Mani @NotFoundMani

If you master only these topics... you'll be prepared for almost every Big Tech interview. ★★★★★ Arrays ★★★★★ Trees ★★★★★ Graphs ★★★★★ DP ★★★★★ Binary Search ★★★★☆ Hashing ★★★★☆ Linked Lists ★★★★☆ Heaps ★★★★☆ Sliding Window ★★★★☆ Greedy Companies change. Patterns don't.

@Wiom_Official Internet suddenly stopped working and I am doing WFH. A generic message without any explanation or expected restoration time is not acceptable. This directly affects my work. Please provide a clear update and restore the service as soon as possible

If Vs code was made in india 😭😭

POV: I understood AWS after making these Architectures ✨

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Which backend stack has the strongest future?

System Design Series - Day 8/30 API Gateway Patterns – The Front Door of Your Microservices API Gateway is the single entry point for all your clients. Without it: - Mobile/web clients call 10+ different services directly - Authentication is duplicated everywhere - Rate limiting, CORS, logging → repeated in every service - Services are fully exposed to the internet With it: - One clean URL for clients - Centralized auth, rate limiting, routing, aggregation - Backend services stay hidden and secure Here’s everything you need to know about API Gateway patterns. What is an API Gateway? Think of it as the hotel front desk Without a front desk: - Guests wander around looking for rooms - No security check - Housekeeping and room service have no coordination With a front desk: - Single check-in point - Routes guests to correct room - Handles security, coordination, and requests API Gateway does exactly that for your microservices. The Problem It Solves Before API Gateway: Mobile app needs user profile + orders: → Calls User Service directly → Calls Order Service directly → Calls Payment Service directly Problems: - Client knows internal service URLs - Multiple network calls (slow on mobile) - Auth tokens sent to every service - No centralized rate limiting or logging - Services exposed to the internet After API Gateway: Mobile app calls one URL: https://api.example. com/profile Gateway handles everything internally: - Authenticates once - Routes and aggregates calls - Returns combined response Benefits: - 1 network call from client - Services completely hidden (security win) - Centralized cross-cutting concerns - Much better client experience Core Responsibilities: 1. Routing Maps external URLs to internal services GET /api/users → User Service GET /api/orders → Order Service 2. Authentication & Authorization Validates JWT/OAuth once at the gateway. Services trust the gateway. 3. Rate Limiting Prevents abuse (e.g., 100 requests/min per user). 4. Request Aggregation Combines multiple backend calls into one response for the client. 5. Protocol Translation Client uses REST → Service uses gRPC (handled at gateway). Advanced Patterns - Circuit Breaker → Prevents cascading failures when a service is down - Request/Response Transformation → Convert old → new API formats - Caching → Cache frequent responses at the gateway level - Logging & Monitoring → Centralized observability When to Use API Gateway Use it when: - You have multiple microservices - External clients (mobile, web, third-party) - You need centralized auth, rate limiting, or aggregation Don’t use it when: - Simple monolith (overkill) - Only internal service-to-service communication - Ultra-low latency is critical (extra hop) Popular Solutions - Kong (open-source, powerful plugins) - AWS API Gateway (managed, serverless) - NGINX + Lua (DIY, lightweight) - Traefik, Envoy, KrakenD Summary API Gateway is not just a proxy. It is the security layer, traffic manager, and aggregator for your entire backend. It simplifies client code, hides internal complexity, and centralizes cross-cutting concerns. Trade-offs: - Extra network hop (adds latency) - Becomes a critical component (make it highly available) Used correctly, it’s one of the most valuable pieces in any microservices architecture. Tomorrow (Day 9): Inter-Service Communication Patterns Questions about API Gateway? Drop them below 👇 #SystemDesign #APIGateway #Microservices #Backend

