DEV Community: Eztosin

🎓 ScholarTrack — An AI-Powered Scholarship Management Dashboard

Eztosin — Sun, 22 Jun 2025 20:28:52 +0000

This is a submission for the Runner H "AI Agent Prompting" Challenge

What I Built

ScholarTrack is an end-to-end AI-integrated platform that allows students to discover, apply for, and manage scholarship opportunities automatically. It leverages Runner H as an autonomous agent that finds scholarships and submits them directly into a fully functional web dashboard built with Bolt and Supabase.

This project streamlines the time-consuming and often overwhelming process of scholarship searching and tracking by delegating it to an AI agent, while providing students with a modern, mobile-friendly dashboard to manage their applications.

🔗 Live Demo: scholartrack-production.app

🔑 Demo Login:

Email: demo@scholartrack.com

Password: demo123456

GitHub Repo: ScholarTrack.git

Here are screenshots from the live platform:

📊 Real-time dashboard with status summaries
📝 Application cards with full CRUD support
🔄 "Claim Demo Data" feature for new users
🤖 API connection with Runner H

How I Used Runner H

Runner H acts as the autonomous AI agent that:

Finds fully-funded scholarships tailored to a student's background
Fills in application details like title, link, deadline, and notes
Sends the data via a POST request to the backend API: https://<SUPABASE_URL>/functions/v1/applications
Can call /applications/summary to generate weekly reports

Example prompt to Runner H:

“Use this student profile to find 3 international scholarships. Apply on their behalf and submit the application details (title, link, deadline, status) to the provided API endpoint.”

The Supabase function is protected with an Authorization header and properly structured to accept Runner H’s submission.

Use Case & Impact

🎯 Use Case

Target Audience: University students, especially in Africa and emerging markets
Problem Solved: Discovering and organizing scholarship applications is hard and tedious
Solution: Delegate it to an AI agent and manage it from a single unified dashboard

💥 Impact

Empowers students with limited time and resources to access funding opportunities
Reduces stress and increases the chances of submitting more applications
Introduces automation into a space that desperately needs it

Social Love ❤️

📢 Shared on:

Let’s make education more accessible with automation! 🚀

Team Submission

Created by:

@eztosin

🧠 Powered by:

Runner H (agent automation)
Bolt (code generation and deployment)
Supabase (auth, DB, API)
Netlify (frontend hosting)

Thanks for reading, and happy prompting! ⚡

June 12: A Landing Page Tribute to Nigerian Democracy

Eztosin — Wed, 18 Jun 2025 07:31:56 +0000

This is a submission for Frontend Challenge - June Celebrations, Perfect Landing: June Celebrations

What I Built

For this challenge, I chose to highlight a celebration that is deeply resonant and significant here in Nigeria: Democracy Day, observed on June 12th. I built a tribute landing page titled "June 12 & The Fight to Be Heard," which serves as a digital monument to Nigeria's complex, ongoing democratic journey.

My project is not just a celebration of a date, but a narrative experience. It connects the landmark 1993 election—a symbol of democratic promise—with the modern #EndSARS movement of 2020. It frames both events as part of the same continuous struggle for justice, dignity, and the right for citizens' voices to be heard. The landing page walks the user through this powerful story, from hope and resistance to the modern echoes of that same spirit.

Demo

The live version of the site is hosted on GitHub Pages. You can experience the full narrative journey by visiting the link below.

Live Demo: June 12 Tribute
- GitHub Repo: Eztosin/June-12-Tribute

Here is a glimpse of the hero section:

Journey

The journey of building this project was both technical and emotional. When I saw the "June Celebrations" theme, I knew I wanted to do something that felt personal and urgent. As a Nigerian, June 12 is more than a public holiday; it's a symbol of a promise and the struggle to uphold it. Connecting that history to the recent #EndSARS protests felt like an important story to tell through design and code.

My process began with defining the narrative flow. I wanted to create a timeline that was not just informative but also evocative. The core challenge was to handle such a sensitive topic with the respect and gravity it deserves, while still creating a visually engaging front-end experience.

Tech Stack & Decisions:

I chose React and Vite for a fast, modern development environment.
Tailwind CSS was essential for rapidly styling the components and creating a clean, consistent design system based on the symbolic colors of the Nigerian flag, with black and red representing the struggle.
The most important library I used was Framer Motion. I am particularly proud of the animated vertical timeline. Using whileInView animations, I could make each historical event fade in as the user scrolls, creating a powerful sense of discovery and progression through the story.
I used react-scroll for the navigation to create a smooth, single-page application feel that guides the user seamlessly through each chapter of the narrative.

One of the things I learned was the importance of typography in setting a tone. I used a bold, impactful font-heading for titles to convey strength, and a clean font-mono for dates to give a sense of historical record.

For the future, I hope to expand this project by adding a "Stories" section where Nigerians can anonymously submit their own short stories related to activism and democracy. I would also like to add more links to educational resources and grassroots organizations that are working to strengthen Nigeria's democratic institutions.

This project is a tribute to the resilience of the Nigerian spirit. Thank you for the opportunity to share this story.

My Submission for the DEV CSS Art Challenge: "The Gift That Sparked the Dream"

Eztosin — Sun, 15 Jun 2025 15:25:51 +0000

This is a submission for Frontend Challenge - June Celebrations, CSS Art: June Celebrations.

For this year's Frontend Challenge: June Celebrations, I chose the "CSS Art" prompt to create something deeply personal. While June is full of vibrant public celebrations, I wanted to honor a quiet, personal one that shaped my entire life: the day my father gave me my first computer. This entry is my tribute to Father's Day.

The theme of my piece is "The Gift That Sparked the Dream." It’s a story, told in three acts, about how a single act of generosity from a parent can ignite a lifelong passion and career.

✨ The Live Demo
First, I invite you to experience the project live. Click through the scenes to see the story unfold.

➡️ View Live Demo

💡 The Concept: A Story in Three Acts
This isn't just a static piece of art. It’s an interactive narrative that progresses through three stages of my journey, each triggered by a click.

🎁The Gift: The opening scene shows the foundational moment—my father giving me the glowing laptop. It's full of potential and quiet excitement.
📚The Learning: The scene shifts to focus on the child, engrossed in the laptop. The father fades into the background, symbolizing his supportive but non-intrusive presence during the long nights of learning and practice.
🎓The Career: The child is now grown, dressed in professional attire. The father is gone from the scene, but his gift's legacy is clear. To celebrate this achievement, the scene comes alive with looping, celebratory fireworks, representing the dream realized.

🛠️ The Technical Approach: How It Was Built
My goal was to create a rich, animated world using modern, efficient, and accessible techniques.

Pure CSS Characters & Clothing
The father and child figures are built from simple <div> elements and styled with CSS. Their clothes aren't extra HTML elements; they are created using a layered linear-gradient trick. The top layer is the shirt, and the bottom layer is the pants, all applied to the single background property of the main body div. This keeps the HTML clean while adding visual detail.

Interactive Storytelling with JavaScript
While the art is primarily CSS, a small amount of JavaScript acts as the "storyteller." It listens for clicks on the scene-toggle buttons (The Gift, Learning, Career) and updates a data-active-scene attribute on the main container. CSS attribute selectors then apply the correct styles, animations, and character positions for each "act" of the story.

