Operations | Monitoring | ITSM | DevOps | Cloud

Let’s start with the obvious: we’d like to have Anthropic as a customer. We greatly admire the work they are doing at the intersection of frontier models + safety. We use lots of different AI tooling at incident.io. We’re all-in at AI at incident.io, both to improve the productivity of our internal team and, more importantly, to provide our customers with superpowers in the form of an AI incident responder.

Hallucinations occur when a large language model (LLM) confidently generates information that is false or unsupported. These responses can spread misinformation that jeopardizes safety, causes reputational damage, and erodes user trust. Augmented generation techniques, such as retrieval-augmented generation (RAG), aim to reduce hallucinations by providing LLMs with relevant context from verified sources and prompting the LLMs to cite these sources in their responses.

Datadog dashboards provide a unified view of your applications, infrastructure, logs, and other observability data—making it easy to monitor health, investigate issues, and share insights across teams. While native Datadog widgets support a broad range of visualization types, some use cases call for more customized representations, particularly when you’re working with unconventional data formats, external sources, or specific transformations.

When your Kubernetes app suddenly has traffic spikes in a distant country, it can be difficult to determine why. Let’s say, for example, we have an e-commerce app that started to receive an unusual surge of visitors from Australia — something we never anticipated. We search for answers in our logs, but without geographic context, we don’t have the full insights we need.

You push a release that touches the checkout flow. Now you’re glued to dashboards and checking Slack, hoping you didn’t introduce a regression that breaks the payment path. You can’t tell if you’ve just shipped a blocker that’s stalling every cart—or some edge case quietly making users bail.

You’ve probably run a lot of Docker containers, but do you know what happens behind the scenes? The Docker container lifecycle is the path a container follows from being created to running, stopping, and finally getting removed. Understanding these steps helps you figure out why a container might not start or when to restart it instead of creating a new one.

If you’ve worked with Kubernetes, you know logs are essential for understanding what’s happening inside your clusters. However, unlike traditional servers, Kubernetes logs present their unique challenges. Pods frequently start and stop, containers restart regularly, and logs stored locally can be lost quickly. Because of this, managing logs in Kubernetes requires a different approach.

Whether it's ensuring a new service is fully equipped with security scanning, logging, and monitoring from the start, enforcing production readiness checks during deployment, or providing temporary credentials for a production database, Workflows directly support Engineering Excellence Initiatives around modernization and standardization.

Our first turnkey dashboard gives you a real-time pulse on engineering health. Every chart is interactive: click any bar or data point to drill down into the underlying PRs, commits, or incidents. And we’re just getting started; more dashboards are already on the way!

If you’re working with containers, SBOMs or any kind of vulnerability scan, you know the drill. Every scan lights up like a Christmas tree. Critical, high, medium and low vulnerabilities. It feels that the list will always go on. The goal is always zero CVEs. And while that sounds great, it’s not realistic. They come at such a high pace, and sometimes they are really hard to resolve. Teams are spending time chasing vulnerabilities that don’t matter.