2026-06-17 07:01
🚀 Automating application migration is now possible with MigIQ! This tool transforms a Spring Boot REST API into Quarkus, showcasing a structured approach that combines graph analysis, automated planning, and parallel execution. Key phases include: 1️⃣ **Analysis**: Understanding code dependencies. 2️⃣ **Requirements**: Gathering context for the target platform. 3️⃣ **Planning**: Creating a detailed task list for migration. 4️⃣ **Execution**: Running tasks in parallel. 5️⃣ **Reporting**:...
Source: Red Hat Developer Blog
Syed M Shaaf
2026-06-17 07:01
🚀 Introducing personal AI notebooks in Red Hat Developer Hub! These notebooks serve as dedicated knowledge bases for specific projects. You can upload your project documents and interact with the AI based on that data, enhancing your workflow. Key benefits include: - Source transparency: Get evidence for every AI claim. - Data isolation: Keep queries relevant to each notebook. - Instant context: New team members can find answers quickly. Explore this feature now in developer preview! 🛠️📄...
Source: Red Hat Developer Blog
Lucas Yoon
2026-06-16 15:34
🚀 Just explored the Red Hat AI Inference on Amazon EKS! This article dives into deploying a two-GPU cluster using NVIDIA L4s, focusing on Kubernetes components like cert-manager for TLS, Istio for service mesh, and KServe for model serving. Key insights include how these elements connect and work together for efficient AI inference. 📊 Learn more about the architecture and components involved! #RedHat #Kubernetes #AIInference #AmazonEKS #CloudComputing
Source: Red Hat Developer Blog
Alexa Griffith
2026-06-16 07:01
EvalHub addresses the reproducibility crisis in AI evaluation by providing immutable records of evaluation runs. By integrating with MLflow, EvalHub captures comprehensive details about each evaluation, ensuring results are not just claims but verifiable evidence. With OCI persistence, evaluation results are stored as tamper-evident artifacts, improving compliance for regulated workloads. Learn more about building a scalable AI evaluation infrastructure! #AI #EvalHub #MachineLearning...
Source: Red Hat Developer Blog
William Caban Babilonia, Matteo Mortari
2026-06-16 07:01
Explore the latest in agentic AI and text-to-SQL! 🖥️ This installment delves into how agentic AI allows LLMs to autonomously interact with databases, improving accuracy in data queries. Unlike traditional chat interfaces, agentic systems learn and adapt, enhancing the user experience in conversational analytics. Stay tuned for more insights on orchestrating these systems! 🚀📊 #AI #DataAnalytics #TextToSQL #AgenticAI #RedHat
Source: Red Hat Developer Blog
Peter Samouelian
2026-06-15 13:12
Learn how to permanently set a network interface administratively down using NetworkManager! This article covers both legacy and new methods. Prior to NetworkManager 1.57, a special configuration file was needed. Now, with versions 1.57 and later, you can use simple nmcli commands to manage interface states without complications. For more details, check out the full article! 📡🔧 #NetworkManager #SysAdmin #Linux #Networking #nmcli
Source: Red Hat Developer Blog
Greg Scott
2026-06-15 07:16
In distributed training, gradient synchronization is a crucial phase often slowed by communication delays. This article explores how Message Passing Interface (MPI) enhances performance using collective operations like All-Reduce to synchronize gradients across GPUs efficiently. It details various parallelization methods: data, tensor, pipeline, and sharded data parallelism—each optimizing workload distribution among GPUs. Additionally, it addresses GPU-aware MPI, which reduces overhead and...
Source: Red Hat Developer Blog
Kushagra Rastogi
2026-06-15 07:16
🌟 Exploring local large language models? Check out the differences between llama.cpp and vLLM! llama.cpp is designed for efficient inference on consumer hardware, allowing users to run models with minimal GPU requirements through quantization. This approach makes LLMs more accessible to developers without dedicated hardware. On the other hand, vLLM excels in high-throughput scenarios, managing multiple requests simultaneously and optimizing GPU utilization. It's ideal for large-scale...