A World of Animation

Subtle Life: The characters have a gentle, looping sway and bounce feature to bring them to life.
Looping Fireworks: To create a gentle celebratory effect on the career page, I used a fun box-shadow trick. Instead of adding dozens of HTML elements, each firework "burst" is generated by a single ::after pseudo-element. Its box-shadow property has over 20 comma-separated values, and each shadow is animated outwards to create a multi-particle explosion. The entire sequence loops to keep the celebration going!
The Final Message: The "Thank you, Dad." message uses a classic typing animation, created by animating the width of the element with the steps() timing function.

A Focus on Accessibility
A project isn't truly complete if it isn't accessible.

Screen Reader Support: The entire scene has a role="img" attribute and a detailed aria-label that describes the artwork, allowing visually impaired users to understand the story. All interactive elements also have ARIA labels.
Respecting User Preferences: The entire project uses a prefers-reduced-motion media query. If a user has motion sensitivity enabled in their operating system, all animations are automatically disabled.

Challenges & Learnings
The most interesting challenge was a visual bug where the child's arm would look "disconnected" during its bouncing animation. I learned that the best fix wasn't to complicate the animation, but to improve the physical "connection" by adjusting the CSS to make the arm overlap the body by a few more pixels. It was a great reminder that sometimes the simplest solution is the most effective.

Conclusion
This project was a joy to create. It was a technical challenge, but more importantly, it was an opportunity to express gratitude. That single gift from my dad didn't just open doors; it booted up a whole new world.

Thank you for this wonderful challenge, DEV team, and a very happy Father's Day to all the dads out there who inspire and support our dreams.

View the Code: You can find the complete source code for this project on my GitHub.

➡️ Github Repo

Math Catcher – Sharpen Your Mind, One Catch at a Time

Eztosin — Fri, 09 May 2025 09:30:29 +0000

This is a submission for the Amazon Q Developer "Quack The Code" Challenge: That's Entertainment!

🎮 What I Built

Math Catcher is a fast-paced, educational arcade game built for the browser where players catch falling balls that display correct answers to math questions.

The concept is simple but engaging:

A math question appears at the top of the screen.
Several balls fall from above, each with a potential answer.
Players move the catcher left or right to grab the correct one.
Score increases with every right catch, and the game gets faster.

The game is built with JavaScript, HTML5 Canvas, and a Custom CSS with minimal use of Tailwind classes for a sleek retro feel. It’s perfect for kids learning arithmetic or adults who want a brain workout!

🚀 Demo

Math Catcher Gameplay

🔐 Test Credentials (if needed for admin/user login):

Admin:
username: admin
password: 2025DEVChallenge

User:
username: newuser
password: 2025DEVChallenge

🎥 Gameplay Preview:

🧠 Code Repository

📂 GitHub: Math-Catcher-Sharpen-Your-Mind-One-Catch-at-a-Time

🤖 How I Used Amazon Q Developer

Amazon Q Developer was an essential assistant throughout the creation of Math Catcher:

🧠 Debugging Help: When balls were falling out of sync or answers weren't registering, Q helped me troubleshoot logic errors in my animation frame and key event listeners.
🎨 Tailwind Tweaks: I asked Q, "How can I refactor my JavaScript math-catching game to use classes for better modularity and scalability?", and it created utility classes to polish the look of buttons, scoreboards, and catch zones.
📦 Optimization: Q helped me refactor my game logic, implement a dynamic timer, and scale difficulty smoothly for mobile responsiveness.

🔍 Pro Tip: Asking Amazon Q specific questions like "How can I make my HTML5 Canvas math game more accessible and mobile-friendly?" gave me precise and usable solutions.

🎓 Educational Value

Not only is Math Catcher fun, it’s also a great tool to help children practice:

Addition, subtraction, multiplication
Quick thinking and coordination
Math fluency under pressure!

📚 Lessons Learned

Simplicity and accessibility make games more engaging.
Amazon Q Developer is a powerful code partner for solo indie projects.
A small project with a meaningful twist can still be impactful.

Built by: @eztosin

⚠️ I am currently a student, and excited to learn through hands-on challenges like this!

Mastering Machine Learning with Python: Foundations and Key Concepts

Eztosin — Mon, 16 Sep 2024 15:39:43 +0000

In today's era of Artificial Intelligence (AI), scaling businesses and streamlining workflows has never been easier or more accessible. AI and machine learning equip companies to make informed decisions, giving them a superpower to predict the future with just a few lines of code. Before taking a significant risk, wouldn't knowing if it's worth it be beneficial? Have you ever wondered how these AIs and machine learning models are trained to make such precise predictions?
In this article, we will explore, hands-on, how to create a machine-learning model that can make predictions from our input data. Join me on this journey as we delve into these principles together.
This is the first part of a series on mastering machine learning, focusing on the foundations and key concepts. In the second part, we will dive deeper into advanced techniques and real-world applications.

Introduction to Machine Learning (ML):

Machine Learning (ML) essentially means training a model to solve problems. It involves feeding large amounts of data (input-data) to a model, enabling it to learn and discover patterns from the data. Interestingly, the model's accuracy depends solely on the quantity and quality of data it is fed.

Machine learning extends beyond making predictions for enterprises; it powers innovations like self-driving cars, robotics, and much more. With continuous advancements in ML, there's no telling what incredible achievements lie ahead - it's simply amazing, right?

There's no contest as to why Python remains one of the most sought-after programming languages for machine learning. Its vast libraries, such as Scikit-Learn and Pandas, and its easy-to-read syntax make it ideal for ML tasks. Python offers a simplified and well-structured environment that allows developers to maximize their potential. As an open-source programming language, it benefits from contributions worldwide, making it even more suitable and advantageous for data science and machine learning.

Fundamentals Of Machine Learning

Machine Learning (ML) is a vast and complex field that requires years of continuous learning and practice. While it's impossible to cover everything in this article, let's look into some important fundamentals of machine learning, specifically:

Supervised Machine Learning From its name, we can deduce that supervised machine learning involves some form of monitoring or structure. It entails mapping one function to another; that is, providing labeled data input (i) to the machine, explaining what should be done (algorithms), and waiting for its output (j). Through this mapping, the machine learns to predict the output (j) whenever an input (i) is fed into it. The result will always remain output (j). Supervised ML can further be classified into:

Regression: When a variable input (i) is supplied as data to train a machine, it produces a continuous numerical output (j). For example, a regression algorithm can be used to predict the price of an item based on its size and other features.

Classification: This algorithm makes predictions based on grouping by determining certain attributes that make up the group. For example, predicting whether a product review is positive, negative, or neutral.

Unsupervised Machine Learning Unsupervised Machine Learning tackles unlabeled or unmonitored data. Unlike supervised learning, where models are trained on labeled data, unsupervised learning algorithms identify patterns and relationships in data without prior knowledge of the outcomes. For example, grouping customers based on their purchasing behavior.

Setting Up Your Environment For Machine Learning

When setting up your environment to create your first model, it's essential to understand some basic steps in ML and familiarize yourself with the libraries and tools we will explore in this article.

Steps in Machine Learning:

Import the Data: Gather the data you need for your analysis.
Clean the Data: Ensure your data is in good and complete shape by handling missing values and correcting inconsistencies.
Split the Data: Divide the data into training and test sets.
Create a Model: Choose your preferred algorithm to analyze the data and build your model.
Train the Model: Use the training set to teach your model.
Make Predictions: Use the test set to make predictions with your trained model.
Evaluate and Improve: Assess the model's performance and refine it based on the outputs.

