Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Enforcing web performance budgets in CI/CD with Sitespeed.io and Slack

Keeping your website fast as new features are introduced is a challenge. Performance regression is common issue that continues to plague websites, especially those of SaaS companies. In performance regression, newly shipped features introduce bloat, leading to slow page loads and reduced user conversion rates. This is exactly what setting performance budgets helps prevent.

[Webinar] Building Quality-Driven Agentic AI in Noisy Big Data Environments

Watch as Itiel Shwartz, Komodor CTO and Co-Founder as he shares hard-won lessons from developing an AI agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking. This webinar covers: Building production ready systems that maintain reliability when 90% of your data is noise. How Komodor developed an AI SRE agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking.

An introduction to GPU time-slicing

GPUs are no longer a niche component. Gamers know them for immersive graphics, workstation users rely on them for balanced performance, and in the age of AI, GPUs have become one of the most in-demand resources in modern infrastructure. They are also expensive. That reality creates two immediate constraints, for individuals and enterprises alike: GPU-backed instances should be provisioned deliberately, and once provisioned, they should be used efficiently.

A cleaner, customizable Bitbucket navigation is here

Last month we shared that a new navigation system is coming to Bitbucket, and we know many of you have been eager to see what it looks like. Today, we’re happy to share that the new navigation is available for to all Bitbucket users. This article covers what’s changing in Bitbucket, when it’s happening, and how you can share feedback with us.

AI Anomaly Detection: Catch AI Cost Surprises Before They Kill Margins

Consider this: traditional cloud cost monitoring was like checking your fuel gauge once a month — after the trip was already over. That model worked when infrastructure scaled slowly. You provisioned resources predictably and paid for stable, linear usage. AI breaks that model. Today, AI costs behave like a high-performance engine with a hypersensitive throttle. A small input, like a prompt change or a single power user, can dramatically increase your fuel burn in seconds.

Drift Under Control: Keep Your Infrastructure Consistent with Continuous Detection, Intelligent Analysis, and Safe Remediation

In cloud-native environments, infrastructure is in constant flux. Teams move fast, leveraging Infrastructure-as-Code (IaC), ephemeral resources, and automation to iterate quickly. But speed brings a cost: configuration drift. A single manual change in the cloud console, an untracked automation script, or an out-of-band fix can cause your infrastructure to fall out of sync with code. Over time, this erodes trust, breaks pipelines, and introduces silent risk.

Why Infrastructure Stability Is Critical for Reliable DevOps Pipelines

Automation in DevOps helps teams move code from a commit to production faster. But it only works when the infrastructure is reliable and consistent. If servers fail, configurations drift, or scaling behaves unexpectedly, even a well-built pipeline can break. Stable infrastructure is what lets teams deploy many times a day with confidence instead of spending hours fixing failed releases. Often, the biggest difference between strong DevOps teams and struggling ones is how dependable their infrastructure is for continuous delivery.

How to Use PostgreSQL AI for Query Writing and Optimization

PostgreSQL AI is gaining attention as SQL complexity increases in production environments. It addresses a common problem: extended queries that accumulate joins, nested logic, and edge cases. Without AI assistance, these queries are often harder to write and review, driving 20–40% of developer time into debugging. In practice, these challenges affect PostgreSQL users in different ways.