GitHub CLI Telemetry Defaults Impact Developer Tools and Open-Source Governance DevOps Insight Apr 15–22, 2026: GitHub CLI telemetry defaults, Copilot sign-up pause, Grafana’s free AI assistant, and Ruby Central turmoil. 📅 Coverage period: Apr 17 - Apr 23, 2026 Read the full analysis 👇 #TechNews #TechnologyTrends #DeveloperToolsAndSoftwareEngineering #DevOps #SoftwareDevelopment #Programming https://lnkd.in/g6bJt2sn

🚀 Introducing DevOpsToolkit — Built for Engineers, by an Engineer After intense building, refining, and pushing boundaries… I’m excited to finally share something I’ve been working on: 👉 A unified DevOps & Platform Engineering toolkit designed to simplify, accelerate, and automate everyday engineering workflows. 🔧 What it brings: • Ready-to-use DevOps scripts (Bash, Python, PowerShell) • Kubernetes & Docker generators (YAML, Helm-ready) • CI/CD pipeline builders (Jenkins, GitHub Actions, GitLab, more) • Cloud-ready configurations (multi-provider mindset) • Security, observability, and automation utilities • Smart AI-powered assistance (early stage, evolving fast) 💡 Built with a simple idea: Instead of searching, rewriting, and debugging the same things again and again… 👉 Why not have everything in one place, ready to use? ⚡ What’s coming next: • BYOK (Bring Your Own Key) for LLM integrations • DevOps command simulation (learn by seeing what happens internally) • Intelligent tool recommendations This is just the beginning — the vision is much bigger: ➡️ A self-evolving DevOps ecosystem with thousands of tools and generators. 🌐 Try it here: https://devopstoolkit.dev/ Would love your feedback, ideas, and brutal honesty 🙌 Let’s build something powerful together. DEVOPS INSTITUTE Agentic DevOps DevOpsCube DevOps Learner Community IBM Amazon Web Services (AWS) #DevOps #PlatformEngineering #Kubernetes #Docker #Cloud #Automation #AI #SRE #DevSecOps #ibmchampion #devopsinstitute #peoplecertambassador #gitlabcertified #devopstoolkit #devopstoolkit.dev

🚨 𝗦𝘁𝗼𝗽 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗠𝗮𝗻𝘂𝗮𝗹𝗹𝘆 — 𝗟𝗲𝘁 𝗚𝗶𝘁 𝗗𝗼 𝘁𝗵𝗲 𝗛𝗲𝗮𝘃𝘆 𝗟𝗶𝗳𝘁𝗶𝗻𝗴 Still running 𝘁𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗮𝗽𝗽𝗹𝘆 from your terminal? That’s not just inefficient — it’s risky. Let’s talk about how combining Terraform + GitHub Actions can completely transform your infrastructure workflow. Every time you push code to your Git repository, a CI/CD pipeline automatically - ♦️ Validates your Terraform code ♦️ Generates an execution plan ♦️ Applies infrastructure changes 🔁 𝗘𝗻𝗱 𝘁𝗼 𝗘𝗻𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 ♦️ You update Terraform code and Push to GitHub ♦️GitHub Actions triggers a pipeline -     ♦️ terraform init    ♦️ terraform validate    ♦️ terraform plan    ♦️ terraform apply 📌 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗖𝗜 𝗖𝗗 𝘄𝗶𝘁𝗵 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 🔐 Safer Infrastructure Changes 👀 Full Visibility - Every change is Git versioned and tracked. 🤝 Team Collaboration ☀️ No manual effort 👑 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝘀𝗲𝘀 ♦️ Use remote backend (S3 + DynamoDB) for state locking ♦️ Separate environments using workspaces or directories ♦️ Add manual approval before terraform apply in production ♦️ Store secrets securely. 👉 We’ll dive deeper into 𝗖𝗜 𝗖𝗗 𝘄𝗶𝘁𝗵 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 in the upcoming posts. Stay tuned!! 🔔 Follow Nitin Kumar for daily valuable insights on LLD, HLD, Distributed Systems and AI. ♻️ Repost to help others in your network. #devops #infrastructureascode #iac #gitops #terraform

