Want To become a Backend Developer?

Here’s a roadmap with essential concepts:

1. Programming Languages

JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.


2. Version Control

Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.


3. Databases

Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.


4. APIs & Web Services

REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.


5. Server & Application Frameworks

Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.


6. Authentication & Authorization

JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.


7. Caching

Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.


8. Message Queues & Event-Driven Architecture

Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.


9. Microservices & Distributed Systems

Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).


10. Testing & Debugging

Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.

11. Containerization & Orchestration

Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.


12. CI/CD (Continuous Integration & Continuous Deployment)

CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.


13. Cloud Platforms

AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.


14. Logging & Monitoring

Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.


15. Security

Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.


16. Scalability & Optimization

Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.

ENJOY LEARNING 👍👍

#backend

Tools & Tech Every Developer Should Know ⚒️👨🏻‍💻

❯ VS Code ➟ Lightweight, Powerful Code Editor
❯ Postman ➟ API Testing, Debugging
❯ Docker ➟ App Containerization
❯ Kubernetes ➟ Scaling & Orchestrating Containers
❯ Git ➟ Version Control, Team Collaboration
❯ GitHub/GitLab ➟ Hosting Code Repos, CI/CD
❯ Figma ➟ UI/UX Design, Prototyping
❯ Jira ➟ Agile Project Management
❯ Slack/Discord ➟ Team Communication
❯ Notion ➟ Docs, Notes, Knowledge Base
❯ Trello ➟ Task Management
❯ Zsh + Oh My Zsh ➟ Advanced Terminal Experience
❯ Linux Terminal ➟ DevOps, Shell Scripting
❯ Homebrew (macOS) ➟ Package Manager
❯ Anaconda ➟ Python & Data Science Environments
❯ Pandas ➟ Data Manipulation in Python
❯ NumPy ➟ Numerical Computation
❯ Jupyter Notebooks ➟ Interactive Python Coding
❯ Chrome DevTools ➟ Web Debugging
❯ Firebase ➟ Backend as a Service
❯ Heroku ➟ Easy App Deployment
❯ Netlify ➟ Deploy Frontend Sites
❯ Vercel ➟ Full-Stack Deployment for Next.js
❯ Nginx ➟ Web Server, Load Balancer
❯ MongoDB ➟ NoSQL Database
❯ PostgreSQL ➟ Advanced Relational Database
❯ Redis ➟ Caching & Fast Storage
❯ Elasticsearch ➟ Search & Analytics Engine
❯ Sentry ➟ Error Monitoring
❯ Jenkins ➟ Automate CI/CD Pipelines
❯ AWS/GCP/Azure ➟ Cloud Services & Deployment
❯ Swagger ➟ API Documentation
❯ SASS/SCSS ➟ CSS Preprocessors
❯ Tailwind CSS ➟ Utility-First CSS Framework

React ❤️ if you found this helpful

Coding Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L

Top Libraries & Frameworks by Language 📚💻

❯ Python
 • Pandas ➟ Data Analysis
 • NumPy ➟ Math & Arrays
 • Scikit-learn ➟ Machine Learning
 • TensorFlow / PyTorch ➟ Deep Learning
 • Flask / Django ➟ Web Development
 • OpenCV ➟ Image Processing

❯ JavaScript / TypeScript
 • React ➟ UI Development
 • Vue ➟ Lightweight SPAs
 • Angular ➟ Enterprise Apps
 • Next.js ➟ Full-Stack Web
 • Express ➟ Backend APIs
 • Three.js ➟ 3D Web Graphics

❯ Java
 • Spring Boot ➟ Microservices
 • Hibernate ➟ ORM
 • Apache Maven ➟ Build Automation
 • Apache Kafka ➟ Real-Time Data

❯ C++
 • Boost ➟ Utility Libraries
 • Qt ➟ GUI Applications
 • Unreal Engine ➟ Game Development

❯ C#
 • .NET / ASP.NET ➟ Web Apps
 • Unity ➟ Game Development
 • Entity Framework ➟ ORM

❯ R
 • ggplot2 ➟ Data Visualization
 • dplyr ➟ Data Manipulation
 • caret ➟ Machine Learning
 • Shiny ➟ Interactive Dashboards

❯ PHP
 • Laravel ➟ Full-Stack Web
 • Symfony ➟ Web Framework
 • PHPUnit ➟ Testing