Source: Red Hat Developer Blog
Cedric Clyburn
2026-06-12 18:28
🚀 **Unlock Faster LLM Inference with Speculative Decoding!** Speculative decoding combines two models: a fast speculator (the hare) that predicts multiple tokens and a larger verifier (the tortoise) that validates them. This method can enhance performance by over three times for predictable tasks like code generation and structured outputs. However, it’s essential to train the speculator model on the same dataset as the verifier for optimal results. Interested in improving your LLM...
Source: Red Hat Developer Blog
Sawyer Bowerman
2026-06-12 07:00
🚀 Model-as-a-Service (MaaS) is now available with Red Hat OpenShift AI 3.4, allowing companies to run their own private AI APIs. MaaS offers a self-service path for developers, providing curated model endpoints while maintaining control over costs and security. It addresses issues like shadow AI and model deprecations by centralizing access and management. This architecture enables efficient AI integration while ensuring data sovereignty and observability. #ModelAsAService #AIIntegration...
Source: Red Hat Developer Blog
Cedric Clyburn
2026-06-12 07:00
🚀 Learn how to deploy virtual machines (VMs) in Microsoft Azure using Red Hat Satellite! This guide explains the setup process for managing both on-premises and cloud workloads seamlessly. It covers prerequisites, including Azure credentials and a Red Hat Satellite instance. Follow step-by-step instructions to create a custom VM image and configure Microsoft Azure as a compute resource within Satellite. Maximize efficiency and maintain centralized management while adapting to cloud...
Source: Red Hat Developer Blog
Øivind Ekeberg
2026-06-11 07:16
Unlock AI evaluations in your CI/CD pipeline with EvalHub's CLI! 🚀 This article details the workflow from setup to production, focusing on how to integrate automated evaluations. Key commands and environment variable configurations streamline the process, enhancing efficiency. Check out the series for more insights on building a scalable AI evaluation infrastructure! #AI #CICD #EvalHub #Automation #DevOps
Source: Red Hat Developer Blog
William Caban Babilonia, Rui Vieira, Matteo Mortari
2026-06-11 07:16
🛡️ Strengthening security in Red Hat's OpenShift AI voice agent is crucial. In a recent article, the implementation of guardrails to prevent prompt injection attacks was discussed, highlighting the importance of prompt engineering. 🔍 Key points include the use of MLflow to track conversation history and evaluating large language models for accuracy. 🛠️ Guardrails such as TrustyAI provide vital defenses against malicious inputs, ensuring reliable interactions in the voice agent. Explore more...
Source: Red Hat Developer Blog
Mike Hepburn
2026-06-11 07:00
Discover how the open-source project llm-d enhances large language model (LLM) inference on Red Hat AI. Traditional load balancers treat LLM requests as stateless, leading to inefficiencies. llm-d optimizes performance by routing requests to GPUs with relevant cached data, significantly reducing time-to-first-token by over 99% and doubling throughput. With intelligent scheduling, it adapts to real-time loads and queue depths, ensuring efficient resource use. This new approach is seamlessly...
Source: Red Hat Developer Blog
Edoardo Vacchi, Madhu Goutham Reddy Ambati
2026-06-10 14:05
🚀 Red Hat Ansible Automation Platform 2.7 is here! This release focuses on enhancing automation for IT teams at enterprise scale. Key updates include a new visual execution environment builder and centralized content catalog to streamline processes. The intelligent assistant now supports bring-your-own-knowledge for tailored guidance. Additionally, the MCP server allows AI agents to manage automation through natural language commands, improving efficiency. Explore the new features and start...
Source: Red Hat Developer Blog
Catherine Choi
2026-06-10 14:05
🚀 Red Hat Ansible Automation Platform 2.7 is now available, enhancing efficiency and intelligence for IT teams. Key updates include: 🔹 A visual execution environment builder for streamlined automation setup. 🔹 A centralized content catalog to easily access trusted collections. 🔹 Ansible development workspaces provide a consistent, browser-based environment. Additionally, the platform introduces an MCP server for AI-driven automation queries and a new intelligent assistant that supports custom...
