DEV Community: Vipul

AI Agent vs Agentic AI: The Difference Everyone Is Talking About

Vipul — Fri, 12 Jun 2026 13:40:59 +0000

Artificial Intelligence is evolving rapidly, and two terms are appearing everywhere in tech discussions, LinkedIn posts, and conference talks: AI Agents and Agentic AI.

Many students, freshers and even experienced IT professionals use these terms interchangeably, While they're closely related, they represent different concepts. Understanding the distinction can help you better navigate the future of AI-powered applications.

What Is an AI Agent?

An AI Agent is a software system designed to perform tasks on behalf of a user.

Think of it as a digital worker that can:

Understand instructions
Access tools and data
Make decisions within defined boundaries
Complete specific tasks

For example, An AI travel assistant that searches for flights, compares prices, and presents options is an AI agent.

Similarly, coding assistants, customer support bots, and automated testing assistants are all examples of AI agents.

Key Characteristics of AI Agents

Goal-oriented
Uses tools and APIs
Executes predefined workflows
Performs tasks with some level of automation

What Is Agentic AI?

Agentic AI refers to the capability of an AI system to act autonomously toward a goal.

Instead of simply responding to commands, it can:

Plan multiple steps ahead
Break large goals into smaller tasks
Adapt when situations change
Decides which tools to use
Evaluate results and adjust actions

In simple terms, Agentic AI focuses on how independently an AI can think and act.

For example, if given the goal:

"Prepare a market research report on electric vehicles."

An Agentic AI system might:

Research industry trends
Gather data from multiple sources
Analyze competitors
Create visual summaries
Generate a final report

All with minimal human intervention.

The Simplest Way To understand the Difference

Think of a car.

The car is the AI Agent.
The self driving capability is Agentic AI.

One refers to the system itself, while the other refers to the level of autonomy and intelligence within that system.

Why Is Agentic AI Becoming Popular?

Traditional AI systems mainly answered questions.

Modern AI systems are expected to:

Complete tasks
Use multiple tools
Interact with software
Manage workflows
Collaborate with humans

Organizations want AI that not only provides information but also helps achieve outcomes. This shift is driving the rise of agentic AI.

Claude Mythos 5 & Fable 5: Anthropic's Next Generation AI Models

Vipul — Thu, 11 Jun 2026 14:52:54 +0000

Artificial Intelligence is evolving rapidly, and Anthropic has introduced two powerful new models; Claude Fable 5 and Claude Mythos 5.

What is Claude Fable 5?

Claude Fable 5 is Anthropic's most advanced publicly available AI model. It is designed to handle complex tasks such as:

Software development
Advanced reasoning
Research and analysis
Long-running AI agent workflows
Vision and document understanding

The model delivers improved accuracy, stronger coding capabilities, and better performance on real-world business tasks.

What is Claude Mythos 5?

Claude Mythos 5 is a restricted-access version intended for trusted organizations and researchers. It offers enhanced capabilities in area such as:

Cybersecurity research
Scientific discovery
Advanced technical analysis
Complex autonomous workflows

Due to it's powerful capabilities, access is currently limited to approved users.

Key Difference

Both models are built on the same foundation, but:

Fable 5 is public version with additional safety controls.
Mythos 5 provides broader access to advanced capabilities for vetted organizations.

Why It Matters

The release of Fable 5 and Mythos 5 highlights the industry's shift toward AI systems that can perform longer, more tasks with greater autonomy. These models are expected to compete directly with the latest offerings from OpenAI, Google and xAI in coding, reasoning, and enterprise AI applications.

Claude Fable 5 brings cutting-edge AI capabilities to a wider audience, while Mythos 5 showcases what the next generation of advanced AI systems may look like. Together, they represent another major step forward in the evolution of intelligent assistants and AI-powered workflows.

What Does "Augmented" Mean in Modern Technology?

Vipul — Wed, 10 Jun 2026 04:52:26 +0000

The word "augmented" appears everywhere in technology today. From Augmented Reality (AR) and Retrieval-Augmented Generation (RAG) to Human Augmentation, the term is becoming increasingly common.

