DevOps & SRE notes
前往频道在 Telegram
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
显示更多📈 Telegram 频道 DevOps & SRE notes 的分析概览
频道 DevOps & SRE notes (@devops_sre_notes) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 258 名订阅者，在 技术与应用 类别中位列第 9 374，并在 美国 地区排名第 2 784 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 13 258 名订阅者。
根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 254，过去 24 小时变化为 3，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 16.60%。内容发布后 24 小时内通常能获得 4.52% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 201 次浏览，首日通常累积 599 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 kubernete, cluster, author, engineering, monitoring 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“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”
凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
13 258
订阅者
+324 小时
+267 天
+25430 天
帖子存档
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
13 264
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
13 264
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
13 264
A popular & widely deployed Open Source Container Native Storage platform for Stateful Persistent Applications on Kubernetes.
https://github.com/openebs/openebs
13 264
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/
13 264
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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/
13 264
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/
13 264
Bulk port forwarding Kubernetes services for local development.
https://github.com/txn2/kubefwd
13 264
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
