Imagine having a concise, AI-generated report of any incident at your fingertips. That’s what Squadcast’s new Incident Summaries feature delivers—instant clarity on ongoing issues, saving precious time during critical moments. At any point in time, any stakeholder or a responder can simply generate and view the incident summary with all important details highlighted, essentially offering a single pane of glass.

Trust doesn’t just happen overnight. For us at incident.io, it’s been a journey—one that’s focused on people just as much as the product. From the start, we knew that building great incident management software wasn’t just about creating features and functionality. It was about building relationships, understanding our users, and truly being there for them when it matters most. Our focus has always been to help teams manage incidents better.

We’ve posted before about how engineers on call at Honeycomb aren’t expected to do project work, and that whenever they’re not dealing with interruptions, they’re free to work on whatever will make the on-call experience better. However, all of our engineering rotations rely on hand-off meetings where they update the Slack groups with everyone who’s on call. During my last shift, a small problem kept causing friction for some of our incident management automation.

Recently, I came across this Reddit post highlighting the challenges of having ineffective alerting rules: And, here at OnPage we have experience with various companies who have dealt with just that, so I felt I should share some of our top tips for creating effective alerting rules in this blog. Read on to discover…

When incidents occur, engineers must jump into action to get systems back to running at peak performance. However, there are a myriad of challenges that can prevent them from resolving the issues swiftly. Imagine a scenario where a team of DevOps engineers manages a cloud-based e-commerce platform that experiences occasional spikes in traffic during peak shopping seasons. During one of those major sales events, the team notices a sharp spike in CPU usage across several critical application servers.

‍Pagerly, a Slack App designed for shift scheduling, makes it easy to create round-robin rotations for various teams. Whether it's support team, engineering team, sales team, customer support or any other department, Pagerly helps manage shift schedules and team rosters within your Slack Workspace. Pagerly app can be installed directly from the Slack App Directory, and it is a most comprehensive rotation app designed to optimize scheduling in Slack.

In recent years, artificial intelligence (AI) has ceased to be a mere technological trend and has established itself as a foundational element shaping the future of Software as a Service (SaaS) and cloud-based software solutions. By 2025, AI's integration into these domains will not just enhance existing functionalities but redefine what is possible in ways we’re only beginning to comprehend.

Change is the only constant in the IT landscape. These changes might involve adding new observability tools, retiring existing monitoring systems, establishing new business units, or integrating IT systems from acquisitions. Managing these changes can challenge even expert ITOps teams. Organizing your monitoring setup can seem overwhelming, especially with issues like monitoring gaps, observability redundancy, complex toolsets, or significant technical debt.

AI is everywhere, and while it’s transforming industries, many organizations are still trying to identify how to use it to achieve tangible value. This is especially true for AIOps, where platforms often fall short of the promises to automate IT operations and improve incident response. As a result, many leaders are skeptical about whether AIOps can deliver measurable results quickly or provide outcome-driven value in IT operations.