Common Libraries and Tools in Python For Machine Learning:

NumPy: Known for providing multidimensional arrays, NumPy is fundamental for numerical computations.
Pandas: A data analysis library that offers data frames (two-dimensional data structures similar to Excel spreadsheets) with rows and columns.
Matplotlib: Matplotlib is a two-dimensional plotting library for creating graphs and plots.
Scikit-Learn: The most popular machine learning library, providing all common algorithms like decision trees, neural networks, and more.

Recommended Development Environment:

Standard IDEs such as VS Code or terminals may not be ideal when creating a model due to the difficulty in inspecting data while writing code. For our learning purposes, the recommended environment is Jupyter Notebook, which provides an interactive platform to write and execute code, visualize data, and document the process simultaneously.

Step-by-Step Setup:

Download Anaconda:
Anaconda is a popular distribution of Python and R for scientific computing and data science. It includes the Jupyter Notebook and other essential tools.

Download Anaconda from this link.
Install Anaconda:
Follow the installation instructions based on your operating system (Windows, macOS, or Linux).
After the installation is complete, you will have access to the Anaconda Navigator, which is a graphical interface for managing your Anaconda packages, environments, and notebooks.
Launching Jupyter Notebook:

Open the Anaconda Navigator
In the Navigator, click on the "Environments" tab.
Select the "base (root)" environment, and then click "Open with Terminal" or "Open Terminal" (the exact wording may vary depending on the OS).
In the terminal window that opens, type the command jupyter notebook and press Enter.

This command will launch the Jupyter Notebook server and automatically open a new tab in your default web browser, displaying the Jupyter Notebook interface.

Using Jupyter Notebook:

The browser window will show a file directory where you can navigate to your project folder or create new notebooks.
Click "New" and select "Python 3" (or the appropriate kernel) to create a new Jupyter Notebook.
You can now start writing and executing your code in the cells of the notebook. The interface allows you to document your code, visualize data, and explore datasets interactively.

Building Your First Machine Learning Model

In building your first model, we have to take cognizance of the steps in Machine Learning as discussed earlier, which are:

Import the Data
Clean the Data
Split the Data
Create a Model
Train the Model
Make Predictions
Evaluate and Improve

Now, let's assume a scenario involving an online bookstore where users sign up and provide their necessary information such as name, age, and gender. Based on their profile, we aim to recommend various books they are likely to buy and build a model that helps boost sales.

First, we need to feed the model with sample data from existing users. The model will learn patterns from this data to make predictions. When a new user signs up, we can tell the model, "Hey, we have a new user with this profile. What kind of book are they likely to be interested in?" The model will then recommend, for instance, a history or a romance novel, and based on that, we can make personalized suggestions to the user.

Let's break down the process step-by-step:

Import the Data: Load the dataset containing user profiles and their book preferences.
Clean the Data: Handle missing values, correct inconsistencies, and prepare the data for analysis.
Split the Data: Divide the dataset into training and testing sets to evaluate the model's performance.
Create a Model: Choose a suitable machine learning algorithm to build the recommendation model.
Train the Model: Train the model using the training data to learn the patterns and relationships within the data.
Make Predictions: Use the trained model to predict book preferences for new users based on their profiles.
Evaluate and Improve: Assess the model's accuracy using the testing data and refine it to improve its performance.

By following these steps, you will be able to build a machine-learning model that effectively recommends books to users, enhancing their experience and boosting sales for the online bookstore. You can gain access to the datasets used in this tutorial here.

Let's walk through a sample code snippet to illustrate the process of testing the accuracy of the model:

Import the necessary libraries:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

We start by importing the essential libraries. pandas is used for data manipulation and analysis, while DecisionTreeClassifier, train_test_split, and accuracy_score are from Scikit-learn, a popular machine learning library.

Load the dataset:

book_data = pd.read_csv('book_Data.csv')
Read the dataset from a `CSV file` into a pandas DataFrame.

Prepare the data:

X = book_data.drop(columns=['Genre'])
y = book_data['Genre']

Create a feature matrix X by dropping the 'Genre' column from the dataset and a target vector y containing the 'Genre' column.

Split the data:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Split the data into training and testing sets with 80% for training and 20% for testing.

Initialize and train the model:

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

Initialize the DecisionTreeClassifier model and train it using the training data.

Make predictions and evaluate the model:

predictions = model.predict(X_test)
score = accuracy_score(y_test, predictions)
print(score)

Make predictions on the test data and calculate the accuracy of the model by comparing the test labels to the predictions. Finally, print the accuracy score to the console.

In this example, we start by importing the essential libraries. Pandas is used for data manipulation and analysis, while DecisionTreeClassifier, train_test_split, and accuracy_score are from Scikit-learn, a popular machine learning library. We then read the dataset from a CSV file into a pandas DataFrame, prepare the data by creating a feature matrix X and a target vector y, split the data into training and testing sets, initialize and train the DecisionTreeClassifier model, make predictions on the test data, and calculate the accuracy of the model by comparing the test labels to the predictions.

Depending on the data you're using, the results will vary. For instance, in the output below, the accuracy score displayed is 0.7, but it may show 0.5 when the code is run again with a different dataset. The accuracy score will vary, a higher score indicates a more accurate model.
Output:

Data Preprocessing:

Now that you've successfully created your model, it's important to note that the kind of data used to train your model is crucial to the accuracy and reliability of your predictions. In Mastering Data Analysis: Unveiling the Power of Fairness and Bias in Information, I discussed extensively the importance of data cleaning and ensuring data fairness. Depending on what you intend to do with your model, it is essential to consider if your data is fair and free of any bias. Data cleaning is a very vital part of machine learning, ensuring that your model is trained on accurate, unbiased data. Some of these ethical considerations are:

Removing Outliers: Ensure that the data does not contain extreme values that could skew the model's predictions.
Handling Missing Values: Address any missing data points to avoid inaccurate predictions.
Standardizing Data: Make sure the data is in a consistent format, allowing the model to interpret it correctly.
Balancing the Dataset: Ensure that your dataset represents all categories fairly to avoid bias in predictions.
Ensuring Data Fairness: Check for any biases in your data that could lead to unfair predictions and take steps to mitigate them.

By addressing these ethical considerations, you ensure that your model is not only accurate but also fair and reliable, providing meaningful predictions.

Conclusion:

Machine learning is a powerful tool that can transform data into valuable insights and predictions. In this article, we explored the fundamentals of machine learning, focusing on supervised and unsupervised learning, and demonstrated how to set up your environment and build a simple machine learning model using Python and its libraries. By following these steps and experimenting with different algorithms and datasets, you can unlock the potential of machine learning to solve complex problems and make data-driven decisions.

In the next part of this series, we will dive deeper into advanced techniques and real-world applications of machine learning, exploring topics such as feature engineering, model evaluation, and optimization. Stay tuned for more insights and practical examples to enhance your machine-learning journey.

