Operations | Monitoring | ITSM | DevOps | Cloud

Build a Docker Monitoring Dashboard in Minutes with Claude MCP + Uptrace

In this video, we use Claude MCP to create and merge Docker container dashboards in Uptrace — directly from the terminal, no manual clicking required. What you'll see: CPU, memory, network, and disk I/O dashboards created with plain text prompts Two dashboards merged into one unified view Dashboard exported as YAML for version control.

3 Things Leaders Must Know About Scaling AI

AI is moving faster than ever, but is your governance keeping up? In this video, Brooke Johnson, Ivanti’s Chief Legal Counsel and SVP of People and Security, breaks down the critical gap between AI adoption and responsible scaling. While speed is rarely the issue, trust and accountability are becoming major roadblocks for IT teams. We explore why nearly 70% of IT pros have witnessed AI hallucinations and how unclear ownership can stall even the most advanced AI initiatives.

Chaos Testing Just Got Easier | New ChaosHub & AI Prompt Library | Resilience Testing | Harness

In this video, we explore two major improvements to the Harness Resilience Testing documentation designed to help you build and manage chaos experiments more efficiently. What's new: ChaosHub Integration AI Prompt Library for Harness MCP These updates make it significantly easier to discover chaos experiments and leverage AI throughout your chaos engineering workflow. If you're using Harness Resilience Testing, this walkthrough will help you get started quickly and make the most of the new documentation experience.

Building a Control Framework for the AI SDLC

Since November, Kosli’s own engineering team has been running a live experiment: what happens to code review when the thing generating the code - and increasingly, the thing reviewing it - is an AI, not a person. Alex Kantor, Kosli’s Director of Technology, walked through that experiment in this webinar: what broke, what it cost to fix, and what four “obvious” assumptions in a standard code review control turned out not to hold once you took the human out of the loop.

Why Internal Agents Must Be Rebuilt with Runtime Context

As we entered 2026, enterprises raced to build internal AI engineering agents, automating incident response, code review, and support. The investment was real, but 88% of these pilots never reached production, and teams are now in rebuild mode, trying to understand why. Live runtime validation was the key architectural decision skipped in these v1 agents and it’s still missing from many v2 designs. Agents need to verify their reasoning against production before they act.

Embracing the Code Review Bottleneck

Roughly a year ago, I left Honeycomb’s SRE team to join the newly formed Tenant team, which works on our Private Cloud offering. This team held some significant challenges on its roadmap if it wanted to demonstrate that the offering was possible, would be worth the cost, and could be done without representing a heavy tax on the rest of the organization.

Monitoring Your Django App Health on Fly.io

Fly.io is a neat choice for deploying Django fast and globally. What it doesn’t really give you out of the box, though, is a deeper picture of an application’s performance. Deployment is only part of the story. No matter which platform you’re using, operating a production application means you need to understand how it behaves. AppSignal helps you fully grasp what happens on the Fly.io server.

Password Policies in Icinga Web

Icinga Web 2 now ships a PasswordPolicyHook that gives administrators and module developers full control over what constitutes a valid password. Instead of hard-coding a single rule set for every deployment, the hook makes password validation an extension point: any module can register a policy, admins select the active one from the configuration UI, and Icinga Web 2 enforces it everywhere a local password is set or changed.

Cost attribution in Grafana Cloud: Manage spend across observability and testing workflows

Knowing what you're spending on observability is useful. Knowing which team, service, or project is driving that spend is what actually lets you act on that information. Cost attribution is a core part of how Grafana Cloud approaches cost management and optimization.