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The Observability Gap: Why Monitoring Data Should Drive Tests

Most teams already know a lot about production. They have dashboards. They have traces. They have alerts. They have enough telemetry to explain what happened after an incident and enough graphs to argue about it for the rest of the week. Then they go to test a change and start from scratch. The integration tests hit a hand-written mock that returns {"status": "ok"}. The load tests replay a CSV somebody exported months ago. Staging is close enough to production right up until it matters.
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AlmaIQ brings unparalleled level of efficiency and effectiveness for IT teams using Collective IQ

AlmaIQ, the intelligent self-service agent for employees just received an incredible boost that expands its role to uniquely help IT teams. Interacting with users through Microsoft Teams, AlmaIQ answers questions about devices and internal processes in natural language. Whereas that intelligence simplified employees lives on the job, it now enables IT teams to interact with Collective IQ at the level of departments, groups, and collections of devices to spot patterns and trends. The overall result: vastly more productive operations and satisfied employees.

Getting Scout Data Into Your AI Workflow

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.

Why This Fortune 500 Chose Agentic AI Over Traditional AIOps

What does real enterprise-ready Agentic AI look like in production? In this video, we break down how a Fortune 500 enterprise used Fabrix.ai’s Agentic AI platform to detect, diagnose, and resolve a critical application issue in just 5 minutes—without moving their data or replacing existing tools. If you're exploring Agentic AI, AIOps, or enterprise automation, this is a must-watch.

Groq vs. GPUs: The future of AI inference in 2026

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.

Women's Day Panel: Navigating the Future of Engineering in the Age of AI

How is AI reshaping engineering—and what does it mean for the future of work? At our first GTA Boston Hub event of the year, we brought together engineering leaders from Boston Consulting Group and Athenahealth to dive into one of the most pressing topics today: the rise of generative AI. In this panel, we explore: Key takeaway: This isn’t “human vs AI”—it’s human augmented by AI. The real advantage lies in how we adapt, collaborate, and lead in this new era.

The Secret to 10x Faster API Testing #speedscale #apitesting #api #automation #production

Stop living in the past. See how to use real production traffic to automate your API testing with zero code changes. Replay real-world patterns in your CI/CD and catch regressions before your users do. Learn more: speedscale.com.

One CLI, Two Audiences: How We Built for Agents and Human

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.