React useEffect Explained Clearly (With Simple Examples)

If you are learning React, chances are useEffect confused you at least once.

Questions like:

  • Why does useEffect run twice?
  • When should I use it?
  • What is dependency array?
  • How is it different from lifecycle methods?

Don’t worry.
In this post, I’ll explain useEffect in the simplest way possible, using real-world analogies and clear examples.


What is useEffect in React?

👉 useEffect is used to perform side effects in React components.

Side effects include:

  • Fetching data from an API
  • Updating the DOM
  • Setting timers
  • Subscribing to events
  • Logging data

Simply put:

useEffect runs code when something changes in your component.


Basic Syntax of useEffect

useEffect(() => {
  // side effect code
}, []);

It has two parts:

  1. Effect function – what you want to do
  2. Dependency array – when you want to do it

Case 1: useEffect Without Dependency Array

useEffect(() => {
  console.log("Component rendered");
});

What happens?

✅ Runs after every render

⚠️ Usually not recommended, can cause performance issues.


Case 2: useEffect With Empty Dependency Array []

useEffect(() => {
  console.log("Component mounted");
}, []);

What happens?

✅ Runs only once, after first render

Equivalent to:

componentDidMount()

👉 Most common use case: API calls


Example: Fetching Data

useEffect(() => {
  fetch("/api/users")
    .then(res => res.json())
    .then(data => setUsers(data));
}, []);

✔️ Fetches data only once
✔️ Avoids infinite loops


Case 3: useEffect With Dependencies

useEffect(() => {
  console.log("Count changed");
}, [count]);

What happens?

✅ Runs only when count changes

👉 Useful for reacting to state or prop changes


Example: Search Input

useEffect(() => {
  fetchResults(searchText);
}, [searchText]);

✔️ Runs only when user types
✔️ Optimized & efficient


Cleanup Function in useEffect 🧹

Some effects need cleanup.

Example:

  • Timers
  • Event listeners
  • Subscriptions
useEffect(() => {
  const timer = setInterval(() => {
    console.log("Running...");
  }, 1000);

  return () => {
    clearInterval(timer);
  };
}, []);

👉 Cleanup runs when:

  • Component unmounts
  • Dependencies change

Why Does useEffect Run Twice in React?

In React Strict Mode (development):

  • React runs effects twice
  • This helps detect bugs

🚨 It does NOT happen in production


Common Mistakes with useEffect ❌

❌ Missing dependency array

useEffect(() => {
  setCount(count + 1);
});

➡️ Causes infinite loop


❌ Incorrect dependencies

useEffect(() => {
  fetchData();
}, []);

But fetchData uses props/state → bug!


When Should You Use useEffect?

Use useEffect when:
✅ You interact with outside world
✅ You perform side effects
❌ Not for simple calculations


Summary Table 📌

ScenarioDependency
Run once[]
Run on every renderNo array
Run on change[state/props]
Cleanup neededreturn () => {}

Final Thoughts

If you remember just one line, remember this:

useEffect syncs your React component with the outside world.

Mastering useEffect will:

  • Improve performance
  • Prevent bugs
  • Make you a better React developer

Best AI Tools for Developers (Free & Paid) – 2025 🚀

Artificial Intelligence is no longer optional for developers. From writing code faster to building AI agents and automating workflows, AI tools are becoming a daily necessity.

In this post, you’ll discover the best AI tools for developers in 2025 — including free and paid options, real use cases, and who should use what.

Whether you are a beginner, working developer, or entrepreneur, this guide will help you choose the right AI tools and boost your productivity instantly.


🔥 Why Developers Should Use AI Tools in 2025

AI tools help developers:

✅ Write code faster
✅ Debug errors efficiently
✅ Build AI agents & chatbots
✅ Automate repetitive tasks
✅ Save time and increase income

In short: AI = productivity + opportunity


1️⃣ ChatGPT – Best AI Assistant for Developers

Best for: Coding help, explanations, debugging, documentation

ChatGPT is one of the most popular AI tools used by developers worldwide.

Key Features:

  • Explains complex code in simple terms
  • Generates boilerplate code
  • Helps with system design & architecture
  • Supports multiple programming languages

Free: Yes
Paid: ChatGPT Plus (for advanced models)

👉 Perfect for students, beginners, and professionals


2️⃣ GitHub Copilot – AI Pair Programmer

Best for: Real-time code suggestions

GitHub Copilot integrates directly into your IDE and suggests code as you type.

