Operations | Monitoring | ITSM | DevOps | Cloud

If you’ve spent any time in developer tooling lately, you’ve probably noticed a pattern: every product is rushing to add a chatbot, an AI summary, or some kind of “magic” button. We get it — it’s tempting. But at Scout, we’ve been deliberately taking a different approach. Instead of building AI into our product first, we’ve focused on making Scout’s data accessible to the AI tools you’re already using.

Network topology describes how devices, connections, and data flows are arranged in a network. It is the blueprint of your network infrastructure. Both matter because they affect performance, scalability, and security. A well-designed topology ensures data reaches its destination quickly and reliably. A poor design creates bottlenecks, security gaps, and troubleshooting nightmares.

Back in 2016, Jonathan Ross founded Groq, the AI chip startup, which went on to enter a non-exclusive licensing agreement with NVIDIA for Groq’s inference technology (as part of a $20 billion deal). The name ‘Groq’ is commonly confused with X (formerly Twitter)’s Grok, which was launched in 2023 as a Gen AI chatbot. As demand for real-time AI continues to grow, inference has become one of the most important and expensive parts of the machine learning lifecycle.

Performance optimization often feels like searching for a needle in a haystack. You know your code is slow, but where exactly is the bottleneck? This is where continuous profiling comes in. In this blog post, we’ll explore how continuous profiling with Alloy and Pyroscope can transform the way you approach performance optimization.

You're tagging Docker images with build numbers. -Build is your latest production release on main. A developer pushes a hotfix to release-v2.1, that run becomes build. -Another merges to develop, build. A week later someone asks: "What build number are we on for production?" You check the registry. -You see,,, on main. The numbers in between? Scattered across feature branches that may never ship. Your build numbers have stopped telling a useful story.

Telegraf is incredibly good at what it does: collecting metrics, logs, and events from just about anywhere and sending them wherever you need. But once Telegraf becomes part of your production telemetry pipeline, spread across environments, teams, regions, and edge locations, the hard part isn’t installing agents; it’s operating them. Configs drift. “Temporary” overrides linger. Rolling out changes across hundreds (or thousands) of agents becomes a careful, manual process.

Half of the Checkly CLI users are already coding agents. This is not a prediction — it's what the data shows today. Since February, more and more agents have been using the CLI to manage and configure their Checkly monitoring setups. Right now, we're at 50% human and 50% agentic CLI users. And we predict that by the end of 2026, it won't be humans using the CLI; the agents will have taken over. The terminal became the primary interface for AI agents doing real work in the Checkly ecosystem.

November 24th, the Opus 4.5 release turned around the entire tech industry. This was the moment when agents became capable. Capable enough to write solid staff-level code. Capable enough to reason about alerts, investigate root causes much faster than most engineers, and set up the reliability layer faster. For me, this feels like an iPhone moment on steroids; the adoption of AI is accelerating much faster than any adoption curve I’ve seen over the past few decades.

The quick download: AI reduces MTTR by helping teams detect issues sooner, pinpoint root causes faster, and resolve incidents with less manual effort. IT downtime costs organizations an average of $9,000 per minute. AI-powered observability can cut incident resolution time by up to 70%. Here’s what it takes to get there. Every minute an incident goes unresolved, the meter is running.

As organizations adopt AI-assisted development and increase their release velocity, they are not only generating more code but also finding more vulnerabilities from static analysis. The traditional remediation workflow of manually triaging issues, creating tickets, and opening individual pull requests (PRs) cannot keep pace. Fixing tens of thousands of vulnerabilities one by one is not a viable remediation strategy.