Coding Interview Resources

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1. Solve a tiny, ignored problem.
2. Solve a big, common problem (even in crowded markets).
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4. Build AI-powered tools using GPT models.

🎯 Follow the SLC Rule:
• Simple: 1–2 core features.
• Lovable: Great UI/UX (design matters!).
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Every job interview has a "Do you have any questions?" part at the end.

But almost every candidate says "No questions from me" or doesn't ask good ones.

Here are 5 interview questions that you can ask to stand out and land offers:

1. I saw you did (XYZ) before joining
(COMPANY). What's your "why", and why did you decide to pursue a career here?

This question takes the interviewer out of the "interviewing" mindset and let's them talk more about their own journey. You can then mention how you relate to their journey, which creates empathy and a stronger connection with the interviewer.

2. If you looked back on this role a year later, what outcomes would indicate that this hire was successful?

This question allows you to see what they're looking for someone to accomplish in the role, and you can then answer their answer by showcasing how you'd do your best to achieve those outcomes.

3. What is the vision of the team in the next 6-12 months, and how would I contribute to having that vision come to life?

This question shows that you're forward-thinking and "in it to win it". You're also getting a deeper understanding of the goals your team has and how you'll play a part in achieving them.

4. What do you believe is the most important skill someone must have in this position?

This question allows you to identify the skill the interviewer wants to see in the candidate, and then you can turn the question back on them and share why you have that skills from your experiences.

5. What are next steps in the interview process, and when should I be hearing back?

This question gives you a timeline of how the interview process will go (if not shared already) and when you should follow-up if you don't hear back from the interviewer.

✅ DSA Roadmap for Coding Interviews 🧠💻

1️⃣ Start with the Basics
– Learn Time & Space Complexity
– Understand Big O notation

2️⃣ Master Arrays & Strings
– Sliding window, Two pointers, Prefix sum
– Practice problems like: Two Sum, Move Zeroes

3️⃣ Dive into Hashing
– Use HashMap/HashSet for fast lookups
– Problems: Longest Substring Without Repeat, Group Anagrams

4️⃣ Linked Lists
– Learn traversal, reversal, cycle detection
– Key problems: Detect Cycle, Merge Two Sorted Lists

5️⃣ Stacks & Queues
– Infix to postfix, parentheses validation, monotonic stack
– Problems: Valid Parentheses, Next Greater Element

6️⃣ Recursion & Backtracking
– Subsets, Permutations, N-Queens
– Key skill: build solution tree and backtrack correctly

7️⃣ Binary Search & Search Problems
– Classic problems: Search in Rotated Array, Koko Eating Bananas
– Understand upper/lower bounds

8️⃣ Trees & Binary Trees
– DFS, BFS, Inorder/Preorder/Postorder
– Problems: Lowest Common Ancestor, Diameter of Tree

9️⃣ Heaps & Priority Queues
– Top K elements, Min/Max heap use cases

🔟 Graphs
– BFS, DFS, Union-Find, Dijkstra’s
– Practice shortest path, connected components, cycle detection

1️⃣1️⃣ Dynamic Programming (DP)
– Start with 1D DP (Fibonacci, Climbing Stairs)
– Move to 2D DP (Knapsack, LCS, Grid Paths)

💡 Tip: Practice on LeetCode, Codeforces, GFG. Use patterns, not memorization.

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Modesql: https://mode.com/sql-tutorial/introduction-to-sql

kudavenkat playlist (first 16): https://www.youtube.com/playlist?list=PL08903FB7ACA1C2FB

SQL Cheatsheet

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

khanacademy SQL course: https://www.khanacademy.org/computing/computer-programming/sql

SQL Book for Beginners

https://t.me/learndataanalysis/36

Practice sql (Very Important)

https://leetcode.com/problemset/database/

https://www.hackerrank.com/domains/sql

https://www.windowfunctions.com/

https://www.sql-practice.com/

ENJOY LEARNING 👍👍

Developers who grow fast in tech tend to combine strong thinking habits with consistent practice and learning routines. These habits compound over time and turn average effort into outsized career progress.

