GitHub Copilot shifts to usage-based billing for AI coding costs

How to Kickstart Learning #Claude Code in 2026 (A Practical Roadmap for Developers & DevOps Engineers) ⏪ Before #Claude Code, #Developers lost 40–60% of their time to repetitive tasks. Non-engineers were locked out entirely. #DevOps meant manual #pipeline babysitting and memorized #CLI rituals. ⚡ What #Claude Code Is, It's an agentic system that reads a codebase, plans a sequence of actions, executes them using real development tools, evaluates results, and adjusts the developer sets the objective and retains control, but the execution loop runs independently. 🧠 First, What Claude Code Really Is Claude Code is not just a chatbot. It’s an AI coding agent that: • Works directly from your terminal • Understands your entire codebase • Writes, edits, debugs, and explains code • Can execute multi-step development tasks 🗺️ Learning #Roadmap, A 5-phase path from basic installation → codebase fluency → agentic workflows → MCP integrations → architecture-level thinking and orchestration. 🛤️ Step-by-Step Roadmap to Get Started 1️⃣ Build Core Foundations (Non-Negotiable) Before Claude Code, you should know: • One programming language (Python or JavaScript preferred) • Basic Git (commit, push, pull, branching) • Command line / terminal usage 2️⃣ Understand Real Development Workflows Learn how software is actually built: • How a project structure looks • How APIs work • How debugging is done • How deployments happen 3️⃣ Get Comfortable with DevOps Basics Claude Code becomes powerful when combined with DevOps: • CI/CD pipelines • Docker basics • Cloud fundamentals (AWS / Azure / GCP) • Infrastructure as Code (Terraform) 4️⃣ Start Using Claude Code (Hands-On) Now comes the real part 👇 Start small: • Ask it to explain your codebase • Generate small functions • Debug errors • Refactor messy code Then level up: • Modify multiple files • Create scripts • Automate repetitive tasks • Generate configs (Dockerfile, YAML, Terraform) 5️⃣ Learn Prompting Like an Engineer Your results depend on how you ask. Bad prompt ❌ “Fix this code” Better prompt ✅ “Fix this API timeout issue, optimize for performance, and explain changes step-by-step” 6️⃣ Move to Advanced Usage (Game-Changer Stage) Once comfortable: • Automate full workflows • Connect with tools (GitHub, CI/CD, databases) • Let Claude handle multi-step tasks • Build small projects end-to-end using AI assistance ⚙️ Real Impact on DevOps & Cloud Engineers Claude Code is already changing how we work: ✅ Faster debugging ✅ Auto-generated infra configs ✅ Reduced manual scripting ✅ Quicker incident resolution ✅ Improved documentation 👉 DevOps is shifting from: “Doing tasks manually” → “Designing intelligent automation” Save this. 👇 ♻️ Share with your #network. ➕ Follow Mahesh Babu Bitra for #AI growth. Boris Cherny Anthropic #ClaudeCode #AI #DevOps #CloudComputing #SoftwareEngineering #GenAI #Automation #FutureOfWork #Developers #Tech2026

GitHub Resets Copilot Pricing as AI Compute Costs Surge: GitHub has announced a significant update to the pricing structure for its Copilot AI-powered coding assistant, responding to the rapidly escalating costs associated with AI compute resources. This change comes at a time when demand for AI tools and services in the software development industry is surging, reflecting the need for organizations to harness advanced technologies to enhance their workflows and productivity. The new pricing model introduces tiered subscriptions, aiming to make Copilot more accessible to individual developers and smaller teams while ensuring that larger organizations can benefit from enhanced features tailored to their needs. GitHub's initiative highlights the importance of balancing affordability with the premium functionalities that AI tools provide, which can dramatically augment coding efficiency. Furthermore, GitHub reiterates its commitment to fostering an ecosystem where developers can leverage AI to streamline their coding processes, improve code quality, and ultimately deliver better software products. With these adjustments, GitHub positions Copilot not just as a tool, but as an essential partner in the modern developer's toolkit, especially as DevOps practices continue to evolve alongside AI advancements. As the industry witnesses a seismic shift towards AI integration, companies are urged to adapt quickly to maintain a competitive edge. Leaning into tools like Copilot could redefine workflows, emphasizing the need for continuous learning and adaptation in DevOps strategies that embrace both tradition and innovation. Read more: https://lnkd.in/gETn8crk 🏆 Elevate your DevOps game! Join our community and learn from industry experts and practitioners.

