Web Development CS JS Python JavaScript Hacking ReactJs Python django Flask CSS Frontend Backend Full Stack Java Node Pdf Books

Coding Problems look complicated because you don’t yet have enough “patterns” in your memory. Feed more problems in your mind.

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🐧 Learn Linux from These 6 Amazing Websites 💻📚 Whether you're a beginner or sharpening your sysadmin skills, these resources can help you master Linux step-by-step: 1️⃣ LinuxOpsys – Great for command-line tips & server guides 🌐 linuxopsys.com 2️⃣ Linux Handbook – Covers terminal commands, scripting, and tutorials 🌐 linuxhandbook.com 3️⃣ Sysxplore – Focused on system internals, hardening, and Linux tools 🌐 sysxplore.com 4️⃣ Linuxize – Simple and clear Linux tutorials for daily use 🌐 linuxize.com 5️⃣ Linux Journey – Gamified Linux learning in a story format 🌐 linuxjourney.com 6️⃣ Linux Survival – Interactive learning environment for beginners 🌐 linuxsurvival.com ✨ Perfect for sysadmins, security enthusiasts, and ethical hackers looking to build a solid Linux foundation. ⚠️ Disclaimer: This post is for educational purposes only. Always verify and practice responsibly in authorized environments. #Linux #Sysadmin #InfoSec #CyberSecurity #LinuxTraining #LearnLinux #LinuxCommands #OpenSource

useful resource for learning Python from scratch @python_assets This is a free book Think Python (https://allendowney.github.io/ThinkPython/). Everything is clearly structured - from basic variables to classes, OOP and recursion Formatted as Jupyter notebooks: you can read the text, run code and complete tasks - all in one place. Directly in the browser, via Colab The notebooks with solutions can be downloaded from this repo on GitHub (https://github.com/AllenDowney/ThinkPython/tree/v3)

useful resource for learning Python from scratch @python_assets This is a free book Think Python (https://allendowney.github.io/ThinkPython/). Everything is clearly structured - from basic variables to classes, OOP and recursion Formatted as Jupyter notebooks: you can read the text, run code and complete tasks - all in one place. Directly in the browser, via Colab The notebooks with solutions can be downloaded from this repo on GitHub (https://github.com/AllenDowney/ThinkPython/tree/v3)

🔰 What Is MERN? MERN Stack is a Javascript Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express, React and Node.js. It is designed to make the development process smoother and easier. 🔰 MongoDB: MongoDb is a NoSQL DBMS where data is stored in the form of documents having key-value pairs similar to JSON objects. MongoDB enables users to create databases, schemas and tables. 🔰 ExpressJS ExpressJS is a NodeJS framework that simplifies writing the backend code. It saves you from creating multiple Node modules. 🔰 ReactJS ReactJS is a JS library that allows the development of user interfaces for mobile apps and SPAs. It allows you to code Javascript and develop UI components. 🔰 NodeJS NodeJS is an open-source Javascript runtime environment that allows users to run code on the server.

🔅 What is Vite? A modern frontend build tool that has quickly gained traction among developers for its remarkable speed and simplicity. Created by Evan You, the developer behind Vue.js, Vite optimizes the development experience by focusing on two primary goals: - Fast Development: Vite provides instant server start and blazing-fast Hot Module Replacement (HMR) for a smoother development workflow. - Optimized Production Builds: With native support for ES modules and efficient bundling through Rollup, Vite ensures that production builds are lean and performant.

💡 Did you know?

Credit card numbers are validated by an algorithm called "Luhn's Algorithm"

Storybook 8.3 for React Native!! - Storybook is an open-source tool for building and showcasing UI components in isolation. - It allows developers to create components independently of their app, making it easier to test, document, and share. - By using a separate environment, you can preview each component’s behavior and appearance without relying on the entire app’s state. Key benefits of the update: - Robust Component Testing with Vitest integration. - Consistent Visual Experience across platforms. - Simplified Setup with streamlined Metro config. - Enhanced Performance with a smaller bundle size.

