Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

Database changes are inherently complex: coordinating schema updates, managing risk, and avoiding downtime all require care. Even when teams improve how they deliver those changes, governance often remains inconsistent, manual, and reactive. In many environments, governance is treated as a separate layer around deployment. Policies are applied unevenly, approvals become bottlenecks, and audit evidence is assembled after the fact, creating gaps in enforcement and increasing operational risk.

This post is also available in German and in French. Tracking environmental impact can be fragmented, time-consuming, and disconnected from operational data. Beyond simply checking ESG reporting boxes or making sure your company is CSRD compliant, actively monitoring environmental impact is the foundation for building an effective sustainability strategy. At Upsun, we know that measuring progress is the first step toward improvement.

Application delivery has accelerated over the last decade. Modern CI/CD pipelines, automated testing, and cloud infrastructure have already raised the baseline. Now AI-assisted coding tools are compressing timelines further still - developers are writing and shipping code faster than ever.

This is a guest post from Adam Atreides. SQL Prompt's March releases focused on Prompt AI improvements and SSMS 22.4 compatibility.

Let me paint a picture. Your company has rolled out Claude or ChatGPT as the standard AI tool. You've connected MCPs to Stripe, your HRIS, Datadog, your CRM, and Slack. A senior engineer set this up because they needed to answer hard cross-system questions and it works beautifully. Now a marketing intern sits down, opens the same LLM harness with the same MCP config, and types "show me revenue by customer for the last 12 months." They get it.