Docker Best Practices for Efficient Builds and Security

Claude Code makes engineers faster. But speed without structure breaks in DevOps. This is where DuploCloud comes in. It takes AI-generated code and turns it into something your team can actually trust, review, and deploy. 🔗: https://lnkd.in/eZRbh6fQ • Claude = fast iteration, coding, problem-solving • DuploCloud = approvals, governance, execution • Together = production-ready DevOps, not just output If you're using AI for DevOps and not thinking about the execution layer, you're leaving a massive gap. Click the link to learn more!

Vibe coding doesn’t fail in production. It fails the moment someone clicks “Deploy” and realizes nobody set up a staging environment. I work with Supabase, Vercel, Lovable, Cursor, and Claude Code every day. These tools are genuinely incredible, you can ship a working MVP in an afternoon. But here’s what no one tells you when the demo goes viral: → Your Google OAuth will break the second you change domains → Your Supabase RLS policies are probably wrong → Your “production” is one branch away from your dev environment → You have no rollback strategy → You have no error logging → You have no idea what happens at 1,000 concurrent users The MVP is the easy part now. AI flattened that curve. The moat is everything below the waterline: CI/CD, branching strategy, environment configs, caching, rate limiting, observability, the boring stuff that keeps real businesses online. Vibe coding gets you to a demo. DevOps gets you to a business. Both matter. Don’t confuse them.

Lightrun: IT is in the Dark Over Coding Assistant Runtime Visibility  Software runs, but sometimes it doesn’t… and that’s often down to a lack of runtime visibility in relation to platform engineering teams being able to trust coding assistants and AI-powered site reliability engineering (SRE) services. It’s an assertion made by software reliability company Lightrun, in its State of AI-Powered Engineering Report 2026, based on an independent poll of 200 SREs and DevOps leaders at enterprises in the U.S., UK and EU. ...

Lightrun: IT is in the Dark Over Coding Assistant Runtime Visibility  Software runs, but sometimes it doesn’t… and that’s often down to a lack of runtime visibility in relation to platform engineering teams being able to trust coding assistants and AI-powered site reliability engineering (SRE) services. It’s an assertion made by software reliability company Lightrun, in its State of AI-Powered Engineering Report 2026, based on an independent poll of 200 SREs and DevOps leaders at enterprises in the U.S., UK and EU. ...

Lightrun: IT is in the Dark Over Coding Assistant Runtime Visibility  Software runs, but sometimes it doesn’t… and that’s often down to a lack of runtime visibility in relation to platform engineering teams being able to trust coding assistants and AI-powered site reliability engineering (SRE) services. It’s an assertion made by software reliability company Lightrun, in its State of AI-Powered Engineering Report 2026, based on an independent poll of 200 SREs and DevOps leaders at enterprises in the U.S., UK and EU. ...

🐳 Mastering Docker — From Basics to Advanced 🚀 I recently dedicated time to deeply understanding one of the most in-demand technologies in modern software development: Docker. What started as learning containers quickly became an eye-opening journey into how real-world applications are built, packaged, deployed, and scaled efficiently. 🔹 Concepts Covered End-to-End: ✅ What is Docker & Why It Matters ✅ Containers vs Virtual Machines ✅ Images, Containers, Registries & Docker Hub ✅ Core Commands (docker run, docker ps, docker pull, docker stop) ✅ Managing Images & Containers Efficiently ✅ Port Mapping & Container Networking ✅ Volumes & Persistent Data Storage ✅ Writing Custom Dockerfiles ✅ Building Images with docker build ✅ Multi-Container Applications with Docker Compose ✅ Environment Variables & Config Management ✅ Logs, Monitoring & Debugging Containers ✅ Cleanup & Optimization Commands ✅ Security Best Practices ✅ Real Project Use Cases with Databases & Web Apps 💡 Biggest Takeaway: Docker is not just a tool — it is a mindset shift. It solves the classic problem of “it works on my machine” by creating consistent environments anywhere: development, testing, staging, or production. Learning Docker also gave me a clearer understanding of deployment pipelines, scalability, DevOps culture, and production-ready engineering. Every developer writes code. Strong developers know how to run it. Professional developers know how to ship it. 🚀 Excited to keep building real-world projects using Docker and modern development workflows. #Docker #DevOps #Containerization #SoftwareDevelopment #BackendDevelopment #FullStackDevelopment #CloudComputing #DeveloperJourney #LearningInPublic #TechSkills #Programming #CareerGrowth #Engineering

𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘾𝙤𝙢𝙛𝙤𝙧𝙩 Module 4: Kubernetes in Practice 𝗗𝗲𝘃 𝗣𝗮𝘁𝗵 0.1 𝟭/ kubectl Fluency: Commands Every Developer Uses 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 • Getting info: get, describe, logs, top • Making changes: apply, delete, scale, rollout restart, rollout status, rollout undo • Debugging: exec -it, port-forward, cp • Working across namespaces: -n flag, context switching • Power moves: --dry-run=client -o yaml to learn, validate before applying • Reading, writing YAML confidently 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 ↳ A structured kubectl exercise set covering 10 common real-world scenarios:  scale a deployment update an image get logs from a crashing pod exec into a running container copy a file out forward a port Timed. Solo. 𝟮/ Logging, Health Checks, Resource Awareness 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 • Logging from containers • Reading logs: single pod, multi-pod with -l labels, --previous for crashed containers • Liveness probes • Readiness probes • Resource requests, limits. Why developers (not just DevOps) should set them • OOMKilled? how to prevent 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 ↳ Add liveness and readiness probes to your Deployment. Add resource requests & limits. Simulate a probe failure and observe what K8s does. Check events 𝟯/ Networking & Storage for Developers 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 • How pods discover, talk to each other • The pattern every backend developer uses: http://service-name.namespace.svc.cluster.local • What a junior dev needs to know about K8s networking • Volumes: when, why your app needs persistent storage • emptyDir: temp shared storage • ConfigMap & Secret volumes: mounting config as files (vs env vars) • PersistentVolumes: what, when they come up, who manages 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 ↳ Configure inter-service communication between two Deployments using service DNS. Add a volume mount for application file storage. Verify data behavior across pod restarts 𝟰/ Diagnosing Real Problems 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 • Systematic debugging: pod status → describe → logs → exec • CrashLoopBackOff: what causes it, how to diagnose • ImagePullBackOff: what causes it, how to fix • Pending: why a pod won't schedule — resource constraints, node selectors, taints • OOMKilled: memory limit hit, what to look for • Reading K8s events: most underused debugging tool • What info to bring when you ask a sr. engr. or DevOps for help 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 ↳ A set of 5 deliberately broken Deployments are provided. Diagnose and fix each one. No hints. Each covers a different failure mode from the concepts above Session replicates real on-the-job debugging 𝟱/ Project Deploy a complete multi-service backend app to K8s end to end — independently or in pairs: • Dockerfiles written, optimized • Images tagged, pushed to registry • K8s manifests written: Deployment, Service, Ingress, ConfigMap, Secret • Health checks, resource limits configured • Application verified working • At least 1 intentional failure introduced. Resolved 𝙄𝙣𝙥𝙪𝙩𝙨 𝙬𝙚𝙡𝙘𝙤𝙢𝙚

I built an autonomous coding agent that improved our production codebase from 5.8/10 to 8.8/10 overnight while I slept. Here's what happened: We're building a fintech platform. Last week I ran a production readiness audit and scored 5.8/10 across security, reliability, testing, infrastructure, observability, code quality, and compliance. 13 critical blockers. Instead of grinding through them manually, I built Cloud Coder: a system that takes a YAML task queue and feeds each task to the local Claude CLI sequentially. Define your tasks, start the runner, go to bed. Night 1: 20 tasks. CSP headers, rate limiting, error boundaries, timeout guards, structured logging. Score jumped to 7.1. Then I added the audit-fix loop. Claude audits the codebase, scores it, generates fix tasks for every gap, executes them, and re-audits. It keeps cycling autonomously until it hits the target. 5 rounds later: 160+ tasks completed. Score: 8.8/10. Zero human intervention after pressing enter. The whole thing is ~500 lines of bash. No API key needed (uses Claude Max). No cloud infra. Just your local CLI and a YAML file. I'm open-sourcing it as a Claude Code plugin. The interesting insight: the code itself isn't the moat. It's the pattern. Use an LLM to score your codebase, generate remediation tasks, execute them, and re-score. Works for any quality dimension you can define. lmk if you like it. putting in a plugin for community and official release app to see how this goes. I'll try to run the auto-improver on itself so the tool itself keeps improving. Try it: https://lnkd.in/gee9d9Ax #ClaudeCode #AI #DevTools #Automation #OpenSource

AI coding agents are rapidly becoming essential for modern engineering teams in their processes of building, reviewing, deploying, and troubleshooting software. As their usage increases, several operational questions arise: - Which agents are being adopted? - What are the associated costs? - How reliable are coding agents within real developer workflows? - Which tools do they invoke, and where are they slowing down, failing, or introducing unnecessary risks in production? Dynatrace provides solutions to these questions by enhancing AI observability for a new generation of coding agents, including Claude Code, Google Gemini CLI, OpenAI Codex CLI, OpenCode, and GitHub Copilot SDK. Read on to learn more. https://lnkd.in/gYacsJp9

Stop calling a 500-line YAML file Infrastructure-as-Code. YAML is not code 🛑 If you can't auto-test it, it's not Code! That's how you end up spreading your source maps to the world; If you think I’m referring to a specific AI company, I don't have a "claw" who you are thinking about 🥹 There's no tool to address this issue today, measure how vulnerable a CI-CD stack is. But the role should be simple "yaml files should always be a few lines long" In my latest article, I break down how I moved our release 'scripts' into full scale testable programs. Multiple defense lines guarantee the product passes tests (unable to push to git remote if not), versioning must follow a clear pattern (unable to deploy if not), and the versioning automatically saved the code version as a minor branch commit+as a git tag. The system blocks bad pushes locally before they ever hit CI runners. Despite it doing everything automatically, there is a clear way to dissociate regular code change pushes from version release intent. Read the full strategy and grab the template as open source here: https://lnkd.in/dkzyQ86G #DevOps #Terraform #CI_CD #gihub_actions Krishnan Ragavendran Paulius Miksys Marwen landoulsi