❯ Go (Golang)
 • Gin ➟ Web Framework
 • Gorilla ➟ Web Toolkit
 • GORM ➟ ORM for Go

❯ Rust
 • Actix ➟ Web Framework
 • Rocket ➟ Web Development
 • Tokio ➟ Async Runtime

Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17

React with ❤️ for more useful content

DSA (Data Structures and Algorithms) Essential Topics for Interviews

1️⃣ Arrays and Strings

Basic operations (insert, delete, update)

Two-pointer technique

Sliding window

Prefix sum

Kadane’s algorithm

Subarray problems


2️⃣ Linked List

Singly & Doubly Linked List

Reverse a linked list

Detect loop (Floyd’s Cycle)

Merge two sorted lists

Intersection of linked lists


3️⃣ Stack & Queue

Stack using array or linked list

Queue and Circular Queue

Monotonic Stack/Queue

LRU Cache (LinkedHashMap/Deque)

Infix to Postfix conversion


4️⃣ Hashing

HashMap, HashSet

Frequency counting

Two Sum problem

Group Anagrams

Longest Consecutive Sequence


5️⃣ Recursion & Backtracking

Base cases and recursive calls

Subsets, permutations

N-Queens problem

Sudoku solver

Word search


6️⃣ Trees & Binary Trees

Traversals (Inorder, Preorder, Postorder)

Height and Diameter

Balanced Binary Tree

Lowest Common Ancestor (LCA)

Serialize & Deserialize Tree


7️⃣ Binary Search Trees (BST)

Search, Insert, Delete

Validate BST

Kth smallest/largest element

Convert BST to DLL


8️⃣ Heaps & Priority Queues

Min Heap / Max Heap

Heapify

Top K elements

Merge K sorted lists

Median in a stream


9️⃣ Graphs

Representations (adjacency list/matrix)

DFS, BFS

Cycle detection (directed & undirected)

Topological Sort

Dijkstra’s & Bellman-Ford algorithm

Union-Find (Disjoint Set)


10️⃣ Dynamic Programming (DP)

0/1 Knapsack

Longest Common Subsequence

Matrix Chain Multiplication

DP on subsequences

Memoization vs Tabulation


11️⃣ Greedy Algorithms

Activity selection

Huffman coding

Fractional knapsack

Job scheduling


12️⃣ Tries

Insert and search a word

Word search

Auto-complete feature


13️⃣ Bit Manipulation

XOR, AND, OR basics

Check if power of 2

Single Number problem

Count set bits

Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

ENJOY LEARNING 👍👍

Learning Python in 2025 is like discovering a treasure chest 🎁 full of magical powers! Here's why it's valuable:

1. Versatility 🌟: Python is used in web development, data analysis, artificial intelligence, machine learning, automation, and more. Whatever your interest, Python has an option for it.

2. Ease of Learning 📚: Python's syntax is as clear as a sunny day!☀️ Its simple and readable syntax makes it beginner-friendly, perfect for aspiring programmers of all levels.

3. Community Support 🤝: Python has a vast community of programmers ready to help! Whether you're stuck on a problem or looking for guidance, there are countless forums, tutorials, and resources to tap into.

4. Job Opportunities 💼: Companies are constantly seeking Python wizards to join their ranks! From tech giants to startups, the demand for Python skills is abundant.🔥

5. Future-proofing 🔮: With its widespread adoption and continuous growth, learning Python now sets you up for success in the ever-evolving world of tech.

6. Fun Projects 🎉: Python makes coding feel like brewing potions! From creating games 🎮 to building robots 🤖, the possibilities are endless.

So grab your keyboard and embark on a Python adventure! It's not just learning a language, it's unlocking a world of endless possibilities.

Is DSA important for interviews?

Yes, DSA (Data Structures and Algorithms) is very important for interviews, especially for software engineering roles.

I often get asked, What do I need to start learning DSA?

Here's the roadmap for getting started with Data Structures and Algorithms (DSA):

𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
1. Introduction to DSA
- Understand what DSA is and why it's important.
- Overview of complexity analysis (Big O notation).

