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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

speedscale

Your Flaky Tests Are a Data Problem, Not a Test Problem

Mar 11, 2026 By Ken Ahrens In Speedscale
Your tests are not flaky. Your test data is. That 401 Unauthorized that fails every Monday morning? The OAuth token in your test fixture expired 72 hours ago. The order_id that works in staging but not in CI? It was hardcoded six months ago and the format changed from integer to UUID in January. The timestamp assertion that passes at 2pm and fails at midnight? You are comparing a hardcoded 2026-01-15T14:30:00Z against Date.now(). These are not test infrastructure problems. Retrying them will not help.
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speedscale

Runtime Validation vs Static Analysis: Why You Need Both

Mar 11, 2026 By Ken Ahrens In Speedscale
Runtime validation does not replace static analysis. They solve different problems. Static analysis catches structural defects in code before it runs. Runtime validation catches behavioral failures by testing code against real production traffic. Enterprise teams adopting AI coding tools need both layers because AI-generated code introduces a new class of defects that neither layer catches alone. According to CodeRabbit's State of AI vs Human Code Generation report, AI-generated pull requests contain roughly 1.7x more issues than human-written ones. Many of those issues pass static checks cleanly.
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speedscale

AI Coding Agents Have a UX Problem Nobody Wants to Talk About

Mar 11, 2026 By Kush Mansingh In Speedscale
The pitch was simple: let AI write your code so you can focus on the hard problems. Three years into the AI coding revolution, and developers are focused on hard problems alright, just not the ones anyone expected. Instead of designing systems and solving business logic, engineers in 2026 spend a startling amount of their day managing the AI itself. Should you use Fast Mode or Deep Thinking? Haiku or Opus? Cursor or Claude Code or Windsurf? Should you write a SKILL.md file or a custom system prompt?
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The fallacy of complacent distroless containers

Mar 11, 2026 By Canonical Ubuntu In Canonical
Join us on our deep dive into Chisel: the tool that brings enterprise-grade traceability to ultra-minimal container images. In this video, we explain why Chisel was created, and how it helps address security challenges in modern container images. We cover why container images often include unnecessary software and dependencies, why building minimal distroless containers can be difficult, and how missing metadata can lead to false confidence in vulnerability scans.
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virtualmetric

Update Management, Content Hub Expansion, and KQL Support

Mar 11, 2026 By VirtualMetric In VirtualMetric
The latest VirtualMetric DataStream release introduces several important capabilities across platform security, data management, and operational workflows. This update strengthens access protection, simplifies infrastructure management, and expands the ways security teams can work with live telemetry. It also extends platform connectivity and improves the user experience across many areas of the interface. Let’s take a closer look.
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canonical

The bare metal problem in AI Factories

Mar 11, 2026 By David Beamonte In Canonical
As AI platforms grow in scale, many of the limiting factors are no longer related to model design or algorithmic performance, but to the operation of the underlying infrastructure. GPU accelerators are key components and are responsible for a large part of the total system cost, which makes their continuous availability and stable operation critical to the output and efficiency of the entire AI platform.
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Resolve's Agents of IT podcast - S2Ep5 - Ari's Hot Takes #itautomation #claude #aiautomation #ai

Mar 11, 2026 By Resolve In Resolve
In this episode of Agents of IT, Ari Stowe and Ian Coppock unpack the recent Claude outage and what it reveals about our growing dependence on AI at work. From developers suddenly returning to Stack Overflow to the infrastructure challenges behind AI scaling, the conversation explores what happens when AI becomes critical enterprise infrastructure. They also discuss how organizations should prepare for AI outages, why “stampede adoption” is the new reality of AI releases, and what resilient, multi-agent architectures could look like going forward.
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circleci

MCP vs. CLI for AI-native development

Mar 11, 2026 By Jacob Schmitt In CircleCI
Summary: The CLI vs. MCP question is really a question about where you are in the development loop. CLIs fit the inner loop: fast, local, zero overhead. MCP servers fit the outer loop: external systems, shared infrastructure, structured access. Most teams need both. AI has put a new kind of scrutiny on developer tooling. When a developer works alongside an AI coding assistant, the tools that assistant can reach, and how it reaches them, directly affect the quality and speed of the work.
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