Operations | Monitoring | ITSM | DevOps | Cloud

When an app breaks and there's no clear clue why, debug logs often hold the answers. They record what the code was doing at each step, making it easier to trace back and spot what went wrong. This guide covers what debug logging is, why it’s useful, and how to use it without turning logs into a wall of noise.

In the AI world, there’s a lot of buzz about creating custom large language models (LLMs) tailored for specific domains, perhaps for better security, context, expertise, or accuracy. It’s an appealing idea: What better way to solve your niche challenges than with a bespoke AI designed just for you? But here’s the thing — building a great LLM isn’t just challenging; it’s prohibitively expensive and resource-intensive.

Development teams managing Kubernetes-based applications face challenges in maintaining visibility and control over their deployment processes. Without a centralized interface, teams struggle to track, monitor, and manage releases across their Kubernetes clusters, leading to potential deployment errors, and difficulties in maintaining consistent deployment workflows.

Blind spots in frontend monitoring can occur when you’re managing complex modern applications. Browser and device variability, user journeys with intricate workflows and multiple touchpoints, and ephemeral frontend components can all create visibility gaps that make it difficult to identify, understand, and resolve the issues impacting user experience.