DEV Community: Mohammad Tahzeeb Khan

Understanding JWT Security Principle

Mohammad Tahzeeb Khan — Sat, 09 Nov 2024 10:06:04 +0000

JSON Web Tokens (JWT) have emerged as a popular and efficient method for implementing secure authentication and authorization in modern web applications.

Let's Connect and Explore more..

Structure of JWT(Token)

A JWT is a compact and self-contained way to securely transmit information between parties as a JSON object. It consists of three parts:

Header: Contains the token type and signing algorithm.
Payload: Encodes the claims, such as user information and permissions.
Signature: Ensures the integrity and authenticity of the token.
Implementing JWT with Spring Security

How JWT Looks Like ---->

In the Above Example.
1.

{
  "alg": "HS384",
  "typ": "JWT"
}

This is the Header, which uses SHA384 Algorithm to Encrypt the data. HMAC-HS384 is a algo with use SHA384. Second thing is type of the Header, which is JWT(Json Web Token)
2.

{
  "id": "D008",
  "name": "Mohammad Tahzeeb Khan",
  "deskno": 1523,
  "post":"Java Developer"
}

This is the Actual data. This data will be transmitted from one-end to another end.
3.

HMACSHA384(
  base64UrlEncode(header) + "." +
  base64UrlEncode(payload),
)

This is the Signature. Which is Responsible for Encrypting the JWT token.

Conclusion
JSON Web Tokens (JWTs) offer a powerful and flexible solution for secure authentication and authorization in modern web applications. By understanding their structure, components, and implementation best practices, you can effectively leverage JWTs to enhance the security of your applications.

Remember, while JWTs provide a robust mechanism, it's crucial to consider security best practices such as:

Strong Secret Keys: Use strong, randomly generated secret keys to sign your JWTs.
Secure Token Storage: Never store JWTs on the client-side for extended periods.
Token Expiration: Set appropriate expiration times to limit the validity of tokens.
Secure Transmission: Transmit JWTs securely over HTTPS to prevent interception.
Regular Key Rotation: Periodically rotate your secret keys to mitigate security risks.
By carefully implementing JWTs and adhering to these security measures, you can significantly improve the security posture of your web applications.

Why Nextjs is Better Than Reactjs

Mohammad Tahzeeb Khan — Thu, 31 Oct 2024 17:15:16 +0000

When it comes to building robust web applications, developers often have to choose between frameworks that seem to offer similar capabilities. Two of the most popular options for modern web development are React.js and Next.js. While React.js has been a go-to library for building component-based UIs, Next.js has emerged as a powerful framework built on top of React, offering extra features that make it a preferred choice for many developers. But why exactly do developers consider Next.js "better" than React.js for certain projects? Let's break down the advantages.

Next.js elevates web development by building on React’s core strengths, offering built-in server-side rendering, optimized performance, and simplified routing—making it the choice for creating fast, SEO-friendly, and scalable applications.

