Operations | Monitoring | ITSM | DevOps | Cloud

For most of the history of software engineering, the primary constraint was production. Code was expensive, skilled engineers were scarce, and shipping features required concentrated human effort. Velocity was limited by how fast people could reason, implement, test, and deploy. That constraint shaped everything from team size, architecture, release cadence, through to how we thought about technical debt. When production is expensive, you optimise for output. You remove friction from shipping.

AI coding agents are getting good at calling tools. Claude Code, Codex, and others can run shell commands, parse JSON, and reason about the results. But they need to know what tools are available and how to use them. That's what skills are for. A skill is a small package of documentation that teaches an AI agent how to use a specific tool. We've built one for Oh Dear.

When you use CertKit, your private keys live in CertKit’s database, encrypted at rest. We’ve written about why the actual risk is smaller than it sounds. But some organizations have policies that prohibit storing private keys with any third party, regardless of how they’re protected. That policy isn’t going away. The Local Keystore enables those organizations to use CertKit and still keep their keys local.

From point-of-sale (POS) terminals to cloud-based applications and mobile devices, reliable connectivity is critical to business operations. Even brief disruptions can negatively impact user experiences, resulting in failed transactions, delayed application responses, or repeated attempts to reconnect. Juniper Mist is an AI-powered networking platform that provides insight into wireless environments, including access point performance and radio frequency health.

A host map is a visual representation of your infrastructure that displays hosts and related resources such as clusters, pods, and containers in a single, interactive view. We introduced the Datadog Host Map more than a decade ago to help you “know thy infrastructure” and answer critical questions: Does everything look healthy? Has anything changed? Does the shape of my environment match what I expect?

Production is a rowdy place of chaos, especially at scale. When you have millions of requests per second flowing through your system, weird things are always happening. Outliers, unusual request patterns, spikes and pulses of traffic from unknown sources, port scanning…it’s all there. To the naked eye, it looks like noise. If you know what you are looking for…patterns emerge. The night sky: every dot is a request. Without intent, it's an undifferentiated field of light.

Discover how Aiven makes the 4 factors of production-ready PostgreSQL easy A database isn't production-ready just because your application can query it. To be truly ready for production, your PostgreSQL setup must be able to survive node failures, block unauthorized network access, handle sudden connection spikes without crashing, and tell you exactly why a query is running slow. Let’s explore these four factors and how Aiven puts being production-ready on easy mode.

Modern engineering teams run on CI/CD. It’s where pull requests get validated, artifacts get produced, and releases get promoted to production. That also makes CI/CD migration very risky because you're not just moving a "tool"; you're moving the workflow that developers use dozens or hundreds of times a day. The good news: disruption is optional.

--- Key Takeaway: Harness AI helped deflect 95% of the platform support tickets for a major financial institution --- These days, success is often measured by what doesn’t happen: When things go right, the software delivery platform is invisible. But what happens when an organization’s delivery velocity increases multifold? Can the platform still stay out of the way?

This is a guest post from Maxime Drobot. This month we’re bringing you official GitHub Actions for Redgate Flyway, usability improvements in Flyway Desktop, and a look at what’s new, what’s in preview. Plus: earlier visibility of code‑review results, helping teams keep quality high and reviews flowing smoothly as AI increases the volume of changes.