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Building a Multi-Page Application with React Router

Piyush Nanwani — Mon, 12 May 2025 05:34:08 GMT

React Router is a powerful routing library built on top of React that helps you add new screens and flows to your application incredibly quickly, all while keeping the URL in sync with what's being displayed on the page.

React manipulates the DOM by injecting components dynamically, creating the illusion of multiple pages while maintaining a single page structure. React Router isn't just about matching a URL to a function or component: it's about building a full user interface that maps to the URL.

In this blog, we will talk about single-page applications (SPAs) and demonstrates how to implement routing with React Router DOM.

What is SPAs ?

Single Page Applications (SPAs) in React use a single page to manage navigation without refreshing. React manipulates the DOM by injecting components dynamically, creating the illusion of multiple pages while maintaining a single page structure. This is crucial for SPAs to function smoothly

We have to install React Router in our react project in order to use them. Use following command on your project file terminal.

npm install react-router-dom

Now you will import the necessary components from react-router-dom in your application.

import ReactDOM from "react-dom/client";
import { BrowserRouter, Routes, Route } from "react-router-dom";
import Layout from "./pages/Layout";
import Home from "./pages/Home";
import Contact from "./pages/Contact";

export default function App() {
  return (
    <BrowserRouter>
      <Routes>
        <Route path="/" element={<Layout />}>
          <Route index element={<Home />} />
          <Route path="contact" element={<Contact />} />
        Route>
      Routes>
    BrowserRouter>
  );
}

const root = ReactDOM.createRoot(document.getElementById('root'));
root.render(<App />);

Layout.js

import { Outlet, Link } from "react-router-dom";

const Layout = () => {
  return (
    <>
      <nav>
        <ul>
          <li>
            <Link to="/">HomeLink>
          li>
          <li>
            <Link to="/contact">ContactLink>
          li>
        ul>
      nav>
      <Outlet />
    
  )
};

export default Layout;

Home.js

const Home = () => {
  return <h1>Homeh1>;
};

export default Home;

Contact.js

const About = () => {
  return <h1>Abouth1>;
};

export default About;

In v6.4, new routers were introduced that support the new data APIs:

createBrowserRouter
createMemoryRouter
createHashRouter
createStaticRouter

The following routers do not support the data APIs:

>:Uses the HTML5 history API for standard web apps.


<HashRouter>:Uses hash-based routing for static servers.

:Uses in-memory routing for testing and non-browser environments.

:Provides static routing for server-side rendering (SSR).


The above components will create browser, hash, memory and static history instances. React Router v6 makes the properties and methods of the history instance associated with your router available through the context in the router object.
createHashRouter is part of the React Router library and provides routing capabilities for single-page applications (SPAs). It's commonly used for building client-side navigation within applications.
Conclusion
React Router allows you to perform single-page routing without reloading the page. This enables faster user experiences because the browser doesn't need to request an entirely new document or re-evaluate CSS and JavaScript assets for the next page. It also enables more dynamic user experiences with things like animation.
Resources
https://reactrouter.com/en/main/route/route
https://www.w3schools.com/react/react_router.asp



How to Integrate React JS with a REST API: A Step-by-Step Guide
Piyush Nanwani — Sat, 03 May 2025 05:30:39 GMT
React is popular frontend JavaScript library for building web application. You need to integrate APIs in your react project for if you want to build production ready web application.
What is a REST API?
REST API stand for A Representational State Transfer Application Programming Interface. It is also known as RESTful API. It is an application programming interface that conforms to the constraints of REST architectural style and allows for interaction with RESTful web services.
When a client request is made via a RESTful API, it transfers a representation of the state of the resource to the requester or endpoint. This information, or representation, is delivered in one of several formats via HTTP: JSON (Javascript Object Notation), HTML, XLT, Python, PHP, or plain text. JSON is the most generally popular file format to use because, despite its name, it’s language-agnostic, as well as readable by both humans and machines.
What is an API?
API is a medium that allows different applications to communicate programmatically with one another and return a response in real time. It is a set of definitions and protocols for building and integrating application software.
In other words, if you want to interact with a computer or system to retrieve information or perform a function, an API helps you communicate what you want to that system so it can understand and fulfill the request.
How to consume REST API's in React.
We will use Fetch API which is browser in-built web API. You are going to use two popular react hook in your project.

useState is a React Hook that lets you add a state variable to your component. We Call useState at the top level of your component to declare a state variable. In below example, initialState is the value you want the state to be initially.

Let's understand what is state in react. State is components memory.
Example of useState hook:
const [state, setState] = useState(initialState)


useEffect : is a React Hook that lets you synchronize a component with an external system. It allows you to perform side effects in functional components. Side effects could be data fetching, subscription, or manually changing the DOM. We will use this hook to fetch API response. It accepts two arguments : a callback function and a dependencies. Second argument is optional. The list of dependencies must have a constant number of items and be written inline like [dep1, dep2, dep3]. React will compare each dependency with its previous value. If you omit this argument, your Effect will re-run after every re-render of the component. useEffect returns undefined.
  Here's an example of useEffect Hook:
  Copy
  Copy
    useEffect(() => {
        // data fetching here
    }, []);



We will fetch the user data from GitHub API. We will display the fetched user data including username, avatar, number of follower, and last but not the least total number of repositories. Here's an example of getting user details using APIs.
useEffect: This hook runs after the component mounts to fetch data.

async/await syntax is used for asynchronous operations.

fetch is used to make a GET request to the specified API endpoint.

.then() is returned a response object.

response.json() parses the JSON response. And to resolve the Response object to JSON format.

.then() used to get actual data.

Error handling using try...catch block.


Copy
Copy
useEffect(() => {
    const fetchData = async () => {
      try {
        const response = await fetch(
          "https://api.github.com/users/AnandAnkur404"
        );

        if (!response.ok) {
          throw new Error("Failed to fetch user data");
        }

        const results = await response.json();
        console.log(results);
        setAvatarURL(results.avatar_url);
        setGithubUserame(results.login);
        setNoOfFollower(results.followers);
        setNoOfRepo(results.public_repos);
      } catch (error) {
        console.log(error);
        setError(error.message);
      }
    };
    fetchData();
  }, []);

Now we will get all the repository data from API.
import "./App.css";
import Button from "react-bootstrap/Button";
import Card from "react-bootstrap/Card";
import { useEffect, useState } from "react";

function App() {
  const [avatarURL, setAvatarURL] = useState();
  const [githubUserame, setGithubUserame] = useState();
  const [noOfFollower, setNoOfFollower] = useState();
  const [repoData, setRepoData] = useState();
  const [noOfRepo, setNoOfRepo] = useState();
  const [error, setError] = useState(null);
async function repoDataURL() {
    try {
      // Getting all the repos of users.
      const response = await fetch(
        "https://api.github.com/users/AnandAnkur404/repos"
      );
      if (!response.ok) {
        throw new Error("Failed to fetch repo data");
      }
      const result = await response.json();
      const list = result.map((item) => (
        <div className="text-center" key={item.id}>
          <a target="_blank" href={item.svn_url}>
            {item.name}
          a>
        div>
      ));
      setRepoData(list);
    } catch (error) {
      console.log(error);
      setError(error.message);
    }
  }
}

Now we will consume data from this API and display.
return (
    <div className="App w-100 min-vh-100 justify-content-center align-items-center d-flex flex-column">
      <Card style={{ width: "18rem" }}>
        <Card.Img variant="top" src={avatarURL} />
        <Card.Body>
          <Card.Title>{githubUserame}Card.Title>
          <Card.Text>
            <h6>No Of Followers {noOfFollower}h6>
            <h6>No Of Public Repos {noOfRepo}h6>
          Card.Text>
          <Button variant="primary" onClick={repoDataURL}>
            List My Repo
          Button>
        Card.Body>
      Card>
      { repoData };
    div>
  );

Conclusion
Here you see how effectively we have integrated Rest API to our react application to build dynamic and data driven web application. In contrast, REST is a set of guidelines that can be implemented as needed, making REST APIs faster and more lightweight, with increased scalability. You can follow the above guideline and use your specific REST APIs to create your own project.
Resources
https://react.dev/reference/react
https://www.redhat.com/en/topics/api/what-is-a-rest-api#overview
https://www.freecodecamp.org/news/how-to-consume-rest-apis-in-react/



Static Site Generation (SSG) vs. Server-Side Rendering (SSR) in Next.js
Piyush Nanwani — Wed, 23 Apr 2025 04:33:29 GMT
What is Next js ?
Next.js is a React framework for building full-stack web applications.
Rendering is one of the features of Next.js that is very useful for developer. In this blog post, we'll explore the key differences between SSG and SSR, their advantages and when to choose one over the other.
Two main rendering strategy of Next.js.
Static Site Generation (SSG)
Static site generation is a technique in web development where it pre-renders pages at build time. That means in production, the page HTML generated when you run next build. SSG results in excellent performance because this HTML will then be reused on each request. This pre-render pages are ready to serve directly to user on each request. This approach is ideal for websites with content that doesn’t change frequently, like blogs or documentation sites.
In Next js, you can statically generates pages with or without data. Lets take a look on example below.
Static Site Generation without data
Using Static Generation, it re-render without fetching data. Here's an example.
function About() {
  return <div>Aboutdiv>
}
export default About

Note that this page does not need to fetch any external data to be pre-rendered. In cases like this, Next.js generates a single HTML file per page during build time.
Static generation with data
Next js provides two important function for fetching data during static generation.

getStaticProps: Fetches data for a specific page.

getStaticPaths: Defines dynamic routes for a page.



getStaticProps.

To fetch this data on pre-render, Next.js allows you to export an async function called getStaticProps from the same file. This function gets called at build time and lets you pass fetched data to the page's props on pre-render.
export default function Blog({ posts }) {
  // Render posts...
}

// This function gets called at build time
export async function getStaticProps() {
  // Call an external API endpoint to get posts
  const res = await fetch('https://.../posts')
  const posts = await res.json()

  // By returning { props: { posts } }, the Blog component
  // will receive `posts` as a prop at build time
  return {
    props: {
      posts,
    },
  }
}


getStaticPath

Next.js allows you to create pages with dynamic routes. Next.js lets you export an async function called getStaticPaths from a dynamic page . This function gets called at build time and lets you specify which paths you want to pre-render.
// This function gets called at build time
export async function getStaticPaths() {
  // Call an external API endpoint to get posts
  const res = await fetch('https://.../posts')
  const posts = await res.json()

  // Get the paths we want to pre-render based on posts
  const paths = posts.map((post) => ({
    params: { id: post.id },
  }))

  // We'll pre-render only these paths at build time.
  // { fallback: false } means other routes should 404.
  return { paths, fallback: false }
}

Advantages of SSG

Speed: Pre-rendered HTML files are served directly, resulting in faster load times.

Scalability: Static files can be easily served by Content Delivery Networks (CDNs), improving your application’s ability to handle more global traffic around the world.


You can use Static Generation for many types of pages, including:

Marketing pages

Blog posts and portfolios

E-commerce product listings

Help and documentation


When should you use Static Generation?
If you want to re-render page ahead of a user's request, and if your content don't change often, then you must choose Static Generation for better performance and scalability.
Server-side Rendering (SSR)
Server-Side Rendering involves generating the HTML for each page on the server when a user requests it. It is also referred as a "Dynamic Rendering". With SSR, there is a server that pre-renders the page – it’s like a template that you plug variables into and the server handles all of the rendering. This happens at request time, so when the user requests the page, they get the server side rendered page. This rendering happens all on the server side and never runs in the browser.
If your page needs to pre-render frequently updated data (fetched from an external API). You can write getServerSideProps which fetches this data and passes it to Page like below.
export default function Page({ data }) {
  // Render data...
}
// This gets called on every request
export async function getServerSideProps() {
  // Fetch data from external API
  const res = await fetch(`https://.../data`)
  const data = await res.json()

  // Pass data to the page via props
  return { props: { data } }
}

SSR is ideal for websites with dynamic or personalised content that changes frequently. You can use Static Generation for many types of pages, including

E-commerce websites.