But what does it actually mean?

At its core, augmented means enhanced by adding something extra. Rather than replacing an existing system, augmentation improves it by providing additional capabilities, information, or functionality.

Think of a car with GPS navigation. The GPS doesn't replace the driver-it augments their ability to reach a destination efficiently. This simple idea is the foundation of many modern technologies.

Augmented Reality (AR)

One of the most well-known example is Augmented Reality.

AR overlays digital content onto the real world, allowing users to interact with both physical and digital elements simultaneously.

Common examples include:

Mobile games like Pokemon GO
AR-powered navigation systems
Virtual furniture placement apps

Unlike Virtual Reality (VR), which creates an entirely virtual environment, AR enhances the real world rather than replacing it.

Retrieval-Augmented Generation (RAG)

In the world of Artificial Intelligence, augmentation plays a crucial role through Retrieval-Augmented Generation (RAG).

Large Language Models are trained on vast amounts of data, but they don't have access to the latest information or organization-specific knowledge.

RAG addresses this challenge by:

Retrieving relevant information from external sources.
Providing that information to the AI model.
Generating a response using both the retrieved data and the model's existing knowledge.

The result is an AI system that can provide more accurate, relevant, and up to date answers.

In simple terms, the AI is augmented with additional knowledge before generating a response.

Human Augmentation

Technology is also being used to augment human capabilities.

Examples include:

Smartwatches that monitor health metrics
AI assistants that improve productivity
Exoskeletons that assist physical labor
Advanced hearing aids and vision enhancement devices

These technologies don't replace human abilities-they strengthen and extend them.

Why Augmentation Is Important

A common misconception is that the technology aims to replace people. In reality, many modern innovations focus on augmentation rather than replacement.

Organizations are increasingly adopting tools that:

Help employees make better decisions
Improve productivity
Reduce repetitive tasks
Enhance access to information

The goal is to create a partnership between humans and technology where each complements the other's strengths.

The Common Pattern

Whether we're talking about AR, RAG, or wearable technology, the pattern remains the same.

Existing Capabilities + Additional Enhancement = Augmented Capability

Examples:

Reality + Digital Information = Augmented Reality
AI Model + External Knowledge = Retrieval-Augmented Generation
Human Skills + Technology = Human Augmentation

Augmentation is one of the defining concepts of modern technology. Instead of replacing what already exists, augmentations enhanced it by adding new capabilities and intelligence.

The next time you encounter the word "augmented", remember this simple definition:

Augmented means making something more capable by adding value, not replacing it.

Forward Proxy vs Reverse Proxy: The Internet's Two Gatekeepers

Vipul — Sun, 07 Jun 2026 06:40:00 +0000

When people start learning networking, cloud, DevOps, or system design, they often hear the terms Forward Proxy and Reverse Proxy.

At first, they sound similar. Both sit between two parties and forward requests. But their purpose is completely different.

A simple way to remember:

Forward Proxy protects clients
Reverse Proxy protects servers

Let's understand this with real-world examples.

Imagine a Corporate Office

You work in a company where employees need internet access.

Instead of allowing every employee to directly browse websites, the company places a gateway in between.

The flow becomes:
Employee -> Proxy -> Internet

The website sees the proxy's IP address, not the employee's.

This is a Forward Proxy.

Now imagine a popular e-commerce website.

Millions of users access the website, but instead of reaching the web servers directly, all requests first go through a gateway.
Users -> Proxy -> Web Servers

Users only see the proxy. The actual servers remain hidden.

This is Reverse Proxy.

What is a Forward Proxy?

A forward proxy sits in front of clients.

It acts on behalf of users when they access resources on the internet.

Request Flow

The destination website doesn't know the real user.

It only knows the proxy.

Common Uses

1. Hide User Identity
Organizations can hide internal IP addresses from external websites.
Employee -> Proxy -> Google
Google sees the proxy IP, not the employee's device.

2. Content Filtering
Companies and schools often block certain websites.

Employee -> Proxy
          - Allow LinkedIn
          - Block YouTube

3. Caching
Frequently accessed content can be stored locally.

Instead of downloading the same file repeatedly:
User -> Proxy Cache -> Response

This reduces bandwidth usage and improves speed.

