Operations | Monitoring | ITSM | DevOps | Cloud

AI code assistants transformed how software is written. They did not transform how it fails. Today, we’re announcing a new MCP integration between Lightrun and Kiro. Kiro now gains live runtime visibility through the Lightrun MCP, grounding AI-assisted development in how code actually behaves at runtime. Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It helps teams move from specification to production faster by turning intent into working code.

AI coding assistants like Amazon Q Developer are transforming how you write software. They can generate entire functions, explain complex code, and help you move faster than ever. But there’s a catch: AI-generated code isn’t always correct. It can introduce subtle bugs, security vulnerabilities, or break existing functionality in ways that aren’t immediately obvious. That’s where continuous integration comes in.

In the AI era, software and new models are being born at a breakneck pace—but they’re also bringing a lot of “baggage” into the world. While AI coding agents are busy accelerating innovation, they’re also excellent at generating a massive byproduct: “digital dust.” Between obsolete releases, orphaned dependencies, and massive model versions, your repository may soon start to look more like a digital junk drawer than a streamlined machine.

In the telecom industry, leaders are under pressure to deliver more—more capacity, more agility, more reliability—while managing rising complexity with fewer resources. The network is the circulatory system of the modern telco, yet it’s still often operated like a patchwork of manual roads, each requiring constant human intervention. This model worked when traffic was predictable and growth was linear.

Most IT leaders agree on one thing: digital employee experience matters. What is less clear is how to operationalize it in ways that deliver measurable business outcomes. Many organizations invest in tools and dashboards, launch experience initiatives, and even measure sentiment. But without an operational model that connects employee experience to core business objectives, IT teams often stay stuck in reactive support. DEXOps changes that.

AWS recently experienced a significant outage. The outage took down major services, including parts of McDonald’s mobile ordering system, some Netflix features, and many other applications that relied solely on AWS infrastructure. This event perfectly illustrates why relying on just one cloud platform can be risky.

AI didn’t replace developers. It replaced the part where you were forced to understand what you just shipped. Now you can prompt your way to a feature, skim the diff, and merge something that “seems reasonable.” And then production does what production always does: finds the one weird browser + one slow network + one user flow that turns your “reasonable” code into a bonfire. So who watches the vibe coder?

We just released React Native SDK 8.0.0, here's what's new, and what's changed. It's been a while since the last major version. The last major release, 7.0.0, shipped on September 2, 2025. After 13 minor and 2 patch releases, it's finally time for a new major version to land: 8.0.0. This version is a maintenance and capability major. This means we: It should be straightforward to upgrade, but check the migration guide for your setup.

Service reliability has outgrown uptime checks and component-level tools, creating friction that slows response, increases toil, and wears teams down. Uptime checks can pass, high availability can be in place, and users still can’t complete basic actions. Pages load slowly, latency spikes, and requests stall — all without a single system flagged as down. Availability measures whether a service is running.

Mining for information about environments is like trying to find gold. Looking for gold can be sifting through silty waters or blasting through a mine. In some cases, the gold nuggets are so small as to be almost invisible, some things look like gold but aren’t, and others are larger nuggets where the miner strikes it rich. Trying to understand how a distributed system works means sifting through vast amounts of telemetry, looking for patterns.