JAVA PROJECTS TO BUILD IN THIS MODERN ERA FOUNDATIONS: CORE JAVA DEVELOPMENT → Build a CLI-based task manager using core Java → Create a file-based note-taking application → Develop a custom logging framework → Build a multithreaded file downloader → Create a basic HTTP server from scratch → Implement a custom JSON parser → Build a terminal-based chat application (Sockets) → Create a Java-based password manager (encrypted storage) → Develop a mini JVM-like interpreter (advanced) → Build a plugin-based application system BACKEND & API DEVELOPMENT → Build a RESTful API using Spring Boot → Create a JWT authentication system → Develop a full user management system (RBAC) → Build a URL shortener service → Create a scalable API gateway (Spring Cloud) → Develop a microservices architecture (Spring Boot + Eureka) → Build a GraphQL API with Spring Boot → Create a real-time notification system (WebSockets) → Develop a rate limiting system → Build a backend for a SaaS product DATABASE & DATA SYSTEMS → Build a custom ORM (like Hibernate basics) → Create a database migration tool → Develop a caching layer (Redis integration) → Build a distributed ID generator (Snowflake-like) → Create a search engine with indexing (Lucene) → Develop a data pipeline (Kafka + Java consumers) → Build a time-series data storage system → Create a database connection pool → Develop a multi-tenant database system → Build a data synchronization service CLOUD, DEVOPS & DISTRIBUTED SYSTEMS → Build a distributed logging system (ELK integration) → Create a CI/CD pipeline automation tool → Develop a containerized Java app (Docker + Kubernetes) → Build a service discovery system → Create a configuration management server → Develop a fault-tolerant system with circuit breakers → Build a job scheduler (like cron service) → Create a distributed cache system → Develop a monitoring system (metrics + alerts) → Build a resilient messaging system (Kafka/RabbitMQ) AI, MODERN APPS & INTEGRATIONS → Build an AI chatbot using Java + OpenAI API → Create a document summarization tool → Develop a recommendation engine → Build a voice assistant (speech-to-text integration) → Create a fraud detection system (ML integration) → Develop a code analysis tool → Build an AI-powered email responder → Create a semantic search system → Develop a RAG-based knowledge assistant → Build an AI-powered resume screening system FULL-STACK & PRODUCT-LEVEL PROJECTS → Build a full-stack e-commerce system (Spring Boot + React) → Create a social media platform backend → Develop a real-time collaboration tool (like Google Docs) → Build a project management tool (like Jira) → Create a SaaS billing and subscription system → Develop a blogging platform (CMS) → Build a learning management system (LMS) → Create a fintech transaction processing system → Develop a ride-sharing backend system → Build a scalable video streaming backend To master Java by building real-world, production-grade systems, get the complete handbook here: codewithdhanian.gumroad.com/l/werbcj

REST API Fundamentals. Core Constraints, Principles, and Practices.

Istio Service Mesh Architecture in Production Control Plane, Data Plane, Sidecar Pattern, Traffic Flow, mTLS & Observability

Preparing for DSA interview, do these leetcode problems! 1. Sliding Window: 3, 76, 239, 424, 904 2. Two Pointers: 11, 15, 167, 42 3. Binary Search: 33, 153, 875, 1011 4. Hashing + Prefix: 1, 49, 560, 128 5. Monotonic Stack: 739, 503, 84, 901 6. Heap (Top K): 215, 347, 973, 703 7. Intervals: 56, 435, 57, 252 8. BFS/DFS: 200, 994, 102, 133 9. Trees: 104, 236, 543, 124 10. DP basics: 70, 198, 322, 300 Just 2-3 per pattern, write the template from memory, then speedrun variants.

