DEV Community: Sharad Raj (He/Him)

System Design of LeetCode

Sharad Raj (He/Him) — Thu, 12 Mar 2026 08:31:09 +0000

What actually happens when you hit "Submit" on LeetCode? 🤔
🔴 Me explaing in the video ➡️

Mind Map on Recent developments in Quantum

Sharad Raj (He/Him) — Tue, 11 Mar 2025 17:02:28 +0000

How to start with Laravel & REST APIs in it, I'm new to it?

Sharad Raj (He/Him) — Thu, 15 Feb 2024 16:01:00 +0000

I've experience with MERN stack but I'm completely new to Laravel, although I've got the basics of php but no oops concepts .... How should I start ? Any resources?

Update: I managed to convince the org to move to MERN and Python.

Have you tried any local alternatives to Copilot?

Sharad Raj (He/Him) — Tue, 05 Dec 2023 12:47:37 +0000

I've been using Copilot from quite a while and would like to know what do you all use ... (*-*)

Genetic Algorithms: Unleashing the Power of Nature in Problem Solving

Sharad Raj (He/Him) — Tue, 05 Dec 2023 12:41:57 +0000

Genetic Algorithms (GAs) are powerful problem-solving techniques inspired by the principles of natural evolution and genetics. They provide a way to search for optimal solutions to complex problems by imitating the process of natural selection. In this article, we will explore the origins of genetic algorithms and understand the essential components such as crossover, mutation, and fitness functions that make them effective tools for problem-solving.

The Origin

Genetic Algorithms were first introduced by John Holland in the 1960s. Holland was inspired by the concept of evolution, where organisms undergo continuous adaptation and improvement through generations. He realized that similar principles could be applied to problem-solving, leading to the development of genetic algorithms.

How do they Work?

Step 1: Initialization:

To begin with, a genetic algorithm creates an initial population of potential solutions. Each solution is represented as a string of genes, which can be thought of as the characteristics or attributes of the solution. For example, consider a problem of finding the shortest path between cities. Each solution can be represented as a string of city names in a specific order.

Step 2: Fitness Evaluation:

After initializing the population, the next step is to evaluate the fitness of each individual solution. The fitness function determines how well a solution solves the problem. It quantifies the quality of the solution based on specific criteria. In the case of the shortest path problem, the fitness function can measure the total distance traveled. Solutions with shorter distances will have higher fitness scores.

Step 3: Selection:

In the selection process, individuals with higher fitness have a higher chance of being chosen as parents for the next generation. This process mimics the idea of “survival of the fittest.” There are various selection methods available in genetic algorithms. One common method is roulette wheel selection, where individuals are selected with probabilities proportional to their fitness scores. Another method is tournament selection, where a subset of individuals competes, and the fittest individual is chosen as a parent.

Step 4: Crossover:

Crossover is a crucial step in genetic algorithms, inspired by genetic recombination in biology. It involves exchanging genetic information between two parent solutions to create offspring. Let’s consider an example where the problem is to find a binary string that matches a target string. In crossover, two parent solutions are selected, and a crossover point is chosen. The genes before the crossover point are exchanged between the parents, resulting in two offspring solutions that inherit traits from both parents. This process increases the diversity in the population and potentially leads to better solutions.

Step 5: Mutation:

Mutation introduces randomness into the population by randomly altering certain genes of an individual solution. This randomness ensures that the algorithm explores a wide range of potential solutions and prevents the algorithm from getting stuck in local optima. Returning to our binary string example, a mutation operation may flip a random bit in a solution. Mutation allows for the introduction of new traits that were not present in the original population, expanding the search space.

Step 6: Repeat Steps 2-5:

The steps of fitness evaluation, selection, crossover, and mutation are repeated iteratively until a termination condition is met. The termination condition can be a maximum number of generations, a desired level of fitness, or a specific number of iterations without improvement. With each iteration, the population evolves, and the solutions tend to improve over time.

Example: Solving the Knapsack Problem

Let’s consider an example to illustrate how genetic algorithms can solve a classic optimization problem called the Knapsack Problem. The problem involves selecting a set of items to maximize the total value while staying within a given weight constraint.

Initialization:

The initial population consists of randomly generated solutions, where each solution represents a combination of items.

Fitness Evaluation:

Each solution’s fitness is evaluated by calculating the total value of the selected items in the knapsack while ensuring the total weight does not exceed the weight constraint.

Selection:

Individuals with higher fitness scores have a higher probability of being selected as parents for the next generation. This selection process promotes the propagation of better-performing solutions.

Crossover:

Two parent solutions are chosen based on the selection process. A crossover point is randomly selected, and the genes beyond that point are exchanged between the parents. The resulting offspring inherit traits from both parents.

Mutation:

To introduce diversity, random mutations occur by flipping a gene or altering its value. In the knapsack problem, a mutation might involve randomly adding or removing an item from a solution.