Source: Red Hat Developer Blog
Catherine Choi
2026-06-10 13:09
🚀 Bazel is an open-source build system that streamlines software builds and tests across multiple languages and platforms. This article discusses methods for building Bazel applications on Automotive Stream Distribution (AutoSD). Three approaches are highlighted: 1️⃣ **Native AutoSD GCC Toolchain**: Direct integration with AutoSD for minimal abstraction. Best for exclusive AutoSD targets. 2️⃣ **S-Core Abstracted Toolchain**: Supports multiple platforms, ideal for CI/CD environments, reducing...
Source: Red Hat Developer Blog
Bilal Elmoussaoui
2026-06-09 07:01
🚀 EvalHub now supports a "bring-your-own-framework" (BYOF) approach, allowing teams to integrate custom evaluation frameworks. This enables organizations to leverage proprietary or academic evaluation harnesses not included in the default provider set. By implementing a simple Python method, users can package their framework, enabling features like experiment tracking and OCI artifact persistence. Learn more about building your custom adapter and the integration process. #EvalHub #AI...
Source: Red Hat Developer Blog
William Caban Babilonia, Rui Vieira, Matteo Mortari
2026-06-09 07:01
🚀 Discover how agentic AI is transforming data access in enterprises! This article introduces the integration of Red Hat OpenShift AI and EnterpriseDB’s PG Airman MCP server, addressing the challenges non-technical staff face with SQL. It highlights a natural language interface that simplifies data queries and ensures compliance with data governance. Stay tuned for more insights in this four-part series! #DataGovernance #AI #PostgreSQL #OpenShift #TechInnovation
Source: Red Hat Developer Blog
Peter Samouelian
2026-06-08 07:01
🚀 Building a local voice agent with Red Hat OpenShift AI can be complex but rewarding. This article provides insights on creating a pizza shop voice agent, focusing on architecture, model selection, and implementation challenges. Key steps include using a voice sandwich architecture, selecting models from the Red Hat OpenShift AI catalog, and ensuring quick execution for natural conversation flow. Explore the full process and learn about performance metrics, agent collaboration, and the...
Source: Red Hat Developer Blog
Mike Hepburn
2026-06-08 07:01
Exploring gang autoscaling on OpenShift is crucial for high-performance workloads like AI/ML training. Traditional Kubernetes scheduling can lead to resource waste when pods can't start simultaneously due to capacity limits. The combination of Red Hat's Kueue and the ProvisionRequest API addresses this issue by coordinating resource availability before scheduling. This ensures that all required pods start together, optimizing resource use. For a deep dive into the setup and benefits, check...
Source: Red Hat Developer Blog
Kevin Hannon, Michael McCune
2026-06-05 03:01
🔧 Interested in managing Red Hat Enterprise Linux (RHEL) more efficiently? The new image mode deployment option allows installation from a bootc image, enhancing consistency and enabling atomic updates. 🖥️ With RHEL 10, you can easily deploy this system using Anaconda and the new bootc kickstart command. This method streamlines the installation process by managing core tasks while ensuring system updates can be performed later. 📥 For more details on how to get started, check the full article...
Source: Red Hat Developer Blog
Jiří Kortus
2026-06-05 03:01
🔍 Struggling with database benchmarking? You're not alone. An article outlines common pitfalls faced when testing AWS RDS PostgreSQL performance. One key issue is the load generator acting as a bottleneck, impacting throughput. Upgrading the client instance can help eliminate this limitation. Another factor is ensuring the test is CPU-bound rather than disk-bound by adjusting parameters. Additionally, increasing the max_wal_size can prevent performance dips during testing. For reliable...
Source: Red Hat Developer Blog
Krishna Magar
2026-06-04 07:16
Unlock the power of chaos engineering with Krkn! 🚀 Krkn now supports over 20 scenario types for Kubernetes, including pod disruptions and network chaos. However, translating your testing intent into precise CLI syntax can be challenging. A new solution simplifies this: a natural language interface that generates validated Krkn commands from plain English. Just describe the failure you want to simulate, and the tool handles the syntax for you. This innovation enhances accessibility, allowing...
Source: Red Hat Developer Blog
Darshan Jain
2026-06-04 07:16
Discover the importance of evaluation collections in AI with EvalHub! This article discusses common pitfalls in AI evaluation, highlighting the need for precise metrics tailored to your deployment context. It introduces evaluation-driven development and how to create personalized collections that meet your specific criteria. The Leaderboard v2 collection serves as a practical example, showcasing how to define benchmarks, weights, and thresholds effectively. Explore how to build a robust...