Media platforms.


Advantages of SSR

Consistent user experience: Users see the latest content as it’s generated on-the-fly, ensuring they always have up-to-date information.

Personalisation: SSR allows you to serve unique content based on user preferences or other dynamic data.


When you should use Server-Side Rendering.
Consider whether your users need real-time data or personalised content. If so, SSR is the better option. SSR pages will always display the latest data because they are generated on the server with every request from the user. With every request, you can always be sure that you have the most recent/up-to-date information.
Conclusion
In this article, we have discussed the two rendering approaches in Next.js: SSG and SSR. I have also provided an example codebase that will help you determine the best rendering approach for your application.
Resources
https://nextjs.org/docs/pages/building-your-application/rendering



ChatGPT: Evolution , Technical features and App making
Sulabh Sharma — Wed, 02 Apr 2025 04:30:48 GMT
ChatGPT is developed by OpenAI. It is an AI chatbot that uses natural language processing to generate human-like conversational responses. It can answer questions, write essays, generate code and more. The "GPT" in ChatGPT stands for "Generative Pre-trained Transformer" indicating its architecture and training method. ChatGPT uses deep learning, a subset of machine learning to produce humanlike text through transformer neural networks. The transformer predicts text including the next word sentence or paragraph based on its training data's typical sequence. Training begins with generic data then moves to more tailored data for a specific task. ChatGPT was trained with online text to learn the human language and then it used transcripts to learn the basics of conversations.
Evolution of ChatGPT
GPT-1: The journey began with the first Generative Pre-trained Transformer (GPT) model introduced by OpenAI in 2018. GPT-1 was a breakthrough in Natural Language Processing (NLP) utilizing 117 million parameters to generate human-like text based on the context provided. It marked a significant step forward in machine learning demonstrating the potential of pre-trained transformers.
GPT-2: Launched in 2019, GPT-2 built upon the foundation laid by GPT-1 expanding the model to 1.5 billion parameters. This massive increase in scale enabled GPT-2 to generate more coherent and contextually relevant text making it a more powerful tool for a variety of NLP tasks. The revolution of GPT has begun.
GPT-3: Released in June 2020, GPT-3 was a monumental leap forward boasting 175 billion parameters. This vast increase in model size brought unprecedented capabilities in natural language understanding and generation.
GPT-3.5: Generative Pre-trained Transformer 3.5 (GPT-3.5) is a sub class of GPT-3 Models created by OpenAI in 2022. On March 15th 2022, OpenAI made available new versions of GPT-3 and Codex in its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002"
GPT-4: It is a multimodal large language model trained and created by OpenAI and the fourth in its series of GPT foundation models. It was launched on March 14th 2023.

https://www.researchgate.net/figure/Illustration-of-GPT-development-history-and-the-rise-of-ChatGPT-The-development-timeline_fig1_374092520
Basic Technical features of ChatGPT models
Model Architecture

Definition: The design of the AI model that determines how it processes and generates text.

Example: Think of it like the blueprint for building a robot. A "Transformer Decoder" is like a design that helps the robot predict and write sentences based on patterns it learned.



Parameters

Definition: The internal "settings" the model uses to make decisions and generate responses. More parameters mean better decision-making.

Example: A model with 175 billion parameters (like GPT-3) is like a chef with 175 billion recipes- it can cook almost anything you ask for.



Token Limit

Definition: The maximum amount of text the model can process in a single interaction. Tokens are chunks of words or letters.

Example: If you ask the model to summarize a 5-page document but its token limit is 2 pages it won’t be able to process the entire document.



Batch Processing

Definition: The ability to handle multiple tasks or inputs at the same time.

Example: Imagine a cashier processing 5 customers at once instead of one by one. A model with good batch processing can answer 5 different users simultaneously without delay.



Training Type

Definition: How the model is trained to understand and generate text.

Pre-trained: Trained on a large dataset before being released.

Reinforcement Learning from Human Feedback (RLHF): Further refined by humans to improve responses.



Example: Pre-trained is like a chef who learned cooking by reading books. RLHF is like a chef who improved their cooking by learning directly from customer feedback.



Few-Shot Learning

Definition: The model's ability to perform tasks after seeing only a few examples.

Example: If you show the model two examples of writing a haiku it can generate its own haikus without needing hundreds of examples.



Latency

Definition: The time it takes for the model to generate a response after receiving input.

Example: A low-latency model is like a fast typist who replies to your text immediately while a high-latency model is like a slow responder who takes minutes.



Multimodal Input

Definition: The ability to process both text and images as input.

Example: You can upload a photo of a math problem and the model (like GPT-4) can solve it by analyzing the image and generating a text response.



Context Retention

Definition: How well the model remembers previous parts of a conversation or input.

Example: If you’re chatting about your favorite movies a model with good context retention will remember your preferences and recommend relevant movies later in the chat.


Comparison of Key technical features of ChatGPT models


Pricing Mechanism of ChatGPT, https://openai.com/api/pricing/


Which Model Should You Choose Based on Your Budget?
1. If You're on a Tight Budget (MVP or Small-Scale App):

Choose GPT-3: It offers great performance for basic use cases like simple content generation, question-answering and basic chatbots. It’s cost-effective and can handle small-scale applications with ease.

2. If You Need Better Performance and More Complex Conversations:

Choose GPT-3.5: It provides improved performance over GPT-3 especially for applications that require ongoing conversations, handling complex customer support queries and larger datasets. It’s a sweet spot between price and performance.

3. If You Need Advanced Features, Multimodal Capabilities, or Large-Scale Enterprise Applications:

Choose GPT-4: While GPT-4 is the most expensive and it offers advanced capabilities such as multimodal processing (text and images) better reasoning and longer context retention. If your app needs complex problem-solving or works with a lot of data (like long documents, images or multilingual content) GPT-4 is worth the higher investment.


Final Thoughts:
When choosing a ChatGPT model for your app development the token limits, batch processing capabilities and the level of complexity your app requires will dictate the costs and performance you can expect.

GPT-3 is the best choice for basic apps with minimal budget and simple tasks.

GPT-3.5 offers a good balance between cost and advanced conversational abilities.

GPT-4 is for high-demand applications that require advanced capabilities but it comes with a higher price tag.


Always consider your app’s specific needs user interaction volume and expected workload when evaluating the most cost-effective model for your app.
Conclusion- The journey began with GPT-1 in 2018 introducing 117 million parameters and pioneering pre-trained transformers for text generation. GPT-2 launched in 2019 scaled up to 1.5 billion parameters improving coherence and contextual relevance. GPT-3 with 175 billion parameters set a new benchmark in 2020 enabling complex tasks like coding and detailed content generation. GPT-3.5 (2022) enhanced conversational abilities and task-specific fine-tuning while GPT-4 (2023) added multimodal capabilities processing both text and images and extended context retention for advanced applications.
ChatGPT models cater to diverse needs:

GPT-3: Ideal for basic tasks and budget-friendly solutions.

GPT-3.5: Balances cost and performance for conversational and customer support apps.

GPT-4: Best for enterprise-level, multimodal tasks and applications requiring advanced reasoning.


Selecting a model depends on your budget complexity and performance needs making ChatGPT a versatile tool for various domains from chatbots to enterprise-level AI applications.
References
https://ttms.com/my/evolution-of-ai-from-gpt-1-to-gpt-4o-key-features-milestones-and-applications/#:~:text=This%20evolution%20of%20GPT%20chatbots,the%20sophisticated%20GPT-4o%20model.
https://platform.openai.com/docs/models
https://platform.openai.com/docs/models



Google Gemini: Evolution, Technical features and app development
Sulabh Sharma — Tue, 18 Mar 2025 03:30:49 GMT
Gemini, formerly known as Bard is a Generative Artificial Intelligence chatbot developed by Google. Based on the Large Language Model (LLM) developed by google Deep Mind, created to compete with OpenAI’s ChatGPT. It was launched in 2023 after being developed as a direct response to the rise of OpenAI's ChatGPT. Gemini AI can process and understand various data types (text, images, audio, code) also present ideas innovatively. Its reasoning capabilities are exceptional. Gemini AI can solve complex problems efficiently and generate high-quality code.
Evolution of Google Gemini
Gemini- Google introduced Gemini As a powerful Successor to PaLM 2 on May 10th 2023. Gemini was developed as a collaboration between DeepMind and Google Brain designed to be multimodal capable of processing text, images, video and computer code simultaneously.
Gemini 1.0- Gemini 1.0 was announced on Dec. 6, 2023, and built by Alphabet's Google DeepMind business unit which is focused on Advanced AI research and development. It comprises three models-Gemini Ultra- designed for highly complex tasks, fully released on February 2024. Gemini Pro- suited for a wide range of tasks. Gemini Nano- Optimized for on-device tasks. Gemini Pro and Nano were integrated into Bard and the Pixel 8 Pro smartphone, respectively.
Gemini 1.5- launched in February 2024 and In May 2024, Google announced enhancements to Gemini 1.5 Pro at the google I/O conference. Upgrades included performance improvements in translation, coding and reasoning features. The upgraded Google 1.5 Pro also improved image and video understanding, including the ability to directly process voice inputs using native audio understanding. The model's context window was increased to 2 million tokens enabling it to remember much more information when responding to prompts. Google announced new features to the Gemini API in May, including the following: https://www.techtarget.com/searchenterpriseai/definition/Google-Gemini

Video frame extraction which lets users upload a video to generate content.

Parallel function calling, which lets users engage in more than one function call at a time.


In June 2024, Google added context caching to ensure users only have to send parts of a prompt to a model once.
Gemini 2.0- Google introduced Gemini 2.0 Flash on December 11, 2024. In an experimental preview through Vertex AI Gemini API and AI Studio. Gemini 2.0 Flash is twice the speed of 1.5 Pro and has new capabilities, such as multimodal input and output, and long context understanding. Other new features include text-to-speech capabilities for image editing and art. The new API has audio streaming applications to assist with native tool use and improved latency.
For more in depth learning of Gemini evolution you can refer to this link- https://timelines.issarice.com/wiki/Timeline_of_Google_Gemini
Basic Technical features of Google Gemini models
Model Architecture

Definition: The design of the AI model that determines how it processes and generates text.

Example: Think of it like the blueprint for building a robot. A "Transformer Decoder" is like a design that helps the robot predict and write sentences based on patterns it learned.



Parameters

Definition: The internal "settings" the model uses to make decisions and generate responses. More parameters mean better decision-making.

Example: A model with 175 billion parameters (like GPT-3) is like a chef with 175 billion recipes- it can cook almost anything you ask for.



Token Limit

Definition: The maximum amount of text the model can process in a single interaction. Tokens are chunks of words or letters.

Example: If you ask the model to summarize a 5-page document but its token limit is 2 pages it won’t be able to process the entire document.



Batch Processing

Definition: The ability to handle multiple tasks or inputs at the same time.

Example: Imagine a cashier processing 5 customers at once instead of one by one. A model with good batch processing can answer 5 different users simultaneously without delay.



Training Type

Definition: How the model is trained to understand and generate text.

Pre-trained: Trained on a large dataset before being released.

Reinforcement Learning from Human Feedback (RLHF): Further refined by humans to improve responses.



Example: Pre-trained is like a chef who learned cooking by reading books. RLHF is like a chef who improved their cooking by learning directly from customer feedback.



Few-Shot Learning

Definition: The model's ability to perform tasks after seeing only a few examples.

Example: If you show the model two examples of writing a haiku it can generate its own haikus without needing hundreds of examples.



Latency

Definition: The time it takes for the model to generate a response after receiving input.

Example: A low-latency model is like a fast typist who replies to your text immediately while a high-latency model is like a slow responder who takes minutes.



Multimodal Input

Definition: The ability to process both text and images as input.

Example: You can upload a photo of a math problem and the model (like GPT-4) can solve it by analyzing the image and generating a text response.



