Operations | Monitoring | ITSM | DevOps | Cloud

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.

Datadog has long supported the monitoring of instrumented Go applications through our Go tracer v1. As the Go ecosystem has continued to mature, we’ve been hard at work collecting feedback and improving upon the tracer’s capabilities and usability features. We are now thrilled to announce the release of our Go tracer v2.0.0. This major update includes better security and stability, and a new and simplified API.

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.

We’ve noticed a lot of our customers spend countless hours building and configuring processors. Either parsing JSON, standardizing log formats, normalizing timestamps, masking PII, de-duplicating logs, the list never ends. Most work revolves around recreating the same processor bundles in multiple processor nodes. Bindplane’s new Blueprints solves that boring, repetitive work by providing pre-built processor bundles you can drop into any pipeline with a single click.

When you run critical IT or Security systems, you are still on the hook if an alert is missed or a pipeline drops data. That is why Cribl Copilot never tries to replace you. Instead, it works as a partner you can question, guide, and overrule.

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.

When shopping for new laptops or computers, you may want to consider your RAM vs storage needs. But if you’re wondering which one is more important, or what the differences are, you’re not alone! We will cover everything you need to know about RAM vs storage, how to upgrade both, and how Internxt can help you free up storage on your device with its virtual drive app, a method to store files in the cloud as if you were interacting with your computer’s hard disk.

Prometheus can be lightweight to start with, but once it’s in production, storage usage tends to grow faster than expected. Managing how long data is kept becomes critical, especially when you're working with limited disk space or tight budgets. This guide outlines the key concepts behind Prometheus data retention, how to configure it effectively, and what to watch out for.

Picture this: a crime scene where the evidence is scattered across five different rooms. There’s a footprint in one, a shattered window in another, a stray shoe on the stairs, and a witness across the street, who only saw part of what happened. Each clue matters in solving the case, but none of them tells the full story on their own.

Observability and monitoring are the cornerstone of resilient, high-performing applications. Nearly every IT or software engineering leader we come into contact with emphasizes the importance of the ability to understand and diagnose what is going on with their applications at all times. Having clear and concise visibility into your applications is no longer optional.