Operations | Monitoring | ITSM | DevOps | Cloud

Uptime reports are now available through the StatusGator v3 API. This new endpoint provides the same uptime report data available in the StatusGator UI, making it easy to access uptime statistics programmatically through the API and MCP server. You can find the endpoint in the API documentation.

Gartner predicts that, by 2029, active AI will be able to autonomously resolve 80% of common customer service issues, reducing operational costs by around 30%. This scenario reinforces the need to move from a reactive model to proactive strategies that prevent incidents from arising, especially Level 1 incidents, which account for a large share of service desk volume.

Canonical Livepatch now officially supports Arm64, further expanding its security patching automation capabilities. For the first time, Ubuntu on an Arm64 machine can apply critical kernel updates, without service interruption or rebooting. Starting with Ubuntu Core 26 for Arm64, and for Ubuntu Core 20 and onwards for AMD64 machines, a wider range of devices and cloud virtual machines can achieve timely vulnerability remediation through Canonical Livepatch.

The conversation about AI and software development has mostly been about velocity. Developers write code faster. Pull requests ship sooner. Backlogs shrink. That part is real, and it matters. But there's a bigger shift happening underneath it, and most engineering leaders I talk to are only just starting to feel its weight. AI hasn't just made developers faster. It has fundamentally expanded who can create and ship software. That changes things in ways that velocity metrics don't capture.

When you’re building with AI, you can get a lot done in 30 seconds. Waiting minutes for CI feedback on your latest change can feel like an eternity. Chunk sidecars are designed to give you feedback fast, running your full test suite against the same Linux environment as CI, directly inside the agentic loop. Traditional CI pipelines can take five or ten minutes to catch a basic lint error or failing unit test.

A malfunction in the baggage handling system at Berlin Brandenburg Airport disrupts the conveyor network that transports luggage across the airport. With more than 70,000 passengers traveling through BER every day and flight schedules timed down to the minute, even a small disruption can quickly lead to delays, missed connections, cancellations, and high costs. Fortunately, the Incident Management team receives the alert in real time and responds immediately.

AI adoption has accelerated faster than most organizations expected. What started with chatbots has quickly evolved into AI systems capable of making decisions across enterprise environments, with the promise of faster service and more efficient teams. But many organizations are discovering an unexpected challenge: as AI usage expands, costs become harder to predict. Most AI platforms operate on token-based pricing models.

AI has made writing code fast, and you can feel it. Commits are up, pull requests are up, new repos spin up over a weekend, and your engineers swear they are faster. But where are all the new products? If every team really got faster, the software you use every day should be getting visibly better. AI helped your engineers ship more code. It didn't help your team ship more products.

Platform engineering has become one of the most discussed topics in cloud native infrastructure. Yet despite the rising focus, most conversations around platform engineering skip over the uncomfortable truths. What actually works at scale? When should you build versus buy? And how do you avoid the traps that trip up even experienced teams?