2. Complexity Analysis
- Time Complexity
- Space Complexity

3. Basic Data Structures
- Arrays
- Linked Lists
- Stacks
- Queues

4. Basic Algorithms
- Sorting (Bubble Sort, Selection Sort, Insertion Sort)
- Searching (Linear Search, Binary Search)

5. OOP (Object-Oriented Programming)

𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
1. Two Pointers Technique
- Introduction and basic usage
- Problems: Pair Sum, Triplets, Sorted Array Intersection etc..

2. Sliding Window Technique
- Introduction and basic usage
- Problems: Maximum Sum Subarray, Longest Substring with K Distinct Characters, Minimum Window Substring etc..

3. Line Sweep Algorithms
- Introduction and basic usage
- Problems: Meeting Rooms II, Skyline Problem

4. Recursion

5. Backtracking

6. Sorting Algorithms
- Merge Sort
- Quick Sort

7. Data Structures
- Hash Tables
- Trees (Binary Trees, Binary Search Trees)
- Heaps

𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
1. Graph Algorithms
- Graph Representation (Adjacency List, Adjacency Matrix)
- BFS (Breadth-First Search)
- DFS (Depth-First Search)
- Shortest Path Algorithms (Dijkstra's, Bellman-Ford)
- Minimum Spanning Tree (Kruskal's, Prim's)

2. Dynamic Programming
- Basic Problems (Fibonacci, Knapsack etc..)
- Advanced Problems (Longest Increasing Subsea mice, Matrix Chain Subsequence, Multiplication etc..)

3. Advanced Trees
- AVL Trees
- Red-Black Trees
- Segment Trees
- Trie

𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
1. Competitive Programming Platforms: LeetCode, Codeforces, HackerRank, CodeChef Solve problems daily

2. Mock Interviews
- Participate in mock interviews to simulate real interview scenarios.
- DSA interviews assess your ability to break down complex problems into smaller steps.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Preparing for a ReactJS interview? Here are some frequently asked questions to help you ace it!

🔹 What is React? Explain its core concepts, including JSX, virtual DOM, and component-based architecture.

🔹 Difference between functional and class components? Dive into hooks vs lifecycle methods.

🔹 What are hooks? Discuss useState, useEffect, and custom hooks.

🔹 Props vs State? Understand the difference and when to use each.

🔹 What is Redux? Know how to manage global state using Redux.

🔹 What are Higher-Order Components (HOCs)? Explain their role in component reusability.

🔹 What is lazy loading? Discuss the benefits of code splitting.

💡 Tip: Always relate these concepts to real-world projects you’ve worked on!

Web Development Best Resources: https://topmate.io/coding/930165

ENJOY LEARNING 👍👍

Typical C++ interview questions sorted by experience

Junior:
- What are the key features of object-oriented programming in C++?
- Explain the differences between public, private, and protected access specifiers in C++.
- Distinguish between function overloading and overriding in C++.
- Compare and contrast abstract classes and interfaces in C++.
- Can an interface inherit from another interface in C++?
- Define the static keyword in C++ and its significance.
- Is it possible to override a static method in C++?
- Explain the concepts of polymorphism and inheritance in C++.
- Can constructors be inherited in C++?
- Discuss pass-by-reference and pass-by-value for objects in C++.
- Compare == and .equals for string comparison in C++.
- Explain the purposes of the hashCode() and equals() functions.
- What does the Serializable interface do? How is it related to Parcelable in Android?
- Differentiate between Array and ArrayList in C++. When would you use each?
- Explain the distinction between Integer and int in C++.
- Define ThreadPool and discuss its advantages over using simple threads.
- Differentiate between local, instance, and class variables in C++.

Mid:
- What is reflection in C++?
- Define dependency injection and name a few libraries. Have you used any?
- Explain strong, soft, and weak references in C++.
- Interpret the meaning of the synchronized keyword.
- Can memory leaks occur in C++?
- Is it necessary to set references to null in C++?
- Why is a String considered immutable?
- Discuss transient and volatile modifiers in C++.
- What is the purpose of the finalize() method?
- How does the try{} finally{} block work in C++?
- Explain the difference between object instantiation and initialization.
- Under what conditions is a static block executed in C++?
- Why are generics used in C++?
- Mention some design patterns you are familiar with. Which do you typically use?
- Name some types of testing methodologies in C++.

