DEV Community: QuanticDev

My Project Got 800 Stars in Two Days on GitHub

QuanticDev — Tue, 03 Nov 2020 11:08:53 +0000

In this article, you will discover how my open-source JavaScript project got 800 stars on GitHub within two days of publishing it. My project is called KOAN (github.com/soygul/koan), and I created it to preserve my general knowledge in Koa and Angular frameworks, as well as Node.js and MongoDB, by making a ready-to-use project template. Upon submitting it to a couple of JavaScript newsletters for review, it exploded and got 800 stars in its first two days of existence! So how did this happen? Did I get lucky? Or did I build something special that people wanted or needed? Or did I simply game the GitHub? Well, read on.

Outline of the article:

KOAN Project
KOAN Demonstration
How to Get Starts on GitHub Then?
Conclusion
You can read the full writeup here: https://quanticdev.com/articles/my-project-got-800-stars-in-two-days-on-github
You can find the live demonstration of KOAN here: https://koan.herokuapp.com

Maximum Value PC Build for Software Engineering

QuanticDev — Wed, 23 Sep 2020 15:26:16 +0000

Today we will build a desktop computer from scratch with the maximum possible value for the money. I am a long time Mac and Linux user on the laptop side for my software engineering tasks, but I am tired of the fan noise. So, I decided to create a desktop build and get the maximum bang for my buck, while retaining great upgradability. I would like to remind you that this build is targeting mainly programming with lightweight photo/video/3D work. Below are the specs:

CPU: AMD Ryzen 5 3600: ~$200
- Alternative: Ryzen 3 3100/3300X: ~$100
- Future: Ryzen 4600/4300X/4100 when they are out.
GPU: NVIDIA GeForce GTX 1650 SUPER: ~$200
- Alternative: GTX 1060 6GB: ~$100: (check out Ebay)
- Future: GTX 3060 when it is out.
MOBO: MSI X570 Tomahawk WiFi: ~$200
- Alternative: MSI B550 Gaming Carbon WiFi: ~$200
RAM: Corsair Vengeance LPX Black DDR4 3200MHz 2x16GB: ~$150
SSD Disk (NVMe): WD Blue SN550 M.2 2280 1TB: ~$100
Power Supply: Corsair RM750X V2 750W: ~$120
Chassis: Fractal Design Define 7: ~$150
- Alternative: Fractal Design Meshify C: ~$90
Keyboard: HyperX Alloy FPS: ~$70
Mouse: Logitech G502: ~$50
Headset: Logitech G Pro
Desk Speaker: Creative Pebble V2
Total: ~$900 - ~$1200

Since it is physically impossible to copy that many images to dev.to, I'll just leave a link to my blog post on YT video if you want to follow the full guide:

Article: https://quanticdev.com/articles/max-value-pc-build-guide
Video guide: https://www.youtube.com/watch?v=LUE3avxvPMA

Big O Time/Space Complexity Types Explained - Logarithmic, Polynomial, Exponential, and More

QuanticDev — Mon, 24 Aug 2020 12:25:05 +0000

Hi all,

Today I will talk about the most important time and space complexity types. Time and space complexities are a measure of a function's processing power and memory requirements. Many time/space complexity types have special names that you can use while communicating with others. While some of the names for complexity types are well known, like linear and constant time, some others are living in the shadows, like quadratic and factorial time. In this video, I will use the big O notation to denote the complexities, which is specifically used to describe the worst-case performance of algorithms. If you want to see or read it, below are the links for the video and article:

Video: https://www.youtube.com/watch?v=GesAhP5jYLo
Article: https://quanticdev.com/articles/primitives/big-o-time-space-complexity-types-explained

Outline of the video/article:

Overview
Constant Time/Space Complexity: O(1)
Logarithmic Complexity: O(logn)
Linear Complexity: O(n)
Polynomial Complexity: O(n^k)
Exponential Complexity: O(2^n)
Factorial Complexity: O(n!)
Alternative Big O Notation
Conclusion

I suggest the video format as it has animations for the references algorithms, which can help you comprehend them easier. Have fun learning!