Additional Resources:

Python Web Scraping: Best Libraries and Practices

Eztosin — Fri, 05 Apr 2024 11:00:00 +0000

The word data should no longer sound strange to you, it’s a four-letter word that holds so much importance in today's world. Accessing the right information or data is essential for startup firms, individuals, developers, or website owners, having the right content to drive traffic to your site can be crucial or seem out of your reach but that’s untrue having Python Web Scraping at your beck and call.

In this article, we’re going to delve into the most important and widely used Python libraries, implementing the best practices involved in scraping the web, dissecting what web scraping means in its entirety and so much more. So join me as we explore the world of Python Web Scraping together!

Introduction to Web Scraping

In simple terms, Web Scraping means extracting contents from a webpage whether authorized or not. This extracted content performed by a user is usually for a specific purpose; for example, a user might need information about weather forecasts, or the latest technology news for a blog. There’s a wide range of needs for scraping the web, from gathering data for research to automating data collection for business insights.

A notable application of web scraping can be extracting names, addresses, and phone numbers of individuals from a website. While this might seem beneficial for profiling and targeting new customers as a business owner, it’s crucial to consider data privacy and ethical considerations in using such scraped data.

It’s important to note that scraping the contents of websites is not universally accepted. Many companies or website owners drastically frown against scraping the contents of their websites; some have built-in security measures that protect their contents from unauthorized access. Therefore, if you need to scrape a particular content without getting blocked, you can refer to companies like ScrapingBee which offer the best practices and pricing to suit your needs.

Getting Started with Python for Web Scraping

Python is considered a high-level programming language, known for its readable syntax making it easier to learn and master, it boasts a vast library and it is an open-source software, allowing its users to contribute to its already amazing features.
To begin using Python for Web Scraping, you’ll need to have Python3 and pip3 installed. If you’re on a Linux Operating system such as Ubuntu 20.04 you can do that with the command below in your terminal

sudo apt update
sudo apt install python3
sudo apt install python3-pip

# Install Python package installer
sudo apt install pip3

Now that you have Python and pip3 installed, here’s a list of common Libraries used for Web Scraping in Python. In this article, we’re going to focus on the most important and frequently used Python libraries for Web Scraping which include:

Requests: In Python web scraping, the request module is considered a top choice, allowing the user to send HyperText Transfer Protocol (HTTP) requests. These requests can be made with various methods such as GET, POST, PUT, DELETE, etc. and then a response is received from the web server to which the request was made. As an essential tool for extracting web content, other libraries used for web scraping often rely on its usage.

pip3 install requests

Example usage:

     import requests

     # Send a GET request and print the response content
     response = requests.get('https://example.com')
     print(response.text)

Beautiful Soup: Who doesn’t like a good meal eh? As its name implies it performs its task beautifully. This remains my favorite Python module for scraping, Beautiful Soup is quite a popular tool used in Python web scraping, with it, you can target specific tags and attributes in HTML when requesting a webpage. It also supports the HTML and LXML parser included in the Python standard library. It can be installed using the command below:

pip3 install beautifulsoup4

Example usage:

     from bs4 import BeautifulSoup

     # Parse HTML content and print the title
     soup = BeautifulSoup(html_content, 'html.parser')
     print(soup.title)

Pyquery: Ideal for scraping JavaScript-rendered contents, Pyquery uses LXML to manipulate HTML and XML making it suitable for dynamic web pages. First, you need to install Pyquery:

pip3 install pyquery

Example usage:

     from pyquery import PyQuery as pq

     # Create a PyQuery object and print text content
     doc = pq(html_content)
     print(doc('p').text())

Scrapy: Scrapy is a powerful Python library for scraping large data. In Scrapy, Requests are scheduled and processed asynchronously, this implies that Scrapy doesn’t need to wait for a request to be processed before it sends another allowing the user to perform more requests and save time. The term “Spider” or “Scrapy Spider” is often used in this context. Spider refers to Python classes that a programmer defines and Scrapy uses the instructions to extract content from a website.

pip3 install scrapy

Example usage:

     import scrapy

     # Define a Scrapy spider and parse response
     class MySpider(scrapy.Spider):
         name = 'example_spider'
         start_urls = ['https://example.com']

         def parse(self, response):
             # Your scraping logic here

Selenium: Selenium helps the user have more control over requests made to websites, allowing more user interaction such as filling forms on sites, clicking links, and navigating through the available pages. Like Pyquery, it allows you to scrape from sites that render JavaScript content helping with automation. It's often used alongside other modules such as Beautiful Soup. Selenium supports headless browsing meaning you can browse websites without the user interface. To be able to use Selenium it can be installed along with the browser drive you intend to use:

pip3 install selenium

Example usage:

     from selenium import webdriver

     # Launch a Chrome WebDriver and print page title
     driver = webdriver.Chrome()
     driver.get('https://example.com')
     print(driver.title)

Key Features and Considerations:

Each library has its strengths and is suited for different scraping tasks.
Requests module is efficient for basic HTTP requests.
Beautiful Soup simplifies HTML parsing and extraction.
PyQuery handles JavaScript-rendered content.
Scrapy is ideal for scraping large datasets.
Selenium enables automated web interaction.

In sum, each library discussed has its strengths and is suited for different scraping tasks. However, successful web scraping also depends on a basic understanding of HTML and CSS. Since most of the requests made and responses received are going to be in this format. HTML is like the human skeleton but for a website, CSS adds visual appeal and design elements to make HTML more beautiful and appealing. A good grasp of HTML tags and CSS selectors is crucial to navigating and extracting content from any website effectively. By combining the capabilities of these Python libraries with a solid understanding of HTML and CSS, developers can unlock the full potential of Python Web Scraping and achieve their data extraction goals.

Understanding HTML Structure:

HTML is the structural backbone of web content, it comprises elements and tags that organize and present texts, images, and videos arranging and structuring them into sections. Each section is defined by specific elements or tags, such as title, heading, or sub-heading. It can even be an image tag or any other multimedia element as the case may be.

In Web Scraping understanding the structure of a website is crucial to a successful data extraction. Each HTML tag often has a class name or ID assigned for easy identification and extraction of relevant information. For instance <div class=”article-content”> is a div tag with a class name “article-content” and may contain the main contents of a web page facilitating targeted extraction.

In addition, CSS (Cascading Style Sheets) affects how a web page is displayed to users. CSS selectors such as class selectors ( .class-name), ID selectors(#id-name), and element selectors (element-name) allow developers to pinpoint and style specific elements on a webpage.

In the context of web scraping, a strong background in CSS elements aids in identifying such structured data and navigating through styled content. Furthermore, CSS can impact JavaScript-rendered content, influencing how it is loaded and displayed. When dealing with dynamic content or interactions driven by JavaScript, considerations of CSS effects are vital for accurate scraping results.

By understanding the structure of HTML with the knowledge of CSS selectors, and how they interact with JavaScript, developers can effectively navigate web pages and extract valuable data for various applications.

Basic Web Scraping Techniques

In this section, we’ll explore some basic techniques of Python Web Scraping using popular libraries such as Requests, Beautiful Soup, and Pyquery.

Example of using Requests

Create a file named request.py and type in the following lines of code then save and exit your text editor. After which you can run the command python3 request.py your output should be similar to mine in the image below.

