Automate Pull Request Reviews with truepr GitHub Action

How many commits have you made just to test if something works in the real environment? Push. Wait for the pipeline. It fails. Fix a config. Push again. Wait again. This is what happens when local dev looks nothing like production. Every fix is a commit, every commit is a 10-minute wait, and none of it is feature work. So I built a local dev platform where developers build and test on a real Kubernetes cluster that mirrors production. Same Dockerfile, same manifests, same ingress. - tilt up — see changes in 1 second instead of pushing and waiting - make ci-local — local gitlab pipeline run to catch failures before you push - Push once and it works, not 15 "fix CI" commits I wrote up how I built this. https://lnkd.in/dAQejEUU #Kubernetes #PlatformEngineering #DevOps #Tilt #GitLab

🗓️ Day 29/100 — 100 Days of AWS & DevOps Challenge Today's task wasn't just Git — it was the full engineering team workflow that makes collaborative development actually safe. The requirement: Don't let anyone push directly to master. All changes must go through a Pull Request, get reviewed, and be approved before merging. This is branch protection in practice. Here's the full cycle: Step 1 — Developer pushes to a feature branch (already done) $ git log --format="%h | %an | %s" # Confirms user commit, author info, commit message Step 2 — Create the PR (Log into GIT) - Source: story/fox-and-grapes - Target: master - Title: Added fox-and-grapes story - Assign a user as reviewer Step 3 — Review and merge (log into GIT as reviewer) - Files Changed tab — read the actual diff - Approve the PR - Merge into master Master now has user story. And there's a full audit trail of who proposed it, who reviewed it, who approved it, and when it merged. Why this matters beyond the task: - A Pull Request is not a Git feature - it's a platform feature. Git only knows commits and branches. The PR is a Git/GitHub/GitLab construct that adds review, discussion, approval tracking, and CI/CD status checks on top of a branch merge. When companies say "we require code review before anything goes to production," this is the mechanism. When GitHub Actions or GitLab CI runs tests on every PR — this is where that hooks in. When a security audit asks "who approved this change?" — the PR has the answer. The workflow is identical across Git, GitHub, GitLab, and Bitbucket: push branch → open PR → assign reviewer → review diff→ approve → merge → master updated → branch deleted Full PR workflow breakdown on GitHub 👇 https://lnkd.in/gpi8_kAF #DevOps #Git #PullRequest #CodeReview #Gitea #BranchProtection #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #GitOps #TeamCollaboration

Most CI/CD pipelines fail for the same reason — no clear stages. After 4 years in DevOps, here's the multi-stage GitHub Actions pipeline I recommend to every engineer on my team: ━━━━━━━━━━━━━━━━━━━ Stage 1 → Test Stage 2 → Build & tag Docker image Stage 3 → Deploy to Staging Stage 4 → Deploy to Production (with manual approval) ━━━━━━━━━━━━━━━━━━━ 3 things that make this bulletproof: 1️⃣ Use needs: to chain jobs — if tests fail, nothing else runs 2️⃣ Tag images with github.sha — every build is fully traceable 3️⃣ Use GitHub Environments for prod — enforces human approval before anything goes live You don't need a complex tool to do this. A single YAML file in .github/workflows/ is enough to build a production-grade pipeline. Save this post for when you set yours up. What does your CI/CD stack look like? Drop it in the comments 👇 #DevOps #GitHubActions #CICD #Docker #Kubernetes #CloudNative #DevOpsEngineer #SoftwareEngineering

🚀 41 seconds. From Git push to live Docker image on Docker Hub. I just built and automated a complete CI/CD workflow using GitHub Actions + Docker — and it took exactly 30 lines of YAML. Here's what happens every time I push to main: ✅ Code is checked out automatically ✅ Docker image builds in seconds   ✅ Health checks run before anything goes live ✅ Image pushes to Docker Hub with zero manual steps No SSH. No "docker build" on my laptop. No human error. Slide 5 shows the image auto-pushed to Docker Hub. Fully automated. Zero manual intervention. The lesson? If you're still deploying manually, you're not doing DevOps — you're doing repetitive work that a 30-line script can handle for free. This is the kind of automation I bring to engineering teams. 🔹 Tech stack: Docker, GitHub Actions, CI/CD, YAML If your team needs someone who ships automation-first, let's talk. 👇 What does your deployment pipeline look like? Drop a comment — I read every one. #OpenToWork #DevOps #GitHubActions #Docker #CICD #CloudEngineering #SRE #InfrastructureAsCode #PakistanTech #HiringDevOps #RemoteWork #TechJobs #DevOpsEngineer #Automation #LinkedIn 💾 Save this post if you're learning CI/CD. 🔄 Share it with someone still deploying manually.