Why developers love it:

  • Context-aware code completion
  • Supports JavaScript, Python, Java, Go, and more
  • Improves coding speed drastically

Free: Limited (students & open-source)
Paid: Yes

👉 Ideal for professional developers


3️⃣ Claude AI – Best for Clean Code & Reasoning

Best for: Logic-heavy coding & explanations

Claude is known for producing cleaner and safer responses compared to many AI tools.

Use cases:

  • Refactoring code
  • Explaining algorithms
  • Writing readable documentation

Free: Yes
Paid: Yes


4️⃣ LangChain – Build AI Agents Like a Pro 🤖

Best for: AI Agent development

LangChain is a framework that helps developers build AI agents, chatbots, and autonomous workflows using LLMs.

Why LangChain is powerful:

  • Connects AI models with tools & APIs
  • Memory, agents, and chains support
  • Widely used in real-world AI products

👉 If you want to build AI Agents, LangChain is a must-learn skill.


5️⃣ Pictory AI – Convert Scripts into Videos 🎥

Best for: Developers & bloggers creating content

Pictory turns text into professional-looking videos automatically.

Perfect for:

  • YouTube Shorts
  • AI explainer videos
  • Tech tutorials

Free: Trial (with watermark)
Paid: Yes

👉 Great tool if you blog + YouTube together


6️⃣ Postman AI – API Development Made Easy

Best for: Backend & API developers

Postman AI helps generate API requests, test cases, and documentation faster.

Benefits:

  • Saves API testing time
  • Improves collaboration
  • Easy debugging

Free: Yes
Paid: Advanced features


7️⃣ Notion AI – Smart Documentation Tool

Best for: Notes, planning, and documentation

Notion AI helps developers:

  • Write technical docs
  • Summarize meeting notes
  • Create roadmaps

👉 Very useful for project planning & learning


🔍 Comparison Table – Best AI Tools for Developers

ToolBest ForFreePaid
ChatGPTGeneral coding
GitHub CopilotCode completion
Claude AIReasoning & logic
LangChainAI agents
PictoryVideo creation
Postman AIAPIs
Notion AIDocumentation

📚 Recommended Book for Developers (Must Read)

If you want to seriously build AI applications and agents, this book is highly recommended:

👉 Generative AI with LangChain and Python

This book covers:

  • LangChain fundamentals
  • Building real-world AI agents
  • Python-based AI workflows

Perfect for developers transitioning into AI.


🎯 Final Thoughts

AI tools are not replacing developers — they are upgrading them.

If you start using these tools today:

  • You’ll code faster
  • Learn smarter
  • Earn more in the future

👉 My advice:
Start with ChatGPT + LangChain and grow from there.

AI Agent Development Roadmap (2025): Skills You Need to Build Intelligent AI Agents

Learn the complete skillset required to build AI agents in 2025. Step-by-step roadmap with tools, examples, and career tips for beginners.

📌 Introduction

Artificial Intelligence is no longer just about chatbots.

Today, AI Agents can think, plan, use tools, and solve real-world problems automatically.
Companies like OpenAI, Google, Meta, and startups are actively hiring developers who can build AI agents.

So the big question is:

👉 What skillset is required to build an AI Agent?
👉 Can beginners learn it?
👉 Is it a good career option in 2025?

Let’s break it down step by step in simple language.


🤖 What Is an AI Agent? (Simple Explanation)

An AI Agent is a system that:

  • Understands user input
  • Makes decisions
  • Uses tools (APIs, databases, browsers)
  • Takes actions automatically

📌 Example:

  • ChatGPT using plugins
  • Auto-trading bots
  • Customer support AI
  • AI that books tickets or writes code

🛠️ Skillset Required to Build an AI Agent

1️⃣ Programming Skills (Foundation)

You don’t need 10 languages.

✔️ Python – most important
✔️ JavaScript – useful for web-based agents

Why?

  • AI libraries are Python-friendly
  • Easy integration with APIs

📌 Beginner Tip:
If you know basic loops, functions, and classes, you are ready.


