Platform engineering is not merely DevOps renamed—it represents a fundamental shift in how organizations build internal developer platforms that reduce cognitive load and accelerate delivery.
The AI revolution is shifting from training to inference. Explore how vLLM, TensorRT-LLM, and MLOps practices are reshaping computing infrastructure for the inference era.
The gap between agentic AI adoption (79%) and production deployment (11%) defines where we stand in 2026. From multi-agent orchestration to guardian agents for governance, this article explores the five key trends shaping autonomous AI systems.
Kubernetes positions itself as the definitive operating system for AI data centers with 15.6 million cloud native developers and AI conformance standards expanding rapidly.
Kubernetes v1.36 (Haru) brings User Namespaces to GA after 6 years of development, introduces tiered Memory QoS protection, and adds alpha support for Workload Aware Scheduling — marking a significant evolution in container security and resource management.
A comprehensive comparison of vLLM, TensorRT-LLM, TGI, and SGLang—the four inference engines dominating AI infrastructure in 2026. Plus the MLOps tools and hardware trends shaping the serving landscape.
Platform engineering has a crisis: 70% of platform teams fail to deliver measurable impact. This article explores why platforms fail, what success looks like, and how to build developer-first platforms that actually get adopted.
