Stack Archive · Specialized Directory

Cloud Platform Engineering

The platforms under the platforms — Kubernetes, infrastructure as code, and the engineering behind reliable cloud foundations.

249analyses
14blueprints
Oct 14updated
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Azure Local on Galleon: When Cloud Must Work Without the Cloud

Azure Local on Armada's Galleon MDC delivers sovereign AI inference that runs fully disconnected, without cloud dependency, for defense and regulated industries.

· 3 min

KubeCon 2026: Kelsey Hightower on AI and the Expertise Reset

Kelsey Hightower at KubeCon: when AI moves this fast, expertise resets. What 'everyone is junior on AI' means for platform engineering teams building on open source.

· 3 min

LLMs on Kubernetes: The Four Threats That Actually Matter

Kubernetes has no visibility into what LLM workloads do. CNCF maps OWASP's Top 10 LLM risks to the infrastructure patterns platform teams already know.

· 3 min

AI-Generated Code and Open Source Risk: What OSSRA 2026 Found

The 2026 OSSRA analyzed 947 codebases and found 581 vulnerabilities per codebase on average — up 107%. Here's what AI-generated code has to do with it.

· 2 min

WebAssembly vs Containers: What WASI 0.3 Actually Changes

WASI 0.3 finalizes the WebAssembly component model, enabling 5–10x faster cold starts than containers. Here's what it means for platform engineering teams.

· 2 min

The $43,800 Hidden Tax on Kubernetes: How Virtual Clusters Fix Platform Team Sprawl

Cluster sprawl consumes 25–30% of platform engineering time. Virtual clusters eliminate the hidden tax — without sacrificing the isolation teams actually need.

· 3 min

AWS Bedrock AgentCore: Architecting Agentic AI for Production

AWS's architectural blueprint separates agentic AI into three layers — orchestration, tools, and knowledge — closing the gap between prototype and production.

· 3 min

The Platform Under the Model: Why Cloud Native Infrastructure Determines AI Production Success

82% of container users run Kubernetes in production. Only 41% of AI developers identify as cloud native. That gap is where most AI production failures happen.

· 3 min

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