Introduction In the world of machine learning and AI, the deployment of Large Language Models (LLMs) has traditionally been a complex task, often requiring expensive infrastructure or cloud-based services. However, Docker's innovative Model Runner is changing the game by allowing developers to run LLMs locally on their machines, without the need for advanced configuration or costly resources. Docker Model Runner is designed to simplify the process of deploying and testing LLMs, empowering developers to experiment with AI models more efficiently. In this article, we will delve into how Docker Model Runner works, its key features, and provide a detailed guide on setting it up for local LLM deployment. Whether you're a seasoned developer or a beginner, Docker Model Runner can significantly enhance your workflow. What is Docker Model Runner? Docker and Its Role in AI Model Deployment Docker is a platform that uses containerization technology to package software and its depend...

Introduction In the ever-evolving landscape of artificial intelligence, the concept of a Multi-agent AI system stands out as a transformative approach to problem-solving. Unlike traditional AI models that function in isolation, multi-agent systems involve multiple autonomous AI agents working collaboratively or competitively to achieve complex goals. From self-driving car coordination to autonomous trading systems and smart factory management, multi-agent systems are becoming integral to modern AI strategies. What is a Multi-agent AI System? Definition A Multi-agent AI system is an architecture where several AI agents operate in a shared environment. Each agent is an intelligent entity capable of: Perceiving its environment Processing data and making decisions Interacting with other agents Adapting based on outcomes Key Characteristics Autonomy : Agents make decisions independently. Collaboration and Competition : Agents may work together or compete. Communication : Agents exchange ...

Introduction Containerization has revolutionized how we build, deploy, and scale applications. Docker has long been the dominant force in this space, but as technology evolves, so do the needs of developers and system administrators. Enter Podman — a powerful, daemonless container engine that addresses many of Docker's limitations. In this comprehensive guide, we explore why Podman is the best Docker alternative . Whether you're a developer, DevOps engineer, or system admin, this guide will help you understand how Podman compares to Docker, and why you might want to make the switch. What is Podman? A Brief Overview Podman (short for Pod Manager ) is a container engine developed by Red Hat. It is designed to manage containers and pods on Linux systems and is fully compatible with the OCI (Open Container Initiative) standards. Key Features Daemonless architecture Rootless container support Docker CLI compatibility Pod concept for grouping containers Better system integration and...

Introduction The Linux kernel is the core of any Linux-based operating system, managing hardware, processes, and system calls. While most users rely on precompiled kernels from their distribution, compiling your own kernel unlocks performance gains, custom functionality, and a deeper understanding of your system. This comprehensive guide walks beginners through the process of compiling the Linux kernel from scratch, including practical examples, tips, and frequently asked questions. Whether you're seeking to improve performance, enable specific hardware, or explore Linux internals, compiling the kernel is a rewarding learning experience. Why Compile the Linux Kernel? Customization Include only the features and drivers you need Optimize kernel size and boot time Performance Tuning Enable real-time or low-latency features Fine-tune memory and CPU scheduling Learning and Troubleshooting Gain in-depth knowledge of Linux internals Debug hardware or system-specific issues Prerequisites B...

Introduction In the rapidly evolving world of DevOps, automating infrastructure is no longer a luxury but a necessity. Tools like Ansible and Terraform are at the forefront of Infrastructure as Code (IaC), offering developers and system administrators powerful ways to define, provision, and manage infrastructure. However, these tools serve different purposes and excel in different areas. If you're wondering whether to choose Ansible or Terraform for your next project, this guide will provide clarity by comparing their architecture, use cases, strengths, and limitations. An Overview of Ansible and Terraform What is Ansible? Ansible is an open-source automation tool developed by Red Hat. It is primarily used for configuration management, application deployment, and task automation. Key Features: Agentless (uses SSH/WinRM) YAML-based Playbooks Push-based model Idempotent tasks Extensive module ecosystem What is Terraform? Terraform, created by HashiCorp, is an open-source tool for ...

Introduction Apache Tomcat is one of the most widely used open-source servlet containers and web servers. Trusted by millions of organizations for its speed, scalability, and flexibility, Tomcat powers applications that rely on Java Servlets and JavaServer Pages (JSP). However, like any software, Apache Tomcat is not immune to vulnerabilities. A critical flaw, CVE-2025-24813, was recently disclosed, which has raised alarm in the IT community. This flaw has the potential to allow attackers to execute malicious actions on affected systems, making it essential for organizations to understand its implications and how to mitigate the associated risks. In this article, we’ll dive deep into the Apache Tomcat flaw CVE-2025-24813, examining what it is, how it works, and how you can protect your systems from its impact. What is Apache Tomcat Flaw CVE-2025-24813? CVE-2025-24813 refers to a security vulnerability in Apache Tomcat versions 9.0.0.M1 through 9.0.98, 10.1.0-M1 through 10.1.34, an...

Introduction In the world of containerization and DevOps, Kubernetes and Docker are two of the most commonly discussed tools. Both play critical roles in deploying and managing containerized applications, but they serve different purposes. Docker simplifies container creation and management, while Kubernetes orchestrates and scales containers efficiently. Understanding their differences and use cases is essential for developers and IT professionals looking to optimize their deployment strategies. Kubernetes vs. Docker: Understanding the Basics What is Docker? Docker is an open-source platform that allows developers to automate the deployment of applications inside lightweight, portable containers. It simplifies software delivery by ensuring consistency across different environments. Key Features of Docker: Containerization: Encapsulates applications and dependencies. Portability: Works across different operating systems and infrastructures. Simplified Deployment: Automates application ...

Introduction In the ever-evolving landscape of artificial intelligence (AI), the need for scalable and flexible environments to deploy AI workloads has never been more critical. Docker, a leading platform for containerization, has transformed the way developers manage applications. One of the innovative solutions in this space is the Docker AI Agent , which brings the power of containers to AI workloads, making deployment faster, more efficient, and highly scalable. In this article, we'll explore how the Docker AI Agent can optimize AI workflows, from basic deployment to advanced use cases. By leveraging Docker's capabilities, AI engineers and developers can run applications in isolated environments, manage dependencies effortlessly, and deploy AI models seamlessly. Whether you're an AI enthusiast or a seasoned developer, this comprehensive guide will provide the knowledge you need to make the most out of Docker for AI. What is Docker AI Agent? Understanding Docker and...