Async Programming in Node.js - Callbacks, Promises, Await, Event Loop

𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝗱 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 JavaScript supports multiple programming paradigms, including Object-Oriented Programming (OOP) and Functional Programming (FP). You can use both paradigms to create robust applications. Here are some key points about OOP and FP in JavaScript: - OOP revolves around objects and promotes encapsulation, inheritance, and polymorphism. - FP emphasizes the use of functions, immutability, and a declarative approach to data manipulation. - You can combine OOP and FP to create powerful applications. Let's look at an example: ```javascript is not allowed, using plain text instead You can define a class that uses functional approaches to manipulate its internal state. For example, a Counter class can have methods like increment, decrement, and reset. These methods can be implemented using arrow functions to ensure the correct context. You can also use function composition to combine multiple functions. For example, you can create a composed function that uppercases a string and appends an exclamation mark. When creating classes with methods, you can make these methods depend solely on the parameters they receive. This leads to more predictable and testable code. Here are some key differences between OOP and FP: - State Management: OOP uses objects to encapsulate state, while FP relies on immutability. - Code Reusability: OOP uses inheritance and polymorphism, while FP uses function composition. - Readability: OOP can have complex hierarchies, while FP can have less readable higher-order functions. - Testing: OOP requires knowledge of internal state, while FP has pure functions that are easier to test. - Performance: OOP can be slower due to state mutations, while FP is generally faster due to optimizations over immutability. You can use both OOP and FP in component-based frameworks like React and server-side applications like Express.js. When blending OOP and FP, consider performance characteristics like garbage collection, higher-order functions, and state mutations. Source: https://lnkd.in/g2ayw_kr

𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝗱 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 JavaScript supports multiple programming paradigms, including Object-Oriented Programming (OOP) and Functional Programming (FP). You can use both paradigms to create robust applications. Here are some key points about OOP and FP in JavaScript: - OOP revolves around objects and promotes encapsulation, inheritance, and polymorphism. - FP emphasizes the use of functions, immutability, and a declarative approach to data manipulation. - You can combine OOP and FP to create powerful applications. Let's look at an example: ```javascript is not allowed, using plain text instead You can define a class that uses functional approaches to manipulate its internal state. For example, a Counter class can have methods like increment, decrement, and reset. These methods can be implemented using arrow functions to ensure the correct context. You can also use function composition to combine multiple functions. For example, you can create a composed function that uppercases a string and appends an exclamation mark. When creating classes with methods, you can make these methods depend solely on the parameters they receive. This leads to more predictable and testable code. Here are some key differences between OOP and FP: - State Management: OOP uses objects to encapsulate state, while FP relies on immutability. - Code Reusability: OOP uses inheritance and polymorphism, while FP uses function composition. - Readability: OOP can have complex hierarchies, while FP can have less readable higher-order functions. - Testing: OOP requires knowledge of internal state, while FP has pure functions that are easier to test. - Performance: OOP can be slower due to state mutations, while FP is generally faster due to optimizations over immutability. You can use both OOP and FP in component-based frameworks like React and server-side applications like Express.js. When blending OOP and FP, consider performance characteristics like garbage collection, higher-order functions, and state mutations. Source: https://lnkd.in/g2ayw_kr

Object-Oriented Programming (OOP) is still one of the most powerful ways to write scalable code — especially in TypeScript. But here’s the reality 👇 👉 Many developers use TypeScript… 👉 Few actually use OOP effectively. So I wrote a simple, practical guide covering: ✅ Core OOP concepts (Encapsulation, Inheritance, Polymorphism, Abstraction) ✅ Real-world TypeScript examples ✅ Interfaces & best practices ✅ When to use (and avoid) OOP If you're building scalable apps with Angular, Node.js, or any large system — this will help 👇 🔗 Read here: https://lnkd.in/dWzaiqEk 💬 What’s your take — do you prefer OOP or functional programming? #javascript #typescript #webdevelopment #programming #softwareengineering #angular #nodejs