✏ Core thinking habits

⦁ Ask “why” before “how” to understand trade-offs, not just syntax or steps.
⦁ Break big tasks into smaller pieces (input, process, output, edge cases) instead of jumping straight into code.
⦁ Write down approach or pseudocode first so you debug logic, not just syntax.

✏ Learning and experimentation

⦁ Treat learning as a daily routine: small coding sessions, experiments, or reading, even 30–60 minutes a day.
⦁ Stay curious about tools, frameworks, and patterns outside your current stack to avoid getting boxed in.
⦁ Use docs, official guides, and community threads as first-class learning sources, not just copy–paste fixes.

✏ Code quality and best practices

⦁ Write DRY, modular, and testable code so features are easier to change and debug later.
⦁ Read others’ code (PRs, open source, senior teammates) to pick up naming, structure, and patterns.
⦁ Add small tests and logs early to catch issues fast instead of relying only on manual clicking.

✏ Feedback, failure, and growth

⦁ Ship early, accept bugs as feedback, and fix quickly instead of hiding unfinished work.
⦁ Ask for code reviews and genuinely act on feedback rather than defending every decision.
⦁ Keep a simple “learning log” of mistakes, insights, and patterns so lessons aren’t forgotten.

✏ Communication and teaching

⦁ Explain your approach simply in standups, comments, or design docs; clear thinking shows in clear communication.
⦁ Teach what you know via mentoring, posts, or short demos—explaining forces you to close gaps in understanding.
⦁ Stay plugged into blogs, talks, and communities to see how others solve real-world problems.

✅ Top YouTube Channels to Learn Coding 📺💻

1️⃣ freeCodeCamp.org
– Full courses on Python, JavaScript, web dev, and data science
– No ads, no fluff – just solid content

2️⃣ Apna College
– Great for beginners in C++, DSA, web dev
– Taught in Hinglish (English + Hindi)

3️⃣ Tech With Tim
– Python tutorials, projects, and game dev with Pygame
– Covers beginner to intermediate topics

4️⃣ The Net Ninja
– Modern web development (React, Node.js, Firebase)
– Clean playlists and short, easy-to-follow videos

5️⃣ CodeWithHarry
– Hindi tutorials for web dev, Python, Java, C++
– Beginner-friendly and practical

6️⃣ Traversy Media
– Covers web technologies, APIs, crash courses
– Ideal for frontend & backend devs

7️⃣ CS50 by Harvard (David Malan)
– World-famous computer science course
– Deep programming foundations

8️⃣ Programming with Mosh
– High-quality tutorials on Python, React, Node, etc.
– Great explanations with visuals

9️⃣ Anuj Bhaiya
– DSA, system design, placement help
– College-friendly Hindi content

🔟 Fireship
– Fast-paced dev content in under 100 seconds
– Great for exploring trending tech/tools

💬 Tap ❤️ for more!

✅ Programming Language Fun Facts 🧠💻

1️⃣ Python 🐍
⦁ Created by Guido van Rossum in 1991
⦁ Known for readability and simplicity
⦁ Tops 2025 charts in AI, data science, and automation

2️⃣ JavaScript 🌐
⦁ Invented in just 10 days by Brendan Eich (1995)
⦁ Runs in every modern web browser
⦁ Powers 95%+ of websites

3️⃣ C 🖥️
⦁ Developed by Dennis Ritchie between 1969-73
⦁ Backbone of OS kernels and embedded systems
⦁ Foundation for C++, C#, Objective-C

4️⃣ Java ☕
⦁ Released by Sun Microsystems in 1995
⦁ “Write once, run anywhere” mantra
⦁ Powers Android apps and enterprise software

5️⃣ Rust 🦀
⦁ Launched by Mozilla in 2010
⦁ Focuses on memory safety without a garbage collector
⦁ Popular for system-level programming

6️⃣ Go (Golang) 🐹
⦁ Created at Google in 2009
⦁ Designed for simplicity and performance
⦁ Great for backend and microservices

7️⃣ TypeScript 🔷
⦁ Microsoft’s superset of JavaScript (2012)
⦁ Adds static typing
⦁ Hot in large frontend projects

💬 Tap ❤️ for more!

