Kubernetes excels at scheduling and isolating workloads, it lacks the context to secure Large Language Models (LLMs), which process untrusted natural language inputs. Highlighting four key risks from the OWASP Top 10 for LLMs, the author argues that security controls shouldn't live within the model runtime (like Ollama). Instead, organizations need a dedicated, LLM-aware policy layer (such as LiteLLM, Kong AI Gateway, or Portkey) in front of the model to enforce validation, filtering, and authorization.
https://www.cncf.io/blog/2026/03/30/llms-on-kubernetes-part-1-understanding-the-threat-model/
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Bulk port forwarding Kubernetes services for local development.
https://github.com/txn2/kubefwd
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The article features an interview with Landon Clipp, who built a multi-tenant GPU-based CaaS platform.
- Bypassing the NVIDIA GPU Operator
- Why gVisor Fails for GPUs
- VM Boot Delays
- Firmware and Memory Security
- Ideal Workload
https://kube.fm/gpu-containers-as-a-service-landon