Context Retention

Definition: How well the model remembers previous parts of a conversation or input.

Example: If you’re chatting about your favorite movies a model with good context retention will remember your preferences and recommend relevant movies later in the chat.


Comparison of Key technical features of Google Gemini models


Pricing Mechanism of Gemini models

Gemini Model Suitability for App Development

Gemini 1.0

Best for: Basic multimodal capabilities, general app development involving text, image, audio or video input handling.

Why: Offers foundational multimodal input capabilities making it useful for applications requiring interactions like chatbots, image processing or content generation.



Gemini 1.5 Pro

Best for: Advanced apps involving reasoning, large-scale data processing, and complex problem-solving (e.g.- code generation tools, advanced AI assistants).

Why: Features enhanced reasoning capabilities, a higher token limit (up to 2 million) and improved efficiency, making it ideal for apps with substantial data and reasoning demands.



Gemini 1.5 Flash

Best for: Real-time apps requiring fast responses (e.g., live chat systems, gaming assistants or real-time recommendation engines).

Why: Optimized for speed and efficiency, with a focus on latency reduction. Its ability to handle up to 1 million tokens also supports moderately complex tasks without high latency.



Gemini 2.0 Flash Experimental

Best for: Cutting-edge apps with real-time multimodal interactions, such as AI-powered virtual assistants, augmented reality (AR) applications, or apps generating native images or audio.

Why: Focuses on real-time multimodal processing and native content generation making it ideal for innovative and interactive applications.




Conclusion
The evolution of Google Gemini models from Gemini 1.0 to Gemini 2.0 Flash Experimental, showcases groundbreaking advancements in generative AI having technical features like model architecture, token limit, content retention, parameters. Gemini can help in app development, depends on the complexity and purpose of the application models can be selected Gemini 1.0 for basic, for advanced reasoning and scalability Gemini 1.5 Pro excels. Real-time interactive apps benefit most from Gemini 2.0 Flash Experimental. With its multimodal prowess and adaptability Gemini redefines AI-driven app development making it a versatile tool for developers aiming to deliver superior user experiences.
References
https://timelines.issarice.com/wiki/Timeline_of_Google_Gemini
https://firebase.google.com/docs/vertex-ai/gemini-
https://firebase.google.com/docs/vertex-ai/gemini-models



Discover technology behind your Smartphones: AI/ML
Sulabh Sharma — Wed, 05 Mar 2025 01:30:10 GMT
Machine learning is a type of artificial intelligence that performs data analysis tasks without explicit instructions. Machine learning technology can process large quantities of historical data, identify patterns and predict new relationships between previously unknown data. Running machine learning (ML) models directly on mobile devices has become increasingly feasible, enabling applications to function offline, reduce latency, and enhance user privacy. Frameworks like TensorFlow Lite, advancements in Large Language Models (LLMs) and tools from OpenAI have been pivotal in this evolution. Running ML on Mobile Phones - TensorFlow/LLM/Gemini/OpenAI" refers to the application and integration of machine learning (ML) technologies directly on mobile devices using specific tools and frameworks
Why Bring AI/ML to Mobile Phones?
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into mobile phones is revolutionizing the way we interact with technology. Here’s why bringing AI/ML to mobile devices is essential:

1. Enhanced User Experience
AI/ML powers smarter apps, such as voice assistants, predictive text and camera enhancements like object recognition and scene optimization, offering personalized and intuitive experiences.

2. Offline Functionality
On-device AI ensures access to features like translations and voice commands without internet connectivity. This is particularly valuable for remote areas or privacy-focused applications like health tracking.

3. Reduced Latency
With computations performed on the device, response times are faster, making applications like real-time gaming and navigation seamless and efficient.

4. Improved Privacy
Processing data locally minimizes the need for cloud servers, ensuring sensitive information stays secure. For example AI-powered facial recognition systems work directly on the phone.

5. Cost and Energy Efficiency
Mobile AI reduces reliance on cloud computing, lowering data transfer costs and energy usage. Frameworks like TensorFlow Lite optimize these computations for low-power devices.

6. Democratization of Technology
AI/ML on phones makes advanced tools like learning apps and translation accessible to a wider audience, empowering users across different regions and demographics
Technologies Powering Mobile AI/ML
1. tensorFlow Lite
tensorFlow Lite (TF Lite) is an open-source, cross-platform deep learning framework launched by Google for on-device inference, which is designed to provide support for multiple platforms, including Android and iOS devices, embedded Linux and microcontrollers. It can convert tensorFlow pre-trained models into special formats that can be optimized for speed or storage. It also helps developers run tensorFlow models on mobile, embedded and IoT devices.
How does TensorFlow Lite work?
1. Select and train a model
Suppose you want to perform an image classification task. The first is to determine the model for the task. You can choose:
–Use pre-trained models like Inception V3, MobileNet, etc.
You can check out the https://www.tensorflow.org/lite/examples here. Explore pre-trained TensorFlow Lite models and learn how to use them for various ML applications in the sample application.
–Create your custom models 
Create models with custom datasets using the https://www.tensorflow.org/lite/guide/model_maker
–Apply transfer learning to pre-trained models
2. Transform models using Converter
After the model training is complete, you need to use the TensorFlow Lite Converter to convert the model to a TensorFlow Lite model. The TensorFlow Lite model is a lightweight version that is very efficient in terms of accuracy and has a smaller footprint. These properties make TF Lite models ideal for working on mobile and embedded devices.

Schematic diagram of TensorFlow Lite conversion process
Source: https://www.tensorflow.org/lite/convert/index
Regarding the selection of pre-trained models section, you may want to get more information about this. TensorFlow Lite already supports many trained and optimized models:

MobileNet: A class of vision models capable of recognizing 1000 different object classes, specially designed for efficient execution on mobile and embedded devices.

Inception v3: Image recognition model, similar in functionality to MobileNet, offering higher accuracy but larger.

Smart Reply: An on-device conversational model that enables one-click replies to incoming conversational chat messages. First- and third-party messaging apps use this feature on Android Wear.


Inception v3 and MobileNet have been trained on the ImageNet dataset. You can retrain on your own image dataset through transfer learning.
You may refer to the information on http://ai.googleblog.com/2017/11/on-device-conversational-modeling-with.html
Get started with LiteRT
This guide introduces you to the process of running a LiteRT (short for Lite Runtime) model on-device to make predictions based on input data. This is achieved with the LiteRT interpreter, which uses a static graph ordering and a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency.
LiteRT inference typically follows the following steps:

Loading a model: load the .tflite model into memory, which contains the model's execution graph.

Transforming data: Transform input data into the expected format and dimensions. Raw input data for the model generally does not match the input data format expected by the model. For example, you might need to resize an image or change the image format to be compatible with the model.

Running inference: Execute the LiteRT model to make predictions. This step involves using the LiteRT API to execute the model. It involves a few steps such as building the interpreter, and allocating tensors.

Interpreting output: Interpret the output tensors in a meaningful way that's useful in your application. For example, a model might return only a list of probabilities. It's up to you to map the probabilities to relevant categories and format the output.


This guide describes how to access the LiteRT interpreter and perform an inference using C++, Java, and Python.
2. Large Language Models(LLM)
Large Language Models (LLMs) are changing smartphone technology, subtly reshaping everything from the device’s core architecture to user interaction. As generative AI integrates deeper into mobile devices, we’re witnessing transformative changes in various aspects of our mobile devices.
How do large language models work?
Machine learning and deep learning
At a basic level, LLMs are built on machine learning. Machine learning is a subset of AI, and it refers to the practice of feeding a program large amounts of data in order to train the program how to identify features of that data without human intervention.
LLMs use a type of machine learning called deep learning. Deep learning models can essentially train themselves to recognize distinctions without human intervention, although some human fine-tuning is typically necessary.
Deep learning uses probability in order to "learn." For instance, in the sentence "The quick brown fox jumped over the lazy dog," the letters "e" and "o" are the most common, appearing four times each. From this, a deep learning model could conclude (correctly) that these characters are among the most likely to appear in English-language text.
Realistically, a deep learning model cannot actually conclude anything from a single sentence. But after analyzing trillions of sentences, it could learn enough to predict how to logically finish an incomplete sentence, or even generate its own sentences.
LLM neural networks
In order to enable this type of deep learning, LLMs are built on neural networks. Just as the human brain is constructed of neurons that connect and send signals to each other, an artificial neural network (typically shortened to "neural network") is constructed of network nodes that connect with each other. They are composed of several "layers”: an input layer, an output layer, and one or more layers in between. The layers only pass information to each other if their own outputs cross a certain threshold.
LLM transformer models
The specific kind of neural networks used for LLMs are called transformer models. Transformer models are able to learn context — especially important for human language, which is highly context-dependent. Transformer models use a mathematical technique called self-attention to detect subtle ways that elements in a sequence relate to each other. This makes them better at understanding context than other types of machine learning. It enables them to understand, for instance, how the end of a sentence connects to the beginning, and how the sentences in a paragraph relate to each other.
This enables LLMs to interpret human language, even when that language is vague or poorly defined, arranged in combinations they have not encountered before, or contextualized in new ways. On some level they "understand" semantics in that they can associate words and concepts by their meaning, having seen them grouped together in that way millions or billions of times
3. Gemini
Gemini, formerly known as Bard, is a generative artificial intelligence chatbot developed by Google. Based on the large language model (LLM) of the same name, it was launched in 2023 after being developed as a direct response to the rise of OpenAI's ChatGPT. It was previously based on PaLM, and initially the LaMDA family of large language models.
How does Gemini work?
1. Set Objectives
The process of integrating Google Gemini Pro begins by understanding your business needs and setting clear goals. Whether you aim to improve user engagement, streamline workflows, automate tasks, or strengthen security, having a solid grasp of your objectives is essential for a successful integration.
2. Utilize Gemini Pro APIs and SDKs
To seamlessly integrate Gemini AI, make use of its APIs and SDKs. This entails incorporating the required code and interfaces to help communication between the mobile app and Gemini Pro’s AI features. Your Google Gemini Pro developer can also utilize tools like Google AI Studio and Google Cloud Vertex Gemini AI to connect Gemini models into your applications.

3. Mapping Data and Configuration
Next, you need to map how data moves and set up Gemini Pro to handle and understand the relevant data sources. This is essential for the AI model to generate useful insights and provide smart responses tailored to the mobile app’s specific context.
4. Testing and Quality Assurance
Thoroughly test the integrated solution to pinpoint and address any possible issues, errors, or bugs. This stage includes testing for technical issues, app performance, security measures, and user experience, guaranteeing that Gemini Pro operates smoothly within the mobile app.
5. Deployment and Monitoring
Once testing is complete, deploy the integrated mobile app. After deployment, closely monitor its performance in real-world situations and use analytics to understand user interactions. This helps in the continuous improvement and optimization of the app.
6. Continuous Improvement and Updates
The final step is to establish a system for ongoing improvement by keeping up-to-date with Google Gemini Pro updates. Businesses should regularly review user feedback and industry trends to make incremental updates, keeping the integrated solution innovative and effective
4. Open AI
OpenAI is a research company that specializes in artificial intelligence (AI) and machine learning (ML) technologies. Its goal is to develop safe AI systems that can benefit humanity as a whole. OpenAI offers a range of AI and ML tools that can be integrated into mobile app development, making it easier for developers to create intelligent and responsive apps.
How Does Open AI works?
OpenAI provides developers with a range of tools and APIs that can be used to incorporate AI and ML into their mobile apps. These tools include natural language processing (NLP), image recognition, predictive analytics and more.
OpenAI’s NLP tools can help improve the user experience by providing personalized recommendations, chatbot functionality, and natural language search capabilities. Image recognition tools can be used to identify objects, people, and places within images, enabling developers to create apps that can recognize and respond to visual cues. 
OpenAI’s predictive analytics tools can analyze data to provide insights that can be used to enhance user engagement. For example, predictive analytics can be used to identify which users are most likely to churn and to provide targeted offers or promotions to those users.
OpenAI’s machine learning algorithms can also automate certain tasks, such as image or voice recognition, allowing developers to focus on other aspects of the app. 