Senior:
- Explain how std::stoi (string to integer) works in C++.
- What is the "double-check locking" problem, and how can it be solved in C++?
- Differentiate between StringBuffer and StringBuilder in C++.
- How is StringBuilder implemented to avoid the immutable string allocation problem?
- Explain the purpose of the Class.forName method in C++.
- Define Autoboxing and Unboxing in C++.
- What's the difference between Enumeration and Iterator in C++?
- Explain the difference between fail-fast and fail-safe in C++.
- What is PermGen in C++?
- Describe a Java priority queue.
- How is performance influenced by using the same number in different types: Int, Double, and Float?
- Explain the concept of the Java Heap.
- What is a daemon thread?
- Can a dead thread be restarted in C++?

ENJOY LEARNING 👍👍

Top Programming Frameworks on GitHub in 2025 👨🏻‍💻⚙️

🔷 React (234,369 stars)
🚀 Vue.js (208,671 stars)
📊 TensorFlow (~186,000 stars)
🔸 Angular (97,453 stars)
🔗 Django (83,095 stars)
💡 Svelte (82,163 stars)
🐍 Flask (69,300 stars)
⚡ Express.js (66,702 stars)
🦄 Laravel (~57,800 stars)
🛠️ Spring Framework (~57,800 stars)

Hey guys,

Today, let’s talk about some of the Python questions you might face during a data analyst interview. Below, I’ve compiled the most commonly asked Python questions you should be prepared for in your interviews.

1. Why is Python used in data analysis?

Python is popular for data analysis due to its simplicity, readability, and vast ecosystem of libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. It allows for quick prototyping, data manipulation, and visualization. Moreover, Python integrates seamlessly with other tools like SQL, Excel, and cloud platforms, making it highly versatile for both small-scale analysis and large-scale data engineering.

2. What are the essential libraries used for data analysis in Python?

Some key libraries you’ll use frequently are:

- Pandas: For data manipulation and analysis. It provides data structures like DataFrames, which are perfect for handling tabular data.
- NumPy: For numerical operations. It supports arrays and matrices and includes mathematical functions.
- Matplotlib/Seaborn: For data visualization. Matplotlib allows for creating static, interactive, and animated visualizations, while Seaborn makes creating complex plots easier.
- Scikit-learn: For machine learning. It provides tools for data mining and analysis.

3. What is a Python dictionary, and how is it used in data analysis?

A dictionary in Python is an unordered collection of key-value pairs. It’s extremely useful in data analysis for storing mappings (like labels to corresponding values) or for quick lookups.

Example:

sales = {"January": 12000, "February": 15000, "March": 17000}
print(sales["February"])  # Output: 15000

4. Explain the difference between a list and a tuple in Python.

- List: Mutable, meaning you can modify (add, remove, or change) elements. It’s written in square brackets [ ].

Example:

  my_list = [10, 20, 30]
  my_list.append(40)
  

- Tuple: Immutable, meaning once defined, you cannot modify it. It’s written in parentheses ( ).

Example:

  my_tuple = (10, 20, 30)
  

5. How would you handle missing data in a dataset using Python?

Handling missing data is critical in data analysis, and Python’s Pandas library makes it easy. Here are some common methods:

- Drop missing data:

  df.dropna()
  

- Fill missing data with a specific value:

  df.fillna(0)
  

- Forward-fill or backfill missing values:

  df.fillna(method='ffill')  # Forward-fill
  df.fillna(method='bfill')  # Backfill
  

6. How do you merge/join two datasets in Python?

- pd.merge(): For SQL-style joins (inner, outer, left, right).

  df_merged = pd.merge(df1, df2, on='common_column', how='inner')
  

- pd.concat(): For concatenating along rows or columns.

  df_concat = pd.concat([df1, df2], axis=1)

7. What is the purpose of lambda functions in Python?

A lambda function is an anonymous, single-line function that can be used for quick, simple operations. They are useful when you need a short, throwaway function.

Example:

add = lambda x, y: x + y
print(add(10, 20))  # Output: 30

Lambdas are often used in data analysis for quick transformations or filtering operations within functions like map() or filter().

If you’re preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.

Here you can find essential Python Interview Resources👇
https://t.iss.one/DataSimplifier

Like for more resources like this 👍 ♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://t.iss.one/free4unow_backup

ENJOY LEARNING 👍👍