utils-devops 0.1.31: A DevOps Superhero for Your Scripts

💡 Learning to Think Like a Problem Solver with Bash As I’ve been diving deeper into Bash scripting, one of the biggest lessons so far has been how powerful a few lines of code can be when it comes to automating everyday tasks. One challenge that stood out was writing a backup script that checks if a directory exists, creates it if it doesn’t, and then copies all .txt files into it, simple but a great exercise in thinking logically and efficiently. (see snippet of the code below) It really clicked for me how automation saves not just time, but mental energy, especially in DevOps, where repeatability and consistency are key. What’s the first script you wrote that made you realise the power of automation? #Bash #DevOps #Automation #Scripting #CoderCo #LearningJourney

Development isn’t just about writing code — It’s about solving problems, learning patterns, and building something that makes life a little easier. The best feeling? Seeing your project actually work after hours of debugging. . . . . #DeveloperMindset #SoftwareEngineering #MERNDeveloper #GenAi #React #Startfromscratch

Jenkins, Vite, and Cursor – three things I can't live without once I discovered them. Jenkins makes deploying to Kubernetes as easy as merging a pull request. We're able to build locally, test tin development and deploy to production reliably within in minutes. See ya later FTP! Vite builds React and Typescript user-interfaces instantly, so my AI agent can update the code and I see the UI change in real-time. I can't believe we used to wait 30 seconds for webpack to build for every line of css that was changed. Cursor brings the best of Ai coding using codex and claude code into a single IDE and manages the headache of figuring out the best practices with each respective model behavior for the task at hand. As the head of product, being able to speak to my computer and see it make the changes I asked for about as quickly as I can ask for them, is absolute magic. The amazing Authentic DevOps team working with me on Embers Protection 2.0 are using these tools creating a world where families can rest assured knowing they have invested in the best technology to defend against wildfires.

The latest update for #Honeybadger includes "The #APM paradox" and "#Python performance #monitoring for #Django, Flask, Celery, and more". #websitemonitoring #DevOps https://lnkd.in/dsTQ33D

#If you work with YAML and JSON regularly — especially in network automation or DevOps — this tool is a gem: #https://lnkd.in/dQrUpqJ2 I have been using it for practice and code validation, and it’s incredibly handy for checking syntax, converting YAML configs to JSON, and verifying structure. ✅ Converts instantly in the browser ✅ Great for validating YAML during learning or scripting ✅ No installation needed — simple and reliable Highly recommend it for anyone practicing YAML/JSON conversions or building automation scripts! #YAML #JSON #NetworkAutomation #DevOps #Python #Ansible #Automation #CodingTools #Learning

Lately, I've been seeing a lot of "starting from scratch" posts about learning new technologies, programming languages, and even #kubernetes. Since I am not very good with theory, if I were to start my Kubernetes journey all over again, I would start by creating my own CLI tool to interact directly with the Kubernetes API. For some, this may not sound like a beginner-friendly advice, but once you start exploring how to talk to the Kubernetes API and analyse its responses, you’ll realise just how much it helps you truly understand what’s happening behind every command. In more detail, this approach can help you build a good understanding of the authentication process, explore the API calls behind each command, and develop a deeper insight into how everything flows within the cluster. To answer potential questions in advance: - NO, it doesn't matter which language you use. Whether it's bash, GO, Python or braille - the important bit is writing the API call and sending it to the Kubernetes API - NO, this isn’t about replacing kubectl. It’s about reinforcing your understanding and making sure you’re not just sailing blindly inside the cluster - NO (another one?), you don't need to learn kubernetes the hard way to create a new cluster. There are many options for provisioning one locally without frying your brain Let me know what you think. Would this approach work for you? Have you ever built something similar while learning a new concept? #Kubernetes #CloudNative #K8s #DevOps #PlatformEngineering #CloudComputing #DevSecOps

Hello everyone. ⚙️ Helm — Simplifying Kubernetes Deployments As my DevOps journey continues, one thing becomes clear: managing Kubernetes configurations manually doesn’t scale. That’s where Helm comes in — the package manager for Kubernetes that brings structure, reusability, and control to complex deployments. --- 🚀 What Is Helm? Helm is to Kubernetes what pip is to Python. It packages Kubernetes manifests into reusable templates called Charts, allowing you to: Deploy apps in one command Version and share configurations Reuse and customize deployments across environments --- 🔍 Why Helm Is Essential in DevOps 1️⃣ Reusability Instead of copying YAML files across projects, define templates once and reuse them for multiple environments. 2️⃣ Version Control Every release of your app is versioned — making rollbacks, upgrades, and debugging painless. 3️⃣ Configuration Management Helm separates values (customizable parameters) from templates, so your deployment logic stays clean. 4️⃣ Seamless Integration with GitOps When combined with ArgoCD, Helm Charts become declarative and automatically synced from Git. --- 🧩 A Typical GitOps Flow GitLab CI/CD → ArgoCD → Helm → Kubernetes → Traefik → Users GitLab CI/CD builds and tests your app ArgoCD pulls the Helm chart and syncs it to the cluster Helm renders and applies Kubernetes manifests Traefik routes live traffic to your deployed app Each tool adds a new layer of automation — together forming a complete, reproducible deployment pipeline. --- 💡 Helm transforms deployment from a set of scripts into a maintainable, scalable delivery system. > “Templates define consistency. Automation delivers reliability.” --- #DevOps #Helm #Kubernetes #ArgoCD #GitOps #CICD #GitLab #Docker #Python #Django #CloudNative #Infrastructure #Automation