# Importing the requests module
import requests

# Specify the URL of the webpage to scrape
url = 'https://dev-tools.alx-tools.com/'

# Send a GET request to the URL and store the response in a variable
request = requests.get(url)

# Check if the request was successful (status code 200)
if request.status_code == 200:
        # Print the HTML content of the webpage
        print(request.text)
else:
        # Print an error message if the request failed
        print('Error:', request.status_code)

Output:

Example of using Beautiful soup:

Create a file named soup.py and type in the following lines of code then save and exit your text editor. After which you can run the command python3 soup.py your output should be similar to mine in the image below.

# Importing the requests module
import requests
from bs4 import BeautifulSoup

# Specify the URL of the webpage to scrape
url = 'https://dev-tools.alx-tools.com/'

# Send a GET request to the URL and store the response in a variable
response = requests.get(url)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    # Create a BeautifulSoup object with the HTML content of the webpage
    soup = BeautifulSoup(response.text, 'html.parser')

    # Print the title of the webpage
    print('Title:', soup.title.text)

    # Print a specific element from the webpage (e.g., the first paragraph)
    print('First Paragraph:', soup.p.text)
else:
    # Print an error message if the request failed
    print('Error:', response.status_code)

Output:

Example code using Pyquery:

In the case of Pyquery, you need to be careful when naming your file to avoid circular importation errors when running your code. In my case, I created a file named pyquery_scraping.py with the following lines of codes after which I ran the command python3 pyquery_scraping.py

# Importing the requests module
import requests
from pyquery import PyQuery as pq

# Specify the URL of the webpage to scrape
url = 'https://dev-tools.alx-tools.com/'

# Send a GET request to the URL and store the response in a variable
response = requests.get(url)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    # Create a PyQuery object with the HTML content of the webpage
    doc = pq(response.text)

    # Print the title of the webpage
    print('Title:', doc('title').text())

    # Print a specific element from the webpage (e.g., the first paragraph)
    print('First Paragraph:', doc('p').eq(0).text())
else:
    # Print an error message if the request failed
    print('Error:', response.status_code)

Output:

Advanced Web Scraping Techniques

In this section, we are going to see some examples of web scraping in Python using popular libraries like Scrapy and Selenium
Example of Python Web scraping using Scrapy:

First, ensure you have Scrapy installed. If not, you can install it using pip:
pip3 install scrapy

Next, create a new Scrapy project and navigate to the project directory:
scrapy startproject dev_tools_scraper
cd dev_tools_scraper/dev_tools_scraper/spiders
Now, create a new spider inside the spiders( /dev_tools_scraper/dev_tools_scraper/spiders) directory. Let's name it dev_tools_spider.py:

import scrapy


class DevToolsSpider(scrapy.Spider):
    """
    A Scrapy spider to scrape data from specified URL
    """

    # Name of the spider
    name = 'dev_tools'


    # URL to start scraping from
    start_urls = [
        'https://dev-tools.alx-tools.com/',
    ]

    def parse(self, response):
        """
        Method to parse the response from each URL
        """

        # Extracting data from the HTML structure
        title = response.css('title::text').get()
        navbar_links = response.css('.navbar-nav a.nav-link::text').getall()
        about_content = response.css('#about .lead::text').get()

        # Yielding the extracted data as a dictionary
        yield {
            'title': title,
            'navbar_links': navbar_links,
            'about_content': about_content,
            }

Save this spider file in the spiders directory of your Scrapy project.

Now, you can run the spider using the following command:
scrapy crawl dev_tools -o output.json
Replace output.json with the desired output file name. This command will execute the spider, scrape the specified URL, and save the extracted data into a JSON file.

You can customize the spider to extract more specific data based on your requirements by using CSS selectors or XPath expressions to target specific elements in the HTML structure.

Output:

Example Python Web scraping using Selenium

Again, ensure you have Selenium installed. You can install it using pip:
pip3 install selenium
Also Download WebDriver based on the web browser of your choice:

For Chrome: Visit the ChromeDriver download page and download the appropriate version for your operating system.
For Firefox: You can get GeckoDriver from the GeckoDriver releases page. For other browsers like Edge, Safari, etc., you can find their respective WebDriver downloads from the official sources

# Import the web driver
from selenium import webdriver
from selenium.webdriver.chrome.service import Service


# Initialize the WebDriver (assuming you have the appropriate driver installed, e.g., chromedriver for Chrome)
service = Service(executable_path="chromedriver.exe")  # Specify the correct path to chromedriver
driver = webdriver.Chrome(service=service)


# Navigate to a website
driver.get('https://dev-tools.alx-tools.com/')


# Get and print the title of the webpage
print("Title of the webpage:", driver.title)


# Close the browser window
driver.quit()

Save this file in your project directory
After running the script you should have a similar output to mine.

Output:

Best Practices and Tips

You’ve just learned a skill that can save you a lot of time and resources: web scraping with Python libraries, In this article, I used a basic website Holberton School - Developer tools because it’s a free-to-use website for learning purposes. While learning is an important part of our journey as developers, it’s important to learn the right way and approach web scraping responsibly and ethically.
Here’s a list of best practices and tips to consider when engaging in web scraping

Tips:

Constant practice ensures mastery. Consider utilizing websites like geeksforgeeks, which offer free Python tutorials on web scraping.
When using Scrapy, always ensure that your ‘.py’ file is located in the spider directory to avoid errors such as “KeyError(f"Spider not found: {spider_name}")”.
Download a web driver with the same version as the browser you intend to use, whether it’s Chrome or Firefox. Refer to their official websites for the correct version.

Common Pitfalls:

Not organizing your scraping files properly can lead to errors and confusion, especially in larger projects.
Not commenting on your code can lead to confusion, employ proper coding ethics
Ignoring website terms of service and scraping without authorization can result in legal issues.

Real-World Applications:

Python libraries like Panda and Matplotlib are often used for data processing and analysis by data analysts.
Web Scraping can be used in various real-world applications, such as:

Extracting the latest news from a blog site for content aggregation.
Gathering product information and prices from e-commerce sites for competitive analysis.
Monitoring changes in stock prices or financial data from financial websites.

By following these best practices, avoiding common pitfalls, and exploring real-world applications, you can harness the power of web scraping effectively and responsibly in your projects.

Conclusion:

In this article, we’ve discussed powerful Python libraries for web scraping and provided practical examples of how to use and install them. We’ve discussed the strengths of each tool and when to use them, based on the size of the project and complexity.

However, this is just an introduction to real problems in Python Web Scraping. To deepen your understanding and gain more practical insights, I encourage you to explore additional resources provided below. These resources include tutorials, documentation, and community forums where you can learn from others and share your experiences.

By continuing to learn and practice Python Web Scraping techniques, you can unlock a world of possibilities in data extraction and analysis. Whether you are a developer, data analyst, or website owner, Python opens doors to valuable tools and libraries for your usage.

Now, it’s time to dive deeper and apply what you’ve learned. Happy Scraping!

Additional Resources:

Scrapy Tutorial.
Freecode Camp Web Scraping Tutorial.
How to Setup Selenium with Chromedriver on Ubuntu.
Python Selenium Tutorial
How to Bypass CAPTCHA with Selenium.
Best Practices for Ethical Web Scraping.
Image by Freepik

Staying Updated on Industry News and Software Tools

Eztosin — Fri, 15 Sep 2023 10:05:10 +0000

In today's fast-paced and ever-evolving world, staying at the forefront of your industry is not just an option; it's a necessity. The rate of innovation is relentless, and without constant adaptation, individuals and organizations risk becoming obsolete. The question is, how do you ensure you're not left behind?