Future of Artificial Intelligence in Mobile Phone
Artificial Intelligence (AI) is poised to revolutionize the mobile phone experience in the coming years. As AI continues to evolve, it will make your smartphone smarter, more intuitive, and increasingly personalized. The key revolution you can expect in the context of the future of artificial intelligence for mobile phone technology:
1.Advancements in AI Chipsets
One of the key drivers of the future of artificial intelligence in mobile phones is the rapid advancements in AI-focused chipsets. Major chip manufacturers like Qualcomm, MediaTek, and Samsung are developing dedicated AI processors that will power the next generation of AI-capable smartphones. These chipsets will enable more AI processing to be done directly on the device, leading to faster response times and enhanced privacy.
2.Generative AI and Multimodal Capabilities
Generative AI, which can create new content like images, text, and audio, is expected to have a significant impact on mobile phones. By 2027, over 1 billion https://www.counterpointresearch.com/insights/genai-capable-smartphone-shipments-to-grow-over-4x-by-2027/ will be shipped that will enable features like AI-powered photo editing, virtual assistants with more natural conversations, and enhanced accessibility for users with disabilities.
3.Personalization and Predictive Features
The future of artificial intelligence (AI) will continue to enhance personalization in mobile phones, with algorithms that learn user preferences and adapt the device's functionality accordingly. This will include features like predictive text, smart content curation, and intelligent task automation. As AI becomes more advanced, it will be able to anticipate user needs and proactively offer suggestions and assistance, making the mobile experience more seamless and efficient.
Conclusion
The integration of AI and ML into mobile devices is revolutionizing technology, enhancing user experiences, reducing latency, and ensuring privacy. Frameworks like TensorFlow Lite, LLMs, Google’s Gemini and OpenAI APIs empower mobile apps with smarter features, real-time processing, and personalized interactions. These advancements make AI more accessible and drive innovation across industries. As AI evolves, mobile development will continue to deliver intelligent, responsive, and transformative solutions for the digital age.
References
https://beonperf.ch/fr-CH/blog-de-beonperf/large-language-model-llm
https://glance.com/us/articles/future-of-artificial-intelligence
https://datasciencedojo.com/blog/openai-and-mobile-app-development/



Step-by-Step Guide to Publishing Apps on Google Play Store
Kashish Nanwani — Tue, 11 Feb 2025 03:30:40 GMT

Publishing your app on the Google Play Store can be a thrilling milestone in your app development journey. It not only allows you to share your creation with millions of Android users but also opens up opportunities for feedback, improvements, and potential earnings. If you're new to the process, don't worry – this guide will walk you through the steps of publishing your app on the Play Store.
Step 1: Create a Google Developer Account
Before you can publish an app, you need to create a Google Developer Account. This account will be used to manage your apps and is required to submit apps to the Play Store.

Go to the Google Play Console.

Sign in with your Google account or create a new one if you don't already have one.

Pay the $25 registration fee (one-time payment).

Once your account is created, you'll have access to the Google Play Console, where you'll manage your apps.
 


Step 2: Prepare Your App for Release
Before uploading your app, make sure it's ready for the public. Here are some critical steps to follow:

Build a Release Version: Ensure you’re building a release version of your app. You don’t want to publish a development build.

Sign Your APK: Your app needs to be signed before submission. Use your private key to sign the APK (Android Package).

Test Thoroughly: Test your app on different devices and screen sizes. Address any bugs, crashes, or performance issues.

Create an App Icon: Prepare a 512x512 pixel icon that will represent your app on the Play Store.


Step 3: Create the App Listing
Your app's listing is what users will see when they search for your app in the Play Store. Here’s what you need to include:

App Name: Choose a unique name that reflects your app's purpose and is easy to remember.

App Description: Write a compelling description of what your app does and why users should download it. Include relevant keywords but avoid spamming.

Screenshots: Provide at least two screenshots of your app’s user interface. These will help users understand the app before they download it.

Category: Select the appropriate category for your app (e.g., Games, Education, Business, etc.).

Content Rating: Choose the appropriate rating for your app based on its content. Google uses this to restrict the app from being shown to inappropriate audiences.

Privacy Policy: If your app collects user data, you’ll need to provide a link to your privacy policy. This is required by Google to comply with data protection regulations.


Step 4: Upload the APK to the Play Console
Once your app and listing are ready, it’s time to upload your APK file to the Play Console:

Go to the Google Play Console.

Click on Create Application.

Choose the language and enter the app title.

Under the Release Management section, select App Releases.

Click on Create Release.

Upload your signed APK file and provide any necessary release notes.

Click Save.


Step 5: Set Pricing and Distribution
Decide whether your app will be free or paid, and select the countries where you want it to be available. You can also enable or disable certain distribution channels, such as Android TV or Wear OS.
Step 6: Submit Your App for Review
Once you’ve completed the necessary steps, it’s time to submit your app for review. Google will review your app to ensure it complies with their Developer Program Policies.
The review process usually takes a few days, but it can take longer if there are any issues or if your app violates any policies.
Step 7: App Goes Live
Once your app is approved, it will be published on the Play Store, and users will be able to download it. You can now monitor its performance, gather user feedback, and update it as needed.
Tips for Success on the Play Store

Keep your app updated: Regular updates with bug fixes, new features, and optimizations will help keep your app relevant and user-friendly.

Use App Analytics: Leverage the Play Console’s analytics to track your app’s performance and user engagement.

Respond to Reviews: Engage with your users by responding to their reviews, whether positive or negative. This can help improve your app’s reputation.


Conclusion
Publishing your app on the Play Store is an exciting process that opens up new opportunities for growth and recognition. By following the steps outlined in this guide, you’ll be on your way to sharing your app with the world in no time.

References-
https://appinventiv.com/blog/how-to-submit-app-to-google-play-store/
https://blog.felgo.com/mobile-development-tips/how-to-publish-an-app-on-google-play
https://www.techmagnate.com/blog/how-to-publish-an-app-on-the-google-play-store/



Masterclass #2: Understanding Web Fundamentals
Sulabh Sharma — Tue, 04 Feb 2025 04:30:24 GMT
In today’s digital world websites are the backbone of online presence and understanding web fundamentals is essential for anyone looking to create, design or optimize a website. Whether you’re a beginner or an aspiring developer learning HTML, CSS and JavaScript will empower you to build engaging and responsive web pages. Masterclass session was conducted on 1st February 2025 at our Noida office about Web fundamentals and this blog contains the key learnings of session.
Topics covered in Masterclass
1. What is the Internet?
The internet is a vast network that connects the whole world, allowing us to access information, communicate and share data instantly. Every website, app and online service relies on this global network of computers to function.

2. How Does the Internet Work?
When you type www.google.com into your browser, several steps happen behind the scenes:
🔹 Web Browser – Your Digital Navigator: The browser (Chrome, Firefox, Edge) helps you access websites by interpreting web code.
🔹 URL – The Internet’s Home Address: A URL (Uniform Resource Locator) like www.google.com tells your browser where to find a website.
🔹 DNS – The Internet’s Phonebook: The Domain Name System (DNS) translates the website name (e.g. google.com) into an IP address.
🔹 Request – Knocking on Google’s Door: The browser sends a request to Google’s servers asking for the webpage.
🔹 Storage – The Internet’s Library: Web servers store databases where websites and data are saved.
🔹 Response – Getting the Webpage: Once the request is processed, Google’s web server sends back the requested page.
This entire process happens in milliseconds!

3. Webpages and Websites
A webpage is a single document on the internet while website is a collection of linked webpages.
🔹 Hyperlinks – The Web’s Connectors: Hyperlinks help navigate between different webpages.
🔹 Static vs. Dynamic Websites:

Static Websites: Fixed content (e.g., basic blogs, portfolio sites).

Dynamic Websites: Interactive, database-driven content (e.g., Facebook, YouTube).



4. HTTP vs HTTPS
🔹 HTTP (Hypertext Transfer Protocol): A communication protocol that allows data transfer between web browsers and servers.
🔹 HTTPS (Secure HTTP): Uses encryption (SSL/TLS) to protect sensitive information, ensuring data security. Eg. banking system
Websites with HTTPS are safer preventing cyber threats like data interception.

5. Frontend vs Backend Development
🔹 Frontend (Client-Side): Everything users see and interact with on a webpage (HTML, CSS, JavaScript).
🔹 Backend (Server-Side): Manages data processing, databases, and user authentication (PHP, Node.js, Python).
A well-functioning website requires both frontend and backend development.

6. HTML: The Structure of the Web
HTML (Hypertext Markup Language) is the foundation of every webpage. It defines the structure of content using tags.
Students wrote live HTML code, creating a simple webpage with headings, paragraphs, links and images. To make your HTML skills more better you can refer to this site- https://www.w3schools.com/html/default.asp

7. CSS: Styling the Web
CSS (Cascading Style Sheets) is used to style webpages making them visually appealing.
🔹 It controls colors, fonts, layouts and responsiveness.
🔹 A basic overview was presented, highlighting how CSS makes websites attractive.

8. JavaScript
JavaScript is the programming language of the web making websites interactive. The mentor conducted a live coding session covering:
🔹 Data Types (Numbers, Strings, Booleans, Arrays, Objects)
🔹 Variables (var, let, const)
🔹 Loops (for, while)
🔹 Conditional Statements (if-else)
🔹 Functions (Reusable blocks of code)
To make your HTML skills more better you can refer to this site- https://javascript.info/ & https://www.programiz.com/javascript/online-compiler/

9. Student Q&A Session
After the session, students asked questions and clarified their doubts on:

Frontend vs Backend development

Best practices for writing clean code

How to start a web development project



10. Class Summary & Next Steps 🚀
Today's masterclass provided a strong foundation in web fundamentals. Students now understand:
✔️ How the internet works
✔️ The difference between webpages & websites
✔️ The role of HTTP & HTTPS
✔️ The importance of frontend & backend development
✔️ Basic coding in HTML, CSS and JavaScript
The next step? Practice! Start coding simple web projects and explore more about web development.

Conclusion
Today’s session was just the beginning! Web development is a vast and exciting field and consistent practice is key. Keep experimenting, building and learning.
Keep exploring and learning. Happy coding.
References
https://www.programiz.com/javascript/online-compiler/
https://javascript.info/
https://www.w3schools.com/html/default.asp



AI in Your Pocket: How Smartphones are Getting Smarter
Kashish Nanwani — Mon, 03 Feb 2025 05:39:59 GMT
Introduction
Imagine a world where your smartphone not only understands your needs but predicts them—this is the reality Artificial Intelligence (AI) has brought to mobile technology. Over the years, AI has evolved from being a futuristic concept to a tangible force in our daily lives. A key milestone in this journey was the launch of Siri in 2011, which made AI assistants widely accessible and transformed the way users interacted with their phones.
The journey of AI in mobile phones is nothing short of transformative. What started as simple devices for communication has evolved into sophisticated, intelligent companions. Early mobile phones offered only basic functionalities, but with the integration of AI, these devices now serve as dynamic tools capable of adapting to and enhancing our daily lives.
Today, AI is at the core of smartphone innovation. Features like facial recognition, augmented reality, and adaptive performance optimization have turned mobile phones into indispensable tools. These advancements not only improve usability but also offer groundbreaking applications in photography, security, and personalized experiences.
AI Trends in Mobile Phones
The integration of Artificial Intelligence (AI) into mobile phones has redefined how we interact with our devices. From enhancing photography to bolstering security, AI drives innovation in mobile technology. Let’s explore the key AI trends shaping the future of smartphones:
1. AI-Powered Photography
Modern smartphones leverage AI to redefine mobile photography, allowing users to capture professional-quality images effortlessly.
Image Recognition
AI algorithms enable smartphones to detect scenes, objects, and faces in real-time. This recognition allows the camera to automatically adjust settings such as exposure, focus, and color balance to suit the identified subject, resulting in optimized photos without manual intervention.

