Operations | Monitoring | ITSM | DevOps | Cloud

The Svelte Team recently announced full observability and tracing support for SvelteKit! This is great news for SvelteKit and Sentry users, since Sentry is already compatible with the new feature! In addition, this is even greater news for the JavaScript ecosystem as a whole because SvelteKit just became the first ESM-based meta-framework to support instrumentation and tracing out of the box.

It’s been a noisy summer in the AI world. Headlines have been filled with doom and gloom: For example, OpenAI’s ChatGPT-5 landing with a thud, and an MIT report claiming 95% of AI pilots are failing. For the sceptics, this is “proof” that AI is just hype. I don’t buy it. The MIT study looked at just 50 projects, a sample size so small you’d fail a basic stats exam for using it. And as someone who uses AI every single day, I can tell you the benefits are real.

Switching status page providers shouldn't mean losing years of valuable incident history. Your service timeline tells the story of your reliability journey—outages you've overcome, maintenance windows you've scheduled, and the trust you've built with transparent communication. Yet most migrations force you to choose: start fresh with a clean slate or manually recreate years of historical data.

A recent survey revealed that developers and engineering teams waste 8+ hours a week on inefficiencies in their role. Poor reporting tools are a main contributor, with Jira being regarded as a frequent source of friction. But since Jira is so deeply embedded in most organizations' infrastructure and processes, replacing it is not really an option. Rather, the solution lies in optimizing how users interact with it rather than abandoning it altogether.

AI coding assistants like Claude Code are becoming core parts of modern development workflows. But as with any powerful tool, the question quickly arises: how do we measure and monitor its usage? Without proper visibility, it’s hard to understand adoption, performance, and the real value Claude brings to engineering teams. For leaders and platform engineers, that lack of observability can mean flying blind when it comes to understanding ROI, productivity gains, or system reliability.

When an application slows down, users rarely think about the database—but in many cases, that’s where the bottleneck lies. Databases sit at the core of nearly every application, storing, retrieving, and processing the information that powers business transactions, analytics, and user interactions. A minor inefficiency in query execution or a spike in resource usage can cascade into multiple issues, starting with degraded application performance, service interruptions, or even downtime.

Today’s enterprise networks aren’t constrained to a single location—they span continents, clouds, and providers, and they’re relied upon by users who can work from anywhere. For network operations teams, that means every issue is a potential scavenger hunt. Is it the app? The WAN? The cloud provider? The ISP? The stakes are high and your tools need to evolve. That’s why the integration of DX NetOps and AppNeta is such a game-changer.

The risk of headline-grabbing incidents, like Samsung’s ChatGPT data leak, related to AI usage outside of the authorization and control of IT (a.k.a. shadow AI) is clear. Most IT teams recognize that a high-profile incident can have serious repercussions. However, the risk of shadow AI goes well beyond the risk of a single incident. In fact, the recent Komprise IT Survey indicates that 79% of organizations have experienced negative outcomes from sending corporate data to AI.

MongoDB’s strength of flexible schema and fast iteration can also hide costly queries until they surface as user-facing latency, replica lag, or spiky CPU. A handful of slow operations can impact the cache, starve other workloads, and cascade into timeouts across services. Monitoring slow queries gives you an early warning system for index gaps and query-plan regressions introduced by code deploys, schema changes, or shifting data shapes.

There’s a new way to ingest AWS metrics into Grafana Cloud that makes observing your AWS resources more cost-effective, easier to operate, and more accurate. You can now stream metrics into the AWS Observability app in Grafana Cloud in near real-time thanks to our new integration with Amazon CloudWatch and Amazon Data Firehose. We’re already using it internally, and we’re finding that it’s not only easier to operate—it’s at least five times more cost-effective.