This blog post will uncover how ilert status pages work, the challenges we encountered while developing this feature, and the problem-solving approaches we adopted.

My name is Tim Gühnemann, and as an AI engineering working student at ilert, I had the privilege of developing and continuous improving ilert AI, ensuring it meets the needs of our customers and aligns with our vision. ‍ Our goal was to provide all our customers with access to ilert AI. We aimed to develop a solution that could adapt dynamically and function independently based on our use cases, similar to the OpenAI Assistant API.

At ilert, one of the key tools in our debugging process is the Event Explorer, which provides an extensive overview of incoming events and their processing lifecycle. By reflecting the event process of an alert source, the Event Explorer allows our team to trace event paths, correlate related data, and identify issues quickly.

We are excited to announce a native integration between ilert and Honeybadger.

With increasing numbers of alerts, keeping focus on the important and most critical alerts proves to be more and more of a challenge. A reduction of alert noise, meaning the prevention of too many created alerts and any kind of user notifications, is needed to ensure efficient alert response. While a detailed explanation of this topic is given in this blog post, a flexible and automated setup for your relevant resources can be achieved with Terraform using the ilert Terraform provider.

After releasing our first version of the ilert dashboard as a static layout, we knew we wanted to take it further by allowing users to customize and arrange widgets freely. We aimed to provide a truly interactive experience, which led us to search for a library that could handle drag-and-drop and resizing functionalities while integrating well with our existing tech stack.

I joined ilert as a student front-end software developer. Before, I was mainly writing iOS apps. Even though I already had some experience with web technologies, diving deep into front-end development was a huge step. Both developing iOS apps and web apps share the same kinds of tasks, such as developing the user interface (UI) and writing app logic. However, the actual development environments are completely different.

ClusterControl by Several9s is one more great partner introduced among ilert integrations for DevOps teams. In this article, learn more about ClusterControl functionality and the benefits of ilert integration.

Read how we set up an experimental chatbot environment that allows us to switch LLMs dynamically and enhances the predictability of AI-assisted features' behavior within the ilert platform. The article includes a guide on how you can build something similar if you plan to add AI features with a chatbot interface to your product.

Building an AI proxy for our AI features was one of the best decisions we made a year ago. In this article, we will share why and what challenges we faced.