Low-Light Photography
In challenging lighting conditions, AI-driven computational photography techniques come into play. By capturing multiple exposures and intelligently merging them, AI reduces noise and enhances details, producing clearer and brighter images in low-light environments.

Real-Time Filters and Effects
AI-powered features offer real-time beautification and augmented reality (AR) effects. These include skin smoothing, virtual makeup, and AR stickers that interact seamlessly with facial movements, providing users with creative tools to enhance their photos and videos instantly.

Video Stabilization
AI contributes to video stabilization by analyzing motion patterns and compensating for unintended movements. This results in smoother, more professional-looking videos, even when recorded handheld or in dynamic situations.

Timeline of AI Milestones in Mobile Technology
Here's a chronological overview of pivotal moments where AI has significantly influenced mobile technology:

2011: Introduction of Siri

Apple launched Siri, the first virtual assistant integrated into a smartphone, enabling voice-activated commands and setting the stage for AI-driven personal assistants.


2014: Google Now

Google introduced Google Now, offering predictive information and voice search capabilities, enhancing user experience through AI.


2016: Samsung's S Voice

Samsung launched S Voice, its AI-powered virtual assistant, marking the company's initial foray into AI integration in smartphones.


2017: Introduction of AI-Specific Hardware

Huawei's Kirin 970 became the first mobile processor with a dedicated Neural Processing Unit (NPU), optimizing AI tasks on smartphones.


2018: AI-Powered Photography

Google's Pixel 3 utilized AI for features like Night Sight, enhancing low-light photography without the need for a flash.


2019: On-Device AI Processing

Apple's A13 Bionic chip featured an advanced Neural Engine, enabling real-time AI processing for tasks like facial recognition and augmented reality.


2020: Samsung's Galaxy AI

Samsung introduced its Galaxy AI suite, enhancing user experience with features like real-time language translation and intelligent photo editing.


2022: AI in Foldable Devices

Samsung's Galaxy Z Fold series incorporated AI to optimize app continuity and multitasking across different screen modes.


2024: Generative AI Features

Samsung's Galaxy S24 Ultra debuted with generative AI capabilities, including real-time voice translation and AI-driven photo editing.


2025: AI-Enhanced User Interfaces

Apple's iPhone 16 series introduced "Apple Intelligence," integrating AI for personalized user experiences and advanced privacy features.



User-Centric Benefits
AI trends aren’t just about cutting-edge technology—they’re about enhancing everyday life for users. From accessibility to personalization, here’s how AI makes smartphones indispensable:

Accessibility for All: AI tools like Apple’s Voiceover provide screen narration for visually impaired users. Advanced systems even describe images aloud, making digital content more inclusive.

Enhanced Productivity: Netflix’s AI-driven recommendation engine, which boasts up to 80% accuracy, demonstrates how predictive technology improves user experience. Similar capabilities are integrated into smartphones for app and content recommendations.

Health and Wellness: Apps like Samsung Health use AI to analyze user data and provide insights on sleep patterns, activity levels, and even stress management tips.


The Future of Artificial Intelligence in Mobile Phones
Artificial Intelligence (AI) is set to transform the mobile phone experience in unprecedented ways. As AI technology advances, smartphones will become smarter, more intuitive, and highly personalized. Here are the key innovations shaping the future of AI in mobile phone technology:


Advancements in AI-Powered Chipsets



    The rapid evolution of AI-centric chipsets is a major driving force behind the integration of AI in mobile phones. Leading manufacturers such as Qualcomm, MediaTek, and Samsung are creating specialized AI processors to power the next generation of smartphones. These advanced chipsets will allow AI computations to occur directly on the device, resulting in quicker response times and improved user privacy.

Generative AI and Multimodal Functionality

    Generative AI, capable of producing new content like images, text, and audio, is set to revolutionize mobile capabilities. By 2027, over a billion smartphones equipped with generative AI are expected to be shipped, enabling groundbreaking features such as AI-driven photo editing, virtual assistants with more natural, human-like interactions, and enhanced tools for accessibility, especially for users with disabilities.

Enhanced Personalization and Predictive Features

    AI will further refine personalization in mobile phones, with advanced algorithms that learn user behavior and preferences to adapt device functionalities accordingly. This includes smarter predictive text, curated content recommendations, and intelligent task automation. As AI grows more sophisticated, it will anticipate user needs and provide proactive suggestions and assistance, creating a seamless and highly efficient mobile experience.

Ethical Considerations
As AI advances, ethical questions around data privacy and security have become more prominent. For example, facial recognition technology has sparked debates about user consent and data protection, leading to regulations like GDPR that aim to safeguard personal information.

AI has transformed smartphones from simple communication tools into personalized, intelligent companions. As these technologies continue to evolve, the possibilities are endless—from smarter photography and assistants to sustainable energy practices and beyond.
References-
https://www.prismetric.com/ai-in-mobile-phones/
https://www.solulab.com/ai-in-mobile-phones/
https://glance.com/us/articles/future-of-artificial-intelligence



Masterclass #1: Introduction to Mobile App Development and Problem-Solving Basics
Sulabh Sharma — Tue, 21 Jan 2025 10:16:04 GMT
Staring the journey of mobile app development can feel overwhelming but with the right guidance it becomes a pathway to innovation and creativity. Welcome to Masterclass on Mobile App Development where we’ll take you through with some Basics of Problem Solving, Mobile App Development, Career Opportunities types of Mobile Apps programming languages basics, industry insights and different apps Native and Hybrid etc. This offline session was conducted on 18th January 2025 at our Noida office, and this blog documents the key learnings for everyone.
Why mobile app development ?
The mobile app industry is booming with millions of apps available across platforms like iOS and Android. Whether you’re an aspiring developer a tech enthusiast or a business owner looking to create an app for your brand, we will help you learning basics of programming and how mobile apps are built in this masterclass.
When we asked students why they want to learn app development? Read their inspiring answers below

Many students shared that they find it fascinating to understand how apps are developed and brought to life.

A Chartered Accountant in the class expressed his desire to automate processes for people inspired by his extensive work with Excel.

One student was keen on learning mobile app development as a creative endeavor want to make exciting games.

Another participant wanted to create apps that bring ease and comfort to people’s lives.

A professional student previously working in a manufacturing company, revealed his interest stemmed from his experience with large machines being commanded through code. He was eager to learn how such technologies work at a deeper level.


Topics covered in masterclass
Our recent Masterclass aimed to demystify this exciting domain by starting with the basics:
1. Problem Solving: The Core Skill

We opened with a session on the basics of problem-solving, emphasizing that every app starts with a problem waiting to be solved. Students were encouraged to think critically, break down problems, and brainstorm solutions that could be transformed into mobile applications


.
2. Explored some fundamentals

Algorithm: The Recipe Instructions
  Just like a cooking recipe outlines step-by-step instructions to prepare a dish, an algorithm is a structured set of steps to solve a problem in programming. It's the plan you follow to achieve a desired outcome.

Variables: The Ingredients
  Variables are the ingredients of your program. They store data, just like flour, sugar and eggs are stored before being mixed to bake a cake
  
  .

Six Basic Computer Instructions: The Cooking Actions

Input: Gathering ingredients from your pantry (data collection).

Output: Plating the finished dish (displaying results).

Processing: Mixing, kneading or blending (performing operations on data).

Storage: Refrigerating leftovers (saving data for future use).

Decision-Making: Deciding when the dish is done based on color or smell (logic to take actions).

Iteration: Stirring continuously until the sauce thickens (repeating tasks until conditions are met)




3. Learn by doing

Students were presented with scenarios and encouraged to:

Break down complex problems into manageable parts.

Think critically to identify efficient solutions.

Collaborate with peers, exploring diverse approaches to problem-solving.




    This activity mirrored challenges faced in real-world app development and highlighted the value of analytical thinking.

Creating flowcharts
  In the next step, students translated their solutions into structured pseudo-designs by:

Defining app functionality and outlining its features.

Visualizing workflows and logic through flowcharts and diagrams.

Writing pseudo-code, serving as a blueprint before actual coding begins.




    These exercises taught participants the importance of planning and organization, ensuring their apps would be efficient and scalable. Please refer to the solved example below.
    
    

The Impact of Active Learning
  By solving queries and creating flow charts, students gained both theoretical understanding and practical skills.

Building the Future: Diving into Mobile App Development

With a solid foundation in place the Masterclass transitioned into an exploration of mobile app development. Here’s what we covered:

Exploring Tools and Frameworks
 Students were introduced to industry-standard tools and frameworks such as:

React Native and Flutter for creating cross-platform apps, Hybrid apps

Kotlin and Swift for building native Android and iOS applications, Native Apps



User Interface (UI) Design Essentials
 Designing user-friendly interfaces is key to any successful app. Participants learned:

Principles of user-centered design.

Basic familiarity wireframes and prototypes to visualize app functionality.

Maintaining consistent and intuitive UI for seamless user experiences.



Frontend and Backend terminologies
 To make apps more functional, students explored API integration basics, including:

How backend and frontend works.

Importance of database.

Understanding secure data communication between the app and servers.



Learning from Real-World Case Studies

Real-world examples were analyzed to understand the strategies behind successful apps, helping students connect theoretical knowledge with practical applications.



Key Takeaways

Understanding the Basics of Programming
 Participants built a basic foundation by learning algorithms, variables, and the six basic computer instructions essential for app development.

Mastering Pseudocode Writing
 Students practiced writing pseudocode and flowcharts allowing them to translate ideas into clear and actionable steps before actual coding.

Developing Problem-Solving Skills
 Through hands-on activities students learned to deconstruct complex problems, analyze solutions, and approach challenges systematically.

Steps of cycle of mobile app development
 From problem-solving to deployment, students were guided through the complete mobile app development lifecycle, empowering them to turn ideas into reality.


Conclusion
This Masterclass laid the groundwork for students to begin their journey in mobile app development. By understanding programming fundamentals, creating flowcharts and writing pseudocode. They are now ready to start writing basic code and with continued learning and practice they will be able to build, enhance and modify apps thus turning their ideas into impactful digital solutions.



Top 5 Programming languages for Mobile App Development in 2025
Sulabh Sharma — Tue, 14 Jan 2025 10:01:10 GMT
Mobile apps are central to our daily lives with global downloads expected to surpass 258 billion by 2025. As the demand for high-quality apps grows the role of programming languages becomes crucial in shaping the future of mobile development. Languages like Swift, kotlin, JavaScript are powering today’s most popular apps and will continue to drive innovation in 2025.
In this blog, We'll explore the Top 5 programming languages for mobile app development, highlighting their Usage, Current stats, growth and future potential in the fast-evolving mobile ecosystem.
1. Swift

Usage: Primarily used for iOS and macOS app development.

Current Stats: Despite its popularity, only 4.6 percent of developers worldwide use Swift. It is believed this community will only grow in size, https://www.coursera.org/articles/programming-in-swift

Growth: Swift is continuously evolving with regular updates from Apple making it the go-to language for iOS development. It's been the preferred language for Apple’s ecosystem since 2014.

Future Scope: As Apple's ecosystem grows with new devices like AR glasses. Swift’s role in developing apps for these devices will likely increase.

Pros of Swift language- Fast performance, Modern Syntax, Memory safety, Active community, Cross-platform.

Cons of Swift language- Limited libraries, Young language, Small Talent Pool, Platform restrictions, Slow adoption.
  


2. Kotlin

Usage: Kotlin is the official language for Android app development recommended by Google.

Current Stats: Serialization and Test have raised their presence by 9 and 6 percentage points respectively. In their tasks, roughly a quarter of Kotlin users apply Ktor, an asynchronous framework for creating microservices, web applications, and more, https://www.jetbrains.com/lp/devecosystem-2021/kotlin/#:~:text=What%20JetBrains%20Kotlin%20libraries%20and%20frameworks%20do%20you%20currently%20use?&text=A%20greater%20proportion%20of%20Kotlin,%2C%20web%20applications%2C%20and%20more.