Build it once. Test the same thing. Ship exactly that. Most teams don't. And that one mistake — rebuilding the artifact in every stage — is silently breaking pipelines everywhere. I've seen it happen first-hand. A bug slipped to production that the test stage had already caught. Not because the tests failed. Because the deploy stage built the code again from scratch. Different binary. Same bug. No one noticed until users did. That's what happens when you don't know how to correctly pass an artifact from one stage to the next. So I put together a full breakdown — real scenarios, actual code snippets, when to use each method, and honest pros and cons — across the three tools most teams are using right now: → Jenkins → GitHub Actions → Microsoft Azure DevOps Whether you're stashing a JAR between stages, passing a Docker image across repos, or just trying to send a version string from one job to another — it's all in there. If you're working with CI/CD pipelines daily, this one's worth a read. Drop a comment if you've been burned by this before. Curious how common it actually is. #DevOps #CICD #Jenkins #GitHubActions #AzureDevOps #SRE #CloudEngineering #Automation #Docker #SoftwareEngineering #PipelineEngineering #BackendDevelopment #TechCareer #CloudNative #DevSecOps

🚀 From GitHub → Jenkins → Docker → Kubernetes This is what a real DevOps pipeline looks like 👇 Most people learn tools in isolation. But in the industry, it’s all about how they connect together. Let’s break down a complete End-to-End CI/CD Workflow 🔥 ━━━━━━━━━━━━━━━━━━━ ⚙️ CI Pipeline (Build + Security) 🔹 Code pushed to GitHub 🔹 Jenkins triggers the pipeline 🔹 OWASP Dependency Check → finds vulnerable libraries 🔹 SonarQube → code quality + security analysis 🔹 Docker → builds container image 🔹 Trivy → scans image vulnerabilities 🔹 Image pushed to registry 💡 Shift-left security starts here ━━━━━━━━━━━━━━━━━━━ 🚀 CD Pipeline (Deployment) 🔹 Jenkins updates image version 🔹 Changes pushed to GitHub 🔹 ArgoCD pulls latest changes 🔹 Deploys to Kubernetes 💡 Fully automated, GitOps-driven deployment ━━━━━━━━━━━━━━━━━━━ 📊 Monitoring & Alerts 🔹 Prometheus → metrics collection 🔹 Grafana → dashboards & visualization 🔹 Alerts → email / notifications 💡 No monitoring = flying blind in production ━━━━━━━━━━━━━━━━━━━ 🎯 What Companies Actually Expect You to Know ✔️ CI → Build + Test + Scan ✔️ CD → Deploy + Automate ✔️ Security → Integrated (not optional) ✔️ Monitoring → Real-time visibility ━━━━━━━━━━━━━━━━━━━ 🔥 Reality Check: Knowing tools ≠ Knowing DevOps Understanding the flow = Real skill 👇 Question for you: Which part of this pipeline do you find most challenging? #DevOps #CICD #Jenkins #Docker #Kubernetes #DevSecOps #CloudEngineering #Automation #SRE #CloudComputing #GitOps #ArgoCD #Prometheus #Grafana #TechCareers #LearnDevOps #PlatformEngineering #HashiCorp #CloudNative

If you are still hand-writing the 10th nearly identical Terraform module, that is not craftsmanship. That is unpaid toil. Same with manually debugging the same Kubernetes YAML issues for the 50th time, or rewriting boilerplate CI/CD config from scratch every new repo. That is not "job security". It is you acting like a very slow, very expensive script. AI is not just for CEOs writing investor emails. It is absolutely for infra and DevOps engineers who are tired of: - Copy pasting the same Terraform patterns with tiny variations - Hunting the same Kubernetes indentation or apiVersion bugs - Rebuilding the same GitHub Actions / GitLab CI skeletons I use AI to generate the first draft of modules, Helm templates, and pipeline configs, then I review, harden, and standardize them. The value is not in typing YAML faster. The value is in designing better architectures, clearer incident patterns, and safer defaults. If we cling to low-value work as our identity, we are volunteering to be replaced by the people who automate it. If we use AI to kill the toil, we get to spend more time on the parts of this craft that actually require judgment and experience. #devops #cloudengineering #platformengineering