Renaming a symbol across a codebase with text search will break things. It matches strings, not definitions. A variable named user gets renamed alongside the class User. Import paths get touched. Comments get changed. Toolpack SDK ships 12 coding tools that work at the AST level. They understand the structure of your code, not just the text. coding.refactor_rename finds every reference to a symbol. The definition, every import, every usage. All renamed correctly. coding.find_references shows exactly which files are affected before you make the change. coding.get_diagnostics checks for new errors after. coding.multi_file_edit handles refactors that touch multiple files. All edits succeed or all of them roll back. No partial changes left behind. The navigation tools cover the other half. coding.find_symbol locates definitions. coding.get_call_hierarchy traces who calls what. coding.get_outline maps the structure of a file. Useful before making changes, not just during. JavaScript and TypeScript get full support through Babel. Python, Go, Rust, Java, C, and C++ get symbol navigation through Tree-sitter. All 12 tools available with tools: true. No external services. Wrote up all 12 with examples: https://lnkd.in/grcfacP6 #AI #LLM #TypeScript #DeveloperTools #OpenSource #SoftwareEngineering #AIAgents

Coding with AI: Initial observations Experiment: Use Claude to refactor the legacy Google test-analytics tool so it can actually be built and deployed. Problem: The tool was built in 2012 using an outdated version of Java, an outdated version of Google Web Toolkit (GWT), and uses POM dependency references that are either outdated or do not currently exist. Assumption: This is a straightforward migration example so Claude should be able to at least get this code to build and deploy. I am not asking it to write anything from scratch. I am asking it to refactor existing code to update references, remove duplicates, and resolve historical library dependencies. Experiment: I will let Claude run largely unmonitored in order to check the assumption using AI to refactor existing code will free up engineer time to make more new features. The assumption I am making is that using Claude as a pair programming partner will not free up enough time to make a significant difference in staffing decisions. Observations: After four hours of work, Claude seems to have resolved most of the issues with dependencies referenced in the POM file. I say "seems to" because I have yet to get the solution to build. Claude resolved the dependency issues by creating local repositories for earlier versions of the dependencies and it even created its own references by adding open source solutions into the project library. The errors in the POM file have been resolved, but the project refuses to build. When I ask Claude to resolve the build errors, it cannot. It gives me a few suggestions for editing the POM file on my own, but it does not take any actions itself. To resolved the build errors, I have to edit the project myself using my own knowledge of Java. Initial Conclusions: Claude initially looked good. The solutions it created to resolve initial build problems got past the initial blockers allowing me to run the Maven builds. Initially that challenge cost me several days of research combing through archived dependencies and old documentation. I still did not manage to resolve POM issues due to legacy Java libraries. I chose to let Claude run as a black box to see what it would do and speed up the throughput per my prior goal. This was probably a mistake. I haven't dug into the Maven build problems outside the initial two, but so far it seems like the solutions Claude implemented have caused at least some of the errors I am seeing. A better solution to using Claude would have been carefully reviewing each solution it proposed and manually implementing my own decisions after reading its suggestions. Further Observations: This experiment was conducted with new agents. I have no insight into what Claude may "learn" as we pair on a consistent code base. It is entirely possible the agent(s) used will start to propose solutions more in alignment with what I would choose as I feed it more information. #AI #Claude #VibeCoding #CowboyTesting #ProgramManagement

🚀 Mastering Promises in JavaScript — The Backbone of Asynchronous Programming As developers, we constantly deal with operations that don’t complete instantly — API calls, file reads, timers, etc. Handling these efficiently is what separates clean code from chaos. Before Promises, we had callback hell 😵 — deeply nested, hard-to-read, and difficult-to-maintain code. 👉 Promises changed the game. 🧠 What exactly is a Promise? A Promise is an object that represents the future result of an asynchronous operation. It acts like a placeholder for a value that will be available: ✔️ Now (rare) ✔️ Later (most common) ✔️ Or never (in case of failure) 🔄 Promise States (Lifecycle) 1. Pending → Initial state 2. Fulfilled → Operation successful (resolve) 3. Rejected → Operation failed (reject) Once fulfilled or rejected, the state is immutable (cannot change again). ⚙️ Creating a Promise const promise = new Promise((resolve, reject) => { const isSuccess = true; if (isSuccess) { resolve("✅ Task completed"); } else { reject("❌ Something went wrong"); } }); 🔗 Consuming a Promise promise .then((result) => { console.log(result); }) .catch((error) => { console.error(error); }) .finally(() => { console.log("✔️ Always runs"); }); 💡 .then() → handles success 💡 .catch() → handles errors 💡 .finally() → runs regardless of outcome 🔥 Promise Chaining (Avoid Nested Code) getUser() .then((user) => getOrders(user.id)) .then((orders) => getOrderDetails(orders[0])) .then((details) => console.log(details)) .catch((err) => console.error(err)); 👉 Each .then() returns a new Promise — that’s why chaining works! ⚠️ Common Mistake // ❌ Wrong .then((res) => { doSomething(res); }) // ✅ Correct .then((res) => { return doSomething(res); }) 👉 If you don’t return, the next .then() gets undefined. 💎 Why Promises are Powerful ✅ Cleaner than callbacks ✅ Better error handling (centralized .catch) ✅ Enables chaining and composition ✅ Foundation for async/await ✅ Makes code readable and maintainable 💬 Final Thought 👉 Promises are not just a feature — they are a mindset shift in handling asynchronous code. #JavaScript #AsyncProgramming #WebDevelopment #Frontend #Coding #SoftwareEngineering #InterviewPrep

