On October 24, NinjaOne launched our 6.0 platform and product updates – including Patch Intelligence AI, which enhances and further automates the NinjaOne Patch Management solution. Just a day later, we saw the impact this new, free feature can make on our customers’ success.

Last week, I attended one of the last conferences of the year with team Mezmo: the Gartner IT Infrastructure, Operations & Cloud Strategies Conference in Las Vegas. Not surprisingly, there were over 20 sessions covering observability and how it is getting increasingly critical in the new complex distributed computing environment. Of course, there were many sessions, including all keynotes that addressed the advent and impact of AI on IT operations and observability.

People keep asking us here at Checkly if and how AI can help create solid and maintainable Playwright tests. To answer all these questions, we started by looking at ChatGPT and Claude to conclude that AI tools have the potential to help with test generation but that "normal AI consumer tools" aren't code-focused enough. High-quality results require too complex prompts to be a maintainable solution.

AI is changing our world, and its impact on observability is no different. This article discusses some of the components of a good observability platform, how AI is well-positioned to revolutionize observability, and how Lumigo Copilot Beta will provide substantial value to customers and partners.

In a 2020 paper, Patrick Lewis and his research team introduced the term RAG, or retrieval-augmented generation. This technique enhances generative AI models by utilizing external knowledge sources such as documents and extensive databases. RAG addresses a gap in traditional Large Language Models (LLMs). While traditional models rely on static knowledge already contained within them, RAG incorporates current information that serves as a reliable source of truth for LLMs.

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.

As digital applications and infrastructures grow increasingly complex, managing and understanding log data has become increasingly vital in achieving practical observability, enabling organizations to detect, diagnose, and prevent issues across their systems. However, traditional log analysis methods often struggle with the volume and complexities of modern log data in cloud-native environments.