🚀 COMPLETE ROADMAP: FROM 4 LPA TO 40 LPA (STEP-BY-STEP PLAN)

PHASE 1: STRONG FOUNDATION (0 – 6 Months)
✅ Master DSA + Core Java
Arrays, Linked List, Stack, Queue, Trees, Graphs, DP
Daily problem solving on LeetCode / CodeStudio
Focus on writing clean & optimised code

PHASE 2: BACKEND DEVELOPMENT (6 – 12 Months)
✅ Become Backend Expert
Spring Boot + REST APIs
Microservices Architecture
SQL + NoSQL Databases
Authentication, JWT, Security Concepts
Build real-world scalable projects

PHASE 3: CLOUD & DEVOPS (12 – 15 Months)
✅ Deployment Skills
Docker & Kubernetes
AWS / GCP / Azure
CI/CD Pipelines
System Monitoring & Scaling

PHASE 4: SYSTEM DESIGN (15 – 20 Months)
✅ Crack High-Level Interviews
Low Level Design (LLD)
High Level Design (HLD)
Load Balancing, Caching, Database Scaling
Design systems like Netflix, Uber, WhatsApp

PHASE 5: ADVANCED ENGINEERING (20 – 30 Months)
✅ Become 10x Engineer
Concurrency & Multithreading
Performance Optimisation
Distributed Systems
Message Queues (Kafka, RabbitMQ)

PHASE 6: BRAND + INTERVIEW PREP (30 – 36 Months)
✅ Personal Branding
Strong GitHub Portfolio
Technical Blogs
LinkedIn Optimisation
Mock Interviews + DSA Revision

PHASE 7: TARGET HIGH-PAY COMPANIES (3 – 5 Years)
✅ Apply For:
MAANG Companies
Product-Based Giants
Remote International Roles
High Paying Startups

🎯 RESULT: 4 LPA ➝ 40 LPA CAREER TRANSFORMATION

Consistency + Smart Learning + Real Projects = High Salary Growth 💰


Reality Check : 👇

₹40 LPA is not magic. 🪄

It’s built with:
Deep skills
Smart job switches
High-leverage projects
Strategic career moves

React ❤️ For More

👨‍🎓System Design Topics: Cheat Sheet for Interview Preparation

☑️ Load Balancing
☑️ API Gateway
☑️ Communication Protocols
☑️ CDN (Content Delivery Network)
☑️ Database
☑️ Cache
☑️ Message Queue
☑️ Generating Unique Identifiers
☑️ Scalability
☑️ Availability
☑️ Performance
☑️ Fault Tolerance and Recovery
☑️ Security and much more

✅ JavaScript Essentials – Interview Questions with Answers 🧠💻

1️⃣ Q: What is the difference between let, const, and var?
A:
⦁ var: Function-scoped, hoisted, can be redeclared.
⦁ let: Block-scoped, not hoisted like var, can't be redeclared in same scope.
⦁ const: Block-scoped, must be assigned at declaration, cannot be reassigned.

2️⃣ Q: What are JavaScript data types?
A:
⦁ Primitive types: string, number, boolean, null, undefined, symbol, bigint
⦁ Non-primitive: object, array, function
Type coercion: JS automatically converts between types in operations ('5' + 2 → '52')

3️⃣ Q: How does DOM Manipulation work in JS?
A:
The DOM (Document Object Model) represents the HTML structure. JS can access and change elements using:
⦁ document.getElementById()
⦁ document.querySelector()
⦁ element.innerHTML (sets HTML content), element.textContent (sets text only), element.style (applies CSS)
Example: document.querySelector('p').textContent = 'Updated text!';

