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Using ReactJS With NodeJS | Ideal Tech Stack For High-Performance Web App Development

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Are you looking to create a full-stack web project? Are you overpowered by the options available in the market? 

Agreed, details like structure and code for the frontend and backend are requisite, but once you choose a library, you can integrate it with a popular framework to work on the development part. 

Are you aware that Node.js and React JS are the two most adopted technologies for web app development? 

React is perfect for the frontend, and Node.js is optimal for the backend. However, if you fuse the two frameworks, the result will be a seamless web app. 

What is React JS? 

You can create user interfaces with the React JS library. Also, you can use reusable components to make code simple to read and maintain. The best part is that these components auto-update when you interact with them. 

With that, you can expect a seamless response that works across many devices. As a result, your application is swift and responsive. React JS should be your first choice if you want to fasten your enterprise app development process. 

With React JS – you can build scalable online apps. Plus, it can help you build the best apps with the framework, even if you don’t know intricate scripting language. 

What is Node.js? 

If you need to build server-side and networking apps, your bet should be on Node.js. Apart from its sizable library of programming modules, you can facilitate the integration of programming languages with API and other third-party libraries. According to the statistics, 42.73% of Web programmers are using Node.js framework, libraries, and IDEs for their complex web development projects. 

Ideally, it runs on Chrome JavaScript runtime. You can build data-intensive real-time apps with Node.js because of its lightweight and efficient I/O model feature. Additionally, you can minimize the development time as it works on multiple platforms. 

Do you know most companies demand scalable software solutions today? Node.js will fit the scalability part as it handles concurrent requests efficiently. Also, the framework features cross-platform compatibility with rapid code execution. 

Going further, let’s look at some reasons to use React JS with Node.js. 

  • You can use Node.js for sequent server connection i.e. if your app’s core is based on real-time data streaming. 
  • You can use React JS and Node.js if you need to handle many requests and maintain server load balance. 
  • Also, you can develop single-page applications in React and use Node.js to build lightweight models for asynchronous data loading through the callback function.
  • You can fuse Node.js and React capabilities to build responsive data-driven multi-device apps. Basically, you can scale up your website performance. 
  • By integrating both technologies – you can expect more ROI and save added effort on app development. 

If you use React JS with Node.js, it can streamline the web development process. 

Here are some applied examples of using React JS with Node.js: 

Do you need to use the frameworks for standard web apps? You can use React JS to render the page and Node.js to handle the requests for the app. Ideally, for a chat app – you can use React JS to render the chat interface and Node.js can handle user communication. 

Steps to merge React JS with Node.js as a backend: 

Firstly, contrive a Node.js project 

You can contrive a Node.js project using a Node package manager (NPM). With this, you can set up a project structure at once. 

Now, set up the server 

It is time to contrive an express.js server – a flexible node.js web app framework. Ideally, you can install it as a dependency. 

Now, contrive a basic express server in a file i.e server.js 

const express = require(‘express’);

const app = express();

const port = 5000; // You can choose any port

app.get(‘/’, (req, res) => {

 res.send(‘Hello from the Node.js backend!’);

});

app.listen(port, () => {

 console.log(`Server is running on port number ${port}`);

});

It is time to initiate the Node.js server with:

node server.js

After that, your Node.js backend will run on your chosen port. 

Quick steps to merge React JS with Node.js as a frontend: 

You can contrive a React app using Create React App – a tool that sets up React projects quickly. 

npx create-react-app my-react-app

Now, you can fetch data from the backend 

You can use the React components to fetch API and request HTTP to the Node.js backend.

import React, { useState, useEffect } from ‘react’;

function App() {

 const [data, setData] = useState([]);

 useEffect(() => {

   // Fetch data from the backend

   fetch(‘/api/data’)

     .then((response) => response.json())

     .then((data) => {

       setData(data);

     });

 }, []);

 return (

   <div>

     <h1>Data from the Backend:</h1>

     <ul>

       {data.map((item, index) => (

         <li key={index}>{item}</li>

       ))}

     </ul>

   </div>

 );

}

export default App;

Now, connect to Node.js backend 

After the above step, you can connect to the Node.js backend by setting up a proxy in your package.json. 

“proxy”: “http://localhost:5000”

With this typical configuration – you can make backend requests without CORS issues. 

Finally, it is time to start your React development server 

cd my-react-app

npm start

As a last step, your React frontend is accessible at http://localhost:3000. In ideal case, you can fetch and display data from the Node.js backend.

Wrapping up

If you integrate React JS with Node.js, it can help you build present-day and scalable web applications. 

With React JS – you can arm the frontend user interfaces, and Node.js can arm serving data and business logic on the server side. 

Are you ready to contrive high-performance web apps with the two technologies? If scalability and flexibility are your foremost concerns, try using both frameworks for app development. 

About the Author: Harikrishna Kundariya, a marketer, developer, IoT, Cloud & AWS savvy, co-founder, and Director of eSparkBiz Technologies. His 12+ years of experience enables him to provide digital solutions to new start-ups based on IoT and SaaS applications.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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