📂 SaaS Stack ┃ ┣ 📂 Frontend ┃ ┣ 📂 React ┃ ┣ 📂 NextJS ┃ ┣ 📂 Vue ┃ ┣ 📂 TailwindCSS ┃ ┗ 📂 Shadcn UI ┃ ┣ 📂 Backend ┃ ┣ 📂 NodeJS ┃ ┣ 📂 Django ┃ ┣ 📂 Laravel ┃ ┣ 📂 FastAPI ┃ ┗ 📂 Express ┃ ┣ 📂 Database ┃ ┣ 📂 PostgreSQL ┃ ┣ 📂 MySQL ┃ ┣ 📂 MongoDB ┃ ┣ 📂 Redis ┃ ┗ 📂 Supabase ┃ ┣ 📂 Auth ┃ ┣ 📂 Clerk ┃ ┣ 📂 Auth0 ┃ ┣ 📂 Firebase Auth ┃ ┣ 📂 Supabase Auth ┃ ┗ 📂 NextAuth ┃ ┣ 📂 Payments ┃ ┣ 📂 Stripe ┃ ┣ 📂 Paddle ┃ ┣ 📂 Dodo Payments ┃ ┣ 📂 Lemon Squeezy ┃ ┗ 📂 Polar ┃ ┣ 📂 Emails ┃ ┣ 📂 Resend ┃ ┣ 📂 SendGrid ┃ ┣ 📂 Mailgun ┃ ┣ 📂 Postmark ┃ ┗ 📂 Amazon SES ┃ ┣ 📂 Storage ┃ ┣ 📂 AWS ┃ ┣ 📂 Cloudflare ┃ ┣ 📂 Google Cloud Storage ┃ ┣ 📂 Supabase Storage ┃ ┗ 📂 Uploadcare ┃ ┣ 📂 Deployment ┃ ┣ 📂 Vercel ┃ ┣ 📂 Netlify ┃ ┣ 📂 Railway ┃ ┣ 📂 Render ┃ ┗ 📂 AWS ┃ ┣ 📂 Domains and DNS ┃ ┣ 📂 Namecheap ┃ ┣ 📂 Hostinger ┃ ┣ 📂 Cloudflare DNS ┃ ┣ 📂 Google Domains ┃ ┗ 📂 SiteGround ┃ ┣ 📂 Analytics ┃ ┣ 📂 Google Analytics ┃ ┣ 📂 Plausible ┃ ┣ 📂 PostHog ┃ ┣ 📂 Mixpanel ┃ ┗ 📂 DataFast ┃ ┣ 📂 Monitoring ┃ ┣ 📂 Sentry ┃ ┣ 📂 LogRocket ┃ ┣ 📂 Datadog ┃ ┣ 📂 NewRelic ┃ ┗ 📂 UptimeRobot ┃ ┣ 📂 DevOps ┃ ┣ 📂 Docker ┃ ┣ 📂 Kubernetes ┃ ┣ 📂 GitHub Actions ┃ ┣ 📂 CI CD ┃ ┗ 📂 Terraform ┃ ┣ 📂 Search ┃ ┣ 📂 Algolia ┃ ┣ 📂 Meilisearch ┃ ┣ 📂 Elasticsearch ┃ ┣ 📂 Typesense ┃ ┗ 📂 OpenSearch ┃ ┣ 📂 AI Integration ┃ ┣ 📂 OpenAI API ┃ ┣ 📂 Anthropic API ┃ ┣ 📂 Replicate ┃ ┣ 📂 HuggingFace ┃ ┗ 📂 Gemini API ┃ ┣ 📂 Integrations ┃ ┣ 📂 Zapier ┃ ┣ 📂 Make ┃ ┣ 📂 n8n ┃ ┣ 📂 Pabbly ┃ ┗ 📂 Webhooks ┃ ┣ 📂 Security ┃ ┣ 📂 SSL ┃ ┣ 📂 Cloudflare ┃ ┣ 📂 WAF ┃ ┣ 📂 Rate Limiting ┃ ┗ 📂 Secrets Management ┃ ┣ 📂 Marketing ┃ ┣ 📂 Search Console ┃ ┣ 📂 Outrank ┃ ┣ 📂 Buffer ┃ ┣ 📂 Analytics ┃ ┗ 📂 Kit ┃ ┗ 📂 Customer Support ┣ 📂 Intercom ┣ 📂 Crisp ┣ 📂 Zendesk ┣ 📂 Tawk ┗ 📂 HelpScout

If you're looking for a good portfolio project, or just a fun way to practice your skills, this one's for you. @ajaypatel_aj walks you through building an admin dashboard using shadcn/ui and TanStack Start. The dashboard will have charts and widgets, a product page, a settings and profile page, and more. freecodecamp.org/news/build-an-…

