Speed Up MVPs and Cloud Migrations with Claude Code

🚀 Cloud-Native E-commerce — Production Deployment (Part 1) Everything was ready. → Terraform ✔️   → EKS ✔️   → ArgoCD ✔️   → CI/CD ✔️  This should have worked. It didn’t. The deployment didn’t fail once. It failed in layers. • External Secrets stuck in OutOfSync   • Pods crashing because secrets weren’t available   • ArgoCD deploying apps before images existed   • Permissions working in CI… but failing inside Kubernetes  At one point, I wasn’t debugging one issue. I was debugging a chain of issues stacked on top of each other. That’s when it hit me: 👉 In distributed systems, nothing fails alone. Everything is connected. I had built: • GitHub OIDC for secure CI/CD   • IRSA for Kubernetes → AWS access   • External Secrets syncing AWS Secrets Manager → Kubernetes   • Terraform-managed infrastructure   • GitOps deployment with ArgoCD  But I missed one thing: 👉 Order and timing are dependencies too. Automation doesn’t guarantee correctness.   It just executes mistakes faster. So I stopped trying to “fix everything quickly”. Instead, I: • Traced every failure step by step   • Mapped dependencies across systems   • Documented root causes (not just fixes)   • Built a real debugging log  🧠 What this phase taught me: • CI (OIDC) ≠ Runtime (IRSA)   • Secrets must exist before apps start   • OutOfSync is a signal, not the problem   • The visible error is often the last domino  Now I don’t just have a working system. I have: • A system I understand   • A deployment I can repeat   • Failures I can fix in minutes  This is Part 1. Part 2 → Where GitOps + CI/CD starts breaking in real systems If you’ve worked with Kubernetes or AWS, you know: 👉 The real learning starts when things break. #DevOps #AWS #Kubernetes #GitOps #Cloud #buildinpublic #learninpublic

🚀 Day 13/15 – Docker Stack (Deploy Apps in Swarm 👇) I learned Docker Swarm helps scale containers across machines. But then I had a question: 👉 “How do we deploy a full application in Swarm?” That’s where Docker Stack comes in 🔥 . . . 🧠 What is Docker Stack? 👉 Docker Stack is used to deploy applications on a Docker Swarm cluster. Instead of running containers manually… 👉 You define everything in a file and deploy it. . . . 📄 What does it use? 👉 It uses the same docker-compose.yml file! Yes 😄 The same file we used for Docker Compose. . . . ⚙️ Key Difference: 👉 docker compose up → Runs locally 👉 docker stack deploy → Runs on Swarm (cluster) . . . 🧱 Example: version: "3" services: web: image: nginx ports: - "80:80" . . . 🚀 Deploy in Swarm: docker stack deploy -c docker-compose.yml my-app . . . 💡 What happens: ✔ App is deployed across multiple nodes ✔ Load balancing is automatic ✔ Containers are managed by Swarm . . . 🔥 Real-world usage: 👉 Production deployments 👉 Scalable applications 👉 Microservices architecture . . . ✨ My takeaway: 👉 Docker Compose = Local setup 👉 Docker Swarm = Cluster management 👉 Docker Stack = Production deployment . . . 💬 Question: Did you know the same Compose file can be used in Swarm? #Docker #DockerStack #DevOps #Cloud #LearningInPublic #Azure #Kubernetes

Has a developer ever said "But it works on my machine!" defensively? What does it mean, exactly? And how does that affect your business? A developer is expected to have many skills, but being great at UX does not mean that they can also provision reliable servers. A full stack developer becomes a professional by building repeatable tools. I offer a complete service, including provisioning servers. Scaling requires server rebuilds and a key part to bringing a project in on time is being able to iterate rapidly. By using automated provisioning tools like Terraform and Ansible, you can iterate on working parts, spinning up and testing environments on a cloud infrastructure is agile. No, this is not a sponsored advert, these tools are free and open source. They are a key part of being ready to ship a scalable infrastructure rapidly. Each new version of the project may require adjustments to infrastructure. You can only maintain launch day velocity with an agile and progressive infrastructure build. You need a developer who can deliver both. Let's talk.

