Operations | Monitoring | ITSM | DevOps | Cloud

For developers building on iOS and macOS, building the most performant software means having access to the latest Mac resources to quickly build, test, and deploy software. Apple’s newest M4 Pro chip represents yet another significant leap in Apple Silicon performance, delivering unprecedented speed and efficiency for development teams.

AWS gives you the power to scale, deploy, and innovate at speed. However, with that speed comes a good amount of complexity. Services multiply, resources balloon, and performance issues sneak in when you least expect them. That’s where monitoring comes in. But it isn’t about checking boxes on dashboards. It’s about getting the most value for every dollar you spend or, maximizing your return on investment (ROI) from AWS monitoring. So, how do you actually do that?

The HaloPSA plugin is a new addition to SquaredUp, and helps you create live dashboards that surface the important metrics – giving you and your team a single pane of glass for help desk performance, asset visibility, and client reporting. Why it matters: If your team uses HaloPSA to manage tickets, assets, and clients, then you already know how vital that data is for running smooth operations.

Here comes a profoundly belated blog post on a behavior change. Better late than never. Due to various bugs with the glob engine on Windows, we decided to rewrite it in CFEngine 3.24.0. Not only does the new glob engine resolve these bugs on Windows, but it also adds support for brace expansion on all platforms. E.g. findfiles.cf command output.

Top Tips is a weekly column where we highlight what’s trending in the tech world and list practical ways to explore these trends. This week, we're going over how you can eliminate data silos in your organization to enable smoother data flows. The free flow of data is one of the clearest signs of organizational health. When data is locked away—isolated in disparate systems that don’t communicate with each other—you’re dealing with a data silo.

There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” But these aren’t checkpoints on a single journey to automation. DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.

You’ve probably felt the pain: data pouring in from every corner of your stack, tools choking on volume, dashboards lagging behind reality, alerts firing (or worse, not firing) without context. If that sounds familiar, it’s time to get serious about telemetry pipelines. Whether you're an SRE trying to stabilize a flapping service or a developer navigating multi-cloud chaos, a telemetry pipeline helps you take control of the data firehose.

We are exploring how we can help on-call engineers troubleshoot incidents more effectively by providing the OpenAI Codex agent with access to real-time observability data in terminals. We've developed an integration and new tool visualizations that connect OpenAI's Codex CLI to the new Datadog MCP server. In this post, we'll share what we've been experimenting with: enabling an AI agent to retrieve production metrics, logs, and incidents from Datadog in real time and act on that context.

For decades, PHP has powered the web using a simple model: process a request, send a response, then shut down. This model, especially in the form of CGI and PHP-FPM, is easy to understand but increasingly inefficient for modern web demands.

Today, we’re announcing the general availability of Lumigo Copilot, the most intelligent AI-powered observability assistant on the market, built for the complexities of modern microservices. Copilot emerged from a simple realization: Distributed systems produce too much fragmented data across too many layers, making troubleshooting slow, reactive, and deeply manual. Copilot changes that.