IT a.m.’s Post

🚀 My Docker Journey – Docker Containers I explored the basics of Docker containers and performed hands-on tasks to understand how containers actually work. In this practical, I learned: ✔️ How to pull an image from Docker Hub ✔️ How to create and run a container ✔️ How to stop, start, and remove containers ✔️ Difference between running and stopped containers Working with commands like: docker pull, docker run, docker ps, docker stop, docker rm This hands-on practice helped me understand how Docker simplifies application deployment using containers. 📌 Sharing my notes for anyone who is starting with Docker. #Docker #LearningInPublic #CloudComputing #BeginnerJourney #TechLearning

Heads up for everyone using GitHub Copilot code review! It's going to start consuming your GitHub Actions minutes from June 1, 2026. Definitely something to factor into future project planning and budget discussions. Good to get the notice early. #GitHub #DevOps

Pushing code is easy… Making sure it works every time is hard 😅 That’s where GitHub Actions comes in. It lets you automate your workflow: • Build your code • Run tests • Deploy automatically All triggered by events like push or PR 🚀 Read more here: https://lnkd.in/gmUXX5MM - By Octopus Deploy

Six engineers. A 22-year-old legacy codebase. GitHub Copilot licenses nobody was really using. Seven weeks later half the team had reached champion status and were already teaching other teams before the engagement ended. Full case study attached.

#My_Journey_Towards_DevSecOps 🚀 Leveling Up My DevOps Journey with Docker! 🐳 I recently completed an amazing Docker learning playlist by Abhishek Veeramalla, Let me share some key takeaways from this journey! 💡 🔹 Understanding Containers Learned how containers package applications with all dependencies, making them portable and consistent across environments. No more “it works on my machine” issues! 🙌 🔹 Containers vs Virtual Machines Discovered why containers are lightweight, faster, and more efficient compared to traditional virtual machines. ⚡ 🔹 Docker Fundamentals Got hands-on with Docker concepts like images, containers, Dockerfile, and basic commands. 🔹 Docker Architecture Explored how Docker works behind the scenes — Docker Client, Daemon, and Registry. 🧠 🔹 Image Lifecycle & Commands Practiced building, running, and managing containers using commands like: #docker #build | #docker #run | #docker #ps | #docker #push and many more. 🔹 Real-world Relevance Now I understand how Docker plays a key role in DevOps, CI/CD pipelines, and scalable deployments. 🌍 ✨ Biggest takeaway: Docker simplifies application deployment and ensures consistency across development, testing, and production environments. #Docker #Containerization #DevOps #DevSecOps #Cloud_Computing #Automation #Learning_Journey #Tech_Growth #Continuous_Learning #IT_Skills #Professional_Growth #Upskilling #Digital_Transformation #Future_Ready #Productivity https://lnkd.in/dwBky2Xx

Built out an end-to-end Kubernetes deployment pipeline from scratch 🚀 ✅ Dockerized a FastAPI app ✅ Deployed with Helm (reusable, parameterized charts) ✅ Added Ingress for domain-based routing ✅ Automated everything with GitHub Actions (lint → template → deploy) ✅ Used Kind for ephemeral clusters in CI Ran into some real-world issues along the way (arguably the best part of the project): ❌ “cluster unreachable” → kubeconfig pointed to localhost in CI ❌ Pods stuck in CrashLoopBackOff ❌ exec format error → containers built on arm64 failing in amd64 environments Worked through each by debugging with kubectl describe / logs and adjusting the pipeline + build process (multi-arch images). 💡 Good reminder that what works locally doesn’t always translate to CI https://lnkd.in/e9kcxNj6

Most Go developers are using GitHub Actions wrong — and it’s costing them speed, reliability, and clarity. I wrote this to break that pattern. The problem isn’t YAML. It’s the way we think about CI/CD. In this article, I walk through: – Why most pipelines become messy and fragile – The mindset shift from “scripts in YAML” → “clean orchestration” – How to structure Go workflows for speed, simplicity, and production reliability – What senior engineers do differently when designing CI Good CI isn’t about making builds pass. It’s about making systems trustworthy. If your pipeline feels slow, confusing, or brittle — this is for you. Read here: https://lnkd.in/dtYNh43T #golang #githubactions #devops #backend #softwareengineering #cicd

In the world of Kubernetes, the Imperative approach is like giving step-by-step directions to fix something quickly on the fly. The Declarative approach is about describing the final result you want in a file and letting the cluster handle the "how." I’ve found that true fluency comes from mastering both: using fast one-line commands to test ideas and YAML files for long-term stability. Balancing that "quick-fix" speed with a permanent record has completely changed my workflow. It’s not just about knowing the tools—it’s about choosing the right language for the task at hand.

It’s easy to get comfortable with tools like Docker or Kubernetes and know how to use them, how to get things running, and how to move forward efficiently. But there’s often a lot more depth behind what’s actually happening at runtime and during execution. Recently I went through “Docker Mastery” (Bret Fisher, Docker Captain), which helped me expand my understanding across Docker, Kubernetes, and Swarm from a more practical, end-to-end perspective. Not just how to use it, but how image builds are structured, how layers and caching affect build consistency and performance, how networking is handled between services, and how these choices impact reliability and maintainability in real environments. Taking a step back to deepen your understanding is both surprisingly engaging and genuinely valuable in the long run 🚀 Thanks to Bret Fisher for the hands-on, practical approach. Highly recommend it to anyone looking to sharpen their skills

I recently switched from GitHub Copilot to Claude Code due to model changes and cost. After using both, here’s my honest take: Claude Code shows strong reasoning in some cases, but in day-to-day development, I haven’t seen a consistent upgrade. The bigger difference is product experience. GitHub Copilot still feels more complete: • Deep IDE integration • Inline suggestions • Smoother workflow Claude Code feels more manual, more prompting, less seamless integration. Right now, Copilot still wins as a developer tool, even if Claude has strengths in specific use cases. Curious to hear from others using Claude Code, where does it actually outperform for you? #SoftwareDevelopment #AItools #DeveloperTools #GitHubCopilot #ClaudeAI #Productivity #Coding #DevWorkflow #ArtificialIntelligence