4️⃣ Q: What is event handling in JavaScript?
A:
It allows reacting to user actions like clicks or key presses.
Example:

document.getElementById("btn").addEventListener("click", () => {
  alert("Button clicked!");
});

5️⃣ Q: What are arrow functions?
A:
A shorter syntax for functions introduced in ES6.

const add = (a, b) => a + b;

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✅ DSA Roadmap for Beginners (2025) 🔢🧠

1. Understand What DSA Is
⦁ Data Structures organize data efficiently; Algorithms solve problems step-by-step
⦁ Why learn: Boosts coding interviews, optimizes code for tech jobs

2. Pick a Programming Language
⦁ Start with Python, C++, or Java for syntax basics
⦁ Focus on loops, arrays, functions before diving deep

3. Learn Time & Space Complexity
⦁ Big-O notation: O(1), O(n), O(n²)
⦁ Analyze efficiency to write better code

4. Master Basic Data Structures
⦁ Arrays & Strings: Indexing, manipulation
⦁ Linked Lists: Insertion, deletion, reversal

5. Explore Stacks & Queues
⦁ LIFO (Stack) for undo operations, FIFO (Queue) for tasks
⦁ Applications: Parentheses balancing, BFS

6. Dive into Trees & Graphs
⦁ Binary Trees, BSTs: Traversal (BFS/DFS)
⦁ Graphs: Adjacency lists, shortest paths (Dijkstra)

7. Learn Sorting & Searching
⦁ Algorithms: Bubble, Merge, Quick Sort; Binary Search
⦁ Understand when to use each for efficiency

8. Tackle Recursion & Backtracking
⦁ Base cases, recursive calls
⦁ Problems: Subsets, N-Queens

9. Work on Dynamic Programming
⦁ Memoization, tabulation
⦁ Classics: Fibonacci, Knapsack, LCS

10. Bonus Skills
⦁ Heaps, Tries, Greedy algorithms
⦁ Practice on LeetCode, HackerRank; build projects like pathfinders

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✅ Coding Interview Questions with Answers [Part-3] 💻🚀

21. Find the longest substring without repeating characters 
Use a sliding window with a set to track characters. 

def length_of_longest_substring(s):
    seen = set()
    left = max_len = 0
    for right in range(len(s)):
        while s[right] in seen:
            seen.remove(s[left])
            left += 1
        seen.add(s[right])
        max_len = max(max_len, right - left + 1)
    return max_len

22. Explain backtracking with N-Queens problem 
Backtracking tries placing a queen in each column, then recursively places the next queen if safe. If no safe position is found, it backtracks. 

def solve_n_queens(n):
    result = []
    board = [-1]×n

    def is_safe(row, col):
        for r in range(row):
            if board[r] == col or abs(board[r] - col) == abs(r - row):
                return False
        return True

    def backtrack(row=0):
        if row == n:
            result.append(board[:])
            return
        for col in range(n):
            if is_safe(row, col):
                board[row] = col
                backtrack(row + 1)
                board[row] = -1

    backtrack()
    return result

23. What is a trie? Where is it used?
A Trie is a tree-like data structure used for efficient retrieval of strings, especially for autocomplete or prefix matching. 
Used in: 
- Dictionary lookups 
- Search engines 
- IP routing

24. Explain bit manipulation tricks 
- Check if number is power of 2: n & (n - 1) == 0 
- Count set bits: bin(n).count('1') 
- Swap without temp: x = x ^ y; y = x ^ y; x = x ^ y

25. Kadane’s Algorithm for maximum subarray sum 

def max_subarray(nums):
    max_sum = current = nums[0]
    for num in nums[1:]:
        current = max(num, current + num)
        max_sum = max(max_sum, current)
    return max_sum

26. What are heaps and how do they work? 
Heap is a binary tree where parent is always smaller (min-heap) or larger (max-heap) than children. Supports O(log n) insert and delete. 
Use Python’s heapq for min-heaps.

