Operations | Monitoring | ITSM | DevOps | Cloud

AI That Matters: Driving Real Outcomes in Network Operations

AI can be a transformative tool in network operations — but only when it’s tied to clear, measurable outcomes. Rather than chasing hype, IT and NetOps teams should focus on solving specific operational challenges like reducing MTTR, cutting costs, and stabilizing infrastructure. AI has real potential when strategically applied, and when aligned with business goals, it becomes a powerful ally in modern network operations.

GitKraken Desktop 11: Meet Your New Development Co-Pilot

Written by author, adapted by AI GitKraken Desktop 11.0 is here, and it’s more than just a version bump. We’re introducing AI-powered features designed to accompany your workflow and help you stay focused on what matters most. It’s changed how I approach commits, and I’m sure it will help y’all too!

Beyond the Bots: Is AI that Only Talks Already Obsolete?

It started with promise: deploy a chatbot, cut service desk costs, and deliver instant support to employees anytime, anywhere. And many large organizations bought in. But several years into this so-called “chatbot revolution,” the results tell a different story—one of inflated expectations and underwhelming outcomes. It’s time to face a hard truth: AI that only talks is no longer enough.

Honeycomb Acquires Grit: A Strategic Investment in Pragmatic AI and Customer Value

We’re excited to share that Honeycomb has completed our first-ever acquisition: we’re joining forces with Grit, bringing on board not only a strong team, but also compelling technology that supercharges our ability to deliver on our mission: to bring observability to every software engineer. This is a strategic move that will help us deepen the value we deliver to customers and accelerate our vision for what modern observability can and should be.

Metrics That Matter: Measuring Developer Productivity in the AI Era

In this episode, Ryan McDonald is joined by Mark Quigley, Head of Platform Engineering at Ninety.io, for a conversation that cuts through the noise around developer productivity metrics and AI. Mark dives deep into how teams can measure what matters—without falling into the trap of turning every measure into a target. He shares how tools like Developer NPS, DORA metrics, and balanced scorecards can help teams optimize for both output and well-being—but only when framed with the right intent.

Elastic extends production-ready AI capabilities for all!

Elastic Security is making your organization safer with general availability of our favorite AI features. Elastic Security is announcing the general availability (GA) of two of our most widely deployed generative artificial intelligence (GenAI) capabilities: Attack Discovery, launched in May, and Automatic Import, launched in August. Elastic’s AI-driven security analytics are providing immense value to many organizations.

How Sentry's AI Autofix Changed my Mind About AI Assistants

Blockchain, IoT, Big Data. If you’ve been around in tech for a while, you know that these kinds of buzzwords come and go: they make a splash going in and fizzle out over time. Seeing many of them come and go over the years has made me skeptical. What are they trying to sell us this time? Some might call it getting grumpy; others might call it becoming an enterprise architect. So you’ll have to forgive me for thinking AI agents seemed like just another buzzword.

CircleCI MCP server: Natural language CI for AI-driven workflows

The pace of software development has changed. With AI coding assistants now embedded into engineering workflows, developers are building faster, shipping sooner, and writing more code than ever before. But as velocity increases, so does the complexity of keeping that code running. When builds fail, developers need answers fast. They need clarity, context, and actionable feedback right where they’re working.

AI Feature Pricing: How To Monetize AI Without Losing Money

If you’re operating a SaaS company these days, chances are you’ve got at least some AI features already on the market and others in the pipeline. That also means you’ve likely encountered one of the top problems with AI in today’s market: It can be prohibitively expensive — to the point where you gamble with actually losing money rather than making a profit with the release of a new AI feature.