Built-in server-side Rendering (SSR)
React.js renders applications on the client side, which can lead to slow initial load times, especially on slower networks. Next.js, on the other hand, provides server-side rendering (SSR) and static site generation (SSG) out of the box. SSR and SSG enable faster load times and improve SEO performance, making it easier for search engines to index content. This makes Next.js an ideal choice for applications where performance and SEO are essential, like e-commerce websites and content-driven applications.
Automatic Code Splitting
With large applications, delivering a single JavaScript bundle can slow down performance. Next.js has automatic code splitting, meaning each page only loads the JavaScript it needs. This keeps pages lightweight, reducing loading time and enhancing user experience. React.js doesn’t have automatic code splitting by default; developers need to set it up manually, which can become time-consuming.
File-Based Routing System
One of the standout features of Next.js is its file-based routing system. Each file in the pages directory automatically becomes a route. For example, pages/about.js corresponds to the /about route. This structure makes it easy to create new routes without configuring any additional settings or third-party libraries. In React.js, routing is typically managed using React Router, which requires extra setup and configuration.
API Routes and Backend Integration
Next.js allows you to create API routes within the same project, simplifying backend integration. You can easily create serverless functions using the /api directory and handle server-side logic without needing a separate backend. React.js, by contrast, is purely a frontend library, and you need a separate server or API setup (e.g., with Node.js, Express, or another backend) to handle server-side logic.
Optimized Performance with Image and Font Optimization
Next.js includes built-in support for image optimization and font optimization, two critical factors for performance. The next/image component optimizes images automatically, providing responsive images in modern formats, lazy loading, and quality adjustments. Additionally, Next.js handles font loading to reduce layout shifts and improve page stability. In React.js, these optimizations would need to be handled manually or through third-party libraries, adding complexity to the development process.
Better SEO Capabilities
Because Next.js supports SSR, SSG, and incremental static regeneration (ISR), it makes achieving good SEO performance much easier than in React.js. React's client-side rendering approach can hinder SEO because search engine bots might not fully render JavaScript-heavy content. With Next.js, developers can generate static pages or render pages server-side to ensure content is readily available for search engine crawlers.
Zero Configuration
Next.js offers a zero-configuration setup for SSR, SSG, CSS support, image optimization, and code splitting, allowing developers to focus on building features without worrying about underlying setup details. React.js is highly flexible, but it requires additional configuration, especially for advanced features like SSR or integrating CSS modules.
Enhanced Developer Experience
Next.js makes development more straightforward with features like:

Automatic Refresh: Pages refresh automatically as you make changes.
Custom Document and App Support: Offers a clean way to set up custom HTML document structures and shared components for layouts.
Middleware Support: Control request and response handling without third-party integrations.
In React.js, these capabilities often require installing and configuring multiple third-party libraries or custom code.

Conclusion
Both Next.js and React.js are powerful tools, but they serve different purposes. Next.js offers a more comprehensive solution for full-featured web applications by building on top of React’s component-based architecture. With features like server-side rendering, file-based routing, API routes, and image optimization, Next.js can help you develop high-performance, SEO-friendly applications quickly.

Ultimately, choosing Next.js over React.js comes down to the needs of the project. If you're looking to create a fast, scalable, and SEO-optimized web application, Next.js can help you get there faster with fewer dependencies and less manual configuration. For simpler applications where SSR and SSG aren't required, React.js remains a fantastic choice.

Yarn vs Bun to Create Vite Project....

Mohammad Tahzeeb Khan — Fri, 27 Sep 2024 14:59:44 +0000

Bun

Purpose: A versatile JavaScript runtime, package manager, and build tool.
Key Features:

Fast performance and low memory usage
Bundler capabilities similar to Webpack or Rollup
Built-in HTTP server and task runner
Support for TypeScript, JavaScript, and WebAssembly

Best Use Cases: Projects that need a unified tool for development, packaging, and deployment.

Step 1 -> Create a vite Project

bun create vite

Step 2 -> Tell the Vite Project to Initialize it.

bun create vite my-vue-app --template vue

In the about command. you can replace vue with any Technology according to your desire. Such as

vanilla, vanilla-ts, vue, vue-ts, react, react-ts, react-swc, react-swc-ts, preact, preact-ts, lit, lit-ts, svelte, svelte-ts, solid, solid-ts, qwik, qwik-ts.

Yarn

Purpose: A package manager for JavaScript projects.
Key Features:

Deterministic installations
Parallel execution of tasks
Offline mode for faster installations
Integration with various build tools

Best Use Cases: Managing project dependencies, handling version conflicts, and ensuring consistent environments across teams.

Step 1 -> Create a vite Project

yarn create vite

Step 2 -> Tell the Vite Project to Initialize it.

yarn create vite my-vue-app --template vue

In the about command. you can replace vue with any Technology according to your desire. Such as

vanilla, vanilla-ts, vue, vue-ts, react, react-ts, react-swc, react-swc-ts, preact, preact-ts, lit, lit-ts, svelte, svelte-ts, solid, solid-ts, qwik, qwik-ts.