Source: Red Hat Developer Blog
William Caban Babilonia, Julian Payne, Marius Ion Danciu
2026-06-04 07:16
Discover how Confidential Containers leverage Trusted Execution Environments (TEEs) on OpenShift bare metal for enhanced workload isolation. At the core are confidential virtual machines (CVMs) that utilize Kata Containers for running Kubernetes pods, ensuring strong security through hardware isolation. 🔒 Remote attestation verifies the integrity of CVMs, ensuring sensitive materials are securely handled. This architecture supports a zero-trust model, enhancing confidentiality and integrity...
Source: Red Hat Developer Blog
Pradipta Banerjee, Leonardo Milleri, Emanuele Giuseppe Esposito, Pei Zhang
2026-06-04 07:16
🔍 In the latest article, we explore the comparison between iSCSI and NVMe/TCP storage protocols in Red Hat OpenShift Virtualization. Both protocols have distinct advantages. iSCSI has been a reliable choice for years but may struggle with modern SSDs due to its single-queue architecture. In contrast, NVMe/TCP is optimized for high-performance flash storage, offering lower latency and higher IOPS. Testing shows NVMe/TCP significantly outperforms iSCSI in VM provisioning and raw disk I/O,...
Source: Red Hat Developer Blog
Sonali Badal
2026-06-04 07:16
🚀 Exciting news! The v0.5.0 release introduces major upgrades for speculative decoding model training. Key features include DFlash algorithm support, which enables single-pass draft token generation, and enhanced online training capabilities. The integration with vLLM’s hidden states extraction system streamlines both online and offline training. Updated documentation provides clear guidance for users. Explore the benefits of the DFlash algorithm and its performance in various tasks....
Source: Red Hat Developer Blog
Helen Zhao, Fynn Schmitt-Ulms, Dipika Sikka
2026-06-04 03:01
Discover how intelligent inference scheduling with llm-d enhances AI performance on Red Hat platforms. The article explores the benefits of optimizing scheduling processes to improve efficiency and resource management in AI applications. Learn how Red Hat AI is leveraging these advancements for better outcomes. #RedHatAI #ArtificialIntelligence #TechInnovation #InferenceScheduling #llmD 🤖📈💡
Source: Red Hat Developer Blog
Edoardo Vacchi
2026-06-03 07:31
🚀 Red Hat OpenShift AI enables teams to build modular AI pipelines using reusable components, streamlining the development process. These standardized building blocks help handle tasks like data preprocessing, model training, and deployment, saving time and reducing duplicated efforts. By leveraging a shared component library, teams can enhance collaboration and ensure consistency across projects. Explore the benefits of composable AI workflows today! 🌐 #RedHat #OpenShiftAI #AIPipelines...
Source: Red Hat Developer Blog
Ana Biazetti, Nelesh Singla, Matt Prahl
2026-06-02 07:01
🚀 Exciting updates from Paketo Buildpacks! UBI 9 and UBI 10 builders are now available, offering multi-architecture support for builds. These builders currently support Node.js, with Java support on the way. Base images are published on Dockerhub, making it easy to get started. Multi-arch options include arm64 and amd64, allowing for flexible application builds. For more details, check the release notes and try using the pack CLI! #PaketoBuildpacks #UBI #MultiArch #NodeJS #DevOps
Source: Red Hat Developer Blog
Costas Papastathis
2026-06-02 07:01
🚀 Exciting advancements in AI deployment! The article outlines how to deploy the Hermes Agent on Red Hat OpenShift AI using GPU-accelerated vLLM model serving. This innovative agent retains user context across sessions, creating a continuous learning experience. Key features include: - Multi-platform capabilities (Telegram, Discord, Slack) - Self-improving skills from multi-step tasks - Seamless integration with OpenShift's production-ready AI infrastructure This deployment is ideal for...