Growth: Kotlin's ease of use and seamless interoperability with Java have made it a favorite for Android developers and its usage continues to rise as the preferred language for Android development.

Future Scope: Kotlin is expected to maintain a dominant position in Android development as Google continues to integrate Kotlin into their frameworks and tools.

Pros of kotlin language- Interoperates with Java, Concise Syntax, Null safety, Official android support, Growing community.

Cons of Kotlin language- Learning Curve for Java developers, Smaller Ecosystem, Slower Compilation, Limited Job Market.
  


3. JavaScript

Usage: JavaScript is primarily used for web development but is a top choice for cross-platform mobile apps via frameworks like React Native, Ionic and Node.js.

Current Stats: JavaScript and HTML/CSS take center stage, with over 63% and approximately 55% adoption by global software developers, https://www.globalapptesting.com/blog/mobile-app-development-statistics-and-facts

Growth: JavaScript frameworks like React Native allow for faster, more efficient development of cross-platform apps. This trend is growing as developers look to write once and deploy on both iOS and Android.

Future Scope: JavaScript will continue to lead cross-platform development, with more businesses adopting frameworks like React Native for cost-effective and scalable solutions.

Pros of JavaScript language- Cross-Platform development, Huge ecosystem, High demand for developers, Rich libraries & frameworks, Fast prototyping.

Cons of JavaScript Language- Performance issues, Callback hell, Single-threaded nature, Difficult debugging, Code maintenance challenges.
  


4. Dart

Usage: Dart is primarily used in combination with Flutter for building high-performance cross-platform mobile apps.

Current Stats: The top three geographies of Dart for programming-language are the United States with 237(43.57%), India with 121(22.24%), United Kingdom with 43(7.90%) customers respectively, https://6sense.com/tech/programming-language/dart-market-share

Growth: The simplicity and efficiency of Dart, combined with Flutter's ability to compile to native code, has led to its rise as a top choice for cross-platform development.

Future Scope: Given the success of Flutter, Dart is poised for continued growth as a language of choice for building efficient, cross-platform apps.

Pros of Dart language- High performance, Flutter integration, Fast development, Cross-platform efficiency, Strong google support

Cons of Dart language- Small talent pool, Limited native support, Relatively new language, Lack of mature libraries, App size concerns.
  


5. Python

Usage: Python is typically used for backend development, but frameworks like Kivy, BeeWare allow developers to build mobile apps but is very limited in use.

Current Stats: According to TIOBE’s Index (2024), https://www.tiobe.com/tiobe-index/ Python usage increased by 9.3% in 2024 but use in mobile app development is very less.

Growth: Python’s simplicity and vast ecosystem make it an attractive option for rapid prototyping and app development, but it faces limitations in terms of performance for 3resource-intensive apps.

Future Scope: While Python may not replace native languages like Swift or Kotlin for mobile development, its growth in fields like AI, machine learning, and web development positions it as a valuable language for mobile developers who want to integrate these features.

Pros of Python language- Easy to learn, Rich ecosystem, Cross-platform frameworks, Rapid prototyping, Versatile

Cons of Python language- Performance limitations, Not natively supported on mobile, Limited mobile-specific tools, App size issues, Not ideal for performance Apps

Why Python is not Popular for Mobile App Development- Python is not popular in mobile app development primarily due to performance and ecosystem limitations. As an interpreted language Python tends to be slower than natively compiled languages like Swift for iOS or Kotlin for Android which are optimized for mobile environments. Mobile apps often require high performance especially for resource-intensive tasks such as complex animations or real-time processing making native languages a more efficient choice. Additionally mobile operating systems have robust, platform-specific development environments with strong integration to their respective APIs, something Python lacks. While frameworks like Kivy and BeeWare offer Python-based development options they are not as mature or feature-rich as their native counterparts, leading to challenges in scalability, usability and access to advanced platform features. Furthermore, Python apps may face compatibility issues or difficulties during the app submission process to app stores which further diminishes its attractiveness for mobile development. Consequently developers often prefer more established technologies like Swift, Kotlin and cross-platform tools like Flutter or React Native for building mobile applications.
  
A Comparative Analysis of Popular Programming Languages
  
Conclusion
  Each of these programming languages has carved out a significant space in the mobile app development ecosystem. Swift and Kotlin dominate their respective platforms, JavaScript powers cross-platform frameworks, Dart is on the rise thanks to Flutter, and Python is an excellent choice for certain mobile applications, especially those integrating advanced technologies. Understanding their roles, adoption, and growth trends will help developers choose the right tools for the rapidly evolving mobile development landscape in 2025.



References

https://survey.stackoverflow.co/2024/

https://www.coursera.org/articles/programming-in-swift

https://6sense.com/tech/programming-language/dart-market-share

https://www.tiobe.com/tiobe-index/





Making Your React App Multilingual
Ankur — Mon, 26 Aug 2024 05:15:56 GMT
Internationalised software supports the languages and cultural customs of people throughout the world. The Web reaches all parts of the world. Internationalised web apps provide a great user experience for people everywhere.
Localised software adapts to a specific language and culture by translating text into the user's language and formatting data in accordance with the user's expectations. An app is typically localised for a small set of locales.
Typically, web apps are localised to just the language or language-region combination. Examples of such locale codes are:

en for English

en-US for English as spoken in the United States

en-GB for English as spoken in the United Kingdom

es-AR for Spanish as spoken in Argentina

ar-001 for Arabic as spoken throughout the world

ar-AE for Arabic as spoken in United Arab Emirates


Create a new Next.js project by using below command line:
npx create-next-app@latest nextjs-app-router-i18n

Add React Intl dependency
npm i -S react react-intl

Create localisation files
The en.json file
{
  "hello": "Hello, world!",
  "welcome": "Welcome to our website"
}

The es.json file
{
  "hello": "Hola Mundo",
  "welcome": "Bienvenido a nuestro sitio web"
}

The fr.json file
{
  "hello": "Bonjour le monde",
  "welcome": "Bienvenue sur notre site Internet"
}

Import useTranslation from react-i18next in your components
// Page.js Entry point
'use client'
import React, {
    useState
} from 'react';
import {
    FormattedMessage,
    IntlProvider
} from 'react-intl';
import messages_en from '../public/locales/en.json';
import messages_fr from '../public/locales/fr.json';
import messages_sp from '../public/locales/sp.json';
import messages_hi from '../public/locales/hindi.json';
import messages_rs from '../public/locales/rus.json';

const messages = {
    en: messages_en,
    fr: messages_fr,
    sp: messages_sp,
    hi: messages_hi,
    rs: messages_rs,
};

const App = () => {
    const [locale, setLocale] = useState('en');

    const handleLanguageChange = (e) => {
        setLocale(e.target.value);
    };

    return (
        <IntlProvider locale={locale} messages={messages[locale]}>
            <div>
                <h1><FormattedMessage id="app.title" />h1>
                <select onChange={handleLanguageChange} value={locale}>
                    <option value="en">Englishoption>
                    <option value="fr">Frenchoption>
                    <option value="sp">Spanishoption>
                    <option value="hi">Hindioption>
                    <option value="rs">Russianoption>
                select>
                <p><FormattedMessage id="app.description" />p>
            div>
        IntlProvider>
    );
};

export default App;

Sample code link : To run the code on your browser.
Conclusion
Next.js offers pretty good support for internationalisation, though recent routing updates have added some complexity. We've shown how to integrate the React Intl library with Next.js, utilizing both the App Router and Pages Router for routing.



Simplifying Git and GitHub for Developers
Ankur — Fri, 16 Aug 2024 03:30:39 GMT
About Git
Git is a Version Control System that intelligently tracks changes in files. Git is particularly useful when you and a group of people are all making changes to the same files at the same time.
A version control system, or VCS, tracks the history of changes as people and teams collaborate on projects together. As developers make changes to the project, any earlier version of the project can be recovered at any time.
What is GitHub ?
GitHub is an online SaaS service. GitHub is a cloud-based platform where you can store, share, and work together with others to write code.
Storing your code in a "repository" on GitHub allows you to:

Showcase or share your work.

Track and manage changes to your code over time.

Let others review your code, and make suggestions to improve it.

Collaborate on a shared project, without worrying that your changes will impact the work of your collaborators before you're ready to integrate them.


Collaborative working, one of GitHub’s fundamental features, is made possible by the open-source software, Git, upon which GitHub is built.
About Repository
A repository, or Git project, encompasses the entire collection of files and folders associated with a project, along with each file's revision history.
Git repositories are self-contained units and anyone who has a copy of the repository can access the entire codebase and its history.
Git Setup
git config --global user.name “[firstname lastname]”
To set a name that is identifiable for credit when review version history
git config --global user.email “[valid-email]”
Basic Git commands.
To use Git, developers use specific commands to copy, create, change, and combine code. These Commands can be directly executed from command line, code editor like VS Code, Intellij idea, or GitHub desktop.

git init : It create a repository and begins tracking the existing directory.

git clone: It used to copy a remote repository that already exist. The clone includes all the project's files, history, and branches.

git add : It stages a file. Git tracks changes to a developer's codebase, but it's necessary to stage and take a snapshot of the changes to include them in the project's history.

git commit : It saves the snapshot to the project history and completes the change-tracking process. In short, a commit functions like taking a photo.

git status : It shows the status of changes as untracked, modified, or staged.

git commit -m “[descriptive message]” : It commits your staged content as a new commit snapshot.

git branch : It will list your branches. a * will appear next to the currently active branch.

git branch [branch-name] : It creates new branch at the current commit.

git merge [branch] : It merge the specified branch’s history into the current one.

git push : It transmit local branch commits to the remote repository branch.

git pull : It fetch and merge any commits from the tracking remote branch.

git log : It show all commits in the current branch’s history.


Example of how we push our remote file to git.


Git after committing changes

Here's an example of git clone.
Command : git clone [URL]

Here's an example of pushing local branch to remote git.

Here's how you create a pull request
Navigate to your repository on GitHub and click on "New Pull Request". Provide a clear description for the Pull Request.
Now we merge branches using command line.
git fetch : Fetch the latest change.
git checkout : "base branch name".
git merge : "New branch name".
Example :

GitHub features
GitHub Codespace : Spin up fully configured dev environments in the cloud with the full power of your favorite editor. GitHub Codespaces is an instant, cloud-based development environment that uses a container to provide you with common languages, tools, and utilities for development. GitHub Codespaces is also configurable, allowing you to create a customized development environment for your project.
GitHub Pull Request : Allow contributors to easily notify you of changes they've pushed to a repository – with access limited to the contributors you specify. Easily merge changes you accept.
GitHub Issues: GitHub Issues are items you can create in a repository to plan, discuss and track work. Issues are simple to create and flexible to suit a variety of scenarios. You can use issues to track work, give or receive feedback, collaborate on ideas or tasks, and efficiently communicate with others. Essentially they serve pivotal point for collaborating or communicating among various element in a system.
CI/CD GitHub: CI/CD automates your builds, testing, and deployment so you can ship code changes faster and more reliably.

Continuous Integration : Automatically builds, tests, and integrates code changes within a shared repository.

Continuous Delivery : Automatically builds, tests, and integrates code changes within a shared repository.

Continuous Deployment : Automatically deploys code changes to customers directly.


GitHub Actions: You can automate, customize, and execute your software development workflows right in your repository with GitHub Actions. You can discover, create, and share actions to perform any job you'd like, including CI/CD, and combine actions in a completely customized workflow.
Conclusion
As we have gone through the Git and GitHub, its common command, its features that how git revolutionize your workflow. GitHub action has CI/CD that simply automate building, testing, deployment so that developer can only focus on writing code and GitHub ensure the quality and efficiency. There are still so much to discover in git.
Resource :
https://docs.github.com/en
https://github.com/features



You Call Yourself a Web Developer and Don't Use Chrome Dev Tools?
Ankur — Wed, 14 Aug 2024 15:42:14 GMT
Chrome Dev Tools is a powerful tool for web developer built into the Google Chrome browser. Chrome Dev Tools is indispensable tool that lets you inspect, edit and debug your code. It helps web developer to build better website and faster.
You can access Chrome dev tools by following commands

macOS: Command+Shift+P

Windows, Linux, ChromeOS: Control+Shift+P


Elements Panel
The HTML and inline CSS codes are displayed in the elements tab. The Elements panel lets you inspect and edit the HTML and CSS of your page. Elements panel gives access to edit on the go so that you can see the immediate changes on the page while you can make changes to your code.

