Interest in generative AI and large language models (LLMs) has exploded in popularity thanks to a slew of announcements and product releases, such as Stable Diffusion, Midjourney, OpenAI’s DALL-E, and ChatGPT. The arrival of ChatGPT in particular was a bellwether moment, especially for developers. For the first time, an LLM was readily available and good enough that even non-technical people could use it to generate prose, re-write emails, and generate code in seconds.

I’m thrilled to announce that Gartner added Monitoring as Code (MaC) as an emerging practice into their Hype Cycles for Monitoring and Observability and Site Reliability Engineering. We are extremely hyped about this recognition and being listed as a vendor innovating in that space. Since we founded Checkly, our vision has been that monitoring should be set up as code and live in your repository; it must be open-source based and feel natural for developers.

We recently ran our first hackathon in quite some time. Over two days, our team collaborated in groups on various topics. By the end of it, we had 12 demos to share with the rest of the team. These ranged from improvements in debugging HTTP request responses to the delightful “automatic swag sharer.” Within our groups, a number of us tried integrating with OpenAI’s GPT to see what smarts we could bring to our product.

D2iQ has introduced an AI assistant that helps organizations resolve problems more quickly and easily, including coding errors and system failures. The new DKP AI Navigator can help reduce the duration and cost of system and application bottlenecks, misconfigurations, and downtime, and ultimately help organizations overcome the Kubernetes skills gap.