Backend Development Learning Roadmap |-- Programming Foundations | |-- Core Language Basics (Python, Node.js, Java, or Go) | |-- Control Flow: Conditionals & Loops | |-- Functions & Modular Design | |-- Error Handling & Exception Management | |-- Data Structures & Algorithms Fundamentals |-- Server-Side Programming | |-- Request & Response Lifecycle | |-- Routing & RESTful API Design | |-- MVC & Modular Architecture Patterns | |-- Middleware Concepts | |-- Template Engines (if applicable) |-- Databases & Storage | |-- Relational Databases (PostgreSQL, MySQL) | |-- SQL Queries, Joins, Indexes & Transactions | |-- NoSQL Databases (MongoDB, Redis) | |-- ORMs (Sequelize, SQLAlchemy, TypeORM) | |-- Caching Strategies & In-Memory Stores |-- Authentication & Authorization | |-- Session-Based Auth & Cookies | |-- Token-Based Auth: JWT, OAuth2 | |-- Role-Based & Permission Systems | |-- Secure Password Storage (bcrypt, argon2) | |-- API Key Management |-- APIs & Integration | |-- RESTful API Design Best Practices | |-- API Versioning & Documentation | |-- API Testing (Postman, Insomnia) | |-- Integrating Third-Party Services | |-- Webhooks & Event-Driven APIs |-- Asynchronous Programming | |-- Promises, Async/Await | |-- Background Workers & Task Queues | |-- Event Emitters & Pub/Sub Patterns | |-- Message Brokers (RabbitMQ, Kafka) | |-- Scheduling Jobs & Cron Tasks |-- WebSockets & Real-Time Systems | |-- WebSockets Fundamentals | |-- Real-Time Libraries (Socket.io, WS) | |-- Pub/Sub for Real-Time Messaging | |-- Synchronization & State Management | |-- Scaling Real-Time Systems |-- Testing & Quality Assurance | |-- Unit Testing Frameworks | |-- Integration Testing | |-- End-to-End Testing | |-- Mocking, Fixtures & Test Coverage | |-- Linting & Formatting Tools |-- Performance & Optimization | |-- Query Optimization & Indexing | |-- Caching Layers & CDN Integration | |-- Load Balancing Fundamentals | |-- Profiling & Benchmarking | |-- Memory Management & GC Tuning |-- Security Best Practices | |-- Input Validation & Sanitization | |-- Rate Limiting & Throttling | |-- CORS & CSRF Protections | |-- Secure Headers & HTTPS | |-- Secrets & Config Management |-- DevOps & Deployment | |-- Environment Configuration | |-- CI/CD Pipelines | |-- Containerization (Docker) | |-- Cloud Hosting & Platforms | |-- Monitoring, Logging & Alerts |-- Real-World Projects & Portfolio | |-- CRUD APIs & Microservices | |-- Authentication & Authorization Systems | |-- Real-Time Chat or Collaboration App | |-- E-Commerce Backend API | |-- Scalable Event-Driven Architecture |-- Continuous Learning & Growth | |-- Reading Official Docs & RFCs | |-- Community Forums & Developer Groups | |-- Open Source Contributions | |-- Keeping Up with Backend Trends & Tools Get the Backend Development Projects Ebook Link: codewithdhanian.gumroad.com/l/ungqng This ebook helps you build practical backend systems step-by-step with real projects, best practices, and production-ready patterns to grow your backend expertise.

HOW I LEARNED SYSTEM DESIGN THE HONEST JOURNEY FROM INTERVIEW FAILURE TO ARCHITECTING PRODUCTION SYSTEMS THAT ACTUALLY SCALE I did not start with system design because I loved it. I started because I failed it. The first time I faced a system design interview, I froze. Not because I didn’t understand coding, but because I had never been taught how to think in systems. I knew how to build features. I didn’t know how to build systems that survive real users, real traffic, and real failures. That failure changed everything. THE WAKE-UP CALL I walked into that interview confident. I walked out confused. I was asked to design a scalable system. I started writing code in my head. That was my first mistake. System design is not about syntax. It is about thinking. What I lacked: → Understanding scale → Knowing trade-offs → Communicating architecture clearly → Breaking down vague problems That moment forced me to rethink how I approached engineering. STEP 1: I STOPPED THINKING LIKE A CODER I had to unlearn something important. Coding solves problems at a micro level. System design solves problems at a macro level. So I shifted my mindset: → From “How do I implement this?” → To “How does this system behave at scale?” Instead of focusing on functions, I started focusing on: → Users → Traffic → Data flow → Bottlenecks → Failure points That shift alone made a huge difference. STEP 2: I LEARNED THE FUNDAMENTALS FIRST Before jumping into complex architectures, I went back to basics. I studied: → Load balancing → Caching strategies → Database design (SQL vs NoSQL) → Horizontal vs vertical scaling → CAP theorem → Consistency vs availability trade-offs At first, it felt overwhelming. But over time, patterns started repeating. System design is not about memorizing solutions. It is about recognizing patterns. STEP 3: I STARTED BREAKING SYSTEMS DOWN Instead of trying to design everything at once, I learned to decompose problems. Every system I designed followed this structure: → Requirements (functional + non-functional) → High-level architecture → Core components → Data flow → Bottlenecks → Scaling strategy → Trade-offs This gave me clarity. And more importantly, it gave me confidence. STEP 4: I STUDIED REAL SYSTEMS This is where things started to click. I stopped learning in isolation and started studying real-world systems: → How messaging systems handle millions of users → How streaming platforms deliver content globally → How marketplaces handle transactions and inventory → How social platforms manage feeds and notifications I realized something powerful: Every large system is just a combination of smaller, well-understood components. STEP 5: I PRACTICED OUT LOUD System design is not just about knowing. It is about explaining. So I practiced: → Speaking while designing → Justifying every decision → Explaining trade-offs clearly → Thinking under pressure At first, it felt awkward. But this is what transformed my interview performance. Because in real interviews, clarity beats complexity. STEP 6: I FAILED AGAIN (BUT BETTER) I didn’t magically become perfect. I failed again. But this time, I understood why. → I missed edge cases → I didn’t estimate scale properly → I overcomplicated parts of the system Each failure became feedback. And slowly, I improved. STEP 7: I STARTED THINKING LIKE AN ARCHITECT This was the turning point. I stopped trying to impress. I started trying to design systems that actually work. My approach became: → Start simple → Scale gradually → Optimize only when necessary → Always consider trade-offs Because real systems are not perfect. They are practical. WHAT I LEARNED THE HARD WAY If you are learning system design right now, understand this: → You don’t need to know everything → You need to think clearly → You need to communicate well → You need to understand trade-offs And most importantly: → You need to practice consistently System design is a skill. And like any skill, it improves with time. FINAL THOUGHT Looking back, that failure was necessary. It forced me to grow beyond writing code. It pushed me to understand systems at scale. It turned me from a developer into someone who can design real-world architectures. If you are struggling with system design, you are not behind. You are just in the process. Keep going. WANT TO MASTER SYSTEM DESIGN FASTER? If you want a structured path that takes you from beginner concepts to real-world system architecture, I’ve put together a complete guide: → codewithdhanian.gumroad.com/l/ntmcf It covers fundamentals, patterns, real-world systems, and practical breakdowns to help you design with confidence. Build systems that scale. Not just code that works.