Real Examples

Corporate internet gateways
School network filters
VPN services
Anonymous browsing services

What is a Reverse Proxy?

A Reverse Proxy sits in front of servers.

Clients don't directly communicate with backend servers.

Instead, requests first reach the reverse proxy.

Request Flow

The user never knows which server actually handled the request.

Common Uses

1. Load Balancing
Traffic can be distributed across multiple servers.

           -> Server 1
User -> RP -> Server 2
           -> Server 3

If 30,000 users visit a website, the load gets shared.

2. Security
Backend servers remain hidden from the internet.

Only the reverse proxy is exposed.

Internet
    |
Reverse Proxy
    |
Private Servers

3. SSL/TLS Termination
HTTPS encryption can be handled by the reverse proxy.

HTTPS User
     |
Reverse Proxy
     |
HTTP Internal Servers

Backend applications don't need to manage certificates individually.

4. Caching
Static content can be served directly.
User -> Reverse Proxy Cache
This reduces backend workload.

Real Examples

Nginx
HAProxy
Traefik
Apache HTTP Server
Envoy Proxy

Easy Trick to Remember

Think about who the proxy is helping.

Forward Proxy
User -> Proxy -> Client

The proxy helps the user.

Reverse Proxy
Internet -> Proxy -> Server

The proxy helps the server.

Embeddings: How Text Becomes Numbers for Semantic Search

Vipul — Fri, 05 Jun 2026 17:19:59 +0000

When using AI-powered systems, documents are not searched the same way traditional databases search text.

Instead of matching keywords, modern RAG systems rely on embeddings - numerical representations of text that capture meaning and context.

Embeddings are the foundation of semantic search.

What Are Embeddings?

An embedding is a list of numbers that represents the meaning of a piece of text.

For example:

"How to deploy kubernetes"

might be converted into:

[0.12, -0.87, 0.45, ...]

While the numbers themselves are not meaningful to humans, they help machines understand relationships between different pieces of text.

Why Convert Text into Numbers?

Computers cannot directly understand language.

To compare meanings, text must first be transformed into a mathematical representation.

Embeddings makes this possible by placing similar concepts close together in a high-dimensional space.

For example:
"How to deploy Kubernetes"
"Kubernetes deployment guide"

will produce embeddings that are very close to each other.
Even though the wording is different, the meaning is similar.

Traditional Search vs Semantic Search

Keyword Search
A traditional search engine looks for exact matches.

Query:
How to deploy Kubernetes

Document:
Kubernetes deployment guide

Although both mean nearly the same thing, keyword matching may miss relevant results.

Semantic Search
Embedding based search compares meaning instead of exact words.

The query and document generate similar embeddings, allowing the system to retrieve the correct result even when the wording differs.

This is the core idea behind semantic search.

How Embeddings Work in RAG

Why Embeddings Matter

Without embeddings:

Search depends on exact keyword.
Relevant documents may be missed.
Retrieval quality decreases.

With embeddings:

Similar meaning can be matched.
Retrieval becomes context aware.
Answer quality improves significantly.

Hallucinations Are Not Always Wrong Facts: Sometimes They're Wrong Interpretations

Vipul — Thu, 04 Jun 2026 17:53:33 +0000

When people hear the term AI hallucination, they often imagine an LLM confidently inventing facts that do not exist.

For example:

"The capital of France is Berlin."

That's an obvious hallucination because the answer is factually incorrect.

However, in real-world AI systems, hallucinations are often much more subtle.

Recently, I experienced a perfect example during the conversation with an AI assistant.

The Question

I asked:

"Is Redis a vector database?"

The assistant immediately responded:

"Yes, Redis is a vector database."

At first glance, the answer seemed reasonable.

After all, Redis supports:

Vector storage
Vector indexing
Similarity search

These are all capabilities associated with vector databases.

But that wasn't actually what I was asking.

The Hidden Problem

My real question was:

"How is Redis classified as a database technology?"