In this article, we'll explore the vital art of staying informed in your industry and mastering the latest software tools. Whether you're in technology or any other sector, the strategies we'll discuss here will help you maintain your relevance and keep pace with the dynamic currents of change.

But first, consider this: What could you achieve with the right knowledge and tools at your disposal? Let's find out.

Methods To Stay Updated:

Staying informed in the fast-paced world of software development is crucial for personal and professional growth. Here are effective methods to keep yourself updated:

A. Online Industry News Platforms: Online platforms like GitHub, StackOverflow, and Reddit programming are hubs of activity for software developers worldwide. These platforms host developer communities where individuals with varying levels of experience and expertise come together to share their ongoing projects and seek visibility, contributions, and industry awareness. Engaging with these platforms involves connecting with experienced software engineers through their social media handles, providing a unique opportunity to learn from their insights and enhance your skills in relevant fields.

Examples: GitHub hosts a wide range of open-source projects, allowing you to explore codebases, contribute to projects, and collaborate with developers globally. Reddit programming subreddits such as r/programming and r/webdev offer discussions, news, and insights into various tech stacks.

B. Email Newsletters: Subscribing to daily updates in developer forums can be a valuable source of information. Platforms like Hashnode, Medium, and Devto house diverse communities of developers with knowledge spanning various tech stacks. By subscribing to their blog posts and newsletters, you'll receive regular updates, tutorials, and insights, keeping you well-informed and up-to-date with industry trends.

Examples: Hashnode hosts blogs from developers worldwide, covering a wide range of topics, from web development to machine learning. Medium features tech-related publications and individual bloggers sharing their expertise in coding, software development, and emerging technologies. By incorporating these methods into your routine, you can stay ahead in the ever-evolving world of software development.

Description Of Software Tools:

To navigate the ever-evolving landscape of the software industry, you'll need the right tools at your disposal. Here are two indispensable ones:

Feedly: Feedly serves as your information hub, thanks to its role as an RSS (Really Simple Syndication) feed aggregator. It allows websites to publish feeds that can be effortlessly parsed and read through RSS applications. Available both as an application and online, Feedly offers a seamless way to access relevant information and insights from across the web, all at your fingertips.
Google Alerts: Google Alerts provides easy access to customized and relevant information. Google simplifies the process by guiding users through setting up alerts for their preferred topics and keywords. With Google Alerts, you can stay informed about precisely what matters most to you.

Benefits Of Staying Updated:

Staying updated offers a multitude of advantages that are indispensable in today's fast-paced world. Let's delve into these essential benefits:

Sustained Industry Relevance: Remaining informed with the latest information is vital. It ensures that individuals and organizations retain their relevance within their respective niches. In an era plagued by misinformation, having access to accurate and up-to-date knowledge becomes a competitive edge.
Fostering Innovative Ideas: Updated information empowers software developers and innovators to refine existing tools and applications continually. It serves as a wellspring of inspiration, enabling them to devise creative solutions to challenges and shortcomings in previous versions. As the saying goes, "A problem shared is a problem half-solved."
Amplified Market Success: Organizations that prioritize data and information consistently outperform their competitors. Staying updated allows businesses to better understand their customers' evolving needs. This, in turn, leads to improved market sales, enhanced customer satisfaction, and an increase in annual revenue.

Conclusion:

The significance of staying updated cannot be overstated. Statistics project an astounding 11.5 million data-related job openings by 2026 resulting in a growing number of over a million learners from around the globe enrolling in the Google data analytics course offered by Coursera, underlining the growing importance of remaining informed.

Organizations have begun to recognize the immense benefits of staying updated, and it's high time individuals and businesses alike adopt this practice. Let us cultivate the habit of staying well-informed about the latest developments in our respective fields. By doing so, we ensure our continued relevance and success in an ever-evolving landscape.

References:

Google Data Analytics Professional Certificate | Coursera
The 3 best RSS reader apps | Zapier
Stay ahead of the curve with Feedly AI
Google Alerts - Google Search

The Relevance of C in Building Efficient Operating Systems

Eztosin — Sun, 20 Aug 2023 00:05:34 +0000

Harnessing C’s Power: The Unrivaled Choice for Operating System Development

Introduction

The creation of a simple, yet powerful tool remains one of the most remarkable innovations the world has ever beheld. This innovation carries the weight of the computing world, revolutionizing how we interact with machines. The C programming language stands as a cornerstone, closely connected to machine language, which empowers developers to explore and manipulate computer systems to change the scope of the world of technology.

As the successor to a previous programming language called B, C was developed in the 1970s by Dennis Ritchie at Bell Laboratories in the USA. It quickly became a cornerstone in the computing world due to its closeness to machine language, empowering developers to explore and manipulate computer systems with unparalleled precision.

In this article, we delve into C's unparalleled relevance and the multitude of benefits it offers. We aim to establish why it deserves its place as the preferred choice for developing operating systems.

Prerequisites

Before delving into the insights offered in this article, it's recommended that readers possess a fundamental understanding of computer hardware and software concepts. This foundational knowledge will greatly enhance the exploration of the myriad benefits associated with learning the C programming language. Whether you're a novice in the programming world or a seasoned professional versed in other languages, this article will shed light on why C holds a unique place in the realm of programming.

The Technological Advancement of C

Originally, C was developed to improve the functions of the Unix operating system (an operating system that acts as a link between the computer and the user). Several renowned scientists made significant improvements to the C library and preprocessor (a step in the compilation process), including Alan Snyder, Mike Lesk, and John Reiser. As a result, C was used to develop version 4 of the Unix operating system in November 1973.

During the 1980s, C gained popularity, and the book “The C programming language” was well-known before the official recognition of C by the American National Standards Institute(ANSI). C compilers that translate program code to machine code (represented as 0’s and 1’s) became available for all modern computer architectures, leading to widespread distribution among governmental and academic bodies.

Since its development, many languages have been structured and syntactically influenced by C’s design. C has firmly established its place for decades in the programming world consistently ranked as one of the top two languages in the TIOBE index, which measures the popularity of programming languages.

Unveiling C’s Relevance in Operating Systems

Have you ever wondered how your computer or smartphones operate behind the scenes? One of the key players in making these systems efficient and powerful is the C programming language.

C is a language that gives developers a direct and powerful way to interact with the computer’s hardware. This unique ability allows C to efficiently control and utilize computer resources, making it an excellent choice for building operating systems that run smoothly and respond quickly.

Let’s take a moment to understand what “kernel capabilities” mean. The kernel is like the brain of an operating system, handling essential tasks such as managing memory, scheduling processes, and handling input/output. C’s kernel capabilities make it possible for developers to create a solid foundation for operating systems, ensuring they work seamlessly with the hardware.

But what does all this technical jargon really mean? Let’s break it down further! Imagine you’re building a house, and C is like having a magic tool that lets you shape every tiny detail of the structure. It allows developers to create functions and features that fit perfectly into the operating system, like customizing your dream home with all the features you want.

And the best part is, C is not only powerful but also portable. That means the programs you write in C can run on different types of computers without much change to your program. So, whether you’re using a Windows PC, a Mac, or a smartphone, C’s portability makes it adaptable to various systems.