Console Panel
In the console, Javascript codes are executed. To get started with the Console panel, enter some JavaScript into the Console and press "Enter." The Console will evaluate the code and print the result. Web developers often log messages to the Console to make sure that their JavaScript is working as expected. To log a message, you insert an expression in console.

When the browser executes your JavaScript and sees an expression like that, it knows that it's supposed to log the message to the Console.
Source Panel
The sources tab displays the files that contain the codes. The Sources panel lets you view and edit your page's JavaScript and CSS code.
Use the Chrome Dev Tools Sources panel to:

View Files.

Edit CSS and JavaScript.

Create and save code snippets of JavaScript.

Debug JavaScript.

Set up a Workspace, so that changes you make in Dev Tools get saved to the code on your file system.



Select a file in the "Files" pane to get started with the Sources panel. This will bring up the contents of that file in the "Editor" pane. From here, you make changes to the code and see those changes immediately reflected on the page.
Network Panel
The network tab displays the files that are being loaded from the URL. Network tab used to analyze network load and resources.

Select a resource in the "Resources" pane to start with the Network panel. This will bring up the details of that resource in the "Details" pane. From here, you can see when the resource was loaded, how long it took to load, and what type of resource it was.
The Network panel lets you:

Record network activity

Inspect network requests

Filter and sort network requests

Search network headers and responses

Change loading behavior

Block network requests

Change network conditions

Debug prefetch speculation rules

Override HTTP response headers

Override headers for multiple URLs with wildcard characters.

Save and export network request data


Performance Panel
Runtime performance is how your page performs when it is running, as opposed to loading. This tutorial teaches you how to use the Chrome Dev Tools Performance panel to analyze runtime performance.

Select a resource in the "Resources" pane to get started with the Performance panel. This will bring up the details of that resource in the "Details" pane. You can check how much time it is taking to load, what type of resource it is, and what size it is.
You can also use the Performance panel to record a performance profile. To record a performance profile, click the "Record" button at the panel's top. This will start recording information about your page's performance. Once you have finished recording then just click the "Stop" button to save the performance profile.
Application Panel
The application tab displays the contents of your browser’s storage. Use the Application panel to inspect, modify, and debug many aspects of your web app including its manifest, service workers, storage, and cache data.
Select a resource in the "Resources" pane to get started with the Application panel. This will bring up the details of that resource in the "Details" pane. You can just see and check what files are being loaded and what cookies and local storage are being used.
Security Panel
Security provides standard security details, such as viewing a site’s HTTPS certificate and TLS status.
The Security panel lets you inspect the security settings of your web page. With the Security panel, you can see what permissions your page has and what security vulnerabilities may be present.
Memory Panel
The Memory panel provides diagnostic tools that let you see the memory distribution of JavaScript objects, discover and isolate memory leaks, get a breakdown of memory allocation by function, and more.

Select a resource in the "Resources" pane to get started with the Memory panel. This will bring up the details of that resource in the "Details" pane. From here, you can check how much of the memory the resource uses, what type of resource it is, and when it was last accessed.
Conclusion
As we saw, how Dev Tools are important for web developer. It allows you to work directly in the browser and see results in real time. You can use it to preview style changes, alter the HTML or help write your JavaScript code and do some debugging.
Resources
https://developer.chrome.com/docs/devtools/
https://www.headspin.io/blog/chrome-devtools-a-complete-guide
https://medium.com/swlh/the-basics-of-chrome-devtools-4d69a102a699
https://codeinstitute.net/global/blog/chrome-dev-tools-tips-tricks/



Top UI Frameworks of React in 2024
Ankur — Thu, 01 Aug 2024 13:35:08 GMT
React is the most commonly used Javascript library in 2024 for front-end development. React is so popular that many UI libraries have built-in custom react components to facilitate easy integration. React UI framework provides a wide range of built in React components like buttons, cards, navigation bar, forms, tables and many more.
In this blog, we will figure out top React UI components. We will talk about their features, strengths, and suitability for React projects development.
Top UI Component libraries for React:
Chakra UI
Chakra UI has over 480k weekly downloads and 34.6k stars on GitHub.
To install Chakra UI into our project, we need to run below commands in our terminal:
npm i @chakra-ui/react @emotion/react @emotion/styled framer-motion

After installing Chakra UI into your project, you need to include ChakraProvider in root of your application.
Button components are used to trigger an event or submit for or navigate to another page.
You have to import the button from the chakra package into your React app.
Here’s an example of a button with icon:
import * as React from 'react'
// 1. import `ChakraProvider` component
import { ChakraProvider } from '@chakra-ui/react'

function App() {
  // 2. Wrap ChakraProvider at the root of your app
  return (
    <ChakraProvider>
      <Stack direction='row' spacing={4}>
          <Button leftIcon={<EmailIcon />} colorScheme='teal' variant='solid'>
            Email
          Button>
          <Button rightIcon={<ArrowForwardIcon />} colorScheme='teal' variant='outline'>
            Call us
          Button>
      Stack>
    ChakraProvider>
  )
}

Output

Radix UI
Radix UI has over 13k weekly downloads on npm.
Simple and easy React based UI to customize. You can only install that particular component in your project and customize it. The vision of radix UI to create an open-source library that the community can use to build accessible design systems.
You will install Radix UI primitives for button interaction into your react app.
npm install @radix-ui/react-focus

Radix themes does not come with a built-in styling system. There’s no css or sx prop, and it does not use any styling libraries internally. Under the hood, it’s built with vanilla CSS.
Here's an example of Button:
import * as React from 'react';
import { Focus } from '@radix-ui/react-focus';
import { Flex, Button } from '@radix-ui/react-focus'

function MyButton() {
    return (
        <Focus>
            <Flex align="center" gap="3">
              <Button variant="classic">Edit profileButton>
              <Button variant="solid">Edit profileButton>
              <Button variant="soft">Edit profileButton>
              <Button variant="surface">Edit profileButton>
              <Button variant="outline">Edit profileButton>
            Flex>
        Focus>
    );
}

Output

Shadcn UI
It’s a collection of reusable components that we can copy and paste code directly into your projects. It is not a component library so you do not have to install it as a dependency. It is not available or distributed via NPM.
Here's an example of Button:
import { Button } from "@/components/ui/button"

export function ButtonSecondary() {
  return <Button variant="secondary">SecondaryButton>
}


Ant Design
Ant design has over 1 million weekly downloads on NPM and 87k stars on GitHub
import React from 'react';
import { Button, Flex } from 'antd';

const App: React.FC = () => (
  <Flex gap="small" wrap>
    <Button type="primary">Primary ButtonButton>
    <Button>Default ButtonButton>
    <Button type="dashed">Dashed ButtonButton>
    <Button type="text">Text ButtonButton>
    <Button type="link">Link ButtonButton>
  Flex>
);
export default App;

Output

React Bootstrap
React bootstrap is one of the popular React UI libraries. It is built on popular bootstrap framework.
React Bootstrap has over 2 million weekly download on NPM and 22k stars of GitHub
Here's an example of using different types of button components in React app.
import Button from 'react-bootstrap/Button';

function ButtonTypes() {
  return (
    <>
      <Button variant="primary">PrimaryButton>{' '}
      <Button variant="secondary">SecondaryButton>{' '}
      <Button variant="success">SuccessButton>{' '}
      <Button variant="warning">WarningButton>{' '}
      <Button variant="danger">DangerButton>{' '}
      <Button variant="info">InfoButton>{' '}
      <Button variant="light">LightButton>{' '}
      <Button variant="dark">DarkButton>
      <Button variant="link">LinkButton>
    
  );
}

export default ButtonTypes;

Output

Material UI
Material UI has over 91.4K GitHub star. It is open source react library that implements Google’s material design.
It includes a comprehensive collection of prebuilt components that are ready for use in production right out of the box, and features a suite of customization options that make it easy to implement your own custom design system on top of your components.
You need to install material UI core libraries and its dependencies.
Here's an example of a button:
npm install @mui/material @emotion/react @emotion/styled

import * as React from 'react';
import Button from '@mui/material/Button';
import Stack from '@mui/material/Stack';

export default function ContainedButtons() {
  return (
    <Stack direction="row" spacing={2}>
      <Button variant="contained">ContainedButton>
      <Button variant="contained" disabled>
        Disabled
      Button>
      <Button variant="contained" href="#contained-buttons">
        Link
      Button>
    Stack>
  );
}

Output

Conclusion
In this blog, we have seen different React UI libraries with their popularity. Choosing the right UI component library which has good community support will definitely accelerate the development of your project. While deciding, weigh the pros and cons in the context of your project. For example, while one library might excel in customization, another might shine in performance. Each libraries comes with its own development benefits, so the best one for your projects largely depends on your project requirements.
References:
https://prismic.io/blog/react-component-libraries
https://ably.com/blog/best-react-component-libraries
https://v2.chakra-ui.com/
https://mui.com/material-ui/getting-started/



The React Hooks you need to know
Piyush Nanwani — Wed, 27 Mar 2024 03:30:35 GMT
In this blog, we'll cover fundamental hooks like useState, useEffect, useRef, and useContext. Then we will talk about custom hooks.
React Hooks
useState
The useState hook allows functional components to manage state. Let's look at a simple example:
import React, { useState } from 'react';

function Counter() {
  const [count, setCount] = useState(0);

  return (
    <div>
      <p>Count: {count}p>
      <button onClick={() => setCount(count + 1)}>Incrementbutton>
    div>
  );
}


The code initializes a state variable named "count" using React's useState hook.

The initial value of "count" is set to 0.

The "setCount" function is a setter function provided by useState hook, used to update the value of "count" later in the component's lifecycle.


useEffect
useEffect enables performing side effects in functional components, such as fetching data or subscribing to external events. Here's an illustration:
import React, { useState, useEffect } from 'react';

function DataFetcher() {
  const [data, setData] = useState([]);

  useEffect(() => {
    // Fetch data from an API
    fetch('https://api.example.com/data')
      .then(response => response.json())
      .then(data => setData(data));
  }, []); // Empty dependency array triggers effect only once on mount

  return (
    <div>
      <ul>
        {data.map(item => (
          <li key={item.id}>{item.name}li>
        ))}
      ul>
    div>
  );
}

useRef
useRef is handy for accessing and interacting with DOM elements directly. Consider the following example:
import React, { useRef, useEffect } from 'react';

function FocusInput() {
  const inputRef = useRef();

  useEffect(() => {
    // Focus the input element on mount
    inputRef.current.focus();
  }, []);

  return <input ref={inputRef} />;
}

useContext
The useContext hook simplifies accessing context values in functional components. Example:
import React, { useContext } from 'react';

const ThemeContext = React.createContext('light');

function ThemedComponent() {
  const theme = useContext(ThemeContext);

  return <p style={{ color: theme }}>Themed Contentp>;
}

Custom Hooks
Custom hooks enable reusing stateful logic across different components. Let's create a simple custom hook for handling form input:
import { useState } from 'react';

function useFormInput(initialValue) {
  const [value, setValue] = useState(initialValue);

  const handleChange = e => {
    setValue(e.target.value);
  };

  return {
    value,
    onChange: handleChange,
  };
}

Now, you can use this custom hook in any component:
import React from 'react';
import useFormInput from './useFormInput';

function Form() {
  const username = useFormInput('');
  const password = useFormInput('');

  return (
    <form>
      <input type="text" {...username} placeholder="Username" />
      <input type="password" {...password} placeholder="Password" />
    form>
  );
}

Another custom hook
Let's create a custom hook called useFetch for handling data fetching using the fetch API.
import { useState, useEffect } from 'react';

function useFetch(url) {
  const [data, setData] = useState(null);
  const [loading, setLoading] = useState(true);
  const [error, setError] = useState(null);

  useEffect(() => {
    const fetchData = async () => {
      try {
        const response = await fetch(url);
        const result = await response.json();
        setData(result);
      } catch (error) {
        setError(error);
      } finally {
        setLoading(false);
      }
    };

    fetchData();
  }, [url]);

  return { data, loading, error };
}

// Usage example in a component
function DataComponent() {
  const { data, loading, error } = useFetch('https://api.example.com/data');

  if (loading) {
    return <p>Loading...p>;
  }

  if (error) {
    return <p>Error: {error.message}p>;
  }

  return (
    <div>
      <h1>Data Componenth1>
      <ul>
        {data.map(item => (
          <li key={item.id}>{item.name}li>
        ))}
      ul>
    div>
  );
}

Now, you can reuse the useFetch hook in any component that needs to fetch data. This promotes code reusability and keeps your components focused on their specific concerns.
Thanks for reading!



