sayzard0xMarioNawfal (@RoundtableSpace)
Obsidian과 Claude Code를 결합해 24시간 동작하는 개인용 AI 운영체제를 만드는 흐름이 소개됐다. 지식 정리, 작업 자동화, 백그라운드 실행까지 지원해 개인 생산성을 크게 높이는 혁신적 AI 활용 사례로 보인다.
connexifyHot take: AIOps is just monitoring with better marketing. Until your NMS can actually correlate an OLT alarm with a customer complaint, it's not AI — it's a dashboard with a chatbot. #TelecomAI #AIOps #StreamingTelemetry
Save this for your next monitoring review.
[AI 시대, 0→1 서비스에서 오픈보다 운영이 더 중요한 이유
AI 시대 스타트업의 새로운 과제인 운영 구조화의 중요성을 다룬 기사. AI 기반 개발 도구로 인해 제품 출시 속도는 빨라졌으나, 운영 안정화와 구조화된 운영 프로세스의 중요성이 부각되고 있다. 초기 서비스 운영 기준의 모호성, 분산된 암묵지, 운영 데이터의 체계화 부재 등 운영 구조의 선행 설계가 필수적이며, AI를 운영 데이터 분석 및 정책 정의에 활용할 수 있다는 점에 초점을 맞추고 있다. '얼마나 빨리 출시하는가'보다 '얼마나 안정적으로 운영되는가'가 핵심 과제로 부상하고 있다.
https://news.hada.io/topic?id=29317
#aiops #startupoperations #aidevelopment #productstability #llmforoperations
Avi Chawla (@_avichawla)
LLM 기반 앱을 실제 사용자에게 배포할 때 필요한 AI 시스템 관측성(observability) 계층을 시각적으로 설명한다. 파이프라인의 각 단계에서 내부 동작을 추적해야 한다는 점을 강조하며, 운영·디버깅에 중요한 개발 가이드 성격의 내용이다.
Avi Chawla (@_avichawla) on X
Layers of observability in AI systems, explained visually: If you’re deploying LLM-powered apps to real users, you need to know what’s happening inside your pipeline at every step. Here’s the mental model (see the attached diagram): Think of your AI pipeline as a series of
Beth Pariseau"IBM is recognizing that they needed to start bringing those products more closely together, so customers could get better benefits ... In the end, Concert [will] be the glue that brings it all together."
Jason Andersen
Check out my writeup of today's #IBMThink news:
Good morning from Boston! Jason Andersen and I break down the top news for #DevOps and #platformengineers at #IBMThink this week, including the general availability of the #IBM Bob #AI coding tool, a long-awaited tie-in with HashiCorp for #AIOps now in public preview and updates to the #Watsonx Orchestrate AI agent platform.
IT Ops Query: IBM Think, Bob agentic IDE, HashiCorp AIOps and more
HabrКак я Zabbix с LLM дружил в свободное время. Архитектурный обзор взаимодействия с нейросетью. Часть 1 «При чем тут ТЗ»
Это первая статья из цикла о том, как я пытался сделать алерты Zabbix в домашней лаборатории чуть умнее, прикрутив к ним локальную LLM и не получить на выходе архитектурного монстра Франкенштейна. В теории хотелось простого: система принимает события мониторинга, понимает их контекст, не дергает лишний раз по пустякам и подсказывает, куда смотреть в первую очередь. Но на практике необходимо начинать не с модели, не с кода и даже не с Docker Compose, а с нормального ТЗ. В процессе написания материал разросся до неимоверных размеров, поэтому пришлось поделить его аж на четыре части. Ссылки буду добавлять по мере выпуска (примерно раз в одну-две недели). Часть 1: Вводная и формирование ТЗ -> вы здесь Часть 2: Выбор локальной LLM Часть 3: Формирование HLD и немного LLD Часть 4: Что из этого вышло
https://habr.com/ru/articles/1031140/
#zabbix #llm #aiops #мониторинг #алерты #тз #itинфраструктура #rca
Как я Zabbix с LLM дружил в свободное время. Архитектурный обзор взаимодействия с нейросетью. Часть 1 «При чем тут ТЗ»
Введение Как мы тебя понимаем, маленький котик Это первая статья из цикла о том, как при четкой формулировке задачи и описании целевой архитектуры получилось собрать для self-hosted инфраструктуры MVP...
Myth: SNMP is dead. Reality: SNMP is not going anywhere. Every OLT, every router, every UPS still speaks it. What you SHOULD be doing is layering streaming telemetry ON TOP of SNMP, not replacing it. #TelecomAI #AIOps #StreamingTelemetry
Tag someone who needs to read this.
Puppet Community Team 
A lot of teams are being told to “use AI in ops” right now. The harder part is figuring out *where it actually helps* day to day without adding risk, noise, or another thing to babysit.
If you’re curious (or skeptical 👀) about AI in ops, join Robin Tatam and Jason St-Cyr as they share their thoughts on where AI can realistically fit into infrastructure operations today. No magic, just using good tools to do better.
Scott McCartyWhat if your AI agent could SSH into your RHEL image and diagnose issues before you push to production?
Red Hat just published a guide for integrating MCP servers into image mode builds. Two intelligence streams: live OS state (journalctl, systemctl) + proactive CVE scanning. No more manual log scraping.
Building Red Hat MCP-ready images with image mode for Red Hat Enterprise Linux | Red Hat Developer
Learn how to leverage the Model Context Protocol (MCP) to connect VS Code or Cursor to two specialized intelligence streams: one for local system telemetry and one for global proactive security