148x Speedup with CloudFront for CDN

Stop building "modern" backends that can't handle 10,000 users. I’ve seen too many projects fail because the team spent 3 months over-engineering a "perfect" microservices architecture before they even had 100 signups. The truth about high-scale systems? The "old" stuff is often the best stuff. Here is my "unpopular" stack for 2026 that actually scales: Laravel: For speed of delivery. The developer experience is still unmatched. Node.js (for the heavy lifting): When I need non-blocking I/O and real-time performance. PostgreSQL over everything: Don't reach for NoSQL until your SQL joins actually break (which is much later than you think). The Load Balancer is your best friend: Stop tweaking code until you've checked your Nginx config. Scaling isn't about using the newest tool on GitHub. It’s about understanding where the bottleneck is before it happens. Agree or disagree? Let’s fight it out in the comments. 👇 #Backend #SystemDesign #SoftwareEngineering #WebDev #Laravel #NodeJS

Day 39 of making my self a BRAND 1 . Successfully Dockerized my Node.js + TypeScript backend using Multi-stage Dockerfile for optimized production image. 2 . Implemented proper build process with tsc + tsc-alias to handle path aliases (@/) in ESM environment. 3 . Configured single-service Docker Compose setup with environment variables support from .env file. 4 . Integrated external services (Aiven Redis, Kafka & MongoDB) seamlessly inside Docker container. 5 . Achieved a clean, lightweight, and production-ready Docker setup that runs perfectly with just one command: docker compose up. #Docker #Dockerize #NodeJS #TypeScript #BackendDevelopment #DockerCompose #MultiStageBuild #ESM #JavaScript #WebDevelopment #DevOps #Containerization #SoftwareEngineering #FullStack #Backend #Programming #Code #Developer #Tech #SoftwareDeveloper #Learning #CareerGrowth #LinkedIn #Success #Project #BuildProcess #Aiven #Redis #Kafka #MongoDB #ExpressJS #CleanCode #ProductionReady #Optimization #Cloud #Infrastructure #Automation #Engineering #TechJourney #DeveloperLife #Coding #100DaysOfCode #LearnInPublic #OpenSource #Skills #Growth #Motivation #Consistency

Excited to share my first serverless CI/CD project! Built and launched a real estate website using Angular and Java 21, hosted entirely on AWS with no servers to manage. Contact form submissions are processed by a Java Lambda function and delivered straight to the realtor's inbox via Amazon SES. Any code changes I push to GitHub automatically deploy to production through a CI/CD pipeline I built with GitHub Actions. https://lnkd.in/evurkQ-a #AWS #Java #Angular #Serverless #CICD #CloudComputing

🔥 From 10 seconds to under 1 second. This one hit different! A few months back, I got the opportunity to explore GraalVM Native Image with Spring Boot as part of a migration from a Java microservice to Google Cloud Functions — and the results genuinely blew me away. ⚡ Cold Start Before: ~10 seconds 🚀 Cold Start After: 0.8 – 1.2 seconds That's an 88% reduction in boot time. For a serverless environment, this is MASSIVE. Cold start latency is one of the biggest pain points when running Java on Cloud Functions — and GraalVM just eliminates it. How? The magic is in AOT (Ahead-of-Time) compilation. Instead of shipping a JAR that the JVM interprets and JIT-compiles at runtime, GraalVM compiles your entire Spring Boot app into a lean, self-contained native binary. No JVM spin-up. No class loading. No warm-up phase. It just runs — instantly. What we explored: ✅ Spring Boot 3.x Native Image with AOT processing ✅ GraalVM Native Image compilation ✅ Serverless-optimized builds for Google Cloud Functions ✅ Significant memory footprint reduction as a bonus GraalVM isn't new — but putting it to work on a real serverless migration and seeing these numbers firsthand? That's a different experience altogether. This was truly a collaborative effort. Really glad to have worked alongside Roshan Gangule And grateful to Bogdan Radian Paris for the support and guidance throughout. 🙌 #Java #SpringBoot #GraalVM #NativeImage #GoogleCloudFunctions #Serverless #Microservices #AOT #PerformanceEngineering #CloudComputing #JavaDeveloper

Day 39 of making my self a BRAND 1 . Successfully Dockerized my Node.js + TypeScript backend using Multi-stage Dockerfile for optimized production image. 2 . Implemented proper build process with tsc + tsc-alias to handle path aliases (@/) in ESM environment. 3 . Configured single-service Docker Compose setup with environment variables support from .env file. 4 . Integrated external services (Aiven Redis, Kafka & MongoDB) seamlessly inside Docker container. 5 . Achieved a clean, lightweight, and production-ready Docker setup that runs perfectly with just one command: docker compose up. #Docker #NodeJS #TypeScript #BackendDevelopment #DevOps #SoftwareEngineering #Dockerization #ProductionReady #MultiStageDocker #TypeScriptDeveloper #NodeJSDeveloper #BackendEngineer #Containerization #DockerCompose #Redis #Kafka #MongoDB #CloudNative #Microservices #BuildInPublic #LearningInPublic #DeveloperJourney #Day39 #100DaysOfCode #CodingJourney #PersonalBranding #TechJourney #SoftwareDeveloper #FullStackDeveloper #DevOpsEngineer #InfrastructureAsCode #CI_CD #ProductionDeployment #CleanCode #OptimizedImage #ESM #PathAliases #TechGrowth #CareerGrowth #DeveloperLife #Programming #WebDevelopment #Engineering #Innovation #TechCommunity #LearnToBuild #SelfImprovement #Consistency #BrandBuilding

