AI 2026: Infrastructure, Agents, and the Next Cloud-Native Shift
2026 AI’s turning point: not models, but infrastructure, agentic runtimes, GPU efficiency, and new organizational forms.
Cloud Native
What I Saw at COSCon'25: The Real State of Open Source in China
From an engineering and organizer’s perspective, real changes at COSCon'25: AI as the default backdrop, discussions returning to engineering issues, and Chinese open source …
The Second Half of Cloud Native: The Era of AI Native Platform Engineering Has Arrived
A decade of cloud native evolution, a look ahead to AI-Native Platform engineering, technical layers, and key changes. KubeCon NA 2025 signals a new era.
Closed-Source Flagships and the Open-Source Twin Phenomenon
Analysis of closed-source model acceleration and open-source ecosystem response, exploring core engineering contradictions and infrastructure evolution.
Lessons from Ingress NGINX Retirement
The retirement of Ingress NGINX reveals technical debt, migration paths, and the trend toward standardized traffic management in cloud native infrastructure.
What Makes an AI Platform Truly Kubernetes-Native?
Discover what defines a truly Kubernetes-native AI platform, key criteria for conformance, and how standardization drives interoperability and growth in cloud-native AI …
Building Efficient LLM Inference with the Cloud Native Quartet: KServe, vLLM, llm-d, and WG Serving
Essential reading for cloud native and AI-native architects: how KServe, vLLM, llm-d, and WG Serving form the cloud native ‘quartet’ for large model inference, their …
The Natural Fit Between AI Inference and Kubernetes
Explore why Kubernetes is the ideal runtime for AI inference — delivering elastic, cost-efficient, low-latency model serving with GPU-aware autoscaling, versioning, and …
From Kubernetes to Qwen: How "Open Source" Has Changed in the AI Era
Exploring the transformation of open source in the AI era, from Kubernetes to Qwen, and revealing the fundamental differences and new opportunities in open source strategies …
YAML to Markdown: Exploring Specification Driven Development and AI-Native Paradigms
Exploring the evolution from YAML to Markdown, revealing how specification-driven development in the AI-native era reshapes the behavior and collaboration of intelligent agents.