I thought GitHub was just a place to store code. I was wrong. My last class changed that completely. We were not just pushing files. We were learning how developers actually work together to get real systems built. Branching. Pull requests. Merging changes. Resolving conflicts. It felt messy at first. I made mistakes. I overwrote changes. I broke things. I had to go back and fix them. But that is when it clicked. GitHub is not about code storage. It is about collaboration. It is about control. It is about building systems where multiple people can work without breaking everything. That is what real DevOps Engineering looks like. In this session, I practiced creating repositories, managing branches, and using version control to track changes. This is the foundation of CI CD and scalable systems in real startup environments. Because without proper version control, automation and infrastructure fall apart. Next, I am focusing on integrating this workflow into real project pipelines. I am building the ability to design automated and scalable business systems using DevOps and AI. If you are building a startup, what part of your system feels the most chaotic right now? #DevOps #AI #Automation #CloudComputing #TechStartups #LearningInPublic #CareerTransition #BuildInPublic #ScalableSystems #DevOpsEngineer

DevOps is no longer just about pipelines. It's about Developer Experience. In 2026, if we are still just writing YAML files all day, are we really evolving? For a long time, the goal was simple: "Automate everything." But now, the focus has shifted. It’s not just about CI/CD anymore; it’s about building Internal Developer Platforms (IDPs) that treat developers as customers. The biggest shift I've seen lately: 1️⃣ AI-Driven Observability: We aren't just collecting logs; we are letting AI predict failures before they happen. 2️⃣ Platform Engineering: Moving away from ticket-based infrastructure to self-service portals. 3️⃣ Security as Code: Not an afterthought, but baked into the very first commit. Tools will change (Jenkins to GitHub Actions, Terraform to OpenTofu), but the mindset of "enabling speed with safety" remains constant. Fellow DevOps folks, what’s the one tool or practice you are betting on this year? #DevOps #PlatformEngineering #CloudComputing #SRE #TechTrends2026

Published: Advanced GitHub Actions Guide ✅ A detailed documentation covering GitHub Actions end-to-end, focused on how workflows are actually structured and used. What’s included: - Core concepts: workflows, events, jobs, steps, runners - Writing workflows with practical examples - CI/CD pipeline flow (build, test, Docker, deploy) - Passing data between jobs (outputs, artifacts) - Matrix builds for multi-environment execution - Reusable workflows and composite actions - Secrets handling and OIDC authentication - Security tooling: Gitleaks, Trivy, SonarQube, OWASP ZAP - Caching, parallel execution, and optimization - Workflow organization and naming patterns - Debugging techniques and common issues The focus is on clarity and structure so it can be used as a reference while building pipelines. DINESH CHALLA #GitHubActions #CICD #DevOps #Automation #SoftwareEngineering #GitHubActions #DevOps #Docker #Kubernetes #CICD #aws

GitHub Actions vs Jenkins — if you can't explain the difference, DevOps interviews will be tough. Picture this: you push your code and want it to build, test, and deploy automatically. That's CI/CD. And for years, Jenkins was the tool everyone reached for. Install it. Configure pipelines. Manage plugins. Done — your automation runs. But here's the catch nobody tells you upfront: Maintaining Jenkins becomes a full-time job on its own. → Server setup and hosting → Plugin updates and conflicts → Manual scaling → Constant maintenance overhead Then came GitHub Actions. No server. No setup. No headache. You write a YAML file inside your repo — and your pipeline is live. Push code → workflow triggers → build, test, deploy. Done. 🚀 But is Jenkins dead? Not even close. Jenkins wins when: → Full control is needed → Complex custom pipelines → On-premise environments GitHub Actions wins when: → Speed and simplicity matter → Cloud-native workflows → Tight GitHub integration So when someone asks "GitHub Actions vs Jenkins?" in an interview — the right answer isn't a winner. It's about use case, team size, and scale. Knowing both — and when to use which — is what separates a good DevOps engineer from a great one. As I transition into AI/ML, understanding CI/CD deeply is becoming even more critical — because ML pipelines need the same rigour. Which do you use at work — Jenkins or GitHub Actions? Drop it below 👇 #DevOps #GitHubActions #Jenkins #CICD #CloudEngineering #Automation #MLOps #BuildInPublic #DevOpsInterview