Operations | Monitoring | ITSM | DevOps | Cloud

In the race to provide full-stack visibility, many modern SaaS platforms have inadvertently created a new problem: information overload. High-end enterprise solutions are designed for companies with dedicated Site Reliability Engineering (SRE) teams that spend their entire day inside a dashboard. But for many businesses, this level of granularity is a distraction. The real question isn’t whether a tool is powerful; it’s whether it fits the everyday needs of your team.

ServiceNow holds a wealth of operational data—but turning that data into dashboards people actually use is a different challenge altogether. Most teams start with what’s available out of the box. Then come the requests: At that point, dashboarding stops being simple. It then has to be “augmented” - with easy shareability, ease of use, contextualization and hierarchy.

This post by an engineer explains how his team uses the.claude folder in Claude Code. The folder is the hidden directory where you store context files, behavioral rules, and automated workflows so Claude understands how to operate in a specific project. He’d set up coding conventions, tool configs, CI integrations. Very engineering-brained. The tool is called Claude Code, so fair enough. I run a web and content team. We write blog posts, tutorials, and technical guides for a living.

Stranded capacity might be costing you more than you realize. It’s time to explore what this means for your data center and how it impacts your bottom line.

AI has rapidly evolved from an experimental technology into a foundational capability for modern enterprises. Today, organizations are no longer asking whether AI should be adopted but how quickly it can deliver measurable operational value.

Bitbucket Pipelines has always been an engine for automating more than just CI/CD, but today, Pipelines takes a first step towards a full agentic automation platform for all the manual, tedious, repetitive work that happens before and after code creation. You’ve probably seen the stat: Development teams spend 84% of their day doing things other than building features. A lot of this work is: This work matters, but it’s not very fun.

Over the last three months, we’ve been exploring what about software development and observability changes with AI, and what doesn’t. Our conclusion: these five principles will still remain true, even when 90% of the code is AI-driven. The agentic AI space is moving fast. Models are improving, context windows are expanding, and the ways people build and operate agents are changing so fast that any thoughts we share could feel dated by the time you read this.

Alerts are meant to help teams respond quickly to problems, but too often they arrive without enough context to be immediately useful. An alert that says “CPU usage is high” still leaves the on-call engineer asking critical follow-up questions: Which service? Which environment? Where do I look next? Validating the alert and triaging the situation is the first step for every engineer. It's a manual step that takes time, extending every potential incident.