Is Artificial Intelligence (AI) is Killing Careers?

Mohammad Tahzeeb Khan — Sun, 15 Sep 2024 14:23:28 +0000

The word AI is not at all new to us. The Question is "Is AI is Really killing the Careers of Software Engineers or Developers?". The Answer is "NAH, not Really.". AI is just a Tool. Artificial Intelligence (AI) has made significant Importance to us in recent years, automating tasks once thought to be exclusively human. This has led to concerns about its potential impact on various industries, including software development. Many people wonder if AI is indeed a threat to the careers of software engineers and developers. But its not 100% True.

The Reality is AI is a Tool, Not a Replacement of Human.

While AI can automate certain aspects of software development, it's essential to understand that it's primarily a tool, not a replacement for human expertise.

Complex Problem-Solving: AI excels at tasks that involve pattern recognition and data analysis. However, it often struggles with complex problem-solving that requires creativity, critical thinking, and domain-specific knowledge.
Human-Centric Design: Designing software that is intuitive, user-friendly, and meets the needs of specific users requires a deep understanding of human behavior and psychology, which is difficult for AI to replicate.
Ethical Considerations: AI algorithms can be biased or make mistakes. Human oversight is crucial to ensure that AI systems are used ethically and responsibly.
Continuous Learning and Adaptation: The software development landscape is constantly evolving. Human engineers are better equipped to adapt to new technologies and trends than AI systems.

New Roles and Opportunities

Instead of replacing software engineers, AI is creating new roles and opportunities. As AI systems become more sophisticated, there will be a growing demand for engineers who can:

1. Develop and maintain AI algorithms: This requires a strong foundation in machine learning, deep learning, and related fields.
2. Ensure AI systems are ethical and unbiased: This involves understanding ethical principles and developing techniques to mitigate bias.
3. Integrate AI into existing software systems: This requires a deep understanding of software architecture and design principles.
Design and develop AI applications for specific domains: This requires domain-specific knowledge and expertise.

Conclusion

While AI is undoubtedly changing the current Fashion of software development, it's not a threat to the careers of software engineers or Developers. Instead, it's creating new opportunities and requiring engineers to develop new skills. By using AI as a tool and focusing on areas where humans excel, software engineers can continue to play an important role in shaping the future of technology.

At the End.

AI is a program or set of code that runs(Execute) on the machine. But Engineer(Human) is a Person who Develops AI, Retrain AI Models, and Developers can Kill the AI as well.....

SmartWork Using AI for Developers

Mohammad Tahzeeb Khan — Sat, 14 Sep 2024 08:32:12 +0000

In this fastest-growing world of software development, the pressure on Developers is to deliver solutions quickly, efficiently, and with high quality can be intense. Developers often juggle multiple projects, complex codebases, and ever-evolving technology stacks. Fortunately, Artificial Intelligence (AI) has stepped in as a powerful ally, transforming how developers approach their work. By automating mundane tasks, offering advanced problem-solving capabilities, and accelerating the development process, AI empowers developers to work smarter, not harder.

The most successful men work smart, not hard.

Two days ago, I came Across an AI Platform that converts Ideas into Code. I coded a website using this AI model in just 2 to 3 hours. This is a great time for me to work on such a Beautiful project with less effort and Time.

I got to know about this AI Platform from a YouTuber called "codewitharry". I saw a youtube video and Started coding a Website, and completed the Website before the deadline.

The Platform which I used is Pieces for Developer. You have to Download this software on your Local machine. And then good to go. You can get your work done just by giving Prompt to the Software or you can also add Plugin to you favorite IDE. It Support more than 10 IDEs, such as VScode, streamline, Intellij, pycharm, etc.

Why to use AI for Development?