🚨 Shipping fast is easy. Shipping securely and reliably is the real challenge. That is exactly what I wanted to solve with my CI/CD GitOps 3-Tier Microservices Platform. Instead of building just another deployment project, I focused on a workflow that reflects real-world DevOps practices: ✅ Automation ✅ Security checks ✅ Deployment traceability ✅ Production-style architecture 🔁 CI flow I implemented GitHub Webhook → Checkout Code → SonarQube Analysis → Trivy Scan → Docker Build → Image Push → Manifest Update 🔐 Security-first focus • SonarQube to catch code quality issues early • Trivy to scan for vulnerabilities before deployment • Multi-stage Dockerfiles to reduce image size and attack surface • ConfigMaps, Secrets, and imagePullSecrets for safer runtime configuration ☁️ Real-world practices • ArgoCD for automated sync and drift detection • AWS EKS for Kubernetes deployment • Envoy Gateway API for structured traffic management • Docker Compose for better local-to-production parity 💡 What this project taught me DevOps is not just about automating deployment. It is about building pipelines that are secure, traceable, and closer to real production workflows. 💬 Comment “GitHub” for the repo link. 👉 In my next post, I’ll break down how I used SonarQube and Trivy to bring a security-first CI approach into this workflow. #DevOps #DevSecOps #Jenkins #SonarQube #Trivy #ArgoCD #Kubernetes #AWS #EKS #GitOps #Docker #CloudSecurity

⚙️ DEVOPS UNLOCK #003 ⚙️ Your GitHub Actions pipeline takes 22 minutes. Your team deploys 8x per day. That's nearly 3 hours of engineer-wait time — daily. Here's how to slash it to under 5 minutes. Pipeline optimization isn't magic. It's understanding where time actually dies. 1. PARALLELIZE with matrix strategy: jobs: test: strategy: matrix: shard: [1, 2, 3, 4] steps: - run: pytest --shard-id=${{ matrix.shard }} --num-shards=4 4 parallel shards = 4x faster test runs. Simple math. 2. CACHE aggressively (this alone saved us 8 minutes): - uses: actions/cache@v4 with: path: ~/.cache/pip key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} 3. GATE on path changes — don't run backend tests for frontend-only PRs: - uses: dorny/paths-filter@v3 id: changes with: filters: | backend: - 'src/api/**' frontend: - 'src/ui/**' 4. USE concurrency groups to auto-cancel stale runs: concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true 5. REUSE workflows with "workflow_call" — stop copy-pasting the same 50-line deploy job across 12 repos. ⚡ Pro Tip: Self-hosted runners on Spot/Preemptible instances with warm Docker layer caches = 70% cost reduction AND faster builds. We went from $800/month on GitHub-hosted runners to $190/month while cutting build time by 60%. The ROI pays for an SRE engineer's tooling budget. What's your pipeline's biggest time sink right now? Let's debug it together 👇 #DevOps #CICD #GitHubActions #PlatformEngineering #Automation #SRE #CloudNative #DevOpsUnlock

⭐ Most platform engineers I know use Cursor for autocomplete. That's like using a excavator to dig a hole with a teaspoon attachment. I spent the last few weeks going deep on Cursor Agent — not the tab-complete, the actual agent mode — specifically for infrastructure and DevOps work. What I found changed how I think about the tool entirely. The agent doesn't just edit files. It: → Queries your live Kubernetes cluster before making a change → Catches open PRs that would conflict with what you're about to do → Investigates a 5xx incident across GitHub, kubectl, and your deploy history — in one conversation → Runs terraform validate, reads the error, fixes it, runs again — without you typing a command But the part nobody talks about: Out of the box, it's generic. It doesn't know your naming conventions, your module patterns, your "never touch this file" rules. Once you configure it properly — 6 files, maybe 2 hours of setup — it's a different tool entirely. I wrote the full breakdown. What MCP actually is, how the agent calls tools under the hood, every config file your team needs to replicate this, and 6 real use cases with exact prompts. If you work in platform or DevOps, this one's worth the read. Part 1 (link in the comment) and Part 2: https://lnkd.in/gpXdFjRU #DevOps #PlatformEngineering #Kubernetes #Terraform #CursorAI #AITools #SRE