You might wonder, “Who uses C for their operating systems?” Well, some of the biggest tech giants, like Microsoft, Apple, and Google, rely on C to build the core of their operating systems. This showcases C’s reliability and popularity in the tech industry.

The Power of C’s low-level access

The key to building an efficient operating system lies in the ability of a programming language to manipulate the hardware of the computer and handle certain critical aspects. C programming excels in handling these critical aspects namely:

Memory Management: Operating systems need to efficiently manage the computer’s memory to ensure smooth program execution and prevent conflicts. C’s direct memory allocation and deallocation access, manipulation capabilities make it well-suited for effective memory management in operating systems.

Process Scheduling: In a multitasking environment, the operating system must efficiently manage processes, allocate resources, and ensure fair execution. C’s versatility allows developers to implement robust process scheduling algorithms.

File Systems: Handling data storage and retrieval is crucial for any operating system. C’s capability to interact directly with hardware enables the development of efficient file systems, ensuring quick and reliable data access.

Interacting with Hardware: As the heart of an operating system, C”s ability to interact directly with hardware allows developers to control low-level operations.

Simplicity and Versatility: C’s simplicity and versatility make it an ideal choice for managing complex algorithms and data structures with concise code, streamlining the operating system development process.

Maturity and Stability: With many additions to the C standard library over the years, C still remains an outstanding and reliable programming language. As a result tech giants rely heavily on building technical systems using the C language

C’s Role in Embedded Systems

An embedded system refers to a combination of computer hardware and software designed to perform specific tasks. With C’s ability to interact with the hardware and boost the functionality of devices, C has been incorporated to ease human life. Examples of these cases include its use in industrial machines, household electronics, automobiles, medical equipment, and more.

Enduring Legacy in the Programming Landscape

C continues to be a widely used and respected programming language among developers worldwide. Academic bodies and various computer science programs have continued to incorporate C in their curriculum, as many find it easier to transition to other programming languages after having a strong foundational background in C.

In addition to its position as a fundamental language for beginners, C remains a top choice for developers worldwide due to its inherent advantages. Its portability allows programs written in C to run on different platforms with little or no modifications. Moreover, its efficiency, reliability, and versatility have cemented its place in technological innovations.

Challenges and Future Prospects

While C has served as the foundation for numerous programming languages and remains the father of languages, it is imperative for programmers to acknowledge its limitations and explore how C may evolve or adapt to meet the changing needs of the evolving technology landscape. The programming community needs to work towards addressing these limitations and continuously improving C’s capabilities.

Conclusion

Having explored the remarkable journey of C from its development by Dennis Ritchie in 1972 to its continuous significance in modern computer architectures, C has undoubtedly made its mark in the computing field.

Throughout our journey, we delved into C’s unique, low-level capabilities that make it stand out from other programming languages, its access to memory allocation and deallocation which gives programmers absolute control to utilize and have access to sufficient memory making it an ideal choice to build technical software and operating systems, its kernel capabilities laid the foundation for a robust operating system, which empowered developers to build software applications that run smoothly on a wide range of devices.

As we steer into the future of the constantly evolving world of technology, C’s strength and adaptability have positioned it as a critical language for developing efficient and portable innovations. Its versatility and reliability will continue to be sought after for a long period of time.

In conclusion, the path of C programming has been inspiring for ages, from its creation to its current status as a dominant force in the programming world. C has proven to be more than just a tool, impacting the lives of developers and users alike, it has become a legend of languages haha.

As we embrace the future, let us continue to remember the power of simplicity and efficiency that C brings, propelling us forward into an ever-evolving world of technological innovation. With C on our side, we can continue to boast of reliability and stability in shaping the destiny of technology itself.

References

C (programming language). (2023, July 30). In Wikipedia. https://en.wikipedia.org/wiki/C_(programming_language)
Lutkevich, B. (n.d.). Embedded System. TechTarget. https://www.techtarget.com/iotagenda/definition/embedded-system
Cognetta, S. (2023, July 26). How to Make a Computer Operating System. Wikihow. https://www.wikihow.com/Make-a-Computer-Operating-System

How To Use and Install a Virtual Machine

Eztosin — Fri, 11 Aug 2023 15:48:42 +0000

Introduction

Welcome to the world of technology, where innovation knows no bounds! Picture this: effortlessly running an entirely different operating system on your computer. Sounds impossible? Well, that is the power of virtual machines, a game-changer for developers worldwide if you ask me.

Though the origins of virtual machines trace back over six decades, their relevance in today's rapidly evolving tech landscape remains unchanged. These virtual wonders have changed how we develop, test, and deploy software, opening doors to limitless possibilities.

In this article, we are going to unveil and explore the fascinating realm of virtual machines, unlocking some of the secrets to their endless potential.

Prerequisites

Whether you're a tech newbie or an experienced developer eager to explore new horizons, this article is tailor-made for you. It will guide you on an exciting journey through different operating systems using virtual machines. Let's unravel the mysteries of these versatile tools and unlock boundless possibilities together!

Understanding Virtual Machines

A virtual machine (VM) is defined as the emulation or imitation of a computer system, equipped with the functionality of a physical or real computer. In this virtual realm, the real machine which oversees the operation is referred to as the "host" while the emulated VM is referred to as the "guest".

The uses of VMs stretch as far as one’s imagination empowering developers to imitate diverse operating systems of their choice.

Some of the advantages of VMs include:

Providing a substitute for a real machine.
Creating multiple isolated environments on a single physical system.
There’s no compliance needed between the guest and the host machine.

There are two types of VMs namely:

System Virtual Machine
Process Virtual Machine

In this article, we delve into the realm of System VMs, which serve as temporal replacements for a real machine, providing the necessary functions required to execute an entire operating system. A hypervisor (computer software or hardware that runs VMs) is required to execute and manage one or more virtual environments.

For more insights into the types of VMs refer here

System Requirements

Before diving into the world of virtual environments on your computer, your machine must meet the necessary specifications for compatibility. Also, VMs are frequently used on Windows, Mac, Solaris, and Linux operating systems. Here are the basic requirements:

A 64-bit i686 Processor: This is very essential to ensure a smooth and efficient performance.
1.3gHz*2 Processor Speed: This is highly recommended to handle the resource-intensive operations of running virtual machines.
At least a 4GB Hard disk space: Sufficient storage space is essential to accommodate the virtual machine files and operating system images.
At least 4GB RAM or more: This ensures optimal performance of running multiple environments. For more demanding tasks, consider upgrading to a higher RAM capacity.

Choosing a Virtualization Software

Managing VMs requires certain tools, there are several software programs available for programmers which can be used to manage virtual environments. However, for our learning journey and ensuring a seamless experience with amazing tools, we will focus on two important tools, namely:

Oracle VM: This software enables you to imitate and run a different operating system on a host machine. For instance, even if your machine has a Windows operating system, Oracle VM allows you to effortlessly run a Linux operating system as a guest.

Vagrant: This is a tool that helps you control your virtual machine. It provides a simple and intuitive way to manage your virtual environments. Just as a car needs a driver, Vagrant allows you to bring your virtual machine to life, and perform various operations with ease.

Why Choose Oracle VM and Vagrant?