🌐 WEBSITES WHERE YOU CAN MAKE MONEY BY HUNTING BUGS Hackers and Cyber Security Experts get paid well but some of them doesn't get chance so easily. So here are some platforms for bug bounty programs to earn good. 🤿Bug Bounty Platforms🥌 HackerOne https://www.hackerone.com Bugcrowd https://www.bugcrowd.com Synack https://www.synack.com Detectify https://cs.detectify.com Cobalt https://cobalt.io Open Bug Bounty https://www.openbugbounty.org Zero Copter https://www.zerocopter.com Yes We Hack https://www.yeswehack.com Hacken Proof https://hackenproof.com Vulnerability Lab https://www.vulnerability-lab.com Fire Bounty https://firebounty.com Bug Bounty https://bugbounty.jp/ Anti Hack https://antihack.me Intigrity https://intigrity.com/ Safe Hats https://safehats.com Red Storm https://www.redstorm.io/ Cyber Army https://www.cyberarmy.id Yogosha https://yogosha.com

REACT. JS VS REACT NATIVE

Atleast react on posts 😂😂

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10 free tools to become top level creator. 1. Idea - Google 2. Research - ChatGPT 3. Script - Notion 4. Recording - Audacity 5. Thumbnail - Canva 6. Editing - Davinci resolve 7. Stock Video - Mixkit 8. Captions - Clipchamp 9. Music & effect - YT Library 10. Scheduling - Buffer

How you guys doing? I hope you are learning something everyday.

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BECOMING A DATA ANALYST IN 2025 Becoming a data analyst doesn’t have to be expensive in 2025. With the right free resources and a structured approach, you can become a skilled data analyst. Here’s a roadmap with free resources to guide your journey: 1️⃣ Learn the Basics of Data Analytics Start with foundational concepts like: ↳ What is data analytics? ↳ Types of analytics (descriptive, predictive, prescriptive). ↳ Basics of data types and statistics. 📘 Free Resources: 1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability 2. Introduction to Data Analytics by IBM (audit for free) : https://www.coursera.org/learn/introduction-to-data-analytics 2️⃣ Master Excel for Data Analysis Excel is an essential tool for data cleaning, analysis, and visualization. 📘 Free Resources: 1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun 2. Chandoo.org: https://chandoo.org/ 🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF. 3️⃣ Learn SQL for Data Queries SQL is the language of data—used to retrieve and manipulate datasets. 📘 Free Resources: 1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/ 2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/ 🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets. 4️⃣ Get Hands-On with Data Visualization Learn to communicate insights visually with tools like Tableau or Power BI. 📘 Free Resources: 1. Tableau Public: https://www.tableau.com/learn/training 2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog 🎯 Practice: Create dashboards to tell stories using real datasets. 5️⃣ Dive into Python or R for Analytics Coding isn’t mandatory, but Python or R can open up advanced analytics. 📘 Free Resources: 1. Google’s Python Course https://developers.google.com/edu/python 2. R for Data Science (free book) r4ds.had.co.nz 🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data. 6️⃣ Work on Real Projects Apply your skills to real-world datasets to build your portfolio. 📘 Free Resources: Kaggle: Datasets and beginner-friendly competitions. Google Dataset Search: Access datasets on any topic. 🎯 Project Ideas: Analyze sales data and create a dashboard. Predict customer churn using a public dataset. 7️⃣ Build Your Portfolio and Network Showcase your projects and connect with others in the field. 📘 Tips: → Use GitHub to share your work. → Create LinkedIn posts about your learning journey. → Join forums like r/DataScience on Reddit or LinkedIn groups. Final Thoughts Becoming a data analyst isn’t about rushing—it’s about consistent learning and practice. 💡 Start small, use free resources, and keep building. 💡 Remember: Every small step adds up to big progress.