In database classification terms, Redis is primarily:

An in-memory database
A key-value database
A multi-model database

It is not generally classified as a dedicated vector database.

The assistant answered a different question:

"Can Redis be used as a vector database?"

instead of:

"Is Redis fundamentally a vector database?"

The result was interesting.
The answer contained correct facts.
Yet the answer was still wrong for the user's intent.

Why This Happens

Large Language Models (LLM) do not truly understand questions the way humans do.

Instead, they predict the most probable interpretation based on patterns learned during training.

When the model saw:

"Is Redis the vector database?"

it likely mapped the question to a common pattern:

"Can Redis perform a vector database functions?"

Since the answer to that interpretation is yes, the model confidently responded with "Yes."

The failure wasn't in factual knowledge.
The failure was in understanding the user's intent.

This Is a Form of Hallucination

Many teams define hallucinations as:

"Any output that does not correctly satisfy the user's request."

Under this broader definition, the Redis example qualifies.
The model generated an answer that was:

Factually supported
Logically consistent
Yet misaligned with the actual question

In other words:

The model hallucinated the meaning of the question.

Why RAG Doesn't Fully Solve This

Many people believe that Retrieval-Augmented Generation (RAG) eliminates hallucinations.

But consider this scenario.

Even if a RAG system retrieves perfect documentation about Redis:

Redis is an in-memory database
Redis supports vector search
Redis supports KNN queries

The LLM still has to interpret the user's question.

If it misunderstands the intent, it may still generate the wrong answer despite having perfect information.

This highlights an important reality:

Not all hallucinations come from missing knowledge.

Some hallucinations come from incorrect interpretation.

The Key Takeaway

When evaluating AI systems, don't only ask:

"Did the model know the answer?"

Also ask:

"Did the model understand the question?"

Because sometimes the most dangerous hallucinations are not invented facts.

They are correct facts applied to the wrong interpretation.

And from a user's perspective, the result is still an incorrect answer.

Understanding Temperature in LLMs: The Creativity Control Knob

Vipul — Wed, 03 Jun 2026 12:14:11 +0000

If you've worked with large language models (LLMs), you have likely come across a parameter called temperature.

Despite its name, temperature has nothing to do with hardware or system performance. It controls how predictable or creative an LLM's responses are.

What Is Temperature?

Temperature influences how the model chooses the next word from its list of possible predictions.

Think of it as a creativity slider:

Low temperature (0-0.3) -> More predictable and focused responses.
Medium temperature (0.5-0.7) -> Balanced creativity and accuracy.
High temperature (0.8-1.5+) -> More diverse and creative outputs.

The higher the temperature, the more willing the model is to choose less likely words.

Example

Prompt:

Explain what Kubernetes is.

Temperature = 0

"Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications."

The answer is consistent and factual.

Temperature = 1

"Kubernetes is like an operating system for your containers, helping applications scale, recover from failures, and run efficiently across clusters."

Still correct, but phrased differently.

Temperature = 2

"Kubernetes acts as the conductor of a container orchestra, ensuring every application plays its part in harmony across a distributed environment."

More creative, but less precise.

When to Use Low Temperature

Low temperature is preferred when accuracy matters:

RAG applications
Technical support chatbots
Code generation
Documentation assistants
Question-answering systems

The goal is consistency and reliability.

When to Use High Temperature

Higher temperatures work better for:

Brainstorming
Story writing
Marketing content
Social media posts
Creative ideation

The goal is diversity and originality.

Temperature in RAG

For RAG systems, temperature is usually kept low (around 0-0.3).

Why?

The retrieved documents already provide the knowledge. The model's job is to use that information, not invent new details.

Higher temperatures can increase the likelihood of hallucinations and inconsistent answers.

Common Misconception

Many people assume:

Higher temperature = smarter AI

Not true.

Temperature only affects randomness. It does not increase the model's knowledge or intelligence.

A higher temperature simply makes the model explore less likely responses.

If you need accuracy, keep it low.
If you need creativity, increase it.