Companies are bleeding money on AWS bills. Not because the cloud is expensive. Because the infrastructure wasn't built with cost in mind from the start. I wanted to understand that problem from the inside, so I built CloudCost, a multi-tier web app where FinOps, security, and resilience were the requirements, not the afterthoughts. Here's what I focused on: → The whole thing runs at roughly $1/day idle. Every single infrastructure decision has a cost reason behind it. → Auto Scaling Group that scales out at 70% CPU and scales back in at 30%. No idle capacity sitting around burning money. → Two layers of self-healing. Docker restarts a crashed container in seconds. ASG replaces a failed instance in minutes. Zero manual intervention either way. → RDS password lives only in Secrets Manager. EC2 fetches it at boot through an IAM role scoped to that single secret ARN. Nothing in code, nothing in env vars, nothing in Terraform files. → Full network isolation. RDS has no public IP. EC2 is unreachable from the internet directly. Everything goes through the ALB. → CloudWatch alarms wired directly to scaling policies, 7-day log retention, basic monitoring only. Detailed monitoring costs extra and 5-minute intervals are enough. → Jenkins running locally in Docker. No extra EC2 spend for the build server. FinOps, security, and resilience are not things you bolt on later. This project was built around that belief. Code + full documentation : https://lnkd.in/daBxh_9z #AWS #DevOps #CloudComputing #FinOps #Terraform #Jenkins #Python

🔹 Post 1 Containers changed the way we deploy apps 🚀 With tools like Docker, you can build once and run anywhere. No more “it works on my machine” issues. #Docker #DevOps #CloudComputing --- 🔹 Post 2 3 Docker commands every beginner should know: ✔ docker pull ✔ docker run ✔ docker ps Start simple. Build fast. Scale smarter. #Learning #Docker #Tech --- 🔹 Post 3 Want faster deployments? Use containers. Use automation. Use consistency. That’s why developers love Docker ❤️ #DevOps #Automation #Cloud --- 🔹 Post 4 Behind every scalable app is a powerful container system. From nginx to MySQL — everything runs smoothly inside containers. Welcome to modern development. #Tech #Docker #Backend --- 🔹 Post 5 Stop managing servers. Start managing containers. That’s the shift happening in tech today. Powered by Docker 🔥 #Cloud #DevOps #FutureOfWork

Shifting FinOps Concerns: Cost-Governance via Local Cloud Emulation 💻️ Non-production environments often represent a significant portion of AWS expenditures. Over-provisioned resources in dev/test sandboxes contribute to cloud waste... the OPPOSITE of direct business value. 😅 LocalStack emulates AWS services locally on your laptop, empowering engineering teams to shift FinOps concerns to the earliest stages of the Software Development Life Cycle (SDLC). With LocalStack you get... 💸 Zero-Marginal Cost Experimentation Developers can prototype with high-cost services (such as EKS, Kinesis, or MSK) without triggering cloud billing or requiring budget approvals. 🏗️ Infrastructure-as-Code (IaC) Validation Test Terraform or CDK templates locally to catch configuration errors before they reach a live environment, preventing costly "failed state" cleanups. 🔄 Automated Lifecycle Management Local environments are ephemeral by design. A docker-compose down ensures 100% resource reclamation, eliminating the risk of persistent "zombie" resources in the cloud. 🧪 Deterministic CI/CD Running integration tests against a local containerized cloud prevents the accumulation of non-production costs during the continuous testing phase. Decoupling the development loop from the AWS billing console allows you to enforce strict cost-governance while simultaneously increasing developer autonomy. 😎 💪

Excited to share a snapshot of an ongoing startup project I’ve been working on — designing and implementing a scalable cloud deployment model from the ground up. This architecture brings together CI/CD, security, and observability into one flow. From code in GitHub, through automated pipelines with Jenkins, quality checks via SonarQube, artifact management with Nexus Repository, all the way to deployment on Amazon Web Services — fully automated using Terraform. At the core, the application runs as a multi-container system using Docker Compose, where services (auth, payments, email, frontend, etc.) communicate through a shared network. Health checks, resource limits, and service dependencies are all defined to ensure resilience and stability. On the infrastructure side: Load balancing, autoscaling, and secure access are built-in Private/public subnet separation improves security Monitoring and alerting via New Relic CDN + WAF layer ensures performance and protection What I’ve found most interesting is how small design decisions (like service communication and health checks) have a big impact on reliability at scale. Still evolving this system, but it’s a solid step toward a production-ready, cloud-native setup. #DevOps #CloudEngineering #AWS #Docker #CI_CD #Kubernetes #Terraform #Microservices #StartupLife #SystemDesign #CloudArchitecture #Observability #Automation