🚀 Why Your Node.js API Uses Too Much Memory Your API starts fine… But after running for some time 👇 🐢 Responses become slow 📈 RAM usage keeps increasing 💥 Server restarts or crashes That’s a memory management issue. 🔍 Common Causes ❌ Memory leaks from global objects ❌ Unclosed DB / Redis connections ❌ Large arrays or objects kept in memory ❌ Event listeners not removed ❌ Caching without limits ❌ Huge file/data processing in memory ✅ What Experienced Node.js Developers Do ✔️ Avoid unnecessary global state ✔️ Close connections properly ✔️ Use streams for large files/data ✔️ Remove unused listeners ✔️ Add TTL / limits to caches ✔️ Monitor heap usage regularly ✔️ Use heap snapshots and profiling tools ✔️ Restart safely with PM2 / process managers ⚡ Simple Rule I Follow If memory keeps growing…
Something is not being released. 💡 Pro Tip Crashes are visible. Memory leaks damage performance silently. ❓ Have you ever debugged memory issues in Node.js? Share your experience! #NodeJS #BackendDevelopment #MemoryLeak #Performance #API #JavaScript #SystemDesign #DevOps

🚨 lockForUpdate() is NOT enough (Real Production Insight) We often use lockForUpdate() in Laravel to prevent race conditions 👇 👉 It works… but only to a certain extent In real production systems, this can still cause issues: ❌ Slow performance under high traffic ❌ Requests waiting too long (lock contention) ❌ Deadlocks when multiple rows are involved ❌ Poor scalability 💡 Example: If 100 users try to buy the same product → 👉 1 gets the lock 👉 99 are just waiting… ⏳ That’s not scalable. 🔧 What real systems do along with locking: ✅ Combine locking with queues (FIFO processing) ✅ Use caching (Redis) for fast checks ✅ Apply idempotency keys to avoid retries ✅ Design APIs to reduce contention 🧠 Key takeaway: 👉 lockForUpdate() solves correctness 👉 But scalability needs more thinking 🚀 Backend engineering is about balancing: ✔ Consistency ✔ Performance ✔ Scalability Curious 👇 Have you faced performance issues with database locks? #Laravel #BackendDevelopment #SystemDesign #WebDevelopment #PHP #Concurrency #Database #Scalability #SoftwareEngineering #APIDesign #Redis #TechContent #Programming #Developers #Coding

🚀 I recently built a Laravel backend project focused on real-world system design challenges under high concurrency. This is not a CRUD demo — it is a production-style backend system simulation designed to handle consistency, race conditions, and idempotent operations. 🧠 Core Engineering Focus: - Concurrency-safe order processing - Redis distributed locking (preventing race conditions on stock updates) - Idempotency key implementation (safe retries for critical operations) - ACID-compliant transactional design - Cache-aside strategy with Redis for read optimization - Clean architecture (Controller → Service → Repository) ⚙️ System Design Decisions: - Stock decrement protected with distributed locks - Idempotent order creation to avoid duplicate transactions - Separation of concerns for scalability and testability - Redis used for both caching and synchronization 📌 Why this project exists: - To demonstrate backend engineering skills beyond CRUD: - data consistency under load - safe concurrent write handling - scalable API structure - production-level Laravel architecture 🔗 Repo: https://lnkd.in/d2b7xTEi

My code goes live in production every time I push to main. No manual SSH. No "let me restart the server." No downtime. Here's what happens when I push: GitHub Actions picks it up. Builds the frontend. Verifies the backend. Then builds 3 Docker images-> frontend, backend, and a background worker. Once built, it SSHs into the production server and deploys. Docker Swarm handles the update new container starts first, old one stops only after the new one is healthy. Users don't feel a thing. After deploy, the pipeline runs health checks. Verifies MongoDB replica set is running. Checks all 5 services are up. If anything fails? Automatic rollback. Previous version stays live. I built this for a Clothing ERP running React, Node.js, BullMQ worker, MongoDB, and Redis all on a single server. Setting up this pipeline was one of the best investments I made. Every fix, every feature production in minutes. How automated is your deployment? #cicd #docker #githubactions #devops #deployment #fullstackdeveloper #nodejs #webdevelopment #hiring #softwareengineering

The Web Development Ecosystem in 2026: More than just "Code." 🚀 Building a modern application is like conducting an orchestra. It’s not just about picking a language; it’s about how these layers communicate: The Frontend: Moving beyond HTML/CSS into reactive ecosystems like React, Vue, and Svelte. The Backend: The engine room. Whether it’s the speed of Go, the robustness of Java (Spring Boot), or the flexibility of Node.js. The Data Layer: The choice between structured SQL (PostgreSQL/MySQL) and the scalable flexibility of NoSQL (Mongoose/MongoDB). The takeaway? Don't try to learn everything at once. Pick one vertical slice (e.g., React + Node + PostgreSQL) and master the flow of data between them. #WebDevelopment #FullStack #SoftwareEngineering #CodingLife #TechStack