Why Chunking Matters in RAG: The Hidden Key to Better Retrieval

Vipul — Mon, 01 Jun 2026 15:53:08 +0000

When people discuss Retrieval-Augmented Generation (RAG), they often focus on embeddings, vector databases, or LLMs. However one of the most critical factors affecting RAG performance is chunking.

A well-designed chunking strategy can significantly improve retrieval accuracy, while poor chunking can lead to irrelevant results and hallucinations.

What is Chunking?

Chunking is the process of breaking large documents into smaller pieces (chunks) before generating embeddings and storing them in a vector database.

For example, instead of embedding a 50-page PDF as a single document, we split it into smaller sections:

Chunk 1: Introduction
Chunk 2: Architecture Overview
Chunk 3: Deployment Process
Chunk 4: Troubleshooting Guide

Each chunk gets its own embedding, making retrieval more precise.

Why Not Store Entire Documents?

Imagine a Kubernetes troubleshooting guide with 100 pages.

If a user asks:

How do I debug a CrashLoopBackOff error?

The system needs to retrieve only the relevant troubleshooting section, not the entire document.

Large documents create embeddings, that represent multiple topics, making retrieval less accurate.

How Chunking Improves Retrieval

1. Better Search Precision
Similar chunks focus on a single topic.

Instead of retrieving an entire document about Kubernetes, the system can retrieve only the section related to CrashLoopBackOff error.

This improves relevance and reduces noise.

2. Reduced Context Window Usage
LLMs have context limits.

Sending entire documents wastes tokens and increases costs.

Chunking ensures only the most relevant information is passed to the model.

3. Improved Answer Quality
Relevant chunks provide cleaner context.

The LLM spends less effort filtering irrelevant information and more effort generating accurate responses.

4. Faster Retrieval
Vector databases search embeddings.

Smaller, focused chunks generally produce more meaningful embeddings, improving retrieval efficiency.

Common Chunking Strategies

Fixed-Size Chunking
Splits text after a fixed number of characters or tokens.

Example:

500 tokens per chunk
50-token overlap

Pros:

Simple to implement
Fast processing

Cons:

May split important information in the middle

Semantic Chunking
Splits text based on meaning, headings, or topic changes.

Example:

Introduction
Installation
Configuration
Troubleshooting

Pros:

Preserves context
Better retrieval quality

Cons:

More complex implementation

Recursive Chunking
Attempts larger splits first and progressively creates smaller chunks when necessary.

Widely used in RAG frameworks because it balances context preservation and chunk size.

Why Chunk Overlap Matters

Without overlap:

Chunk 1:
Kubernetes automatically restarts failed containers.

Chunk 2:
The CrashLoopBackOff state indicates repeated failures.

The relationship between the two chunks may be lost.

With overlap:

Chunk 1:
Kubernetes automatically restarts failed containers.
The CrashLoopBackOff state...

Chunk 2:
The CrashLoopBackOff state indicates repeated failures...

Overlap helps preserve context across chunk boundaries.

Choosing the Right Chunk Size

There is no universal answer.

Typical starting points:

Content Type                  Suggested Size
--------------------------------------------------
Technical Documentation       300-800 tokens
Blog Articles                 500-1000 tokens
Source Code                   Function/Class level
PDFs & Manuals                500-1500 tokens

The best size depends on your data and retrieval goals.

In RAG system, embeddings, vector databases, and LLMs often get most of the attention. But chunking is the foundation that determines whether the right information is retrieved in the first place.

Good retrieval starts with good chunks.

Understanding ORMs - The Bridge Between Code and Databases

Vipul — Sat, 30 May 2026 11:34:21 +0000

When building applications, developers constantly interact with databases -- storing users, fetching orders, updating products, and much more.

But writing raw SQL queries everywhere can become repetitive, difficult to maintain, and error-prone.

This is where ORMs(Object Relational Mappers) come in.

What is an ORM?

An ORM is a tool or framework that allows developers to interact with databases using programming language objects instead of writing raw SQL queries.

Instead of this:

SELECT * FROM users WHERE id = 1;

You can write something like:

User user = userRepository.findById(1);

The ORM automatically converts your code into SQL queries behind the scenes.