CodeWithMe Project Overview-CodeWithMe is a full-stack,online coding platform that allows users to browse, solve, and submit solutions to Data Structures & Algorithms (DSA) problems directly in the browser. The platform features an integrated code editor, real-time code execution via Judge0, an AI-powered DSA tutor (Google Gemini), video editorial solutions hosted on Cloudinary, and a complete admin panel for content management. Deployment: Frontend on Vercel, Backend on Render. How It Works- For Regular Users:Sign Up,Browse Problems,Open a Problem,Write Code,Run Code,Submit Code,Get AI Help,Watch Editorial,View Solutions,Track Progress. For Admin Users:Access Admin Panel,Create Problem,Update Problem,Delete Problem,Upload Video Solution,Delete Video. How code execution works: -User submits code + language -Backend maps language to Judge0 language ID (JS=102, Java=62, C++=76) -Each test case is sent sequentially to Judge0 CE API (ce.judge0.com) with wait=true -Judge0 returns status (Accepted=3, Wrong Answer=4, TLE=5, etc.) -Results are aggregated and saved as a Submission document -If all test cases pass → status = "accepted" and problem is added to user.problemSolved AI System Prompt Capabilities: -Act as DSA tutor scoped to the current problem only -Provide hints, debug code, explain solutions, analyze complexity -Refuses non-DSA topics -Receives full problem context (title, description, test cases, starter code) RateLimiter: Two layers of protection using Redis: 10-second cooldown (LeetCode-style) — Uses Redis SET NX EX for a per-route+IP lock. Sliding window — Max 60 requests per hour, implemented via Redis Sorted Sets. Tech Stack Summary: ⚛️ Frontend Framework: React 19 + Vite 7 🎨 Styling: TailwindCSS 4 + DaisyUI 5 🧠 State Management: Redux Toolkit (RTK) 📝 Forms & Validation: React Hook Form + Zod 💻 Code Editor: Monaco Editor (@monaco-editor/react) 🌐 HTTP Client: Axios 🛣️ Routing: React Router v7 ↔️ UI Panels: react-resizable-panels 📖 Markdown Rendering: react-markdown + remark-gfm ✨ Icons: Lucide React 🟢 Backend Runtime: Node.js + Express 5 🍃 Database: MongoDB Atlas (Mongoose 9) ⚡ Caching / Token Blocklist: Redis (Mock in dev, Redis Cloud in prod) 🔐 Authentication: JWT + bcrypt + cookie-parser ⚙️ Code Execution: Judge0 CE (hosted API) 🤖 AI Assistant: Google Gemini (@google/generai) ☁️ Video Hosting: Cloudinary ✅ Input Validation: validator.js (backend) 🚦 Rate Limiting: Custom Redis-based middleware Deployed Link: https://lnkd.in/g-HSStaj #ReactJS #ViteJS #NodeJS #ExpressJS #JavaScript #TypeScript #WebDev #FullStack #MongoDB #Mongoose #Redis #Cloudinary #BackendDevelopment #DatabaseDesign #API #AI #GoogleGemini #GenerativeAI #Judge0 #MonacoEditor #CodingPlatform #SoftwareEngineering #ReduxToolkit #RTK #ReactHookForm #Zod #Authentication #JWT #CyberSecurity

Over time, I’ve found myself more and more interested in functional programming, not as a theoretical exercise, but as a way to think more clearly about backend software. A lot of the problems we deal with every day come from complexity that slowly builds up in the code: hidden state, unclear flows, implicit failures, and business rules that become harder to reason about over time. That is part of what led me to write this post: What Functional Programming Means for a Backend Engineer. It is a reflection on how functional ideas have influenced the way I think about backend development, and why I believe they can help us build software that is easier to understand, test, and evolve. Here it is: https://lnkd.in/ewGXuURh I’d genuinely love to hear whether functional programming has changed the way you think about backend engineering too.

