Operations | Monitoring | ITSM | DevOps | Cloud

By 2026, the GPU shortage isn’t a supply-chain hiccup anymore. It’s baked into the system. Even after pouring billions into CapEx, most enterprises still want 40% more GPU capacity than they actually have. And it’s not because they’re chasing moonshots. Technology companies are training foundation models while serving inference for millions of users on the same clusters. AI labs are juggling fine-tuning, evaluation, and real-time experimentation side by side.

On March 2, 2026 at 23:30:24 UTC, we experienced an issue where the Zoom AI scribe was unable to join calls, rendering Zoom meeting transcription unavailable for all users. On March 2, 2026 at 23:30:24 UTC, we experienced an issue where the Zoom AI scribe was unable to join calls, rendering Zoom meeting transcription unavailable for all users. The issue persisted from approximately February 28 through March 5, 2026.

When a critical incident triggers, there’s no time to figure out who to call. That decision needs to be made well before the incident arrives. A dedicated escalation policy for critical incidents gives your team a clear path to follow the moment things go wrong, rather than leaving it to whoever happens to be around. This guide covers the key decisions involved in building that policy.

Low-priority incidents are easy to deprioritise in the moment. Each one feels small and manageable. But without a proper escalation policy, they pile up quietly and reviewing them later often tells a different story. A simple escalation policy is usually all it takes to keep them from slipping through entirely.

Short Summary: In this guide, we address performance problems that occur when PostgreSQL queries very large tables containing low-cardinality boolean fields. It also demonstrates how composite indexes, created and tested with Devart tools, allow PostgreSQL to locate the required rows directly instead of scanning the entire table.

Short Summary: This article explains how to expand your CLI query tool so it can handle multiple databases. First, we focus on key relational systems, then move to data warehouses. We’ll use dbForge Edge as a reference point, showing how different databases can be managed in one place while keeping automation simple.