🚀 Major Release: create-quality-automation v2.3.0 After comprehensive code review and user feedback, we've rebuilt our quality automation CLI around a core philosophy: use best-in-class existing tools, don't reinvent the wheel. Key Improvements: 🔧 Replaced custom package.json handling with @npmcli/package-json (the official npm library) 🔒 Integrated gitleaks for production-grade secret scanning ⚡ Added actionlint for advanced GitHub Actions workflow validation 📝 Switched to markdownlint-cli2 for robust documentation validation 🪟 Enhanced Windows compatibility by removing shell dependencies 🎯 Added Node.js 20+ requirement enforcement with proper lazy loading Why This Matters: Rather than building custom solutions, we now leverage mature, battle-tested tools that teams already trust. This approach delivers better reliability, more features, and easier maintenance. Perfect for: • JavaScript/TypeScript projects • Python development • Teams implementing DevOps best practices • Open source maintainers Get started in seconds: npx create-quality-automation@latest The tool automatically configures ESLint, Prettier, Husky, lint-staged, GitHub Actions, and more - all with production-ready defaults. #DevOps #QualityAutomation #AIBuilderLab /why not subscribe to my newsletter AIBuilderLab?

🔥 Automating Test Generation with GitHub Copilot + Pytest + GitHub Actions ⚙️🤖 Tired of writing unit tests manually? Let AI handle it for you! 💡 I recently built a Copilot-Pytest-AutoGen pipeline that automatically: ✅ Detects changed Python files in a pull request ✅ Generates Pytest test cases for all functions using GitHub Copilot (no CLI required) ✅ Runs the tests, validates coverage, and ✅ Posts a summary comment back on the PR — all hands-free! 🚀 🧩 How It Works 1️⃣ Developer raises a Pull Request 2️⃣ GitHub Action triggers the copilot_test_gen.py script 3️⃣ It asks Copilot AI to write runnable Pytest tests 4️⃣ The pipeline: Runs all generated tests Measures coverage Detects untested functions Regenerates tests for missing coverage 5️⃣ If all tests pass ✅ → it commits and pushes automatically 6️⃣ A summary like this appears in your PR 👇 🤖 Copilot Test Automation Summary • Status: ✅ success • Result: 15 tests passed, 0 failed • Coverage: 92% Check workflow logs for details. ⚙️ Tech Stack 🧠 GitHub Copilot (AI-powered test generation) 🧪 Pytest + pytest-cov (validation & coverage) ⚡ GitHub Actions (CI/CD automation) 🐍 Python 3.10+ 🧠 Why This Matters 💬 It transforms testing from manual & repetitive → automated & intelligent. 💡 Boosts test coverage, ensures code quality, and lets you focus on logic instead of boilerplate. 💾 Integrates seamlessly into your existing CI/CD pipeline. 🧱 What Happens Under The Hood Detects modified .py files in src/ Auto-creates or updates corresponding tests/test_<module>.py files Runs pytest with coverage Cleans up invalid or failed test generations Posts results directly to your PR ✅ 🚀 Example Workflow Output 🚀 Starting Copilot Test Generator... 📂 Changed files detected: [src/mathops.py] 🧠 Generating tests via Copilot... ✅ Generated test file: tests/test_mathops.py 🧪 Running pytest... ........... [100%] ✅ Tests passed — Coverage: 92% 🚀 Committed & pushed automatically! 🧩 Real-World Use Ideal for: DevOps & CI/CD pipelines QA automation teams Projects adopting AI-assisted software engineering ❤️ Takeaway This is a glimpse into how AI + DevOps can supercharge developer productivity. GitHub Copilot isn’t just a coding assistant — it’s becoming your CI teammate.🧠💪 🙏 Special Thanks to Manikanta Aisetty for the automation idea. ✨ Future Enhancements – Copilot-Pytest-AutoGen 🧠 Smarter prompt engineering for deeper context 🧪 Automatic mocking and dependency injection 📊 Intelligent coverage ⚙️ Parallel test generation for faster CI 📈 Visual QA insights dashboard 🧬 Hybrid AI model (Copilot+GPT) 🤖 Long-term: a self-learning, self-healing test agent From auto-generated tests ➜ to an autonomous QA system that continuously improves. If you’d like the GitHub Action YAML + Python generator script, Pls go and check this repo: https://lnkd.in/gqy9hvbH #Copilot #AI #DevOps #Pytest #Automation #CICD #GitHubActions #Python #Productivity