Operations | Monitoring | ITSM | DevOps | Cloud

OpenLLMetry extends the industry-standard OpenTelemetry framework with features specifically for monitoring large language model (LLM) and GenAI application behavior.

In 2008, Nuance hired me to join their Healthcare Speech Recognition team as a “Release Engineer.” DevOps wasn’t a thing yet — Patrick Debois and Andrew Shafer wouldn’t hold their first “DevOpsDays” until 2009. But I was lucky that “Release Engineer” at Nuance meant “jack of all trades” who wrote Makefiles, bash scripts, Perl, and Java to build and release code to a fleet of hundreds of on-premise Linux machines.

Every customer lost costs telecom providers five to seven times more than keeping an existing one. The industry experiences 15-25% annual churn rates, resulting in billions of dollars in lost revenue, as competitors readily absorb these dissatisfied customers. Warning signs typically manifest before departure, but traditional detection methods often identify them too late, after customers have already decided to switch providers.

Tips and best practices to help you get up and running with FireHydrant's Model Context Protocol integration. Manage incidents, alerts, and retrospectives directly through AI assistants like Claude or Cursor. Welcome to the FireHydrant MCP Server user guide! This guide will help you get up and running with FireHydrant's Model Context Protocol integration, allowing you to manage incidents, alerts, and retrospectives directly through AI assistants like Claude or Cursor.

TL;DR Missed GitKraken AI All Access Week? Here’s what went down, why devs are still buzzing about it, and how you can still get in on powerful AI tools that level up your Git workflow.

Some people can get an AI assistant to write a day’s worth of useful code in ten minutes. Others among us can only watch it crank out hundreds of lines of crap that never works. What’s the difference? There are some skills specific to AI development. There are also properties of the codebase we’re working in that make it amenable to AI assistance. Most AI demos use projects created from scratch with AI in mind—cute.

When I spoke at AWS London earlier this year, I had the chance to discuss something that more and more teams are starting to feel: traditional observability doesn’t cut it for AI systems. In AI, “Is it running?” is no longer enough. We have to ask, “Is it right?” When I delivered that line, I saw the heads nodding. Everyone’s excited to build with LLMs, but when it comes to actually monitoring them in production? That’s where things fall apart.

Cloud Observability’s curated Application Monitoring dashboards improve troubleshooting with best practices from Google SREs.