Operations | Monitoring | ITSM | DevOps | Cloud

On May 14, 2026, users across multiple regions began reporting problems with Slack, including messaging failures, sign-in issues, and problems loading attachments and images. While the outage did not affect every user, reports quickly showed the issue was widespread enough to disrupt business communication for organizations around the world. StatusGator identified the incident through customer outage reports and triggered an Early Warning Signals alert at 14:21 UTC.

IncidentHub's latest product updates include a new Business plan with Teams support, early outage detection v1, and more integrations with ticketing systems. The public status now includes a disable feature. As before, many features are driven by feedback, and I am grateful to all our customers who have shared their feedback with us.

OpenTelemetry has become the default way to instrument applications and ship telemetry. The hard part has never been the data model. It’s been picking a backend that handles OTLP without quietly turning into a per-metric bill or a black box that swallows your data. Netdata is a native OTLP backend.

The discussion around AI in infrastructure and operations has become increasingly model-centric. Teams want to know what model a platform uses, how current it is, how much reasoning capacity it has, and how quickly it can be updated as the model landscape shifts. Those are reasonable questions, but they tend to arrive too early. In production operations, the more consequential question is what happens to the data before any model is asked to interpret it.

meshIQ MFT Flow Intelligence converts fragmented file transfers into observable transaction ecosystems, ensuring secure, timely delivery across hybrid environments while reducing operational risk and enhancing regulatory compliance for modern enterprise operations.

Every second of a flight test produces a torrent of telemetry from engines, sensors, and control systems. Aerospace teams have captured this data for decades to verify performance and maintain safety, yet analysis often happens long after the mission ends. Engineers wait for downloads, conversions, and compliance checks before they can interpret results. That delay turns telemetry into a historical record instead of a feedback loop.

The first time an AI assistant suggests "restart the service" during a live incident and nobody on the bridge can tell whether that suggestion came from a current runbook, a stale wiki page, or thin air, you stop caring about model benchmarks. You start caring about what the agent actually knew, where that knowledge came from, and whether you can trust the chain of reasoning behind it.

Here is a reality that every network admin eventually runs into: users report slow apps, dropped calls, and broken connections, and the first instinct is to blame the ISP or the cloud provider. The ticket gets escalated, the ISP pushes back, and hours later, you find out the problem was sitting inside your own building the whole time. A saturated switch port. A misconfigured VLAN. A flaky patch cable in the server room.