Operations | Monitoring | ITSM | DevOps | Cloud

After three decades of working with databases, one thing I’ve seen over and over is this: we don’t treat our development and test environments with the same respect we do our production systems. Not because people don’t care. Far from it. It’s usually because teams are under pressure, everyone’s juggling multiple priorities, and the quickest path forward often wins the day.

Teams that find a weekly rotation too long and a daily rotation too short often end up choosing between 2-day and 4-day rotations. This guide compares both these rotations across three key criteria. For each criterion, we have discussed how it works for 2-day and 4-day rotations and recommended what to choose when. To make it easy, we also included a comparison table for a quick overview. This gives you all the information you need at a glance. Let’s dive in! Table of contents.

Most IT teams don’t lack AI policies. They lack policies that survive a Git push. In many organizations, AI governance is a paper tiger. There are comprehensive documents outlining data usage, approved models, and risk management. On an auditor's desk, these policies look complete. But inside the workflow, the reality is different. AI tools are being embedded directly into IDEs, CI pipelines, and internal automation scripts.

AI agents are quickly shifting from experimentation into day-to-day operations. That shift is showing up in the data. McKinsey’s latest State of AI research highlights both broader AI use and the growing focus on “agentic AI,” even as many organizations still struggle to scale safely. For mid-market IT teams, agents can feel like the unlock: automate repetitive workflows, reduce backlog pressure, and deliver more output without expanding headcount.

The promise of Artificial Intelligence in Site Reliability Engineering (SRE) is seductive: an autonomous system that never sleeps, instantly detects anomalies, and fixes broken infrastructure while humans focus on high-value work. However, the gap between a demo-ready chatbot and a production-grade Autonomous AI SRE is vast. In complex, noisy environments like Kubernetes, a “naive” implementation of Large Language Models (LLMs) is not just ineffective, it can be dangerous.

PostgreSQL ODBC drivers are no longer background components. For teams running BI, reporting, and ETL on PostgreSQL, the drivers directly affect how fast queries run, how reliably dashboards refresh, and whether data pipelines remain stable as usage grows. As PostgreSQL moves deeper into analytics stacks, these capabilities are driving the demand for these tools, a trend reflected in broader ODBC market growth.

We know that AI has been transformational for engineering and it will continue to be, so stop me if this sounds familiar. Imagine an engineering lead opening a pull request for a critical security patch and finding five hundred lines of AI-generated code. While the solution is (mostly) usable, it follows a pattern no one on the team recognizes. This shift away from manually writing every line of logic has introduced a unique level of complexity for teams.

Automation has become more than just a "nice to have" choice. It's an essential part of the modern business landscape, promising increased efficiency, reduced costs, and improved accuracy. However, despite its potential benefits, many organizations struggle when trying to implement automation. In this article, we'll explore some of the most common implementation mistakes we've encountered and how to navigate them effectively.

OpenClaw (née MoltBot, née ClawdBot) is taking over the world. Everyone is spinning their own, either on a VPS, or their own Mac mini. But here's the problem: OpenClaw is brand new, and its security posture is mostly unknown. Security researchers have already found thousands of publicly available instances exposing everything from credentials to private messages.