Artificial intelligence

Learn how the Red Hat OpenShift AI observability summarizer transforms raw time-series data from Prometheus into actionable, human-readable insights for platform teams. Discover the five-layer pipeline architecture and how it reduces noise and increases signal for a focused answer.

This article describes an enterprise-preferred topology that separates fleet management from cluster hosting for independent scaling and clean operational boundaries.

This article discusses the benefits of diffusion LLMs, a revolutionary approach to language models that offers a dynamic tradeoff between accuracy and performance. The article covers the architecture, evolution, and real-world statistics of this technology, including examples of open source models like LLaDA 2.X and Mercury 2.

Discover how Helion, a Python embedded domain-specific language, abstracts low-level parallelism details to allow developers to write GPU operations using simple, intuitive PyTorch-like syntax. Automatically generate hundreds or even thousands of Triton variants for optimal performance.

Learn how to use OpenViking context database instead of traditional flat vector storage to provide AI agents with persistent, structured memory.

This article describes how to onboard a project and the results using two tools, CodeCov and CodeRabbit.

Discover OpenCode, a model-neutral AI coding assistant that supports over 75 providers, including OpenAI, Anthropic Claude, Google Gemini, and local large language models (LLMs) via Ollama. Switch models on demand, compare outputs, avoid vendor lock-in, and even run fully offline with local models. Learn how to set up your environment in Red Hat OpenShift Dev Spaces.

Learn to modernize enterprise Java for Kubernetes using the four-path framework. Explore cloud-native patterns like Sidecars, Sagas, and RAG to build resilient, AI-ready systems.

Learn how to combine KServe and llm-d to optimize generative AI inference, improve performance, and reduce infrastructure costs. This article demonstrates the integration architecture and provides practical guidance for AI platform teams.

Learn how to automate documentation updates for code changes using Code-to-Docs, an open source GitHub Action. This tool uses AI to analyze your code changes, identify affected documentation files, and generate updated content. Get started with this guide.

Learn about critical lessons from building an MCP-powered AI agent for ServiceNow, including how to structure testing environments, best practices for implementing safeguards, and a phased approach to deploying enterprise AI integrations.

Dive into the Q1’26 edition of Camel integration quarterly digest, covering the

Explore new features in Red Hat build of Kueue 1.3, including integration with JobSet for efficient batch job scheduling, support for LeaderWorkerSet for distributed ML workloads, and the introduction of v1beta2 APIs. Learn how to get started with the updated Kueue operator.

Learn how speculative decoding in vLLM can significantly increase throughput without altering a model's output quality, resulting in 19% cost savings at scale for enterprise AI. This post benchmarks gpt-oss-120B with Eagle3 speculative decoding on vLLM and demonstrates consistent throughput and latency improvements across varying concurrency levels, datasets, tensor-parallelism settings, and draft-token budgets.

Discover how Red Hat optimized for human maintainability and significantly increased AI-assisted productivity by formalizing architectural constraints into machine-readable rules, custom lint rules, and deep documentation. Learn about the three layers they built and the impact on development.

Learn how to leverage the Model Context Protocol (MCP) to connect VS Code or Cursor to two specialized intelligence streams: one for local system telemetry and one for global proactive security. Discover the benefits of using MCP servers for Red Hat Enterprise Linux and Red Hat Lightspeed for Red Hat Enterprise Linux to diagnose issues with your image mode for Red Hat Enterprise Linux servers.

Explore how Red Hat AI simplifies agent deployment with OpenClaw, showcasing model inference, safety guardrails, agent identity, and persistent state. Learn about vLLM, Llama Stack, and Models-as-a-Service (MaaS) options, and discover the benefits of agent identity and zero trust with Kagenti and AuthBridge.

Learn how Fromager, an open source project, helps protect Python dependencies by rebuilding entire dependency trees from source, providing network-isolated builds, and managing dependencies as a verifiable map. Discover how Fromager ensures supply chain verifiability, ABI compatibility, and customization.

Learn how to use Claude AI as a pattern matcher to refactor legacy test suites and save time. Discover how to install and set up Claude for large-scale refactoring, and follow a step-by-step guide to remove hard-coded paths and add FMF metadata.

Learn how to run OpenClaw on Red Hat OpenShift with production-grade security and observability. We cover default-deny network policies for blast radius containment, container-level sandboxing with OpenShift, Kubernetes Secrets for credential management, and end-to-end OpenTelemetry tracing with MLflow, so every decision your AI agent makes is isolated, auditable, and safe by default. Whether you're a developer exploring AI agents for the first time or a platform engineer thinking about running agentic workloads at scale, this is the infrastructure story that makes it production-ready.

Learn how to build security hygiene into OpenClaw by using containers for isolation, role-based access control (RBAC) for user access permissions, and secrets for sensitive information. This article explores how to use infrastructure powered by open source technology to help protect these workflows.

Learn how to use Lola, a unified package manager for AI context. Treat your AI context as versioned, auditable code with Lola modules and marketplaces. Improve your AI assistant workflow with this open source tool.

Learn how to build AI-assisted development workflows using harness engineering, a practice that emphasizes structured context over free-form tickets. Discover the two techniques that led to more consistent and predictable results.

Explore how Red Hat OpenShift AI was used to run an unsupervised AI agent for 24 hours, improving validation loss by 2.3% without human intervention. Learn about the container build, Kubernetes deployment, and key findings.

Learn how to set up distributed tracing for an agentic workflow based on lessons learned while developing the it-self-service-agent AI quickstart. This post covers configuring OpenTelemetry to track requests end-to-end across application workloads, MCP servers, and Llama Stack.