Oracle VM and Vagrant are ideal choices, especially for absolute beginners. Their user-friendly interfaces and straightforward installation process make them accessible to everyone. Additionally, these tools have gained popularity among programmers worldwide for their reliability and versatility in handling virtual environments. To learn more about other virtual machine software, refer here.

Installing Oracle VM and Vagrant: Your First Virtual Environments

Both Oracle VM and Vagrant offer users the flexibility to create diverse virtual environments supporting multiple operating systems. In this guide, we will walk through the installation process of Oracle VM and Vagrant enabling you to set up a Linux operating system like Ubuntu.

To ensure secure and reliable downloads of the software, you can follow the links provided below:

Download OracleVM here

After clicking the above link, you should be redirected to the Oracle VM website's download page. Choose the link that matches your operating system (Windows, MacOS, or Linux).

Download Vagrant here

Once you click the link, you will be taken to the Vagrant website's download page. Choose the link that matches your operating system(Windows, MacOS, or Linux).

After the download is complete, navigate to the downloaded files and initiate the installation process for both Vagrant and Oracle VM.

Follow the on-screen instructions, just like you would when installing any other application or game on your computer.

Next, you'll need to open the Terminal on MacOS or the Command Prompt on Windows:

For MacOS Users:

Go to your Applications folder, then open the "Utilities" folder.

Look for the "Terminal" application and launch it.

For Windows Users:

Click on the Start menu and type "Command Prompt" in the search bar.

Select the "Command Prompt" app from the search results to open it.

Once you have the Terminal (MacOS) or Command Prompt (Windows) open, you're ready to proceed with setting up your virtual environments using Vagrant and Oracle VM.

Set Up Your Virtual Environments

Regardless of your operating system, you'll need to enter the following commands into your terminal or command prompt. This step will guide you through the process of creating your virtual environments using Vagrant and Oracle VM. Before proceeding, ensure you have a working internet connection, as it is crucial for the setup process.

Please note that after entering the commands, you should see the images displayed below in your terminal (MacOS) or command prompt (Windows). These images will serve as confirmation that you have successfully created your virtual environments.

vagrant box add ubuntu/focal64

After executing the above command, note that it may take a while to download depending on your network connection, so exercise some patience with your computer system.

The command "vagrant box add ubuntu/focal64" allows you to add the Ubuntu image from the internet, Vagrant will download the specified virtual machine image and store it locally on your computer.

In this case, "ubuntu/focal64" refers to the official Ubuntu 20.04 LTS (Focal Fossa) virtual machine image that is hosted on the Vagrant cloud. The "focal64" part indicates that it is a 64-bit version of Ubuntu 20.04.

The next step is to create a vagrant file that will hold the "ubuntu/focal64" virtual machine using the command:

vagrant init ubuntu/focal64

The above command should display something similar to the image below:

Please make sure to run the command "vagrant init ubuntu/focal64" in the directory of your choice. These commands are only needed once to create a working VM. On subsequent uses of your virtual environment, you only need to start your machine and change into it using the command below:

vagrant up

The command "vagrant up" starts your VM and you should see something similar to the image below. Please note that the startup may take a while, and you should see the message "Machine Booted and Ready!" like in the image below. If you don't see the expected message, you may need to start your machine all over again using "vagrant reload".

To change into your machine, use the command below:

vagrant ssh

After the execution of the above command, your Command Prompt (Windows) or Terminal (MacOS) should display something similar to the image below:

Congratulations! You've successfully created a working VM and you are now ready to explore the Linux operating system. This opens up a world of possibilities for your projects, allowing you to use your preferred applications and software in your Linux environment. For instance, if you need to program using the "emacs" text editor you can run the command to install:

apt install emacs

This command "apt install emacs" allows you to install the "emacs" text editor on your Linux operating system.

For more Vagrant commands, you can use the command below it lists all the commands associated with it:

vagrant --help

Once you've completed your projects and you wish to shut down the virtual environment, you can use the "exit" command and then use "vagrant halt" to close all operations.

Now that you're all set, go ahead and unleash your creativity in this Linux environment!

Some Common Troubleshooting Issues During Installation

Here are some tips for some common issues:

A message saying "unable to initialize vagrant at an early stage": If you encounter this issue, it is likely due to an outdated version of PowerShell. To resolve it, update your PowerShell to the latest version. You can find the latest version and installation instructions on the official PowerShell website.

A message that says "Time up, failed to connect with box In the specified time frame": If you face this error, you may need to increase the allocated time for booting your virtual machine. You can do this by adjusting the timeout settings in your Vagrant file. For detailed instructions on modifying the timeout settings, refer to the Vagrant documentation or the official Vagrant community forums.

Conclusion

VMs have been in existence for a long time and they are still relevant in the constantly evolving world of technology, Whether you are a Full stack developer, frontend, or back-end developer, it is a tool to include in your toolbox.

Numerous possibilities for using VMs continue to amaze me. Start exploring the world of virtual machines today and unlock a new realm of possibilities, grasp the opportunity to make your projects easier, and expand your knowledge of varieties of operating systems.

DEV Community: Eztosin

🎓 ScholarTrack — An AI-Powered Scholarship Management Dashboard

What I Built

How I Used Runner H

Use Case & Impact

🎯 Use Case

💥 Impact

Social Love ❤️

Team Submission

June 12: A Landing Page Tribute to Nigerian Democracy

What I Built

Demo

Journey

My Submission for the DEV CSS Art Challenge: "The Gift That Sparked the Dream"

Math Catcher – Sharpen Your Mind, One Catch at a Time

🎮 What I Built

🚀 Demo

🧠 Code Repository

🤖 How I Used Amazon Q Developer

🎓 Educational Value

📚 Lessons Learned

Mastering Machine Learning with Python: Foundations and Key Concepts

Introduction to Machine Learning (ML):

Fundamentals Of Machine Learning

Setting Up Your Environment For Machine Learning

Steps in Machine Learning:

Common Libraries and Tools in Python For Machine Learning:

Recommended Development Environment:

Step-by-Step Setup:

Using Jupyter Notebook:

Building Your First Machine Learning Model

Let's break down the process step-by-step:

Data Preprocessing:

Conclusion:

Additional Resources:

Python Web Scraping: Best Libraries and Practices

Introduction to Web Scraping

Getting Started with Python for Web Scraping

Example usage:

Example usage:

Example usage:

Example usage:

Example usage:

Key Features and Considerations:

Understanding HTML Structure:

Basic Web Scraping Techniques

Example of using Requests

Example of using Beautiful soup:

Example code using Pyquery:

Advanced Web Scraping Techniques

Example Python Web scraping using Selenium

Best Practices and Tips

Tips:

Common Pitfalls:

Real-World Applications:

Conclusion:

Additional Resources:

Staying Updated on Industry News and Software Tools

Table Of Contents:

Introduction:

Methods To Stay Updated:

Description Of Software Tools:

Benefits Of Staying Updated:

Conclusion:

References:

The Relevance of C in Building Efficient Operating Systems

Harnessing C’s Power: The Unrivaled Choice for Operating System Development

Introduction

Prerequisites

The Technological Advancement of C

Unveiling C’s Relevance in Operating Systems

The Power of C’s low-level access

C’s Role in Embedded Systems

Enduring Legacy in the Programming Landscape

Challenges and Future Prospects

Conclusion

References

How To Use and Install a Virtual Machine

Introduction

Prerequisites