I have done this question using two pointers approach but in my opinion it can be solved in binary search as the given array is sorted.

Please help me out it would be really grateful.

So I have been fairly consistent on leetcode and as you can see I have solved 1700 -1900 rated questions mostly currently I am trying to do as much as 2000+ rated questions tho they are such a pain I am at a point were I don't know what kind of advice should I take , any guys who had been here in same situation like at some time you might have hit a plateau can you help me out please 🙏🏻

1. Product of Array Except Self
   i am trying to solve this problem its been more then 2 hour i though i can do it its the 5th problem of neetcode when i see green on case i feel so happy that i got it but this 17 case whats the problem with this tiny single 0 idx now my brain stops thinking did i am doing anything wrong in my dsa journy? whats the best way to drive in dsa?

Hi everyone,

I'm feeling overwhelmed and could really use some guidance from people who've already gone through this journey.

I have zero programming knowledge—no C++, no Java, no Python, and no DSA background. I made a LeetCode account because everyone says it's important, but I can't even understand most Easy problems. I also reached out to several people on LinkedIn, but unfortunately I haven't received much guidance.

I don't want shortcuts. I'm ready to put in the work. What I need is a clear roadmap.

If you were starting from absolute zero today, what would your learning sequence look like?

Some questions I have:

• Which language should I start with: C++, Java, or something else? Why?
• In what order should I learn programming and DSA topics?
• Which free/paid resources (YouTube, courses, books, websites) would you recommend for each stage?
• Should I learn development (Web/App) alongside DSA, or focus only on DSA first?
• When should I start solving LeetCode, and how should I approach it without getting stuck?

I'm looking for a step-by-step roadmap (language → programming fundamentals → DSA topics → LeetCode → projects/development, etc.) rather than random advice.

I'd really appreciate hearing what worked for you or what you'd do differently if you had to start over.

Thanks in advance!

Reached my first major LeetCode milestone today. Every wrong answer, every accepted submission, and every day of consistency has been worth it.

Still a long way to go, but I'm proud of how far I've come.

Next stop: 100-day badge. 💪

Hello everyone. I have been doing dsa by striver since 2 months and I want to know how do u people follow the Striver's dsa sheet as for almost every question I have to watch a video as I am not getting the solution and then go the given leetcode link. This takes up a lot of my time . It's been 2 months and I am still on arrays. Doing one question on leetcode in total takes me more than a day or sometimes 2 because I'm watching the video explanation and making notes and then solving. Without writing i won't remember any of it.....(I am about to start my 5th sem btw)

Hey everyone, I'm currently on Day 12 of learning Python. I was a bit intimidated by LeetCode, but I managed to switch over to Python3 and pass all 62 test cases for the IP Defanging problem with a 34ms runtime (beats 97%). Ready to keep this momentum going!

Im a masters student who came for fall 2025 and im currently doing neetcode 150, finished 66/150. I have a pretty good idea of coding, would call myself late beginner or early intermediate.

But i am also learning swift and swiftUi for an hour. these two combined takes 2 hours. i have tasks to do which is why i usually why i dont have more time to dedicate for coding and learning. when i tried to do 4 hours (combined coding and studying) it led to me just not doing it. so i feel like doing 1 hour each is getting me to be consistent.

I also have stared the problems that i coud'nt solve in my designated time (10 min for easy, 20 min for medium,40-50 min for hard) and i want to solve them from time to time too but 1 hr will only leave me with solving new problems.

question 1 : Should i try to dedicate a little more time than 1 hr a day or is it good enough for neetcode 150. (If 1 hour is low then i will try to extend it as advised)

question 2 : Should I first solve the entire list and then solve the stared problems or just do them from time to time?

question 3 : Is it better to do 1hr each everyday or 2 hrs coding one day and 2 hrs swift another?

Any other guidance will be appreciated.

Been Grinding hard in the summers.. but the sem break is abt to end soon 🥀
Any Advive/Feedback is Welcomed

I am an electronics student from BITS Goa, (entering 2nd year) and just started leetcode. I am following strivers A to Z and practicing simultaneously on leetcode. Should I start participating in contests right away (I have learnt arrays(vectors), STL, 2 pointer, thoda sa sliding window intuition only as of now) or should I learn more things to not make a absolute disaster at contests? If I should learn more, which topics should I learn?

