Operations | Monitoring | ITSM | DevOps | Cloud

Sovereignty, Liquid Cooling, and the New Infrastructure Hierarchy with Gavin Dudley

The AI revolution is here, and data centers are its beating heart! Gone are the days of data centers being just overlooked IT infrastructure; they are now the "cool kids on the block," essential for powering the most significant technological shift of our generation.

Revolutionizing User Experience with Agentic AI

Revolutionizing User Experience with Agentic AI Agentic AI integration creates prompt-driven interfaces that simplify access to information and ticketing for users. AI incident correlation reduces service downtime, boosting productivity. Ring deployment automates patch management for controlled updates, while lifecycle management of gateways enhances security. Autonomous endpoint management tackles time and data challenges, leading to more efficient operations. The focus is on leveraging technology to innovate and optimize resources.

AI + Dark Mode: Introducing AI-Powered Insights and The Long Awaited Dark Mode

Join the live stream at 11 am ET, here. Launch Week’s Friday drop delivers two of the most-requested upgrades we’ve ever shipped: Together, they turn Bindplane into a cooler , and smarter , place to manage observability and SecOps telemetry. A full suite of extensive AI features will be rolling out over the coming weeks. This is just the beginning!

MCP = Observability + Code, a Real-life Example

Our bot is hitting an error. We can see it in the distributed trace. Here, see what happened when we noticed it: Austin fired up Claude Code (hooked up to Honeycomb with its MCP tool) and got it to find the error, fix it, deploy, and check that the fix worked. It got a little overconfident at first, but the ending is happy. IRL this took 22 minutes; the video speeds up the AI agent interactions and cuts out waiting. This video includes Austin Parker, Jessica Kerr, and Ken Rimple.

Why Cribl Copilot Editor is Built for the Human, First and Foremost

I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI.

From RPA to Agentic AI: Understanding the Shifting Landscape of Enterprise Automation

Over the past decade, organizations have embraced automation in waves – starting with basic task scripts and Robotic Process Automation (RPA), then moving to hyperautomation, and now exploring “agentic AI” as the next frontier. Each step in this evolution has expanded the scope of what can be automated, and revealed new challenges. This blog offers a detailed comparison of RPA, hyperautomation, and agentic AI, their key differences, strategic advantages, and potential drawbacks.

Hyperparameter tuning for LLMs using CircleCI matrix workflows

Hyperparameter tuning is a critical step in optimizing large language models (LLMs). Parameters such as learning rate, batch size, weight decay, and number of training epochs can significantly affect convergence behavior and final model performance. While several approaches like grid search or random search are widely used, executing them manually is inefficient; especially when each training run is compute-intensive.