Operations | Monitoring | ITSM | DevOps | Cloud

Every growing business hits a moment of truth somewhere between "we just launched" and "we just lost a customer because our site was down for 40 minutes." It's then that you realize the free monitoring tool you set up six months ago—the one that felt more than adequate at the time—is no longer pulling its weight.

Table of Contents GPU sharing with NVIDIA hardware is becoming easier to adopt in Kubernetes but it hasn’t been easier to observe. Time-slicing lets multiple workloads share the same GPU. MPS allows CUDA workloads to execute concurrently. Schedulers like KAI make it easier to manage these shared environments. But sharing a GPU introduces a problem that is easy to underestimate.

Would a single click on Approve in your service desk satisfy an auditor reviewing a high-risk change, a purchase order, or a vendor contract? Often the answer only becomes clear when someone requests a signed copy and the ticket has nothing to show. Much of this traces back to terminology. Many organizations treat electronic signatures, digital signatures, and approvals as interchangeable, and ITSM tools tend to call every sign-off an approval.

How many minutes of battery runtime are left in the unit protecting your primary rack right now? Most monitoring deployments can answer a question like that for every switch, server and virtual machine in the building. Ask about power and the dashboard goes quiet. Backup power tends to fall between two owners. Facilities buys the hardware and books the service visits, while IT owns everything plugged into it.

To reduce vendor sprawl, start with inventorying what your tools do, who depends on them, and where their capabilities overlap. Then decide which tools to retain, connect, or replace. When going away from tool sprawl, you need to aim for a smaller toolset that your team can manage while still supporting the work your organization needs. This guide walks through spotting unnecessary fragmentation, assessing whether consolidating makes sense or not, and transitioning to it.

AI log analysis helps IT teams turn massive volumes of operational data into actionable insight. By applying statistical methods, machine learning (ML), semantic analysis, and generative AI, organizations can identify unusual behavior, connect related signals, and investigate probable root causes faster. But AI-generated answers should not be mistaken for proof.

Language servers give editors structured, real-time feedback: diagnostics, hover docs, autocomplete, and other guidance that would otherwise surface later. Language servers already exist for many of the languages and tools developers use every day, but Claude Code doesn’t automatically receive their feedback.

Based on the LeadDev panel discussion "Governance Is the Platform Problem Worth Solving," hosted in partnership with Harness, August 5, 2026. AI agents are no longer waiting for a human to approve their next move. They open pull requests, adjust configurations, and act on behalf of the people who deployed them — often faster than any review cycle can keep up.

94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. Based on the InfoQ webinar "Platform Engineering in the Age of AI," featuring panelists from Harness, DKB, and Shine, August 18, 2026. 94% of engineering leaders say the AI metrics that matter most to them are missing.