Operations | Monitoring | ITSM | DevOps | Cloud

With PostgreSQL 19 on the horizon, query planning could get smarter thanks to new query tools and faster maintenance. The release is still a few months away, but Beta 1 already shows where PostgreSQL is headed. Released on June 4, 2026, PostgreSQL 19 Beta 1 previews most of what’s planned for the final release.

On June 17, 2025, AT&T permanently shut down its email-to-text and text-to-email gateway. Emails sent to @txt.att.net and @mms.att.net stopped reaching phones, and any automated workflow that relied on that address went dark overnight (AT&T support) . For IT Ops, MSPs, facilities and energy ops and incident response teams, this was not a minor inconvenience.

In January, we announced that Grafana Labs had assumed maintenance of the business intelligence (BI) plugins created by Volkov Labs, and committed to a six-month maintenance period. Today, we’re sharing an update: we're extending our maintenance commitment through the end of 2026. As announced earlier this year, that commitment includes maintaining compatibility with recent Grafana releases while handling bug fixes, security updates, and community contributions on a best-effort basis.

Kafka is brilliant at moving events around, but sooner or later someone wants to actually query those events, perhaps aggregations, dashboards, or ad-hoc analytics over billions of rows. That is where ClickHouse comes in. It's the option for when stream processing is more than you need, but warehouse query latency is more than you'll tolerate.

The first stage of AI SRE maturity is a laptop, a throwaway cluster, and zero production access. Here’s how to set it up, and what to watch for. AI SRE (Site Reliability Engineering) agents are AI-powered systems that automate the most time-consuming parts of incident response: triaging alerts, correlating logs and metrics, generating root-cause hypotheses, and proposing remediation steps.

Modern infrastructure teams are expected to deliver cloud-like speed, consistency, and reliability, even when their workloads run on physical servers. Bare metal remains essential for many environments: private clouds, Kubernetes clusters, AI infrastructure, edge sites, regulated platforms, and large Ubuntu estates. But operating physical infrastructure at scale is difficult when provisioning, patching, monitoring, and lifecycle management are handled by disconnected tools and manual processes.

This is a follow-up to Sergiy’s post Errors, traces, logs, metrics: when to reach for what. Modern observability platforms, like Sentry, give developers a lot of choice. For a given problem, should you use traces, profiles, metrics, logs? If you take away one thing from this post, I hope it’s this: when in doubt, start by adding a few targeted log lines.

How many times should your service desk solve the same problem before it becomes shared knowledge? A senior agent on a 14-person service desk we worked with last quarter had answered the same question four times in two days for four different employees. The solution was already documented but buried in a wiki nobody could find. That is exactly the gap ITSM knowledge management is designed to close.

What makes one endpoint management tool better than another? Not the feature list. Almost every tool claims patching, asset tracking, and automation. What matters is whether it holds up across a few hundred machines, and how much time it hands back to your team. For most IT teams, a good tool needs to: We looked at 10 of the best endpoint management software tools for 2026. We read through G2 and Gartner Peer Insights ratings, checked vendor pricing pages, and went through user reviews.

In the previous post in this series, we covered why Virtual Machine (VM) Live Migration in Kubernetes is difficult: a VM’s IP is its identity, and the “new” VM on the destination node has to come up with the same IP, this something that Kubernetes is not known for, and on top of that, traffic has to switch over only after network security policies are in place.