GitHub Copilot Pulls Drawstring On Tighter Developer Usage Limits: GitHub Copilot, the AI-powered code completion tool, is undergoing changes as it tightens its usage limits for developers. Due to the surge in its popularity among software engineers, GitHub has implemented stricter controls to ensure the tool is used effectively and judiciously. This move acknowledges the vast potential of AI in enhancing coding efficiency while balancing the need for responsible usage. The adjustments to Copilot are designed to foster a more sustainable development environment. By limiting the extent of its code generation capabilities, GitHub aims to encourage developers to engage more deeply with their coding processes rather than relying solely on automated suggestions. This strategic pivot could lead to an overall improvement in software quality and maintainability as developers become more hands-on in their approach. Furthermore, GitHub’s decision reflects a broader trend in the DevOps community where reliance on automation tools is continually being assessed. As organizations seek enhanced productivity, balancing automation with active developer engagement is becoming crucial. Issues such as code authenticity and ownership are raised, prompting discussions about how generative AI tools should fit into the software development lifecycle. As the industry evolves, the implications of these changes will be closely watched. Developers and organizations alike must navigate the fine line between leveraging AI-driven tools and maintaining the human element in coding practices. GitHub's new strategy aims not just at refining Copilot’s use but also at shaping the future landscape of coding in the DevOps arena. Read more: https://lnkd.in/gS4FjVB5 ⚡ Supercharge your DevOps expertise! Join our community for cutting-edge discussions and insights.

The journey from a local URL to a fully rendered UI in the browser is a complex one, but it all starts with a reliable environment. With performance familiarity, I’ve found that Dockerizing the entire development lifecycle is non-negotiable for consistency. In 2026, gaining the ability to package and ship applications efficiently is the baseline for high-impact DevOps and microservice stability. This Docker Learning Roadmap for 2026 breaks down the transition from images to complex orchestration: https://lnkd.in/gzHQkHqm Are you using multi-stage builds to keep your 2026 production images lean and secure? #Docker #DevOps #Containerization #CloudComputing #SoftwareDev

🚀 Day 4 Complete – Deep Dive into Docker & Containerization! 🐳 Today was one of the most insightful sessions so far in my learning journey. I explored Docker, a powerful tool that plays a crucial role in modern software development and deployment. This session was completely hands-on and helped me understand how real-world applications are built, packaged, and deployed efficiently. 🔍 What I Learned Today: 🔹 Understanding Containerization I started by learning why containerization is important. Unlike traditional setups, Docker allows applications to run in isolated environments, ensuring consistency across development, testing, and production. This eliminates the classic “it works on my machine” problem. 🔹 Core Docker Concepts I gained a strong foundation in: ✔️ Containers – Lightweight, runnable instances of applications ✔️ Images – Blueprints used to create containers ✔️ Docker Registry – Storage and distribution system for Docker images (like Docker Hub) ✔️ Docker Architecture – How Docker Engine, Client, and Host work together ✔️ Docker Desktop – Tool to manage containers locally 🔹 Hands-on Setup & First Execution Installed Docker Desktop and created my Docker Hub account. Successfully executed my first container using the "hello-world" image, which verified that Docker is correctly set up on my system ✅ 🔹 Running Real Applications in Docker Worked with the "welcome-to-docker" image and explored: ✔️ Detached mode ("-d") – Running containers in the background ✔️ Port Mapping ("-p") – Connecting container ports to local machine ports This helped me understand how real applications are accessed from a browser. 🔹 Practicing Docker Commands Practiced essential commands that are widely used in development and DevOps workflows: 👉 "docker pull" – Download images 👉 "docker images" – List available images 👉 "docker run" – Run containers 👉 "docker ps" – View running containers 👉 "docker stop" & "docker start" – Manage container lifecycle 🔹 Working with Amazon Linux Container This was the most exciting part! 🎯 ✔️ Started an Amazon Linux container locally ✔️ Installed Java 17 inside the container ✔️ Deployed my first application (JAR file) inside the container ✔️ Tested it from my local system 📈 My Key Takeaways: ✨ Docker simplifies deployment and improves consistency ✨ Containers are lightweight and faster compared to virtual machines ✨ Real-world projects rely heavily on Docker for scalability and portability ✨ Hands-on practice is the best way to truly understand DevOps tools 📚 Learning Progress So Far: ✔️ Microservices & REST API fundamentals ✔️ Building Spring Boot applications ✔️ Deploying apps on AWS EC2 ✔️ Linux commands & environment setup ✔️ Docker & containerization 🔥 Feeling more confident day by day as I move closer to building production-grade applications! #Docker #DevOps #Java #SpringBoot #AWS #Containerization #LearningJourney #SoftwareEngineering #TechSkills #CareerGrowth