Why ORMs Exist

Applications are written in object-oriented languages like Java, Python, or C#, while databases store data in tables and rows.

ORMs act as a translator between these two worlds.

Popular ORM Frameworks

Java

Hibernate
JPA
MyBatis

Python

SQLAlchemy
Django ORM

JavaScript/Node.js

Sequelize
Prisma
TypeORM

.Net

Entity Framework

Advantages of ORMs

Faster Development

Developers write less SQL and more business logic.

Cleaner Code

Database operations become easier to read and maintain.

Database Independence

Switching databases becomes easier because ORM handles many DB-specific differences.

Security

Most ORMs help prevent SQL Injection attacks using parameterized queries.

Automatic Mapping

ORMs automatically map database tables to application objects.

The Hidden Trade-Offs

ORMs are powerful, but not perfect.

Performance Overhead

Generated queries may not always be optimized.

Complex Queries Become Difficult

For advanced reporting or analytics, raw SQL is sometimes easier.

Learning Curve

Understanding ORM internals is important to avoid slow queries.

"Magic" Problem

Developers may not realize what SQL is being generated behind the scenes.

How ORMs Work Internally

When you define a class like:

class User {
    int id;
    String name;
}

The ORM maps it to a database table:

id    name
1     ABC

The ORM:

Tracks object changes
Generates SQL queries
Executes them
Converts database rows back into objects

All automatically.

Understanding robots.txt - The Tiny File That Controls Search Engine Crawlers

Vipul — Sat, 30 May 2026 10:56:16 +0000

When people think about SEO (Search Engine Optimization), they usually focus on:

keywords
backlinks
content
page rankings

But there's a small file quietly working behind the scenes on almost every website:

robots.txt

Despite being just a plain text file, it plays an important role in how search engines interact with a website.

What is `robots.txt`?

robots.txt is a file placed in the root directory of the website that tells search engine crawlers which parts of the site they are allowed or not allowed to crawl.

Example:

https://example.com/robots.txt

Search engine bots from platforms like Google Search, Bing usually check this file before crawling a website.

Why Does `robots.txt` Exists?

Not every page on a website needs to appear in search results.

Some pages are:

internal
temporary
duplicate
admin-related
irrelevant for public search

Instead of wasting crawler resources, websites use robots.txt to guide bots efficiently.

A Simple `robots.txt` Example

User-agent: *
Disallow: /admin/

Meaning:

Applies to all bots (*)
Prevents crawling of /admin/

Simple, but powerful.

How Search Engine Crawling Works

Typical flow:

Search engine discovers website
Bot requests /robots.txt
Website responds with crawler rules
Bot follows allowed paths
Pages get indexed

This process is part of what makes search engines scalable across billions of websites.

Common `robots.txt` Rules

Allow Everything

User-agent: *
Disallow:

Block Entire Website

User-agent: *
Disallow: /

This blocks all crawling.
Extremely dangerous if accidentally deployed to production.

Block Specific Directory

User-agent: *
Disallow: /private/

Block Specific File

User-agent: *
Disallow: /secret.pdf

`robots.txt` and SEO

Good use of robots.txt can improve SEO by:

reducing crawl waste
improving indexing efficiency
hiding duplicate pages
prioritizing important content

But incorrect usage can destroy rankings.

A single wrong line can accidentally remove an entire website from search visibility.

One Important Misconception

robots.txt is not a security mechanism.

Many developers mistakenly think:

"if it's in robots.txt, nobody can access it."

That's incorrect.

Example:

Disallow: /internal-financial-data/

This actually exposes the existence of sensitive folders publicly.

Anyone can simply visit:

https://example.com/robots.txt

and view blocked paths.

Real security should use:

Authentication
Authorization
VPNs
Firewalls

-- not robots.txt.

How CDN Improves Website Performance

Vipul — Thu, 28 May 2026 16:11:27 +0000

Every time you open a website, a lot happens behind the scenes.
Images, Videos, CSS files, JavaScript, APIs -- all of these need to travel from a server to your device.

Now imagine if that server is located thousands of kilometers away from the user.

The result?