The pipeline was green. Deployment said successful. Production was running the wrong code for 3 hours. No alerts. No red dashboards. Nothing. I've been burned by silent CI/CD failures more times than I'd like to admit. The dangerous ones aren't the crashes — they're the failures that look like success. Here are the 3 that hurt the most: 1. Docker cached the wrong image Build finished in 12 seconds. Felt fast. Turned out Docker served a previously cached layer. Yesterday's code went to production. The build log looked completely normal. 2. Tests reported zero failures — because they never ran The test framework found no matching files, ran zero tests, exited with code 0. Green badge. A real bug reached production that the tests should have caught. 3. Deployment succeeded. Old code still running. Kubernetes rollout reported complete. New image never actually pulled — node had the old one cached with imagePullPolicy: IfNotPresent. "Deployment succeeded" and "new code is live" are not the same thing. The root cause in every case was the same: The pipeline verified that steps executed — not that outcomes were correct. The fixes aren't complex: → Embed Git SHA in every image. Verify it post-deploy → Fail the pipeline if zero tests ran → Never use :latest in Kubernetes. Always deploy with image SHA I wrote the full breakdown with code examples for GitHub Actions, Jenkins, and Kubernetes on Dev.to. Link in the comments 👇 Have you hit a silent pipeline failure? Drop it below — genuinely curious what broke. #DevOps #CICD #Docker #Kubernetes #Jenkins #SRE #PlatformEngineering #Cloud

𝗖𝗜/𝗖𝗗 — 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿𝗵𝗼𝘂𝘀𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 👇 𝗖𝗜 — 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 🔹 𝗖𝗼𝗱𝗲 -> Developers push to a shared repo (𝗚𝗶𝘁𝗛𝘂𝗯, 𝗚𝗶𝘁𝗟𝗮𝗯) 🔹 𝗕𝘂𝗶𝗹𝗱 -> Code is compiled and packaged (𝗚𝗿𝗮𝗱𝗹𝗲, 𝗪𝗲𝗯𝗽𝗮𝗰𝗸, 𝗕𝗮𝘇𝗲𝗹) 🔹 𝗧𝗲𝘀𝘁 -> Automated tests run to catch issues early (𝗝𝗲𝘀𝘁, 𝗝𝗨𝗻𝗶𝘁, 𝗣𝗹𝗮𝘆𝘄𝗿𝗶𝗴𝗵𝘁) 𝗖𝗗 — 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 / 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 🔹 𝗣𝗹𝗮𝗻 & 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 -> Changes are reviewed and staged for deployment (𝗝𝗜𝗥𝗔, 𝗖𝗼𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲) 🔹 𝗗𝗲𝗽𝗹𝗼𝘆 -> Shipped to production via 𝗗𝗼𝗰𝗸𝗲𝗿, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝗔𝗿𝗴𝗼 or 𝗔𝗪𝗦 𝗟𝗮𝗺𝗯𝗱𝗮 🔹 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 -> Infrastructure managed via 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 🔹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 -> System health tracked via 𝗣𝗿𝗼𝗺𝗲𝘁𝗵𝗲𝘂𝘀, 𝗗𝗮𝘁𝗮𝗱𝗼𝗴 The whole loop runs automatically on every 𝗰𝗼𝗱𝗲 𝗽𝘂𝘀𝗵 — making 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝗳𝗮𝘀𝘁𝗲𝗿, more 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲, and fully 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱. This is exactly what I implemented for my 𝗘𝗖𝗦 𝗙𝗮𝗿𝗴𝗮𝘁𝗲 project (full project post coming soon 👀). 📌 Credit: ByteByteGo CoderCo #CICD #DevOps #GitHub #Docker #Kubernetes #Terraform #CloudComputing #CoderCo #LearningInPublic #AWS

GitOps: Why I Stopped Running kubectl Manually A while back I made a rule for myself: no more manual kubectl apply in production. Ever. It felt uncomfortable at first. Like giving up control. But the reality is — it was the opposite. Once we moved to a full GitOps workflow with ArgoCD, every change became: — Versioned in Git — Reviewed via pull request — Automatically synced to the cluster — Fully auditable Rollbacks went from a 30-minute fire drill to a simple git revert. Deployment confidence went through the roof. And the best part? Teams that previously depended on the "infra guy" could now self-serve their own deployments safely. GitOps is not just a deployment strategy. It's a cultural shift — from "who did what and when" to "the repo is the single source of truth." If you're still doing manual deployments, try this: pick one non-critical service and move it to GitOps. See how it feels. You probably won't go back. #GitOps #ArgoCD #Kubernetes #DevOps #ContinuousDelivery #SRE