Why Edge Computing Matters in the AI Era

Ritika Kumar — Tue, 12 May 2026 16:49:09 +0000

Cloud Computing is a well-known term and a concept that almost every organization uses today. Previously, every firm faced the issue of setting up its own data centers, and of course, it was a tedious task to maintain servers and bear huge infrastructure costs.
Then came Cloud Computing.

Cloud Computing helped organizations save costs by providing only the services they required, such as servers, storage, networking, databases, and computing power, without managing the physical infrastructure themselves.

But with the evolution of AI, cloud computing alone is no longer a complete solution.

Why?

Because AI today is not just powering websites and applications. It is running:

self-driving cars
IoT devices
smart cameras
drones
robotics
intelligent assistants

These systems require real-time information and instant decision-making. Sending every request to a distant cloud server and waiting for the response introduces latency, which is not practical for modern AI-driven systems.

And this is where Edge Computing enters the picture.

Honestly, I used to ignore this topic during my college days, thinking, “It’s not important.” 🤭

But as usual, whatever we ignore somehow appears in exams🫣.
So, without beating around the bush😜, let’s understand edge computing in the simplest way possible.

Imagine you own a shop and want to get your car washed. But the car washing station is very far from your home.

Now think about the problems:

It takes time to travel
You waste energy
Lose customers because the shop is closed😣

But if the car washing station is near your home, things become much easier:

Less travel time
Quicker service
Manage your customers efficiently😊

Edge computing works in the exact same way.

Instead of sending data all the way to centralized cloud servers for processing, the computation happens near the device itself.

For example, imagine having an AI assistant on your smartphone that works even without internet connectivity using a dedicated AI chip from companies like NVIDIA, Qualcomm, or Apple.

When you ask the assistant a question:

The processing happens locally on the device
The AI model runs directly on the chip
You get a response almost instantly

Now compare this with cloud-only processing:

Your request travels to cloud servers
GPUs process the request
The response travels back to your device

This round trip may only take seconds, but for real-time systems, even milliseconds matter.

Imagine a self-driving car waiting for cloud instructions before applying the brakes.

Scary, right? 🤦‍♀️

That is why edge computing is becoming extremely important for modern AI systems. It helps reduce:

Latency
Bandwidth Consumption
Internet Dependency
Response Time

In the next blog, we will discuss how the future of AI is hybrid.😎. Stay tuned.

Create your first Docker Image

Ritika Kumar — Sat, 09 May 2026 16:58:33 +0000

Imagine you and your team have built an app. Now the testing team wants to test it.
Normally, they may need to install:

Node.js
npm packages
dependencies
environment setup
configurations

Even after all that, errors can still happen because their system may be different from yours.
Docker solves this problem by packaging your app, runtime, dependencies, and configuration into one image. The tester only needs Docker installed. They can run your image directly and test the app.

Follow these steps to create your own Docker image:

Create the folder structure displayed in the image.

Create a server.js file and add the following code inside the file.
It is a demo code that displays the OS information when the user opens the URL in the browser.

const http = require("http");
const os = require("os");

const server = http.createServer((req, res) => {
  res.writeHead(200, { "Content-Type": "text/html" });

  res.end(`
    <html>
      <body style="font-family:sans-serif;padding:40px;">
        <h1>Running Inside Docker</h1>
        <p><strong>Container Name:</strong> ${os.hostname()}</p>
        <p><strong>OS:</strong> ${os.platform()}</p>
        <p><strong>Architecture:</strong> ${os.arch()}</p>
        <p><strong>Node Version:</strong> ${process.version}</p>
        <p><strong>Current Time:</strong> ${new Date()}</p>
      </body>
    </html>
  `);
});

server.listen(3000, () => {
  console.log("Server running on port 3000");

Add the following information to the package.json file.
The package.json file contains information about the Node.js project, such as its name, version, main file, and dependencies.

{
  "name": "docker-identity",
  "version": "1.0.0",
  "main": "server.js"
}

Add the following code to your dockerfile:

FROM defines the base image. Here, node:trixie already includes Linux, Node.js, npm, and required system libraries.

WORKDIR sets /app as the working directory inside the container.

COPY copies files from your local system into the container.

RUN executes a command while building the image. Here, it runs npm install.

CMD defines the command that runs when the container starts.

FROM node:trixie
COPY package.json /app/
COPY src /app/src
WORKDIR /app/
RUN npm install
CMD ["node", "src/server.js"]

Run the following command to build the Docker image.

build: It is used to build the custom image.
. : It is the current directory
-t: It is the tag flag that is used to give name and version to the application

docker build -t node-app:1.0 .

After the build is successfully completed, run the following command to containerize your app.

run: It is used to run the image as a container.
-p: It is a flag that is used in port binding. Here, 3000 is the port of the container, whereas 8080 is the port of your system. So, we set 8080 as the port where you can publicly access the application.
node-app: It is the name of the application.

docker run -p 8080:3000 node-app:1.0

After the container runs successfully, open localhost://8080 to see your app.
The following output is displayed:

Why This Is Useful

Now this image can run on any system that has Docker installed.

The tester does not need to install Node.js, npm, or project dependencies manually. Everything required to run the app is already packed inside the Docker image.

Even if the tester is using Windows, macOS, or Linux, the container will run the same way.

DEV Community: Ritika Kumar

Why Edge Computing Matters in the AI Era

Create your first Docker Image

Why This Is Useful