Automating Repetitive Tasks

One of the most immediate and tangible benefits of AI for developers is the automation of repetitive tasks. From writing boilerplate code to running routine tests, AI tools save developers countless hours, allowing them to focus on more important and creative aspects of their projects.

Code Completion: Tools like GitHub Copilot and Tabnine use AI to predict and complete lines of code, significantly speeding up coding. These tools learn from a wide variety of codebases, helping developers by suggesting contextually relevant code snippets.
Code Formatting: AI-driven tools such as Prettier and Black can automatically format code according to a specified style guide, ensuring that your code is always neat and consistent across large teams.

2. Enhancing Debugging and Error Resolution

Debugging is an essential but time-consuming part of development. AI tools now streamline this process by identifying bugs, errors, or inefficiencies in the code more quickly and intelligently than ever before.

Intelligent Bug Detection: AI-based tools like DeepCode and SYNK can analyze code in real-time, flagging potential vulnerabilities, inefficiencies, or bugs. These tools continuously learn from vast datasets, providing developers with cutting-edge recommendations to fix issues early in the development process.
Error Resolution Assistance: Platforms like StackOverflow now integrate AI-driven bots that can provide developers with relevant answers to coding errors or bugs they encounter. Microsoft's Visual Studio Code and JetBrains IDEs are also integrating AI assistants for real-time debugging help.

3. Accelerating Project Management

AI isn't just useful for writing and testing code—it also plays a key role in managing the software development lifecycle, streamlining workflows, and improving communication between team members.

Smart Task Prioritization: AI-powered project management tools like Jira Smart Commit and Zenhub use machine learning to predict project bottlenecks, prioritize tasks based on importance, and optimize task allocation to reduce workload imbalances.
Automated Documentation: Writing project documentation is a tedious task that AI can handle effectively. Tools like Document360 and Scribe use AI to automatically generate detailed documentation based on project notes, code comments, and interactions with the system, leaving developers free to focus on their code.

4. Improving Collaboration with AI-Powered DevOps

AI can also assist in the collaboration between development and operations (DevOps), making the entire process from coding to deployment more efficient.

Continuous Integration/Continuous Deployment (CI/CD): AI-driven tools like Harness and CircleCI use machine learning to identify bottlenecks in the deployment pipeline, automate build optimizations, and reduce errors in production releases.
Monitoring and Performance Analysis: AI-powered monitoring tools like Datadog and New Relic help track application performance in real-time, automatically detecting anomalies and offering suggestions for optimization. This reduces the downtime and maintenance headaches developers often face.

5. Fostering Creativity and Innovation

By taking over repetitive and mundane tasks, AI allows developers to focus more on creativity and innovation. Whether it's brainstorming new features, designing intuitive user experiences, or architecting robust systems, developers can channel more energy into problem-solving and creative thinking.

Idea Generation and Prototyping: AI tools like Figma’s smart design assistant help generate layouts, UX ideas, and visual designs, allowing developers to focus on functionality while ensuring the user interface remains elegant and usable.
Innovation through AI APIs: Platforms like IBM Watson and Google Cloud AI provide AI APIs that developers can integrate into their applications, enabling advanced features like natural language processing, image recognition, and machine learning without needing to develop these systems from scratch.

Conclusion

The future of software development lies in Smart-Work, where AI becomes an indispensable partner for developers. By automating repetitive tasks, enhancing debugging and error detection, improving project management, and fostering creativity, AI helps developers achieve higher productivity and code quality. Whether you’re a beginner or a seasoned developer, embracing AI in your workflow will help you stay competitive, allowing you to focus on what matters most—innovation, problem-solving, and delivering high-quality software.

For More Follow me on:
Instagram : md.tahzeeb.k
Youtube : TheCodeCortex
LinkedIn : Mohammad Tahzeeb Khan
Github : Mohammad Tahzeeb Khan
Website : www.tahzeeb.dev