Imagine solving problems in O(n²) when O(n) is possible. The sliding window technique can change your life! The sliding window technique is a genius way to handle problems that require continuous subarrays. Instead of recalculating everything, we "slide" a window across the data, updating values efficiently. 📌 Where it works best: ✅ Finding the maximum sum of a subarray (Kadane’s Algorithm) ✅ Checking for anagrams in a string ✅ Longest substring without repeating characters Sliding windows can be fixed-size or variable-size, depending on the problem. Once you master it, it saves tons of unnecessary computations. Have you ever used sliding window to optimize a problem? What’s a tricky case where it didn’t work?

Both explore graphs, but one goes deep while the other goes wide. Which one is better? Depth-First Search (DFS) and Breadth-First Search (BFS) are core algorithms in graph theory, but they serve different purposes. 📌 DFS (Depth-First Search):✅ Best for exploring all paths (e.g., cycle detection, backtracking)✅ Uses recursion or a stack✅ Works well for tree problems 📌 BFS (Breadth-First Search):✅ Best for finding shortest paths in unweighted graphs✅ Uses a queue for level-wise traversal✅ Ideal for problems like word ladder or shortest path in a maze The key is knowing when to use which. If you need shortest paths, BFS is the way. If you need to explore all possibilities (like in backtracking), DFS is your friend. Have you ever used BFS or DFS in a real-world project? Which one do you find more intuitive?

If you still use Bubble Sort, we need to talk… 😅 Sorting is one of the most fundamental operations in CS. Yet, many people stick to slow algorithms like Bubble Sort when there are better choices! 📌 Key sorting algorithms and when to use them: ✅ QuickSort – Fast on average (O(n log n)), great for general sorting ✅ MergeSort – Stable and works well with linked lists ✅ HeapSort – Useful when sorting large datasets in place ✅ Counting/Radix Sort – If elements have a small range, these can be faster than O(n log n)! Choosing the right sorting algorithm depends on the constraints, not just speed. Sometimes stability or memory usage matters more. What’s your go-to sorting algorithm, and why? Have you ever used a non-standard sorting technique?

Why check every pair in O(n²) when you can solve it in O(n)? The two-pointer technique is pure magic! The two-pointer technique is one of the simplest ways to optimize problems involving arrays or linked lists. Instead of using nested loops, we use two indices that move towards each other or in the same direction. 📌 Examples where it shines: ✅ Finding pairs that sum to a target (Two Sum for sorted arrays) ✅ Checking if a string is a palindrome ✅ Merging two sorted lists It’s super useful, but it only works when the problem has a sorted structure or a clear left-to-right movement. What’s your favorite problem that uses two pointers? Have you ever found a creative use for it?

Hi, I am Hemanth from Hyderabad, India. I am a prefinal year B.Tech student from CS background. My goal is to enter a good product based company like FAANG. I am planning to start my web development journey by understanding and learning. Do you have any suggestions for a beginner like me to start my journey?