Google’s next-gen coding agent signals a major shift in how software gets built. We’re moving beyond AI as a “copilot” to AI as an active teammate—agents that can read codebases, plan tasks, write features, run tests, and even update tickets across your DevOps stack. This isn’t just about faster coding—it’s about redefining the developer role. Instead of writing every line, developers will guide, review, and architect while AI handles execution and repetitive work. As these agents integrate directly into workflows (GitHub, Jira, CI/CD), they’re turning software development into a more autonomous, intent-driven process—where you define the goal and the system figures out the steps. https://bit.ly/48ughWT

🚀 From Ansible Errors to Automated CI/CD — My DevOps Learning Journey (Real Insights) Over the past few days, I’ve been diving deep into AWS & DevOps, and here are some practical learnings that every beginner (and even experienced engineers) should know 👇 🔹 1. Ansible Playbook Mistakes → Big Learning While working on automation: Missed small YAML details (---, indentation) Faced role not found issues Learned difference between script vs copy + command 👉 Lesson: In DevOps, small mistakes = big failures 🔹 2. Installing “Latest” Java Isn’t Always Straightforward I initially used: openjdk-11-jdk But then realized: Better approach → default-jdk Why? It installs the latest stable version supported by your OS 👉 Lesson: Always think future-proof, not hardcoded 🔹 3. Understanding Webhooks Changed Everything ⚡ Before: I thought systems keep checking for updates Now: I understand webhooks = event-driven automation 👉 Example: Code push → triggers build automatically No manual work. Pure automation. 🔹 4. CodeBuild Doesn’t Work Alone (Important Insight) I assumed: AWS CodeBuild will trigger automatically on commit ❌ Reality: Needs integration with AWS CodePipeline OR webhook setup 👉 Lesson: DevOps is about connecting services, not just using them 🔹 5. Biggest Mindset Shift Moving from: ❌ Running commands manually To: ✅ Building systems that run automatically 🔥 What I’m Learning Next CI/CD pipelines using AWS CodePipeline + CodeBuild Advanced Ansible automation Moving towards AI-powered DevOps (Agentic AI 👀) 💡 For Anyone Starting DevOps Don’t just watch tutorials. 👉 Break things 👉 Fix errors 👉 Build real projects That’s where real learning happens. If you’re also learning AWS / DevOps, let’s connect 🤝 I’d love to learn from your journey too! #DevOps #AWS #Ansible #CICD #CloudComputing #Automation #LearningInPublic #TechJourney