27. Find kth largest element in an array 

import heapq
def find_kth_largest(nums, k):
    return heapq.nlargest(k, nums)[-1]

28. How to detect cycle in a graph? 
Use DFS with visited and recursion stack. 

def has_cycle(graph):
    visited = set()
    rec_stack = set()

    def dfs(v):
        visited.add(v)
        rec_stack.add(v)
        for neighbor in graph[v]:
              if neighbor not in visited and dfs(neighbor):
                return True
            elif neighbor in rec_stack:
                return True
        rec_stack.remove(v)
        return False

    for node in graph:
        if node not in visited and dfs(node):
            return True
    return False

29. Topological sort of a DAG 
Used to sort tasks with dependencies. 

def topological_sort(graph):
    visited, result = set(), []

    def dfs(node):
        if node in visited:
            return
        visited.add(node)
        for neighbor in graph.get(node, []):
            dfs(neighbor)
        result.append(node)

    for node in graph:
        dfs(node)
    return result[::-1]

30. Implement a stack using queues 

from collections import deque

class Stack:
    def init(self):
        self.q = deque()

    def push(self, x):
        self.q.append(x)
        for _ in range(len(self.q) - 1):
            self.q.append(self.q.popleft())

    def pop(self):
        return self.q.popleft()

    def top(self):
        return self.q[0]

    def empty(self):
        return not self.q

💬 Double Tap ♥️ For Part-4!

✅ Coding Interview Questions with Answers [Part-2] 💻🚀

11. What is a sliding window algorithm?
A technique for solving problems involving arrays or strings by maintaining a window that slides over data. It helps reduce time complexity by avoiding nested loops.
Example: Finding the max sum of subarrays of size k.

12. Detect cycle in a linked list.
Use Floyd's Cycle Detection Algorithm (Tortoise and Hare).
⦁ Move two pointers at different speeds.
⦁ If they meet, a cycle exists.
⦁ To find the cycle start, reset one pointer to head and move both one step until they meet again.

13. Find the intersection of two arrays.
Use a HashSet to store elements of the first array, then check each element in the second array.
⦁ Time: O(n + m)
⦁ Space: O(min(n, m))

14. Reverse a string or linked list.
⦁ For a string: Use two-pointer swap or Python's slicing.
⦁ For a linked list: Use three pointers (prev, curr, next) and iterate while reversing links.

15. Check if a string is a palindrome.
Use two pointers from start and end, compare characters.
Return false if mismatch, true if all characters match.

16. What are the different sorting algorithms?
⦁ Bubble Sort
⦁ Selection Sort
⦁ Insertion Sort
⦁ Merge Sort
⦁ Quick Sort
⦁ Heap Sort
⦁ Radix Sort
Each has different time and space complexities.

17. Explain quicksort vs. mergesort.
⦁ Quicksort: Divide and conquer, picks a pivot.
⦁ Average: O(n log n), Worst: O(n²), Space: O(log n)
⦁ Mergesort: Always divides array into halves, then merges.
⦁ Time: O(n log n), Space: O(n), Stable sort

18. What is a binary search tree (BST)?
A tree where left child < node < right child.
⦁ Efficient for searching, insertion, deletion: O(log n) if balanced.
⦁ Unbalanced BST can degrade to O(n)

19. Inorder, Preorder, Postorder traversals.
⦁ Inorder (LNR): Sorted order in BST
⦁ Preorder (NLR): Used to copy or serialize tree
⦁ Postorder (LRN): Used to delete tree

20. Implement LRU Cache.
Use a combination of HashMap + Doubly Linked List.
⦁ HashMap stores key-node pairs.
⦁ Linked list maintains access order.
⦁ When cache is full, remove the least recently used node.
Operations (get, put): O(1) time.

💬 Double Tap ♥️ For Part-3!

✅ Coding Interview Questions with Answers [Part-1] 💻🚀

1. What is the time and space complexity of your code?
Time complexity measures how the runtime grows with input size. Space complexity measures memory used. Always analyze both to optimize your solution.