2️⃣ Understanding APIs (Very Important)

AI agents communicate with:

  • AI models
  • Databases
  • External tools

You should know:

  • REST APIs
  • JSON data format
  • HTTP methods (GET, POST)

👉 Bonus skill: GraphQL


3️⃣ Basics of Artificial Intelligence

You don’t need advanced math.

Just understand:

  • What is Machine Learning?
  • What is a Neural Network?
  • What is a Large Language Model (LLM)?

📌 Focus on concepts, not equations.


4️⃣ Prompt Engineering (Most Underrated Skill)

AI agents work based on instructions.

You must learn:

  • How to ask clear questions
  • How to guide AI behavior
  • How to reduce wrong answers

Example:
❌ “Write code”
✅ “Write clean JavaScript code with comments and error handling”

Good prompts = smart agents.


5️⃣ Working with AI Models (LLMs)

You should understand:

  • Tokens
  • Context window
  • Model limitations
  • Cost control

Popular models:

  • GPT
  • Claude
  • Gemini
  • Open-source LLMs

6️⃣ Data Handling & Databases

AI agents store memory and results.

Learn basics of:

  • SQL or NoSQL
  • Vector databases (basic idea)
  • Reading & writing data

📌 JSON + simple database knowledge is enough to start.


7️⃣ Tool Usage & Automation

Modern AI agents:

  • Call APIs
  • Use browsers
  • Execute functions

Learn:

  • Function calling
  • Tool integration
  • Simple automation logic

This is what makes an agent powerful.


8️⃣ Problem-Solving Mindset (Most Important)

Tools change. Skills remain.

A good AI agent builder:

  • Understands the problem
  • Breaks it into steps
  • Designs logic
  • Tests edge cases

💡 This skill gives you long-term success.


🗺️ Beginner Roadmap (Simple Path)

  1. Learn Python basics
  2. Understand APIs & JSON
  3. Learn AI concepts
  4. Practice prompt engineering
  5. Build small AI agents
  6. Add tools & memory

👉 Within 3–6 months, you can build real projects.


💼 Career & Money Opportunities

AI Agent skills can help you earn via:

  • Freelancing
  • SaaS products
  • YouTube & blogging
  • Startup jobs
  • Automation services

📈 Demand is increasing every month.


📢 Final Thoughts

You don’t need to be an AI expert to start.

✔️ Start small
✔️ Learn consistently
✔️ Build real projects

AI agents are the future of software development.

📚 Recommended Book

If you’re serious about building AI agents and intelligent applications, this book is one of the best resources to get started:

👉 Generative AI with LangChain and Python – From Zero to Hero


🔔 Call to Action (Very Important for Subscribers)

👉 Bookmark LearnersStore.com
👉 Subscribe for AI, JavaScript, and Developer tutorials
👉 Share this post if it helped you

What is Horizontal Scaling? (Simple Explanation for Beginners)

One-line answer:

Horizontal scaling means increasing system capacity by adding more machines (servers) instead of upgrading a single machine.


Brief Explanation

Horizontal scaling (also called scale out) is a technique used to handle more traffic by adding multiple servers that work together as a group.

Instead of making one server bigger and more powerful, you create many smaller servers and distribute the load among them using a load balancer.

Simple example:

  • One server can handle 1,000 users
  • Traffic increases to 5,000 users
  • Solution → Add 4 more servers
  • Load balancer distributes traffic equally
Users
  ↓
Load Balancer
  ↓
Server 1   Server 2   Server 3   Server 4   Server 5


Why Horizontal Scaling is Important

  • Handles high traffic efficiently
  • Improves availability (if one server fails, others work)
  • Easier to scale gradually
  • Commonly used in cloud environments

Horizontal Scaling vs Vertical Scaling

Horizontal ScalingVertical Scaling
Add more serversUpgrade existing server
High availabilitySingle point of failure
More complexSimpler
Used in microservicesUsed in monoliths

Real-World Examples

  • Web applications: Add more app servers during peak traffic
  • Microservices: Each service can scale independently
  • Cloud platforms: AWS Auto Scaling, Kubernetes replicas

Where Horizontal Scaling is Used

  • Microservices architecture
  • Cloud-based applications
  • High-traffic websites
  • Distributed systems

Key Requirements for Horizontal Scaling

  • Load balancer
  • Stateless services (or shared state like Redis/DB)
  • Proper monitoring and health checks

Summary

Horizontal scaling allows applications to grow by adding more servers, making systems more reliable, scalable, and fault-tolerant. It is the preferred scaling method in modern cloud and microservices-based applications.


