React MFE Template with AWS Cognito and i18n Support

Today I focused on building the async backbone of my media processing system using NestJS + BullMQ + Redis. Implemented an upload confirmation pipeline with a producer–consumer architecture that separates API traffic from heavy background processing. Set up Redis in Docker, built a scalable job queue, and split the system into API and worker apps using a clean, modular architecture. Big takeaway: designing for async from day one makes systems more resilient, scalable, and production-ready. Step by step turning this into a cloud-native media pipeline 🚀 #NestJS #BackendEngineering #SystemDesign #Redis #AsyncArchitecture https://lnkd.in/giE46bsx

If you need quick and dirty work like a small API, a script, or glue code, Node.js is still very good. Keep it simple and use TypeScript if you want type checking. Pick one small web framework like Express or Fastify and try hard to NOT add lots of extra packages. A big part of 'Node breaks' pain is dependency bloat. If you are building a bigger backend service that must run calm in production, Go is a strong default, obviously Go gives real types, fast builds, and one compiled binary you can ship anywhere. It also makes Docker simpler. A basic Go program that imports net/http is about 10 MB, and many real microservices land around 13 MB to 17 MB unstripped. That is small enough that image size stops being a problem. For containers, use a multi-stage Docker build and copy the final Go binary into a minimal runtime image. Alpine is fine. If you want the smallest image and you do not need a shell, use scratch. For microservices support, Go tends to feel better because you can get common service needs wired up faster. If you want batteries included patterns, look at go-zero https://lnkd.in/g_qMD9TY If you want cross language building blocks like service discovery, tracing, and logging components, look at Dapr (dapr/dapr). Though do not expect Go to be perfect, if your stack uses MongoDB, then be ready for friction with the official Go MongoDB driver. Some teams choose a third party library because they dislike the official driver APIs (including me). Also, editor support matters. If VSCode Go IntelliSense feels flaky, try GoLand. It is paid, has a 30 day trial, and students can get it free

🚀 Excited to Share My Users Microservice (TypeScript + MongoDB) I’ve built a production-ready Users Microservice in TypeScript that you can plug into any project — so you don’t have to handle user management yourself. Just focus on building your core features. ✨ Key Features Sign up & Login Sign in with Google Update password Forgot password with OTP Hashing password Reset password Access & Refresh tokens Secure token handling via cookies MongoDB integration Graceful startup & shutdown (production-ready) Winston logger integrated with AWS CloudWatch 🔍 Actuator Endpoints (Infra Monitoring) /actuator/health → Real DB + service health status /actuator/liveness → Process health (K8s liveness probe) /actuator/readiness → Traffic readiness (K8s readiness probe) /actuator/system → OS + memory breakdown (Admin access only) The repository includes: Clean folder structure Well-defined routes Detailed README.md .env.example for easy environment setup This service is designed with scalability, monitoring, and real-world production needs in mind. If you're building microservices or scaling your backend, this might save you a lot of time. 🔗 Check out the repository link below and explore the full documentation. https://lnkd.in/g37QAhTD #microservice #backend #nodejs #ai #agents #users #service Would love your feedback and thoughts!

Ever wondered how platforms like Render or Vercel handle auto-deployment so smoothly? The short answer: CI/CD pipelines are the backbone of it all. Instead of relying on managed platforms, I decided to build the entire flow myself — end to end. 🔹 Every git push to main triggers a GitHub Actions workflow 🔹 The workflow securely SSHs into an AWS EC2 instance 🔹 Pulls the latest code 🔹 Rebuilds the Docker image 🔹 Restarts the container using Docker Compose 🔹 And the updated app goes live automatically In simple words: git push → GitHub Actions → EC2 → Docker rebuild → live app Building this manually forced me to understand what tools like Render and Vercel abstract away — Docker internals, SSH authentication, AWS networking, and real-world deployment failures. It was challenging, occasionally frustrating, but incredibly rewarding. This project gave me a much deeper appreciation for how production deployments actually work behind the scenes. 📌 Repo + CI/CD diagram attached https://lnkd.in/gRUCxBa9 #CI_CD #DevOps #Docker #AWS #GitHubActions #LearningByBuilding