Slow loading time
High latency
Buffering
Poor user experience

This is exactly the problem a CDN (Content Delivery Network) solves.

What is a CDN?

A CDN is a globally distributed network of servers that stores cached copies of website content closer to users.

Instead of serving content from a single origin server, the CDN delivers files from the nearest edge server.

For example:

Your main server may be in Mumbai
A user opens the website from Germany
Instead of Germany --> Mumbai communication for every request, the CDN serves cached content from a nearby European edge location.

This significantly reduces distance and improves speed.

Without CDN

Request Flow

User --> Origin Server --> Response

if the origin server is far away:

Higher latency
Slower downloads
More load on main server

Example:
A user in the US accessing a server hosted in India may experience noticeable delay because data must travel across continents.

With CDN

Request Flow

User --> Nearest CDN Edge Server --> Response

Now:

Reduced latency
Faster content delivery
Lower bandwidth usage on origin server
Better scalability

The user gets content from the nearest available location instead of the primary server.

How CDN Improves Performance

1. Reduce Latency
Latency is the time taken for data to travel between client and server.

The shorted the distance, the lower the latency.

CDNs place servers across multiple geographic regions so users connect to nearby locations.

This improves:

Website load time
Video streaming
API response speed
Gaming performance

2. Cache Static Content
CDNs mainly cache static files like:

Images
CSS
JavaScript
Fonts
Videos

When multiple users request the same file:

The CDN serves it directly from cache
The origin server is not contacted repeatedly

This reduces server workload dramatically.

3. Handles Traffic Spikes
Suppose a website suddenly goes viral.

Without CDN:

The origin server may crash due to huge traffic

With CDN:

Traffic gets distributed across multiple edge servers

This improves availability and reliability during high traffic events.

Real-World Example

Think of a CDN like food delivery warehouses.

Without CDN:

Every order comes directly from the main factory

With CDN:

Products are stored in nearby warehouses
Delivery become much faster

Similarly, CDNs store website content closer to users worldwide.

Additional Benefits of CDN

Better Security
Many CDNs provide:

DDoS protection
Rate limiting
Web Application Firewall (WAF)
Bot protection

Lower Infrastructure Cost
Since cached content is served by CDN servers:

Less bandwidth is consumed on origin servers
Lower compute usage
Reduced hosting cost

Improved SEO
Search engines consider website speed as a ranking factor.

Faster websites often get:

Better user engagement
Lower bounce rate
Improved search rankings

Popular CDN Provides

Some commonly used CDN providers are:

Cloudflare
Akamai Technologies
Amazon Web Services CloudFront
Google Cloud CDN
Fastly

Large platforms like streaming services, e-commerce sites, and social media platforms heavily rely on CDNs.

Final Thoughts

CDNs are one of the biggest reasons modern websites feel fast globally.

Instead of depending on a single distant server, content is distributed across edge locations worldwide, reducing latency and improving scalability.

The bigger the audience, the more important CDN becomes.

Even small websites today use CDNs because performance directly impacts user experience.

Inside Load Balancers - The Hidden Traffic Controllers of the Internet

Vipul — Tue, 26 May 2026 13:57:25 +0000

Whenever thousands or even millions of users open an application at the same time, one big question arises:

"How does a single website handle so much traffic without crashing?"

The answer is usually a Load Balancer.

A load balancer sits between users and backend servers and distributes incoming traffic intelligently.

Instead of this:

Users --> One Server

Modern systems work like this:

Users --> Load Balancers --> Multiple Servers

This prevents any single server from becoming overloaded.

What Actually Happens Behind the Scenes?

Suppose you open:

https://myapp.com

Your request first reaches the load balancer, not the application server directly.

The load balancer then decides:

"Which backend server should handle this request?"

It checks things like:

Current server load
Active connections
Server health
Response times

and forwards your request to the best available server.

How Does It Choose Servers?

Load balancers use different algorithms.

Round Robin

Request 1 --> Server A
Request 2 --> Server B
Request 3 --> Server C

Least Connections

Traffic goes to the server handling the fewest users currently.

Weighted Distribution

More powerful servers receive more traffic.

