Automation and management

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Article

How to plan your RHEL lifecycle with AI

Samiksha Saxena +1

Discover how the Model Context Protocol server for Red Hat Lightspeed transforms the manual process of managing a RHEL fleet lifecycle into an AI-driven strategy.

ai-ml
Article

Vibes, specs, skills, and agents: The four pillars of AI coding

Rich Naszcyniec

Explore the four pillars of AI coding: vibes, secs, skills, and agents, and learn how they can improve the coding quality and reduce the encoding/decoding gap. Discover the benefits of a spec-driven approach and the importance of modular specs and skills in achieving harmony.

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Article

Integrate Red Hat Advanced Cluster Management with Argo CD

Francisco De Melo Junior

Learn how to integrate Red Hat Advanced Cluster Management with Argo CD for efficient application control. Discover how to use both push and pull models, and configure Argo CD to watch Policy resources.

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Article

Automate AI agents with the Responses API in Llama Stack

Michael Dawson

Learn how the Responses API in Llama Stack automates complex tool calling while maintaining granular control over conversation flow for AI agents. Discover the benefits and implementation details.

OpenShift + Pipelines 2
Article

Smarter multi-cluster scheduling with dynamic scoring framework

Jian Qiu

The Dynamic Scoring Framework is an open source project that helps automate cluster scoring based on Prometheus metrics for intelligent workload distribution in multi-cluster environments. It acts as a bridge between your monitoring system and the Placement API, continuously evaluating clusters and updating their scores. The framework consists of three key components: DynamicScorer, DynamicScoringConfig, and DynamicScoringAgent.

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Article

Fine-tune AI pipelines in Red Hat OpenShift AI 3.3

Ana Biazetti +2

Learn how to fine-tune AI pipelines in Red Hat OpenShift AI 3.3. Use Kubeflow Trainer and modular components for reproducible, production-grade model tuning.

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Article

Performance and load testing in Identity Management (IdM) systems using encrypted DNS (eDNS) and CoreDNS in OpenShift clusters

Josep Andreu Font +1

This blog post presents the performance analysis of Identity Management (IdM) when encrypted DNS (eDNS) is introduced in an Red Hat OpenShift 4.20 cluster. The post includes a detailed discussion on the architecture of the setup, tuning of IdM, and performance testing of the system under various scenarios. The results demonstrate that the system can sustain high throughput and low latency even with eDNS, making it a suitable choice for secure and high-performance DNS solutions.