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#055 - From Enterprise Java to Kubernetes and AI-Driven Infrastructure with Dan Hicks (Boomi)

Dan breaks down the fundamental similarities and stark differences between application development and platform engineering. He shares the unexpected hurdles he faced during his transition, from complex networking and CoreDNS latency to the harsh realities exposed by chaos testing in cloud environments.

Agno Monitoring & Observability with OpenTelemetry and SigNoz

Learn how to implement end-to-end monitoring and observability for Agno-based AI systems using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Agno workflows, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability—so you can debug faster, optimize latency, and confidently run AI systems at scale.

Secure and Compliant DevOps in an AI-Enabled World

Is Your DevOps Strategy Ready for the AI Era? AI is accelerating modern software delivery—but it’s also raising the stakes for security, compliance, and auditability. As AI-driven change increases, many organizations are discovering that incomplete DevOps practices are creating new risk. Based on insights from 800+ global IT professionals, the 2026 State of DevOps Report reveals why vendor‑backed, enterprise‑grade DevOps platforms are becoming critical for managing AI‑driven risk and meeting evolving regulatory demands.

Playwright Myths Busted: Speed, Flakiness, Production Monitoring & AI Test Generation

Playwright is too hard, too slow, and too flaky — right? In this webinar, Stefan busts six common end-to-end testing myths and shows how to reuse your Playwright tests as production monitors with Checkly. He covers codegen, trace viewer, UI mode, flakiness root causes (and fixes), and a quick look at Playwright MCP for AI-assisted test generation.

Why SaaS is Dying (and what's next) #speedscale #saas #data #datasecurity #devops #technews

Traditional SaaS is a data trap. It’s time to stop sending your most valuable asset to third parties. Enter BYOC (Bring Your Own Cloud): the future of data sovereignty, where the software comes to you. Visit: speedscale.com.

Checkly Playwright Reporter: A Cloud Dashboard for Your Playwright Tests

The Checkly Playwright Reporter is an npm package that sends the results of npx playwright test to Checkly as a cloud test session, including traces, screenshots, videos, and full debugging context. Run your Playwright suite in CI or locally, and every result gets a persistent, shareable home in Checkly with AI-powered analysis, richer trace-derived views, and a direct path to production monitoring. It does not replace Playwright. It makes the output of Playwright much easier to work with.

Defeating Context Rot: Mastering the Flow of AI Sessions | Harness Blog

In Part 1, we argued that most dev teams start in the wrong place. They obsess over prompts, when the real problem is structural: agents are dropped into repositories that were never designed for them. The solution was to make the repository itself agent-native through a standardized instruction layer like AGENTS.md. But even after you fix the environment, something still breaks. The agent starts strong.

AI Is an Amplifier, Not a Shortcut

There’s a version of the AI story that engineering leaders want to hear. It goes like this: adopt AI coding tools, watch output multiply, ship faster, do more with less. Clean. Simple. Boardroom-ready. The data tells a different story. Not a worse one. Just a more honest one. We recently analyzed 2,172 developer-weeks of real coding activity across teams using GitHub Copilot, Cursor, and Claude Code. The headline numbers are striking: power users show 4-14x higher activity than non-users.

What Metrics to Monitor in Your Vibe Coded App

These days, using a tool such as Cursor, GitHub Copilot, Zed, or Claude makes it easier than ever to develop and deploy applications. You express your requirements, receive the completed project back as output, and there you have it! You now have an application that is in production and functioning. However, the surprise comes after the app has been deployed. When your app breaks or behaves abnormally, it may not be immediately obvious what is wrong or how to fix it.