Repeat Steps 2-5:

The fitness of the offspring is evaluated, and the selection, crossover, and mutation steps are repeated. This iterative process allows the algorithm to explore different combinations of items, gradually improving the fitness of the population.

By repeating these steps for a specific number of generations or until a termination condition is met, genetic algorithms converge towards optimal or near-optimal solutions.

That is it, now you know about them ... at least the basics :)

GAs are super cool, they solve problems using the magic of natural selection and genetics. They work by creating a population of possible solutions, then using steps like fitness evaluation, selection, crossover, and mutation to improve those solutions over time. It's like a never-ending game of survival of the fittest, but for solutions to your problems. These algorithms have been used in all sorts of fields, from optimizing stuff to scheduling and even machine learning. So if you're looking for a versatile and effective way to solve problems, give genetic algorithms a try!

Naya Tab: A NEW TAB Browser extension like Brave Browser's new tab page

Sharad Raj (He/Him) — Fri, 08 Jul 2022 10:07:24 +0000

I created this browser extension called "Naya Tab".

This new tab extension adds functionality similar to the brave browser's new tab page.

You can add shortcuts to websites and change wallpaper for the new tab page.

I was trying to publish it on Chrome Webstore but unfortunately I ran into some payment related issues so for now it's only available for firefox & edge.

You can install it on Firefox & Edge by visiting:

https://addons.mozilla.org/en-US/firefox/addon/naya-tab/

https://microsoftedge.microsoft.com/addons/detail/naya-tab-a-new-tab-exte/nclaeoangfbocckefkmmiabicmfcpolg

You can support me at: https://github.com/sponsors/sharadcodes
for India UPI: iamsharadraj@okaxis

I will make it open source once it get published on chrome and edge webstore.

Get this Browser Extension now!

Sharad Raj (He/Him) — Mon, 13 Jun 2022 13:54:39 +0000

I created this browser extension called "Naya Tab".

This new tab extension adds functionality similar to the brave browser's new tab page.

You can add shortcuts to websites and change wallpaper for the new tab page.

I was trying to publish it on Chrome Webstore but unfortunately I ran into some payment related issues so for now it's only available for firefox.

As for the edge store, it's under review so I'll post here when it gets published.

You can install it in Firefox by visiting:

https://addons.mozilla.org/en-US/firefox/addon/naya-tab/

https://microsoftedge.microsoft.com/addons/detail/naya-tab-a-new-tab-exte/nclaeoangfbocckefkmmiabicmfcpolg

You can support me at: https://github.com/sponsors/sharadcodes
for India UPI: iamsharadraj@okaxis

I will make it open source once it get published on chrome and edge webstore.

Created AnonPasteBin Using appwrite's Anonymous Login functionality

Sharad Raj (He/Him) — Thu, 12 May 2022 15:00:25 +0000

Overview of My Submission

I came to know about appwrite on March 11th so had literally 1 day to submit the project...... but it turns out it's a dead simple setup for any developer and I've to say it's an amazing dev experience.

So I made this Anonymous Pastebin Project AKA "AnonPaste" to test out the anonymous login functionality provided by appwrite and how it works behind the scenes.

The project is dead simple, it does following:

As soon as you are on the create paste page you are automatically logged in using anonymous login.
When you create a paste after providing title and text it saves and redirects the user to the View Paste Page.
View Paste Page extracts the ID of the document AKA paste from the route and uses it to load the document AKA paste and show it to the user.

Submission Category:

Web2 Wizards

Link to Code

sharadcodes / appwrite-pastebin-devto-hackathon

DEV.to appwrite Hackathon Submission | AnonPaste webapp

DEV appwrite Hackathon project

Anonymous PasteBin AKA AnonPaste

So I made this "AnonPaste" Anonymous PasteBin Project to test out the anonymous login functionality provided by appwrite and how it works behind the scenes.

The project is dead simple, it does following:

As soon as you are on the create paste page you are automatically logged in using anonymous login.
When you create a paste after providing title and text it saves and redirects the user to the View Paste Page.
View Paste Page extracts the ID of the document AKA paste from the route and uses it to load the document AKA paste and show it to the user.

Setup

Clone repo.
Install appwrite. Steps are at: https://appwrite.io/
Cd to repo folder and npm i
Open appwrite dashboard.
Create a collection named pastess with following string fields
- txt size 10000.
- title & code with size leave it as…

View on GitHub

Additional Resources / Info

Video Demo:

Screenshots:

How do you manage releases on Github ?

Sharad Raj (He/Him) — Mon, 29 Nov 2021 05:12:12 +0000

Are you using any Github Marketplace App/Action or have you written your own pipeline or are you using something entirely different?
Let's discuss (* - *)...

PWA to APK Github Action

Sharad Raj (He/Him) — Wed, 24 Nov 2021 06:18:21 +0000

My Workflow

So I've created this PWA to APK Action which currently coverts the project generated by bubblewrap-cli to APK.