A month ago I started a side project: a small CLI to figure out where my AI coding tokens go. A month later it's a Rust daemon, a Next.js + Supabase cloud dashboard, a VS Code / Cursor extension, signed macOS / Linux / Windows binaries, and a marketing site. I'm a frontend dev. I haven't written a Rust line by hand the entire time. AI built every layer. The thing that actually unlocks this isn't the model — it's the workflow. Four rules I keep that turn AI agents from clever assistants into reliable contributors: 1. One canonical context file. Claude Code, Codex, and Cursor each want their own (CLAUDE_md, AGENTS_md, .cursorrules). They drift apart in a week. I keep one canonical SOUL_md, and the rest are 3-line pointer stubs. Every agent reads the same doc. No drift. 2. Every fix ships with a test that fails when the fix is reverted. Unit tests plus bash end-to-end scripts pinned to specific issues. Negative-path provable. Without this, AI silently regresses things. 3. Hard formatter + lint wall. Prettier, ESLint, cargo fmt, clippy -D warnings — non-negotiable on every PR. AI drifts in style across sessions; the gate keeps the codebase consistent across hundreds of PRs. 4. One issue, one branch, one PR. Each release milestone has a single epic "control issue" listing every sub-task in execution order, with ADRs locking the spec before any code is written. The agent never has to figure out scope or architecture — those decisions live in the issue and the ADR. It just picks the next issue and ships the smallest change that closes it. The result: a 6 MB Rust cost tracker, a Next.js + Supabase cloud, a Cursor extension, signed macOS/Linux/Windows binaries — all built solo by a frontend dev who hasn't written a Rust line by hand. The tool I built (budi) now tracks AI coding cost per Linear ticket across Claude Code, Cursor, Codex, and Copilot. So I can see exactly how much each ticket cost the workflow. The tool measures itself. Built mostly with Claude Code + Opus. Open source, MIT. Link in the comments. Honest ask, because I genuinely need help here: I'm a frontend dev, not a founder. I've never marketed a product in my life. I have no clue how to actually get this in front of the people who would use it. Hacker News? YouTube demos? Newsletters? Cold DMs to dev advocates? If you've shipped a developer tool and something worked — I'd take any advice you have. I'm also looking for beta testers for the cloud dashboard. If you or your team is curious where your AI coding spend actually goes, send me a DM and I'll get you set up. #ai #developertools #opensource #claude #cursor #codex

Why Node.js feels fast → Because of Async Programming When I started backend development, understanding Synchronous vs Asynchronous execution completely changed how I think about building scalable systems. Let’s break it down: Synchronous (Sync) – Tasks run step-by-step – Each task waits for the previous one Example: Reading a file → next line runs only after file is read Asynchronous (Async) – Tasks don’t wait – Other operations continue while waiting Example: Reading a file → rest of code keeps running In Node.js, async is handled using: – Callbacks – Promises – async/await Why Async matters: – Handles multiple users efficiently – Improves performance – Prevents blocking operations Now the next level — Parallel Execution Multithreading – Multiple threads in one process – Shared memory – Faster but complex Multiprocessing – Multiple processes – Separate memory – More stable but heavier Worker Threads (Node.js) – Used for CPU-intensive tasks – Run in parallel – Prevent blocking the main event loop Real-world insight: While working on backend projects, I realized async programming is the backbone of scalable applications. In short: – Sync → Simple but blocking – Async → Efficient and scalable – Worker Threads → Best for heavy computations Key takeaway: If you want to build fast and scalable systems, understanding async + parallelism is essential. FAQs: 1. Is Node.js single-threaded or multi-threaded? - Node.js uses a single-threaded event loop but leverages background threads internally. 2. Does async mean parallel execution? - No. Async means non-blocking, not necessarily parallel. 3. When should I use Worker Threads? - For CPU-intensive tasks like image processing or heavy computations. 4. Are Promises better than Callbacks? - Yes. They are cleaner and easier to manage. 5. Can async code still block the app? - Yes, if CPU-heavy tasks run on the main thread. #JavaScript #NodeJS #AsyncProgramming #BackendDevelopment #WebDevelopment #Multithreading