Agentic AI is reshaping software development in 2026. From LangGraph and CrewAI to Microsoft's new Agent Governance Toolkit, discover how autonomous agents are becoming production-ready teammates for infrastructure, security, and DevOps workflows.
The AI infrastructure landscape has undergone a seismic shift in 2026. From vLLM and TGI to NVIDIA Blackwell B200 and agentic systems, explore the technologies defining production-ready AI at scale.
A comprehensive guide to the agentic AI framework landscape in 2026. From LangGraph to CrewAI to OpenAI Agents SDK, we examine the trade-offs, use cases, and production considerations for building autonomous multi-agent systems.
By end of 2026, 40% of enterprise applications will embed AI agents. Explore the multi-agent frameworks, A2A protocol, security challenges, and practical applications of agentic AI in software development.
The DevOps landscape in 2026 is transforming through agentic AI, platform engineering maturity, GitOps standardization, OpenTelemetry adoption, and supply chain security requirements. From AWS DevOps Agent to self-architecting systems, discover how these converging trends are reshaping software delivery.
The AI infrastructure landscape of 2026: vLLM dominates inference, AMD and TPUs challenge NVIDIA, vector databases mature for RAG, and AI observability becomes essential for production ML systems.
How vLLM's PagedAttention innovation, multi-hardware support, and distributed parallelism strategies made it the dominant open-source LLM inference engine in 2026, delivering 2-4x throughput improvements.
A comprehensive comparison of the three dominant multi-agent AI frameworks—CrewAI, LangGraph, and AutoGen—helping enterprises choose the right foundation for their agentic AI systems in 2026.
The CNCF's new Kubernetes AI conformance program aims to solve portability and predictability challenges for AI workloads running on the 80% of enterprises already using Kubernetes.
The vLLM Korea Meetup 2026, held in Seoul on April 2nd, delivered more than just technical presentations—it offered a window into how AI inference infrastructure is consolidating around vLLM as a common layer. Hosted by the vLLM KR Community with support from Rebellions, SqueezeBits, Red Hat APAC, and PyTorch Korea, the event drew field engineers […]
vLLM v0.19.0 brings full Google Gemma 4 architecture support, speculative decoding with zero-bubble async scheduling, and significant Model Runner V2 maturation for improved throughput and efficiency.
The latest vLLM release adds Google Gemma 4 architecture support with MoE, multimodal, and tool-use capabilities, plus breakthrough performance improvements through zero-bubble async scheduling.
The vLLM project releases v0.19.0 featuring Gemma 4 architecture support, zero-bubble async scheduling with speculative decoding, Model Runner V2 enhancements, and ViT full CUDA graph capture for improved inference performance.
The latest LiteLLM releases bring cosign image verification, improved audit logging exports to S3, SSO security fixes, and a streamlined UI migration to Ant Design.
The first v1.50 preview release brings table pagination labels, improved entity relation cards, and BUI component migrations - here's how to upgrade your developer portal.
Six key takeaways from Amsterdam show cloud-native has moved decisively from experimentation to execution - with AI workloads, data sovereignty, and platform engineering dominating the conversation.
vLLM v0.19.0 ships with Google Gemma 4 support, zero-bubble async scheduling with speculative decoding, Model Runner V2 improvements, and contributions from 197 developers.
Ollama's latest release moves to Apple's MLX framework, unlocking unified memory benefits and faster local LLM performance on Mac.
OpenAI acquires Promptfoo, bringing AI security testing and prompt injection detection into its growing safety-focused product suite.
OpenClaw's March 2026 release removes nodes.run, hardens plugin security, and restructures background tasks into a proper control plane.