Mastering State Management in React
Piyush Nanwani — Wed, 20 Mar 2024 03:28:17 GMT
In this blog, we'll explore various state management strategies, covering prop drilling, the useContext hook, useReducer, and popular state management libraries.
Prop Drilling
Prop drilling refers to the process of passing down props through multiple levels of nested components to reach a deeply nested child component. While it's a common practice, it can lead to code complexity and reduced maintainability.
Example of Prop Drilling
Consider a scenario where you have a deeply nested component that needs access to a prop passed by a parent component. Each intermediate component in the hierarchy would need to receive and pass down the prop:
// ParentComponent.js
const ParentComponent = () => {
  const sharedProp = 'Hello, Prop Drilling!';
  return <IntermediateComponent propToPass={sharedProp} />;
};

// IntermediateComponent.js
const IntermediateComponent = ({ propToPass }) => {
  return <ChildComponent propToReceive={propToPass} />;
};

// ChildComponent.js
const ChildComponent = ({ propToReceive }) => {
  return <p>{propToReceive}p>;
};

Click here to run this code
useContext()
Simplifying with useContext
The useContext hook offers a cleaner solution for accessing values throughout the component tree without prop drilling. It allows components to subscribe to a context created by a provider.
Example using useContext
// AppContext.js
import React, { createContext, useContext } from 'react';

const AppContext = createContext();

export const AppProvider = ({ children }) => {
  const sharedValue = 'Hello, useContext!';
  return <AppContext.Provider value={sharedValue}>{children}AppContext.Provider>;
};

export const useAppContext = () => {
  return useContext(AppContext);
};

Now, any component within the AppProvider can access the shared value directly:
// ChildComponent.js
import React from 'react';
import { useAppContext } from './AppContext';

const ChildComponent = () => {
  const sharedValue = useAppContext();

  return <p>{sharedValue}p>;
};

Click here to run this code
useReducer
State Management with useReducer
The useReducer hook is ideal for managing more complex state logic. It allows you to update the state based on the previous state and an action.
Example using useReducer
// CounterReducer.js
const counterReducer = (state, action) => {
  switch (action.type) {
    case 'INCREMENT':
      return { count: state.count + 1 };
    case 'DECREMENT':
      return { count: state.count - 1 };
    default:
      return state;
  }
};

export default counterReducer;

// CounterComponent.js
import React, { useReducer } from 'react';
import counterReducer from './CounterReducer';

const CounterComponent = () => {
  const [state, dispatch] = useReducer(counterReducer, { count: 0 });

  return (
    <div>
      <p>Count: {state.count}p>
      <button onClick={() => dispatch({ type: 'INCREMENT' })}>Incrementbutton>
      <button onClick={() => dispatch({ type: 'DECREMENT' })}>Decrementbutton>
    div>
  );
};

Click here to run this code
State Management Libraries
Exploring State Management Libraries
Several state management libraries simplify and streamline state management in large-scale applications. Notable libraries include Redux, MobX, and Recoil.
Example using Redux
Redux is a popular state management library. First, install it:
npm install redux react-redux

Now, create a Redux store, actions, and reducers:
// store.js
import { createStore } from 'redux';
import rootReducer from './reducers';

const store = createStore(rootReducer);

export default store;

// reducers.js
const initialState = { count: 0 };

const counterReducer = (state = initialState, action) => {
  switch (action.type) {
    case 'INCREMENT':
      return { count: state.count + 1 };
    case 'DECREMENT':
      return { count: state.count - 1 };
    default:
      return state;
  }
};

export default counterReducer;

Now, integrate Redux in your React component:
// CounterComponent.js
import React from 'react';
import { useSelector, useDispatch } from 'react-redux';

const CounterComponent = () => {
  const count = useSelector(state => state.count);
  const dispatch = useDispatch();

  return (
    <div>
      <p>Count: {count}p>
      <button onClick={() => dispatch({ type: 'INCREMENT' })}>Incrementbutton>
      <button onClick={() => dispatch({ type: 'DECREMENT' })}>Decrementbutton>
    div>
  );
};

Conclusion
Whether you opt for prop drilling, useContext, useReducer, or leverage state management libraries like Redux, each method has its merits. Experiment with these techniques to discover which best fits your project's needs and enhances code maintainability and scalability.
Happy coding!



React Components: Function-Based vs Class-Based
Piyush Nanwani — Sat, 16 Mar 2024 12:26:35 GMT
Choosing function based components or class based components is a common question a lot of us face when starting your React journey. In this blog, we will talk about both of these approaches and guide you along with examples.
Function-Based Components
In programming, we make functions to reuse the code and make it more readable. A function is just a block of code given some label, and we can execute that block of code by using that label i.e function name.
Our web applications are simply made of HTML, CSS and JavaScript. And in React we simply breakdown our apps into smaller parts called components. So, components are merely the HTML, CSS, JS chunks.
Example:
import React from 'react';

const FunctionComponent = () => {
  return <p>Hello, Function Component!p>;
};

Click here to run code
Features:

Simplicity: Functions are concise, making the code easy to read and maintain.

Nothis Keyword: Eliminates confusion around the this keyword, simplifying the component structure.


Class-Based Components
Class-based components have been a traditional way of defining React components, incorporating the concepts of ES6 classes.
Example:
import React, { Component } from 'react';

class ClassComponent extends Component {
  render() {
    return <p>Hello, Class Component!p>;
  }
}

Click here to run code
Features:

Lifecycle Methods: Enables the use of lifecycle methods like componentDidMount, componentDidUpdate, etc.

State Management: Supports local state management using this.state and this.setState().


When to Use Function-Based Components?

Functional Paradigm: When adhering to a functional programming paradigm or following a more functional approach.

Simplicity: For simpler components without the need for state or lifecycle methods.

Hooks: When utilizing React Hooks for state and side effects.


Example: Function-Based Component with Hooks
import React, { useState, useEffect } from 'react';

const FunctionWithHooks = () => {
  const [count, setCount] = useState(0);

  useEffect(() => {
    document.title = `Count: ${count}`;
  }, [count]);

  return (
    <div>
      <p>Count: {count}p>
      <button onClick={() => setCount(count + 1)}>Incrementbutton>
    div>
  );
};

Click here to run code
When to Use Class-Based Components?

Legacy Codebases: When working with older codebases that extensively use class components.

Lifecycle Methods: For components requiring lifecycle methods for tasks like data fetching or DOM manipulation.

Local State: When managing local state using this.state is necessary.


Example: Class-Based Component with State
import React, { Component } from 'react';

class ClassWithState extends Component {
  constructor(props) {
    super(props);
    this.state = {
      count: 0,
    };
  }

  componentDidMount() {
    document.title = `Count: ${this.state.count}`;
  }

  componentDidUpdate() {
    document.title = `Count: ${this.state.count}`;
  }

  render() {
    return (
      <div>
        <p>Count: {this.state.count}p>
        <button onClick={() => this.setState({ count: this.state.count + 1 })}>
          Increment
        button>
      div>
    );
  }
}

Click here to run code
Conclusion

Function-based components and class-based components are both essential to React development, providing different approaches for doing the same thing.

The introduction of hooks has allowed function components to handle state and side effects, blurring the distinction between the two techniques.

So, you will find old code written using class based syntax and new with function based syntax.

You should understand the differences between each so that you can work on projects or third party packages that use either or both of the two approaches.  
  Thanks for Reading!





Understanding React Concepts: State and Prop
Piyush Nanwani — Sat, 09 Mar 2024 08:14:28 GMT
React, with its declarative and component-based approach, relies on key concepts like state and props to manage and pass data. This blog aims to explain the differences between state and props with examples.
State
State is the internal handling of data of a component. It provides the component's dynamic properties, which might change over time.
Features:

Local to Component: State is specific to the component it belongs to.

Mutable: It can be modified using setState.

Component-owned: Controlled and managed by the component itself.


Props
Props (short for properties) are external inputs passed to a component. They allow the parent component to communicate with and customize its child components.
Features:

Immutable: Props are read-only and cannot be modified by the child component.

Received from Parent: Passed down from parent to child.

Pure Functions: Components that rely only on props are pure and deterministic.


Examples of State and Props
State Example:
Consider a counter component that manages its count using state.
import React, { useState } from 'react';

const Counter = () => {
  const [count, setCount] = useState(0);

  const increment = () => {
    setCount(count + 1);
  };

  return (
    <div>
      <p>Count: {count}p>
      <button onClick={increment}>Incrementbutton>
    div>
  );
};

In this example, count is a piece of state managed by the Counter component.
Props Example:
Now, let's create a Greetings component that receives a name prop.
import React from 'react';

const Greetings = (props) => {
  return <p>Hello, {props.name}!p>;
};

Here, name is a prop passed from the parent component when rendering Greetings:
import React from 'react';
import Greetings from './Greetings';

const App = () => {
  return <Greetings name="John" />;
};

Understanding the difference between state and props is important for React developers. State enables components to manage internal dynamic data, while props help in communication between parent and child components.
Thanks for reading!



React Ecosystem Overview
Piyush Nanwani — Mon, 04 Mar 2024 03:30:23 GMT
I recall the early days of my journey with React. Upon completing a course or a book, I would feel a sense of accomplishment, thinking I had mastered React, only to discover there were still unknown aspects. This cycle repeated itself - learning, happiness, discovery of more unknowns. And then I realized how big the React Community is plus it keeps on evolving even when you are learning or sleeping or doing anything.
So, if one knows the React roadmap specific to their field, it helps in navigating in this React ocean. So, I have covered some topics in this blogs which I think will help you in developing a better understanding of the possibilities of React plus how to be in touch with ongoing and future updates.
The React Ocean
React powers web, mobile apps, windows apps, mac os apps, tv - android, apple tv, AR, VR etc. So, whatever platform you want to build your React apps these are the things that you would need to know:

Platform knowledge:

So, if you are building Android / iOS apps. Knowing about how Android / iOS works, what are the limitations of your underlying platform, what are the powers or uniqueness of that platform.


React Skills

JavaScript Skills ( Refer to this blog )

Software Development Lifecycle

Best Practices


Mind maps of React Ecosystem

React Learning Path

Core Concepts

Styling

Web Development Technologies

Web Development Tools

Mobile Development ( Refer to this blog )







React powers various platforms, and understanding your chosen platform is key. Master React and JavaScript skills, embrace the software development lifecycle, and adopt best practices. To truly learn React, dive in, explore, and let curiosity guide you through this ever-evolving ecosystem.
If you found this helpful, like this. And if you have any doubt or feedback do write a comment.
Thanks for reading!
Helpful links:
JavaScript Essentials for React Native
Top State Management Libraries in React Native
React Native Roadmap for Developers in 2024
Comparing React.js, React Native, and Native Mobile App Development
Why is React Native so Popular? Discover the Top Reasons!
React Resources