Is Windows Good for Developers Again? - A Review by a Senior Software Engineer

QuanticDev — Fri, 10 Jul 2020 13:18:44 +0000

Hello chaps, I am the QuanticDev, a senior software engineer with a decade of experience. I have started my development career with Windows back in middle school. I mostly did PHP back then, along with some basic C/C++ using Borland C++ Builder 6, and it worked out great! However, since then, Windows became the underdog of software development. With the power of free and open-source software, Linux has taken over the server world. The easiest way to write server-side apps that target Linux is to use a Unix based system like macOS or Linux, and not Windows! In addition, Windows never had a great package manager. There were attempts like Chocolatey or Scoop, but they are no match to Homebrew on macOS or 10 different package managers found on Linux systems. Now that we have the second version of Windows Subsystem for Linux, as well as the shiny new Windows Package Manager called “winget”, did the tables turn? Let’s find out.

Video: https://www.youtube.com/watch?v=QnHGGmLMKO0
Article: https://quanticdev.com/articles/is-windows-good-for-developers

Outline of this video/article:

Overview
Windows Package Manager (winget)
Windows Subsystem for Linux 2 (WSL 2)
Downsides of WSL and winglet
Is Window with WSL 2 and winget a Worthy Developer OS?

Kadane's Algorithm and Its Proof - Max/Min Sum Subarray Problem

QuanticDev — Thu, 18 Jun 2020 13:33:46 +0000

I have previously published an article on max/min subarray sum problem using Sliding Window technique. Now it is time to one-up it with Kadane's Algorithm, which also handled arrays with negative numbers. It is slightly more complex than Sliding Windows Technique but it is a frequent ingredient of many programming interview questions. It is also a prime example of Dynamic Programming.

You can find the full writeup here: https://quanticdev.com/algorithms/dynamic-programming/kadanes-algorithm
If you prefer a video instead: https://www.youtube.com/watch?v=4csAswCkXZM

How to Find Funding for Your Project

QuanticDev — Tue, 09 Jun 2020 12:28:44 +0000

It is quantic time that we investigate options for financing your projects. It doesn't matter if you are looking to fund a new business idea or just a side project, there are options at all levels. For instance, there are microgrants up to $5000 for small side projects. For bigger business ideas, you have recurring grants, government funds, and crowdsourcing options. You can even go with advertising on your projects, sponsorships, and selling relevant merchandise. The final step is to look for venture capital, of course.

I have just published an aggregate article from all other sources, and you can find it here: https://quanticdev.com/articles/how-to-fund-your-project
Analysis of all the resources listed on the article can be found as a video: https://www.youtube.com/watch?v=uOX3IU_OSig

Funding options that we will investigate:

Crowdfunding
- Kickstarter & Indiegogo
- Patreon
- Crowdfunder
Grants
- Facebook SM Grants
- AwesomeFoundation
- Gitcoin
- Python Software Foundation Grants
- Tyk’s Side Project Fund
- Unitary Fund
Open-Source Focused Grants
- InternetFonden of Sweden
Lemonade Stand Guide (list of funding types)
- Donation Button
- Bounties
- Sponsorware
- Books & Merchandise & Advertising & Sponsorship
- Get Hired to Work On a Project
- Start a Project For Your Company
- Consulting & Paid Support
- Software as a Service
- Open Core & Paid License
- Venture Capital

Lockable Tree - Google Interview Question

QuanticDev — Thu, 14 May 2020 09:17:11 +0000

Lockable tree is a great programming interview question asked by Google, and it is a very well thought out one. A lockable tree is a tree with nodes that can be locked if none of the ancestors or descendants is locked. In the question, we are asked to implement locking/unlocking operations that should run in O(h) time where h is the height of the tree.

This is a very well-crafted interview question by Google. Both the requirements and the question itself are quite clear, which is a rarity in the industry. Often, the interviewers will intentionally make the question a little obscure, so they can observe how you do your requirements analysis and if you can communicate with the interviewers clearly. However, in this case, the requirements are clear cut, which I think reflects how Google operates.

I am pretty sure most of the folk here can solve this by themselves. But if you want the full solution with requirements analysis, improvement, and solution steps, here it is: https://quanticdev.com/algorithms/trees/lockable-tree

Essential Software for Working From Home

QuanticDev — Wed, 13 May 2020 15:19:49 +0000

Working from home is the new necessity, maybe even the future of many jobs. The University of Chicago recently published a paper on how many jobs can be done at home. Based on the study in the United States, they concluded that 34 percent of the jobs could be done at home, and those jobs account for 44 percent of all wages. That is a pretty significant percentage. Given the circumstances, there must be a lot of people scrambling to get their working-from-home setup going.