I’ve started a new project called awrust, a lightweight, MIT-licensed AWS emulator written in Rust, and just published the first post in the series. This one focuses on building a clean foundation for emulating S3: setting up a minimal async HTTP server (Tokio + Axum), adding structured logging with tracing, and documenting architectural decisions from day one. No full S3 yet, just small vertical slices with observability built in. More to come as I start implementing the first real S3 endpoints. https://lnkd.in/ehDkcdaw

Building my first CI/CD Pipeline: Automation from Local to Cloud! I’ve successfully set up my first automated CI/CD pipeline today! As I'm diving deeper into backend development, I wanted to move beyond just writing code and start understanding how professional-grade deployment works. What I achieved today: ✅ Built a Node.js & Express backend using ES Modules. ✅ Implemented Unit Testing with Jest to ensure code reliability. ✅ Configured GitHub Actions to automate the integration process. ✅ Handled Environment Variables and Cross-OS compatibility using cross-env. The Workflow: Every time I push code to GitHub, an automated runner triggers. It sets up the environment, installs dependencies, and runs my tests. If everything passes, it's ready for the next stage (Deployment)! Github Link: https://lnkd.in/gQ27iKyb

My colleague introduced me to Kiro last week. I haven't looked back. It's an AI-powered application development tool by AWS that builds a spec before it writes a single line of code. Prompt → Requirements → Architecture → Code No more guessing. No more fragile outputs. Just structured, production-ready applications. Quick tip before you sign up: Sign up with Google, GitHub, or AWS Builder ID and you get 500 free credits for 30 days. Free tier drops to 50/month after that. Worth trying. Check it out at kiro.dev #Kiro #AWS #AITools #AppDevelopment #AgenticAI

🚀 Excited to announce Agent Plugins for AWS! An open-source library that equips AI coding agents with the skills to architect, deploy, and operate on AWS. Plugins provide packaged expertise, bundled into a single shareable unit. No need to consume MCP servers, subagents, skills,... from multiple sources ! Support for Claude Code and Cursor (see Cursor marketplace) The deploy-on-aws plugin is available now: you can use it to deploy applications to AWS with architecture recommendations, cost estimates, and IaC deployment. 🔗 Repository: https://lnkd.in/gK6aqejc 🔗 Blog: https://lnkd.in/gdfmn2qK 🔗 Claude plugins: https://lnkd.in/gpPuRVHt 🔗 Cursor marketplace: https://lnkd.in/gr5UYbqx and blog: https://lnkd.in/gvRmQy8K

Backend now deploys itself. GitHub → OIDC → AWS Lambda. Push to dev and it ships. Set up a CI pipeline that installs dependencies, builds the artifact, and deploys automatically — end to end. No console uploads. No long-lived credentials. Took a couple iterations to get the IAM trust and deployment flow right, but that’s part of building real systems. Automated packaging. Automated deployment. Zero manual steps. This is the kind of infrastructure that keeps velocity high and removes friction from shipping.

🚀 New article is live on dev.to After deploying my personal website to AWS, the natural next step was making sure new features could be shipped quickly, safely, and consistently. A solid follow-up to any frontend deployment is setting up a CI/CD pipeline, and in my case I used GitHub Actions to automate the entire process: from building the React app to deploying it on AWS S3 + CloudFront. In this article, I share my perspective on how to: - Built a React SPA deployment pipeline with GitHub Actions - Use OIDC authentication to securely deploy to AWS (no long-lived credentials) - Sync build artifacts to S3 static hosting - Handle CloudFront cache invalidation to avoid blank pages after deploys - Structure the pipeline into clear install, build, and deploy stages Feel free to take a look and share your thoughts. It would be greatly appreciated! 👉 Full write-up on dev.to: https://lnkd.in/deygZkW6 #aws #githubactions #cicd #cloudfront #s3 #react #devops #frontend #webengineering