👉 Check the book on Amazon – Highly recommended for system design interviews

CQRS Supporting Architecture Explained: A Simple Guide for Beginners

When applications grow bigger, handle more users, or need better performance, the traditional way of writing code (where the same model handles both reading and writing data) starts to fail.
This is where CQRS comes in.

In this article, we’ll simplify CQRS (Command Query Responsibility Segregation) and explain how the supporting architecture works behind it — in clean, easy-to-understand language.


What is CQRS?

CQRS stands for Command Query Responsibility Segregation.

It means:

  • Commands → Modify data (Create, Update, Delete)
  • Queries → Read data (Get, List, Search)

Instead of using the same model or service for both, CQRS separates them.

This makes your application:

  • More scalable
  • Easier to maintain
  • Faster for reads
  • Flexible for writes (business logic)

Why Do We Need CQRS?

Traditional applications use one model for both reading and writing.
As the app grows:

  • Read operations increase heavily
  • Write operations get more complex (validation, rules, workflows)
  • Database becomes a bottleneck
  • Code becomes messy
  • Scaling becomes difficult

CQRS solves this by dividing responsibilities.


CQRS Supporting Architecture – Explained Step by Step

CQRS architecture has two main sides:

  1. Command Side (Write Model)
  2. Query Side (Read Model)

Let’s break it down with a real-world example.


1. Command Side (Write Model)

This side handles:

  • Create
  • Update
  • Delete

Each command performs a specific task and follows business rules.

Command Side Components:

✅ Command
A message describing what you want to do.
Example: CreateOrderCommand, UpdateStockCommand

✅ Command Handler
Executes the business logic for the command.

Example:

  • Validate input
  • Apply business rules
  • Update the database
  • Publish events

2. Query Side (Read Model)

This side is designed for fast reads.

It:

  • Uses optimized data models
  • Can have denormalized tables
  • Is designed for speed, not business logic

Query Side Components:

✔ Query
Request for data:
Example: GetUserByIdQuery, GetOrdersListQuery

✔ Query Handler
Fetches the data quickly from the read database.


3. Event Bus / Message Broker

After a write happens, the system may need to notify other parts.

This is done using events.

Examples:

  • OrderCreatedEvent
  • UserRegisteredEvent

These events are published to a message broker, such as:

  • Kafka
  • RabbitMQ
  • AWS SNS/SQS
  • Azure Service Bus

These events help keep the read model updated.


4. Read Database (Optimized for Queries)

The read side may use:

  • SQL Database
  • NoSQL (MongoDB, DynamoDB)
  • Elasticsearch
  • Redis

It is usually separated from the write database to allow:

  • Independent scaling
  • Faster reads
  • Different structure from write model

5. Sync Between Write and Read Models

Whenever write happens:

  1. Command Handler updates Write DB
  2. Event is published
  3. Event Handler listens and updates Read DB

This ensures Read DB is always up-to-date.


Benefits of CQRS Supporting Architecture

✔ High scalability

You can scale “reads” separately from “writes”.

✔ Better performance

Read side is optimized for speed.

✔ Clean architecture

Command and Query responsibilities are separated.

✔ Easier to add features

Adding new query or command is straightforward.

✔ Event-driven communication

Improves reliability in distributed systems.


When Should You Use CQRS?

Use CQRS when:

  • You have heavy read traffic
  • Business rules are complex
  • You need real-time updates
  • You are building microservices
  • You want to scale different parts independently

Avoid CQRS for:

  • Small applications
  • Simple CRUD projects
  • Early-stage prototypes

Simple Example to Understand CQRS

Let’s say you’re building an e-commerce app:

User places an order → Command Side

  • PlaceOrderCommand is triggered
  • Validations happen
  • Order saved in Write DB
  • OrderPlacedEvent is published

App shows order status → Query Side

  • GetOrderStatusQuery fetches data
  • Query handler fetches from Read DB
  • Response is fast and optimized

Conclusion

The CQRS supporting architecture helps developers build:

  • scalable
  • maintainable
  • high-performance

applications by splitting responsibilities between commands and queries.

This approach shines in large-scale systems, event-driven environments, and microservices.

If implemented correctly, CQRS can drastically boost application speed, stability, and flexibility.