DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
Mostrar más📈 Análisis del canal de Telegram DevOps&SRE Library
El canal DevOps&SRE Library (@devopslibrary) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 19 407 suscriptores, ocupando la posición 6 952 en la categoría Tecnologías y Aplicaciones y el puesto 34 858 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 19 407 suscriptores.
Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 162, y en las últimas 24 horas de 13, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 15.12%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.09% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 932 visualizaciones. En el primer día suele acumular 1 376 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como kubernete, cluster, infrastructure, storage, configuration.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
Traceway is a self-hosted observability platform that ingests OpenTelemetry traces and metrics, groups exceptions automatically, and gives you endpoint performance, distributed tracing, and alerts — all in a single binary. No OTel Collector or separate time-series database required.https://github.com/tracewayapp/traceway
As part of an ongoing series, the Developer Experience SIG interviews organizations about their real-world OpenTelemetry Collector deployments to share practical lessons with the broader community. This post features Adobe, a global software company whose observability team has built an OpenTelemetry-based telemetry pipeline designed for simplicity at massive scale, with thousands of collectors running per signal type across the company’s infrastructure.https://opentelemetry.io/blog/2026/devex-adobe
Exports helm release, chart, and version statistics in the prometheus format.https://github.com/sstarcher/helm-exporter
The open-source, datacenter-scale inference stack. Dynamo is the orchestration layer above inference engines — it doesn't replace SGLang, TensorRT-LLM, or vLLM, it turns them into a coordinated multi-node inference system. Disaggregated serving, intelligent routing, multi-tier KV caching, and automatic scaling work together to maximize throughput and minimize latency for LLM, reasoning, multimodal, and video generation workloads.https://github.com/ai-dynamo/dynamo
This article explains how to use the In-Place Pod Resize feature in Kubernetes, combined with Kube Startup CPU Boost, to speed up Java application startup.https://piotrminkowski.com/2025/12/22/startup-cpu-boost-in-kubernetes-with-in-place-pod-resize/
This is a story about a tricky issue I resolved recently.https://dev.to/datton94/how-my-client-hit-linux-kernel-network-limits-on-aws-eks-3am5
More and more enterprises want the benefits of AI-assisted coding, automatic completions, suggestions, and inline generation, without sending their source code to external APIs. This has naturally increased interest in self-hosted coding assistants, where all inference runs on internal hardware and all models stay inside a controlled environment. We built a complete prototype of such a system. In this article, we walk through its architecture, explain how Kubernetes is used to deploy it, and how different system parameters interact to determine real-world performance. In a separate post, we study how the llama.cpp inference server behaves under increasing load.https://medium.com/@ferraricorneloup.teo/inside-a-self-hosted-ai-coding-assistant-architecture-kubernetes-deployment-and-llama-cpp-158330a12441
A personal knowledge base that turns markdown notes into a semantically-connected, AI-augmented knowledge graph. Atomic stores knowledge as atoms — markdown notes that are automatically chunked, embedded, tagged, and linked by semantic similarity. Your atoms can be synthesized into wiki articles, explored on a spatial canvas, and queried through an agentic chat interface.https://github.com/kenforthewin/atomic
Run multiple agents in parallel — each in its own container, with its own workspace, collaborating on your code or project files simultaneously.https://github.com/GoogleCloudPlatform/scion
A lightweight, single-binary OpenTelemetry viewer for local development. Visualize traces, logs, and metrics from your instrumented applications — no Docker, no databases, no complex setup.https://github.com/mesaglio/otel-front
DriftHound is a Rails WebApp that receives Terraform drift reports via API and provides visibility into infrastructure drift across your projects.https://github.com/drifthoundhq/drifthound
After successfully adopting Terraform for GitHub repository management, the next step in our Infrastructure as Code (IaC) journey was clear: dogfood our own product and manage our domains and DNS zones using the DNSimple Terraform provider.https://blog.dnsimple.com/2025/11/managing-domains-terraform-dnsimple
After a few years of writing open-source Terraform modules, I've picked up a few syntax tricks that make code safer, cleaner, and easier to maintain.https://rosesecurity.dev/2025/12/04/terraform-tips-and-tricks.html
Instead of managing node groups, installing Karpenter, configuring the VPC CNI plugin, deploying the AWS Load Balancer Controller, setting up the EBS CSI driver, and keeping all of those components updated and compatible with each other - you enable a single flag and AWS handles all of it.https://darryl-ruggles.cloud/a-complete-terraform-setup-for-eks-auto-mode-is-it-right-for-you
¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
