Bruno Meseguer

This article will guide you through the process of rapid prototyping using Apache Camel, from the initial concept to the final deployment on OpenShift, all within a fully web-based, low-code interface.

This tutorial gives you a unique chance to learn, hands-on, some of the basics of large language models (LLMs) in the Developer Sandbox for Red Hat OpenShift.

This article explains how to use Red Hat OpenShift AI in the Developer Sandbox for Red Hat OpenShift to create and deploy models.

This solution demonstrates an implementation to build a platform that synthesizes conversations across multiple different communication channels and services.

The Edge to Core Pipeline Pattern automates a continuous cycle for releasing and deploying new AI/ML models using Red Hat build of Apache Camel and more.

The Camel Quarkus runtime lets you run integration processes with low memory usage, fast startup, and high performance. Learn how you can try it out at no cost.

This article explains the benefits of domain-specific languages and compares Camel's Java, XML, and YAML DSLs to help you choose the right one for your team.

Experiment with Camel K integration in a fully web-based experience using the Developer Sandbox for Red Hat OpenShift—no local installs needed.