Operations | Monitoring | ITSM | DevOps | Cloud

The proliferation of managed LLM services like OpenAI, Amazon Bedrock, and Anthropic have introduced a wealth of possibilities for generative AI applications. Application engineers are increasingly creating chain-based architectures and using prompt engineering techniques to build LLM applications for their specific use cases.

In my last blog post, I explained why we decided to destroy one third of our infrastructure in production just to see what would happen. This is part two, where I go over the big day. How did our chaos engineering experiment go? Find out below!

According to a recent study, failing tests alone cost the enterprise software market an astonishing $61 billion annually. This figure mirrors the vast number of resources devoted to rectifying software failures, translating into about 620 million developer hours lost each year. On average, engineers spend 13 hours to resolve a single software failure, a statistic that paints a stark picture of the current state of debugging efficiency.

“O11y”, also known as Observability, is changing how we handle system performance. This guide will walk you through the essentials of O11y and how to implement it effectively. Let's dive in.

Software changes so rapidly that developing on the cutting edge of it cannot fall to a single person. When it comes to asynchronously disseminating information about projects, code comments, PR conversations, Slack, RFCs, and other investigatory documents do a wonderful job, but no amount of async communication replaces the magic of two brains bouncing ideas off of each other.

In today's fast-paced digital economy, businesses are constantly seeking innovative solutions to streamline their operations, enhance agility, and drive growth. As enterprise IT infrastructure environments get more distributed and complicated to meet evolving demands, the need for robust IT monitoring, management and automation becomes even more important.