2. What is the difference between an array and a linked list?
Arrays store elements contiguously with fast access by index. Linked lists store elements as nodes connected by pointers, allowing easy insertion/deletion but slower access.

3. How does a HashMap work internally?
It uses a hash function to convert keys into indexes in an array. Collisions are handled by chaining (linked lists) or open addressing.

4. What is recursion? Give an example.
Recursion is a function calling itself to solve smaller subproblems.
Example: Factorial(n) = n × Factorial(n-1), with base case Factorial(0) = 1.

5. Explain stack vs. queue.
Stack: Last In First Out (LIFO), like a stack of plates.
Queue: First In First Out (FIFO), like a line at a store.

6. What is a binary search and when to use it?
Binary search efficiently finds an item in a sorted array by repeatedly dividing the search interval in half. Use on sorted data for O(log n) time.

7. What is the difference between BFS and DFS?
BFS (Breadth-First Search) explores nodes level by level using a queue.
DFS (Depth-First Search) explores as far as possible along a branch using a stack or recursion.

8. What is dynamic programming?
A method to solve problems by breaking them into overlapping subproblems and storing solutions to avoid repeated work.

9. Solve Fibonacci using memoization.
Memoization stores already calculated Fibonacci numbers in a cache to reduce repeated calculations and improve performance from exponential to linear time.

10. Explain two-pointer technique with an example.
Use two pointers to traverse data structures simultaneously.
Example: Find if a sorted array has two numbers summing to a target by moving pointers from start and end inward.

💬 Double Tap ♥️ For Part-2!

✅ 🔤 A–Z of Programming 💻

A – API (Application Programming Interface)
Interface for programs to communicate with each other.

B – Bug
Error or flaw in a program that causes incorrect results.

C – Compiler
Tool that converts code into executable machine language.

D – Debugging
Process of finding and fixing bugs in code.

E – Exception
An error detected during execution, often requiring handling.

F – Function
Reusable block of code that performs a specific task.

G – Git
Version control system for tracking code changes.

H – HTML (HyperText Markup Language)
Standard language for building web pages.

I – IDE (Integrated Development Environment)
Software that combines tools for coding, testing, and debugging.

J – JavaScript
Language for building interactive web applications.

K – Keyword
Reserved word with special meaning in a programming language.

L – Loop
Structure for repeating a block of code multiple times.

M – Module
File containing reusable code, functions, or classes.

N – Namespace
Container to organize identifiers and avoid naming conflicts.

O – Object-Oriented Programming (OOP)
Paradigm based on objects and classes to structure code.

P – Parameter
Value passed to a function to customize its behavior.

Q – Query
Instruction to retrieve data, often from databases.

R – Recursion
Function that calls itself to solve a problem.

S – Syntax
Rules that define how code must be written.

T – Try-Catch
Error-handling structure to catch exceptions.

U – UI (User Interface)
Part of the program users interact with visually.

V – Variable
Named storage for data in a program.

W – While Loop
Loop that continues as long as a condition is true.

X – XML
Markup language for storing and sharing structured data.

Y – YAML
Readable format used for config files in DevOps and backends.

Z – Zero-based Indexing
Common system where counting in arrays starts at 0.

💬 Tap ❤️ for more!

Useful Free Resources To Crack Your Next Insterview
👇👇

Job Interviewing Skills Tutorial Free Course

https://bit.ly/3RvG31E

Interview Training for Hiring Managers and Teams Free Udemy course

https://bit.ly/3fCgxe8

Coding Interview Prep Free course by Freecodecamp

https://www.freecodecamp.org/learn/coding-interview-prep/

Cracking the coding interview free book

https://t.me/crackingthecodinginterview/272

Python Interview Question and Answers for freshers

https://www.careerride.com/python-interview-questions.aspx

50 coding interview Questions book

https://www.byte-by-byte.com/wp-content/uploads/2019/01/50-Coding-Interview-Questions.pdf