@Wiom_Official Again

Don’t overcomplicate it. • Build a Password Manager to learn file handling, hashing (not full crypto) • Build a URL Shortener to understand routing, IDs, and persistence • Build a Todo App with deadlines to practice CRUD and basic state • Build a Web Scraper to learn requests, parsing, and rate limits • Build a CLI Expense Tracker to master logic, files, and edge cases • Build a Log Analyzer to work with files, timestamps, and patterns • Build a Simple Recommender using similarity rules (not ML magic) • Build an Email Automation Script using SMTP and scheduling Projects. Not tutorials.

> Claude = coding. ($20/mo) > Supabase = backend. (Free) > Vercel = deploying. (Free) > Namecheap = domain. ($12/yr) > GitHub = version control. (Free) > Resend = emails. (Free) > Clerk = auth. (Free) > Cloudflare = DNS. (Free) > PostHog = analytics. (Free) > Sentry = error tracking. (Free) > Upstash = Redis. (Free) > Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$19 There has never been a cheaper time to build.

Vibe-coding tools you should know about • @claudeai - AI for coding + debugging • @cursor_ai - AI code editor • @Replit - cloud coding + instant deployment (mobile app) • @vercel - hosting • @github - AI coding assistant • @NanoBanana - Image generation • @midjourney - More image options beyond NanoBanana • @rainbowdotme - Web3 wallet connections • @RevenueCat - For subscriptions • @Lovable - AI app and website builder • @rork - For mobile apps • @Dribbble - For design discovery • @v0 - UI component generator • @zeddotdev - Fast collaborative editor • @zencoderai - AI coding agent platform • @codeium - Free AI autocomplete • @tabnine - AI code completion • @supabase - Backend/database • @ChatGPTapp - General AI assistant • @AskAider Terminal-based AI pair programmer • @boltdotnew - Prompt-to-app generator

If you’re learning Backend in 2026, master these 8 concepts: 1️⃣ REST API design ↓ 2️⃣ Authentication (JWT / OAuth) ↓ 3️⃣ Database indexing ↓ 4️⃣ Caching (Redis) ↓ 5️⃣ Rate limiting ↓ 6️⃣ Logging & monitoring ↓ 7️⃣ Docker basics ↓ 8️⃣ System design fundamentals Frameworks change. Concepts don’t.

As a developer, Please Slap yourself if you are unable to explain even 10 from below terms : Indexing Clustering Denormalization Normalization Read replicas Leader–Follower replication Multi-leader replication Quorum Consensus CAP theorem BASE ACID Eventual consistency Strong consistency Snapshot isolation MVCC (Multi-Version Concurrency Control) Two-phase commit (2PC) Three-phase commit (3PC) Write-ahead logging (WAL) Checkpointing Compaction Rebalancing Resharding Data locality Hot partition Split-brain Failover High availability (HA) Horizontal scaling Vertical scaling Load balancing Connection pooling Caching Materialized views Secondary indexes Composite index Covering index Bloom filter LSM tree B-tree Query planner Cost-based optimizer Deadlock Lock escalation Optimistic locking Pessimistic locking Dirty read Phantom read Read skew Write skew Data skew Backpressure Circuit breaker Throttling Rate limiting CDC (Change Data Capture) Logical replication Physical replication Geo-replication Federation Data lake Data warehouse Columnar storage Row-based storage Time-series partitioning Hash partitioning Range partitioning Consistent hashing Data migration Schema evolution Schema registry Idempotency Exactly-once semantics