You'll need to install bubblewrap-cli which requires just a few steps, you can follow this guide for it.

Submission Category:

Phone Friendly

Yaml File or Link to Code

actions.yml: code
Action Github Repository:

sharadcodes / pwa-to-apk-action

A Github Action for Converting your PWA to android app.

Welcome to pwa-to-apk-action 👋

Github Action for Converting your PWA to android app.

✨ Demo

How to use?

To use this action add following to your workflow file

- name: PWA to APK action
  uses: sharadcodes/pwa-to-apk-action@v1.0.0
  with:
    project-root-folder: 'your_android_project_folder_root' # This should be the root folder which contains
                                                            # all the files generated by bubblewrap-cli

What about the Future?

Well I'm planning to remove the extra step in which you have to upload the android project folder generated by bubblewrap-cli in the next release of this action so stay tuned by watching the repo :), a star will also do a fine job.

Author

👤 Sharad Raj Singh Maurya

Show your support

Give a ⭐️ if this project helped you!

View on GitHub

Additional Resources / Info

Demo repository

sharadcodes / pwa-to-apk-action-test

This repository contains files generated by bubblewrap-cli which will be used in awsm-pwa-to-apk-action

Welcome to pwa-to-apk-action-test 👋

This repository contains files generated by bubblewrap-cli which will be used in pwa-to-apk-action, it's a one time process, I'll automate this step further so that you only have to push to your web app and the project along with apk get's generated automatically, currently only apk generation is automated.

Author

👤 Sharad Raj Singh Maurya

Show your support

Give a ⭐️ if this project helped you!

View on GitHub

TODO

Automating the step in which you have to upload the android project folder generated by bubblewrap-cli.
Fixing the generated apk signing error.

There are some issues with the generated apk so I don't recommend to use it for production. I will be fixing many things in the upcoming days :)

Tools for generating REST API docs rapidly?

Sharad Raj (He/Him) — Thu, 11 Nov 2021 17:37:28 +0000

So let's assume you have this REST API in whatever foo language, how will you document it if in hurry or you don't want to do complex things just for the sake of it.

Let's have a discussion and share resources (-.-)

PS: It's 11pm here so I may be on sleep mode after some time.

So here is a new Gatsby, Hugo & Jekyll theme (-.-) 🔥🔥

Sharad Raj (He/Him) — Thu, 11 Nov 2021 17:28:06 +0000

I'm posting this after a long time, I think a year maybe. So making the comeback with creating some themes and sharing FOSS side projects that I created for myself.

So first one in the list is this Gatsby theme.
Actually, I created this theme first for Jekyll then Hugo and then for Gatsby.

How it looks

Here are all the repository links:

Thank you for reading (-.-)

DEV Community: Sharad Raj (He/Him)

System Design of LeetCode

Mind Map on Recent developments in Quantum

How to start with Laravel & REST APIs in it, I'm new to it?

Have you tried any local alternatives to Copilot?

Genetic Algorithms: Unleashing the Power of Nature in Problem Solving

The Origin

How do they Work?

Step 1: Initialization:

Step 2: Fitness Evaluation:

Step 3: Selection:

Step 4: Crossover:

Step 5: Mutation:

Step 6: Repeat Steps 2-5:

Example: Solving the Knapsack Problem

Initialization:

Fitness Evaluation:

Selection:

Crossover:

Mutation:

Repeat Steps 2-5:

Naya Tab: A NEW TAB Browser extension like Brave Browser's new tab page

Get this Browser Extension now!

https://microsoftedge.microsoft.com/addons/detail/naya-tab-a-new-tab-exte/nclaeoangfbocckefkmmiabicmfcpolg

Created AnonPasteBin Using appwrite's Anonymous Login functionality

Overview of My Submission

Submission Category:

Link to Code

sharadcodes / appwrite-pastebin-devto-hackathon

DEV.to appwrite Hackathon Submission | AnonPaste webapp

DEV appwrite Hackathon project

Anonymous PasteBin AKA AnonPaste

Setup

Additional Resources / Info

How do you manage releases on Github ?

PWA to APK Github Action

My Workflow

Submission Category:

Yaml File or Link to Code

sharadcodes / pwa-to-apk-action

A Github Action for Converting your PWA to android app.

Welcome to pwa-to-apk-action 👋

✨ Demo

How to use?

What about the Future?

Author

Show your support

Additional Resources / Info

sharadcodes / pwa-to-apk-action-test

This repository contains files generated by bubblewrap-cli which will be used in awsm-pwa-to-apk-action

Welcome to pwa-to-apk-action-test 👋

Author

Show your support

TODO

Tools for generating REST API docs rapidly?

So here is a new Gatsby, Hugo & Jekyll theme (-.-) 🔥🔥

How it looks

Gatsby Theme Serial Programmer

Jekyll Theme Serial Programmer

Hugo Theme Serial Programmer