GitHub pauses Copilot sign-ups as AI coding drives up compute demand: GitHub has temporarily paused signups for its AI-powered coding assistant, Copilot, after it experienced an overwhelming demand. The tool, designed to enhance coding efficiency, utilizes machine learning to suggest code in real-time, essentially acting as a pair programming partner. This pause indicates both the popularity of Copilot and potential challenges in scaling the service to meet user needs. Developed in collaboration with OpenAI, GitHub Copilot showcases advancements in AI technology within the software development realm. It has gained traction among developers for its ability to reduce coding time and help navigate complex codebases. However, as demand surged, GitHub recognized the necessity to ensure stability and service quality before reopening signups. The decision to pause signups raises questions about the future of AI in DevOps practices. Developers are increasingly relying on AI tools to streamline workflows, but maintaining service quality is essential for sustained user satisfaction and productivity. As GitHub navigates this juncture, the expectations from users and the technology's evolution will play a critical role in shaping the next steps for Copilot and similar tools in the market. Read more: https://lnkd.in/gGn7p6-C 🏅 Champion your DevOps career! Join our winning community and reach new heights of success.

🚀 Kubernetes Workflow: From Code to Production If you are learning DevOps or cloud-native engineering, this is one of the most practical Kubernetes flows to understand. 👇 🧭 End-to-End Kubernetes Workflow 1️⃣ Code Application 💻 Write your application code and prepare it for containerization. 2️⃣ Build Docker Image 🐳 Package the app with dependencies into a Docker image. 3️⃣ Push to Registry 📦☁️ Push the image to a container registry (Docker Hub / ACR / ECR / GCR). 4️⃣ Create Deployment (YAML) 📄⚙️ Define desired state in Kubernetes manifest files (Deployment, resource limits, environment variables, etc.). 5️⃣ Pods Created 🧩 Kubernetes schedules and runs Pods based on your Deployment spec. 6️⃣ Service Created 🌐 Expose Pods internally (ClusterIP) or externally (NodePort/LoadBalancer) with stable networking. 7️⃣ Ingress / Load Balancer 🔀 Route external traffic, manage host/path rules, and enable TLS termination. 8️⃣ Users Access App 👥✅ Your app is now reachable and scalable in production. ⭐ Why Kubernetes Is Powerful 📈 Auto Scaling: Scale up/down based on traffic and resource usage 🩺 Self-Healing: Restart/replace unhealthy containers automatically 🔄 Rolling Updates: Zero-downtime deployments ⏪ Rollback: Revert quickly to a stable version 🔐 ConfigMaps & Secrets: Manage config and sensitive data securely 📊 Monitoring: Observe metrics, logs, and health in real time 🛠️ Quick Kubernetes Commands Cheat Sheet kubectl get pods kubectl get svc kubectl get deployments kubectl apply -f deployment.yaml kubectl describe pod <pod-name> kubectl logs <pod-name> kubectl scale deployment my-app --replicas=3 kubectl rollout undo deployment/my-app 💡 Simple Explanation Kubernetes is an open-source platform that automates deployment, scaling, and management of containerized applications. 🎯 Goal: Build reliable, scalable, and highly available applications in production. 🚀 Kubernetes Workflow: From Code to Production Here is the simple production journey every backend/devops engineer should know: 1️⃣ Code application 2️⃣ Build Docker image 3️⃣ Push image to registry 4️⃣ Apply Kubernetes Deployment YAML 5️⃣ Pods are created and managed 6️⃣ Service provides stable networking 7️⃣ Ingress/Load Balancer exposes app 8️⃣ Users access your application Kubernetes helps teams ship faster with: ✅ Auto scaling ✅ Self-healing ✅ Rolling updates ✅ Easy rollback ✅ Secure config management ✅ Real-time monitoring If you are preparing for DevOps / SRE / Cloud interviews, master this workflow and practice these kubectl commands daily. 💪 #Kubernetes #DevOps #Docker #CloudComputing #SRE #PlatformEngineering #Microservices #CICD #SoftwareEngineering #TechCareer