One of the Most Important Features:

Health Checks

Load balancers continuously monitor backend servers.
If a server crashes or becomes unhealthy:

Server B --> Down

the load balancer automatically stops sending traffic to it.

This is one of the main reasons modern applications achieve high availability without users noticing failures.

Layer 4 vs Layer 7

Some load balancers only understand:

IP addresses
TCP/UDP ports

These are called:
Layer 4 Load Balancers

Others understand full HTTP requests including:

URLs
Headers
Cookies

These are:
Layer 7 Load Balancers
This allows advanced routing like:

/api --> API Servers
/image --> Image Servers

HTTPS and SSL Termination

Handling HTTPS encryption on every backend server is expensive.

Client HTTPS --> Load Balancer decrypts traffic --> Backend servers receive HTTP

This process is called:
SSL Termination
It improves performance and simplifies certificate management.

Why Load Balancers Matter

Without load balancers:

Servers crash during traffic spikes
Applications become unreliable
Scaling becomes difficult

With load balancers:

Traffic is distributed efficiently
Failed servers are isolated automatically
Applications scale horizontally

That's why almost every modern platform -- from streaming services to cloud applications -- depends heavily on load balancing behind the scenes.

DEV Community: Vipul

AI Agent vs Agentic AI: The Difference Everyone Is Talking About

What Is an AI Agent?

What Is Agentic AI?

The Simplest Way To understand the Difference

Why Is Agentic AI Becoming Popular?

Claude Mythos 5 & Fable 5: Anthropic's Next Generation AI Models

What is Claude Fable 5?

What is Claude Mythos 5?

Key Difference

Why It Matters

What Does "Augmented" Mean in Modern Technology?

Augmented Reality (AR)

Retrieval-Augmented Generation (RAG)

Human Augmentation

Why Augmentation Is Important

The Common Pattern

Forward Proxy vs Reverse Proxy: The Internet's Two Gatekeepers

Imagine a Corporate Office

What is a Forward Proxy?

What is a Reverse Proxy?

Easy Trick to Remember

Embeddings: How Text Becomes Numbers for Semantic Search

What Are Embeddings?

Why Convert Text into Numbers?

Traditional Search vs Semantic Search

How Embeddings Work in RAG

Why Embeddings Matter

Hallucinations Are Not Always Wrong Facts: Sometimes They're Wrong Interpretations

The Question

The Hidden Problem

Why This Happens

This Is a Form of Hallucination

Why RAG Doesn't Fully Solve This

The Key Takeaway

Understanding Temperature in LLMs: The Creativity Control Knob

What Is Temperature?

Example

When to Use Low Temperature

When to Use High Temperature

Temperature in RAG

Common Misconception

Why Chunking Matters in RAG: The Hidden Key to Better Retrieval

What is Chunking?

Why Not Store Entire Documents?

How Chunking Improves Retrieval

Common Chunking Strategies

Why Chunk Overlap Matters

Choosing the Right Chunk Size

Understanding ORMs - The Bridge Between Code and Databases

What is an ORM?

Why ORMs Exist

Popular ORM Frameworks

Advantages of ORMs

The Hidden Trade-Offs

How ORMs Work Internally

Understanding robots.txt - The Tiny File That Controls Search Engine Crawlers

What is robots.txt?

Why Does robots.txt Exists?

A Simple robots.txt Example

How Search Engine Crawling Works

Common robots.txt Rules

robots.txt and SEO

One Important Misconception

How CDN Improves Website Performance

What is a CDN?

Without CDN

With CDN

How CDN Improves Performance

Real-World Example

Additional Benefits of CDN

Popular CDN Provides

Final Thoughts

Inside Load Balancers - The Hidden Traffic Controllers of the Internet

What Actually Happens Behind the Scenes?

How Does It Choose Servers?

One of the Most Important Features:

Layer 4 vs Layer 7

HTTPS and SSL Termination

Why Load Balancers Matter

What is `robots.txt`?

Why Does `robots.txt` Exists?

A Simple `robots.txt` Example

Common `robots.txt` Rules

`robots.txt` and SEO