Developer tools

Discover how Red Hat OpenShift 5 empowers developers with native AI agent sandboxing, dynamic GPU allocation, and upgraded local-to-cloud developer tooling.

Learn how to use Claude Code to automate Testing Farm onboarding, generate TMT metadata, and configure multi-environment CI pipelines in GitHub and GitLab.

Implement LLM tool calling with Apache Camel using bounded loops, ai-tool components, and MCP for security-focused, vendor-neutral enterprise integrations.

Test Lightwell dependency upgrades locally with JFrog Artifactory and Podman to identify vulnerabilities and apply quick fixes.

Prepare for Red Hat Developer Hub 2.1: Learn what's changing for front-end plug-ins, what to update in your configuration, and how to test before you upgrade.

Learn about significant updates to the Clang bytecode interpreter, with roughly 640 new commits since the previous report. Improvements include support for virtual bases, proper dynamic_cast handling, and the use of tail-calls to enhance performance.

Resolve OpenShift Dev Spaces production issues, including Dynatrace conflicts and Podman regressions, with technical fixes and CI/CD automation guidelines.

Learn how to configure Red Hat OpenShift Dev Spaces for enterprise security with SCM OAuth, air-gapped registries, and PKI trust.

Learn how to configure Red Hat OpenShift Dev Spaces at scale. Master multi-tenant namespace isolation, resource quotas, and etcd cleanup guardrails.

Learn how to unify Kubernetes and OpenShift authentication with Kube AuthKit, a Python library that simplifies token management across environments.

Learn how to author Red Hat Developer Hub golden path templates to scaffold Go microservices and monitor Tekton, Argo CD, and Quay in a unified portal.

Implement a regression detector for Red Hat AI Inference, catching performance regressions in CI/CD before release. Learn how in this guide.

Connect AI coding agents to Red Hat Developer Hub to enforce governance rules, prevent compliance violations, and ground architectural choices in your catalog.

Explore free-threaded Python 3.14 on RHEL. Learn how to install python3.14t, manage thread safety, and optimize multi-core CPU performance for your apps.

Learn how to install and configure OpenCode, an open source AI coding assistant, for local development with Ollama, OpenVINO, or Red Hat AI.

Improve large language model inference speed with Speculators 0.6.0's FastMTP-style fine-tuning.

Simplify Red Hat Developer Hub software template authoring with rhdh-templates, an agent skill for best practices and local validation.

Evaluate AI agents on Red Hat OpenShift AI with IBM CLEAR and EvalHub. Learn how to automate analysis for recurring failure patterns in AI agent execution.

Learn how to generate AI-assisted Ansible playbooks with Red Hat's VS Code extension for faster, more accurate code generation.

Red Hat OpenShift Dev Spaces meets Ansible development tools, giving every automation developer a governed, consistent environment in under five minutes.

Streamline Red Hat Ansible development with the MCP server: Reduce context switching and non-deterministic behaviors for reliable automation.

Download latest Red Hat Trusted Artifact Signer releases.

Boost AI coding agent performance with AGENTS.md and Agent Skills. Learn how to standardize project context, structure tasks, and share skills across teams.

Learn how PyTorch's Inductor compiler and vertical fusion optimize GPU code for faster neural network layers

Upgrade your PyTorch training and inference with TorchComms, the new back-end layer for modern large-scale ML systems.