Ultimate Guide to Machine Learning Interviews

https://t.me/datasciencefun/820

ENJOY LEARNING 👍👍

🔅 Python Function

Top 50 Coding Interview Questions 💻🚀

1. What is the time and space complexity of your code?
2. Difference between array and linked list.
3. How does a HashMap work internally?
4. What is recursion? Give an example.
5. Explain stack vs. queue.
6. What is a binary search and when to use it?
7. Difference between BFS and DFS.
8. What is dynamic programming?
9. Solve Fibonacci using memoization.
10. Explain two-pointer technique with an example.
11. What is a sliding window algorithm?
12. Detect cycle in a linked list.
13. Find the intersection of two arrays.
14. Reverse a string or linked list.
15. Check if a string is a palindrome.
16. What are the different sorting algorithms?
17. Explain quicksort vs. mergesort.
18. What is a binary search tree (BST)?
19. Inorder, Preorder, Postorder traversals.
20. Implement LRU Cache.
21. Find the longest substring without repeating characters.
22. Explain backtracking with N-Queens problem.
23. What is a trie? Where is it used?
24. Explain bit manipulation tricks.
25. Kadane’s Algorithm for maximum subarray sum.
26. What are heaps and how do they work?
27. Find kth largest element in an array.
28. How to detect cycle in a graph?
29. Topological sort of a DAG.
30. Implement a stack using queues.
31. Explain the difference between pass by value and reference.
32. What is memoization vs. tabulation?
33. Solve the knapsack problem.
34. Find duplicate numbers in an array.
35. What are function closures in Python/JavaScript?
36. How does garbage collection work in Java?
37. What are lambda functions?
38. Explain OOPs concepts: Inheritance, Polymorphism, Encapsulation, Abstraction.
39. What is multithreading vs. multiprocessing?
40. Difference between process and thread.
41. Implement a binary heap.
42. Explain prefix sum technique.
43. Design a parking lot system.
44. Find median in a stream of numbers.
45. Detect anagram strings.
46. Serialize and deserialize a binary tree.
47. Implement a trie with insert and search.
48. Explain design patterns like Singleton, Factory.
49. Discuss trade-offs between readability and performance.
50. How do you debug a tricky bug?

💬 Tap ❤️ for detailed answers!

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?

On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.

And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.

The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

🧑‍💻 How to Crack Coding Interviews in 2025 💥🚀

1️⃣ Understand the Interview Format
⦁ Phone screen, technical rounds, system design, behavioral
⦁ Research company-specific patterns on sites like Glassdoor

2️⃣ Master Data Structures & Algorithms
⦁ Arrays, Strings, Linked Lists, Trees, Graphs
⦁ Sorting, Searching, Recursion, Dynamic Programming
⦁ Practice daily on LeetCode, HackerRank, Codeforces

3️⃣ Learn Problem-Solving Patterns
⦁ Sliding window, Two pointers, Fast & slow pointers
⦁ Backtracking, Greedy, Divide & Conquer
⦁ Understand when & how to apply them

4️⃣ Write Clean & Efficient Code
⦁ Focus on readability, naming, and edge cases
⦁ Optimize time & space complexity
⦁ Explain your approach clearly during interviews

5️⃣ Mock Interviews & Peer Coding
⦁ Practice with friends or platforms like Pramp, Interviewing.io
⦁ Get comfortable thinking aloud and receiving feedback

6️⃣ Prepare for Behavioral Questions
⦁ Use STAR method (Situation, Task, Action, Result)
⦁ Highlight teamwork, problem-solving, and adaptability

7️⃣ Know Your Projects & Resume
⦁ Be ready to explain your role, challenges, and learnings
⦁ Discuss tech stack and decisions confidently

8️⃣ Stay Calm & Confident
⦁ Take a deep breath before coding
⦁ Think aloud, clarify doubts
⦁ It’s okay to ask for hints or discuss trade-offs

💬 Double Tap ❤️ For More!