Operations | Monitoring | ITSM | DevOps | Cloud

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

Asset management software is becoming the backbone of modern organizations in 2026. With rising demand for automation global compliance pressure and the need for accurate real time visibility companies are rapidly shifting from outdated manual processes to intelligent digital asset management solutions.

For most Central IT leaders, tech debt isn't a surprise. It's the silent tax on every roadmap, every quarterly plan, every conversation about why things take so long. Modern cloud application platforms (true PaaS environments) give IT leaders a path to unwind years of accumulated complexity while simultaneously accelerating innovation. You no longer have to tolerate the tax.

Engineering teams today manage infrastructure spread across multiple clouds and tools. Whether this happened through gradual accumulation or deliberate strategy, the result is the same: complexity that slows teams down. Managing each cloud separately with different tools and workflows is a bottleneck to delivery speed, operational efficiency, and platform reliability.

If there’s one thing clear from our AI-powered observability webinar, it’s that observability has officially graduated from a “nice-to-have” to a business-critical discipline, and AI is helping lead that charge. Our webinar brought together guest speaker Stephen Elliott, Group VP at IDC, and Ranbir Chawla, former SVP of Engineering at RB Global, for an hour of insights that mixed data, experience, and hard-won lessons from the trenches.

Most AI programs don’t fail on model quality. They fail because the experience makes people either over-trust or quietly avoid the system. Employees often use AI more than leaders realize, frequently without training or guardrails. Interfaces that just “show an answer” without confidence, provenance, or recourse create two risks: blind reliance and shadow use.

Since launching Harness in 2017, we’ve been on a mission to unlock faster innovation by removing the bottlenecks that slow software engineering teams down. From day one, we believed that the biggest obstacles in engineering weren’t in writing code — they were in everything that followed.

Choosing the proper Home Assistant hardware can be overwhelming. Whether you’re new to home automation or a seasoned pro, the hardware you select can make or break your experience. This comprehensive guide will demystify the requirements, delve into the various options, and help you make an informed decision. From the compact Raspberry Pi to the powerful Intel NUC, we’ve got you covered. So, strap in, and let’s dive into the world of Home Assistant hardware!

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.

In our increasingly online world, managing cloud, AI, and other tech spend has shifted from a good idea to an absolute necessity. But even when cost management is a priority, how do you get busy development teams and engineers actively engaged in the new practices? New initiatives are often viewed as more work on the team’s plate, which is an understandable deterrent to adoption. That leaves FinOps proponents struggling to get others on board.