Source: Red Hat Developer Blog
Gerald Trotman
2026-06-02 07:01
🚀 Discover the future of AI development with Evaluation-Driven Development (EDD) using EvalHub! EDD transforms traditional test-driven development by focusing on measurable performance gaps instead of simple pass/fail outcomes. 🔍 Key Steps in EDD: 1️⃣ Define clear evaluation criteria before coding. 2️⃣ Measure quality with gradient scores for deeper insights. 3️⃣ Iterate based on data to optimize performance. EvalHub streamlines this process, ensuring effective AI outcomes through transparent...
Source: Red Hat Developer Blog
William Caban Babilonia, Matteo Mortari
2026-06-02 07:01
🚀 The vLLM Semantic Router enhances model efficiency by routing requests to the appropriate models based on complexity. However, a recent study found that the pretrained model had an 80% accuracy rate, leading to a 20% misrouting rate. This highlights a critical need for improved accuracy in enterprise deployments. To address this, a fine-tuning pipeline was established on OpenShift AI, significantly boosting routing accuracy from 80% to 98.5%. This adjustment ensures that models handle...
Source: Red Hat Developer Blog
Christopher Nuland
2026-06-02 07:01
🚀 The Red Hat build of Cryostat 4.2 is now generally available, enhancing Java monitoring on OpenShift. 📊 Key features include SQL query support for JDK Flight Recorder data, allowing for in-console analysis without downloading files. 🔍 The update also introduces async-profiler integration for better stack trace capture and smart triggers for dynamic recording management. 🔒 New observability features like audit logging and improved thread dump analysis enhance security and system tracking....
Source: Red Hat Developer Blog
Syed M Shaaf, Chris Mah
2026-06-01 07:01
🛡️ Protecting your Kubernetes Operator from OOMKill is crucial. Kubernetes operators, which manage applications automatically, have a vulnerability linked to unfiltered informer caches. This can lead to memory exhaustion and crash your operator, exposing it to potential denial-of-service attacks. To mitigate this, ensure your cache is filtered by labels and implement best practices during updates. Learn more about safeguarding your cluster! #Kubernetes #DevOps #CloudComputing #Security...
Source: Red Hat Developer Blog
Rishabh Singh, Ugo Giordano
2026-06-01 03:01
Accurate timing is crucial across various industries, as applications rely on the system clock to reflect real-world time. The challenge lies in achieving consistent accuracy everywhere. ⏰ Most systems use Network Time Protocol (NTP) for millisecond accuracy, while Precision Time Protocol (PTP) can reach up to 100 nanoseconds. Global Navigation Satellite System (GNSS) is another option, but it faces risks like jamming. 🌍 To ensure reliability, a solution combines GNSS as the primary source...
Source: Red Hat Developer Blog
Joseph Richard
2026-05-29 07:01
🔍 Red Hat OpenShift Container Platform introduces key updates in system management! Starting with version 4.21, new clusters will automatically allocate system-reserved resources based on node size, addressing past memory and CPU competition issues. Additionally, version 4.22 introduces CPU limit enforcement for system daemons, enhancing stability. These changes aim to improve node performance while maintaining compatibility with existing setups. #OpenShift #RedHat #Kubernetes #CloudComputing...
Source: Red Hat Developer Blog
Neeraj Krishna Gopalakrishna
2026-05-29 03:01
Unlock the potential of performance analysis with Claude! 🚀 This article explores how Claude simplifies the challenging task of analyzing large CPU profiles and traces, particularly with the Go Green Tea garbage collector. It highlights how Claude identifies bottlenecks and suggests optimizations effectively. Key aspects include analyzing CPU profiles using Go's pprof tool and optimizing atomic operations for better performance. Claude also aids in recognizing patterns in trace files to...
Source: Red Hat Developer Blog
Archana Ravindar
2026-05-28 07:16
🚀 Introducing LogAn: a new approach to log analysis that addresses the limitations of Large Language Models (LLMs). Traditional LLMs struggle with the vast volume of log data, often processing mostly routine messages rather than critical errors. LogAn offers a solution by utilizing a template mining algorithm called Drain, which compresses logs into unique templates for efficient analysis. Developed by IBM Research and open-source, LogAn combines log templatization and semantic analysis to...
Source: Red Hat Developer Blog
Rahul Shetty, Aman Vishwakarma