Most people mix everything together in cloud projects. Build the image. Configure the server. Deploy the infrastructure. All in one place. That works… until it doesn’t. So I decided to separate concerns properly 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐀𝐌𝐈 𝐁𝐮𝐢𝐥𝐝 𝐰𝐢𝐭𝐡 𝐏𝐚𝐜𝐤𝐞𝐫 + 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 I built a workflow where: • Packer creates a reusable AMI • GitHub Actions validates & builds it automatically • Terraform deploys infrastructure using that AMI No manual steps. No guesswork. What I focused on (this is where it gets real) Instead of just “making it work,” I focused on: • Reusability → AMI already has NGINX pre-installed • Consistency → Every EC2 launch is identical • Automation → AMI build runs on every push • Separation → Image ≠ Infrastructure How the flow works • Push code → triggers GitHub Actions • Packer validates & builds AMI • AMI ID stored in manifest • Terraform fetches latest AMI (via tags) • EC2 instance is deployed automatically Issues I had (and why they matter) • Packer build failed → unsupported instance type → fixed by switching to a compatible type • Terraform failed → undeclared VPC reference → fixed by using default VPC These are small errors, but in real environments, they break pipelines fast. What this project reinforced: • Infrastructure is not just about provisioning. • It’s about designing systems that are predictable under change. • Separating image creation (Packer) from deployment (Terraform) is a big step toward production-grade workflows. If you’re learning DevOps, this is one pattern worth mastering early. Would you keep AMI builds separate like this, or bundle everything into one pipeline? Check first comment for project repo. #DevOps #AWS #Terraform #Packer #InfrastructureAsCode #CloudEngineering #CICD #GitHubActions The Pistis Tech Hub

A client came to me with a problem… They had 8 microservices ready for production But no real infrastructure behind it. They needed something that was: • Highly available • Secure • Cost-efficient • Actually production-ready And like most teams… Their first thought was: 👉 “Let’s use Kubernetes” I told them… hold on. You don’t always need Kubernetes to go to production. So instead, I designed this 👇🏽 A lean AWS microservices architecture using ECS (EC2 launch type) Here’s what i designed: → React frontend (hosted in AWS Amplify/Cloudflare) → Public ALB as the only entry point → ECS cluster running in private subnets → 1 gateway microservice exposed → 7 internal microservices (completely private, communicate through aws service discovery) → Private RDS + Redis for the data layer Everything isolated. Everything controlled. Nothing exposed unnecessarily. Security looked like this: ALB → Gateway only Gateway → internal services only Internal services → never public No shortcuts. Then came the real challenge… 💰 Cost Because this was a startup. So we optimized hard: → ECS on EC2 instead of Fargate → NAT instance instead of NAT Gateway 👀 → Shared RDS instance (multiple DBs) → VPC endpoints to reduce NAT traffic Same outcome. Lower cost. Final result? • Highly available across AZs • Secure by design • Scales when needed • Cost stays predictable (~$300/month) • All implemetion with infrastructure-as-code tool - Terraform. A lot of people over-engineer too early. The best architecture isn’t the most complex one. It’s the one that fits where you are right now. If you’re building microservices and not sure what AWS setup makes sense… I’ve probably been there already 👍🏽

🚨 I just ran ONE command… and 3 servers came to life. No cloud. No AWS. No fancy UI. Just a single Vagrantfile. Day 25 of my DevOps journey… and this felt like entering the “real world” 🌍 Until now, I was working with: 👉 One server 👉 One service 👉 One setup But today I learned something BIG: Real applications don’t run on one machine. So, I built this locally: 🖥️ web01 → Frontend server 🖥️ web02 → Another frontend (scaling 👀) 🖥️ db01 → Database server All connected. All working together. And the craziest part? I didn’t create them one by one. I just wrote a Multi-VM Vagrantfile and ran: 👉 vagrant up ⚡ Boom. 3 machines created 3 OS booted 3 roles assigned 🤯 That moment when you realize: You’re not just learning commands anymore… You’re learning system design 💡 Key lessons from today: • Real apps = multiple services (not one server) • Each service should run independently • Infrastructure can be defined in ONE file • Scaling is just… adding another block of code ⚠️ Also learned the hard way: 👉 You MUST assign unique IPs to each VM 👉 You MUST specify VM name while using vagrant ssh 👉 More VMs = more RAM usage (your laptop will remind you 😅) 🔥 Bonus: I even used AI to generate a starter multi-VM setup… But here’s the truth: If you don’t understand the architecture, AI won’t save you. 📈 Mindset shift: Yesterday: “Let me run a server” Today: “Let me design a system” Day 25 done ✅ And this is where DevOps starts feeling like real engineering. #devops #IaC #vagrant #systemdesign #techjourney #devopsjourney #learninpublic #techcommunity #softwareengineering #softwaredevelopment