Today I am going to give you the essential list of software for working from home. Below is the software that I will review in this article. They are ordered based on my quality assessment, which you will see in the article/video:

Team Chat
Slack
Google Hangouts
Microsoft Teams
Mattermost
Video Conferencing
Zoom
Discord
join.me
Jitsi
Blue Jeans
Online Office Suite: Mail, Calendar, and File Sharing
Microsoft Office 365
Google Suite
Project Management
Trello
AirTable
Asana
Pivotal Tracker
Wrike
Jira (!)
All-in-One
GitHub

Know the Difference: Subarray vs Substring vs Subsequence vs Subset

QuanticDev — Thu, 16 Apr 2020 12:19:14 +0000

Today we are going to make a comparison of subarray vs substring vs subsequence vs subset. These are all similar concepts but have important differences. Especially in an interview situation, you need to be extra careful about your choice of wording. If a question asks you to return a subsequence and you return a subset, you might fail the interview. Or the interviewer can directly ask you about the differences between these concepts as they are quite important. Let me start by describing each concept with examples. Finally, I will give you a comparison table.

Video: https://www.youtube.com/watch?v=uzhN-QhzR2g
Article: http://quanticdev.com/algorithms/primitives/subarray-vs-substring-vs-subsequence-vs-subset

State of Software Engineering in 2020

QuanticDev — Tue, 07 Apr 2020 13:26:00 +0000

It is time to talk about the state of software engineering in 2020 and beyond. Software engineering has seen explosive growth over the last 20 years, and it seems to be keeping that momentum up. According to Fortune data, total revenue of top 15 technology companies in the world was a record 1.67 Trillion US Dollars in 2019, which is up 2% from 2018. There are more software companies than ever now. In addition, existing non-software companies are introducing more software components into their products, anything from cars to washing machines. The future is software, but not all software is created equal. Identifying the most promising and fastest-growing areas of software can help you take off your career and projects. Investing in a growing area helps you to find a job easier and get paid better, and helps you in finding funding for your projects. So, let us not waste time and get right into it. This video uses GitHub Octoverse data as the basis, plus my experiences and observations as a software engineer with a decade of experience.

Video: https://www.youtube.com/watch?v=GesAhP5jYLo
Article: https://quanticdev.com/articles/software-engineering-in-2020

Outline of this video/article:

Overview
Growth of Programming
Growth of Open-Source
Top Libraries
Trending Projects
Top Programming Languages
Data Science and Machine Learning
Cloud Computing, DevOps, and Security
Conclusion

Sliding Window Technique + 4 Questions (Algorithms Series)

QuanticDev — Tue, 31 Mar 2020 12:03:06 +0000

Sliding Window Technique is a method for finding subarrays in an array that satisfy given conditions. We do this via maintaining a subset of items as our window, and resize and move that window within the larger list until we find a solution. Sliding Window Technique is a subset of Dynamic Programming, and it frequently appears in algorithm interviews. In this video, you will learn how Sliding Window Technique works (with animations), tips and tricks of using it, along with its applications on some sample questions.

Video: https://www.youtube.com/watch?v=jM2dhDPYMQM
Article: http://quanticdev.com/algorithms/dynamic-programming/sliding-window

In the video, you will find the solutions to the following questions, as well as their time and space complexities:

• Easy: Statically Sized Sliding Window: Given an array of integers, find maximum/minimum sum subarray of the required size.
• Medium: Dynamically Sized Sliding Window: Given an array of positive integers, find the subarrays that add up to a given number.
o Variation (Medium): Same question but for an array with all integers (positive, 0, negative). The optimal solution is Kadane's Algorithm, but Sliding Window can still be applied with modifications (not recommended though).
• Medium: Flipping/Swapping: Given an array of 0's and 1's, find the maximum sequence of continuous 1's that can be formed by flipping at-most k 0's to 1's.
• Hard: Strings: Given a string and n characters, find the shortest substring that contains all the desired characters.

Most Popular Programming Languages 2010 - 2023 (extrapolated)

QuanticDev — Mon, 10 Feb 2020 13:23:34 +0000

Most popular programming languages by pull requests on GitHub public repositories. Original GitHub data is for 2012 - 2020. Rest of the data is extrapolated using linear regression.

Animation on YT: https://www.youtube.com/watch?v=LjWn2aJ3o2g