Operations | Monitoring | ITSM | DevOps | Cloud

An application slows down every month-end. You open Hyper-V Manager, the virtual machine looks healthy, and the ticket closes without a cause. Next month it happens again. Native consoles show live state and keep no history behind it, so nobody can prove whether the fault was the guest, the host, or a storage path shared with nine other workloads. That gap sets what a Hyper-V deployment costs to run, measured in unplanned downtime and in engineering hours spent guessing. This guide covers.

Routine vulnerability fixes are rarely difficult in isolation. The interruption that they cause is the problem: pick up the ticket, trace the dependency, update the package or image, regenerate files, run the checks, open the pull request, then return after deployment to close the loop. That repeated handoff was a good candidate for automation with agentic pipelines. The goal was simple: start the day with a tested pull request instead of another ticket to pick up.

Canonical is now part of the Open Secure AI Alliance, announced by NVIDIA with partners across cloud computing, cybersecurity, enterprise software, open source foundations, and AI research. The alliance aims to develop and share open technologies, techniques, and tools to safeguard software and agents in the age of AI.

Across the global landscape, telecommunications operators have already deployed machine learning for predictive maintenance, customer care chatbots, and anomaly detection. However, as the industry transitions toward Autonomous Networks Level 4 (AN L4), where networks can make intent-driven, predictive decisions and perform closed-loop management with minimal human intervention, a fundamental architectural challenge has emerged.

Giordano “Gyo” Piazza, GitKraken’s Director of Engineering, has spent the past month heads down on the next version of Kepler. When he got on a call to talk through it, he skipped the changelog and went straight to the part he actually wanted to talk about: how agents show their work.

Database monitoring is the continuous tracking of a database's health, performance, and resource consumption, covering metrics like query latency, throughput, connections, and replication lag. In the AI era, it has expanded to include a fifth dimension: spend. Modern teams monitor not just whether the database is fast, but whether the workload it serves is worth what it costs to run. In May 2025, an engineer named Bohan Zhang stood up at PGConf.dev in Montreal and broke every scaling rule in the book.