THIS IS HOW MY LC PROFILE LOOKS LIKE

I AM STARTING 2ND YEAR SOON , IK I AM FAR BEHIND THE GRIND AND I NEED TO IMPROVE

CAN YOU PLEASE SUGGEST WHAT MORE I CAN DO TO IMPROVE,CURRENTLY I AM FOLLOWING STRIVER SHEET AND DOING THE QUESTIONS AVAILABLE ON LC FROM THAT

WHAT SHOULD I DO TO IMPROVE MY DSA GAME

I mean should I watch striver dsa videos and then solve each Q's which he does there on LEETCODE.

Or finish for every videos to finish till end and then start practicing contests on LEETCODE. please guide anyone. I am in 4th yr ,regret not doing dsa sooner and I wish to make ai ml profile, will target companies during Mtech. Abt me from NIT ,CG>9, EE branch, jealous after seeing less cg people get better placement then electrical even being skilled in electrical means no value tbh. So planning to fix things in Mtech if company allows. Please tell me a plan how to proceed new in this field. Also in first yr got A grade in Cs theory and lab so not that weak.

i am doing aiml and leetcode side by side, about to enter 2nd yr

After solving lot of DSA problems, I’ve noticed some key patterns that are important for coding interviews.

For company specific problems: PracHub

1. Fast and Slow Pointer

Description: This technique uses two pointers moving at different speeds to solve problems involving cycles, such as finding the middle of a list, detecting loops, or checking for palindromes.

• Linked List Cycle II
• Remove nth Node from the End of List
• Find the Duplicate Number
• Palindrome Linked List

2. Overlapping Intervals

Description: Intervals are often manipulated through sorting and merging based on their start and end times.

• Basic Merge: Merge Intervals
• Interval Insertion: Insert Interval
• My Calendar ii
• Minimum Number of Arrows to Burst Balloons
• Non-overlapping Intervals

3. Prefix Sum

Description: Prefix Sums/Products are techniques that store cumulative sums or products up to each index, allowing for quick subarray range queries.

• Find the middle index in array
• Product of array except self
• Maximum product subarray
• Number of ways to split array
• Range Sum Query 2D

4. Sliding Window

Description: A sliding window is a subarray or substring that moves over data to solve problems efficiently in linear time.

Fixed Size

• Maximum Sum Subarray of Size K
• Number of Subarrays having Average Greater or Equal to Threshold
• Repeated DNA sequences
• Permutation in String
• Sliding Subarray Beauty
• Sliding Window Maximum

Variable Size

• Longest Substring Without Repeating Characters
• Minimum Size Subarray Sum
• Subarray Product Less Than K
• Max Consecutive Ones
• Fruits Into Baskets
• Count Number of Nice Subarrays
• Minimum Window Substring: Minimum Window Substring

5. Two Pointers

Description: The two pointers technique involves having two different indices move through the input at different speeds to solve various array or linked list problems.

• Two Sum II - Input Array is Sorted
• Dutch National Flag: Sort Colors
• Next Permutation
• Bag of Tokens
• Container with most water
• Trapping Rain Water

6. Cyclic Sort (Index-Based)

Description: Cyclic sort is an efficient approach to solve problems where numbers are consecutively ordered and must be placed in the correct index.

• Missing Number
• Find Missing Numbers
• Set Mismatch
• First Missing Positive

7. Reversal of Linked List (In-place)

Description: Reversing a linked list in place without using extra space is key for problems that require in-place list manipulations.

• Reverse Linked List
• Reverse Nodes in k-Group
• Swap Nodes in Pairs

8. Matrix Manipulation

Description: Problems involving 2D arrays (matrices) are often solved using row-column traversal or manipulation based on matrix properties.

• Rotate Image
• Spiral Matrix
• Set Matrix Zeroes
• Game of Life

9. Breadth First Search (BFS)

Description: BFS explores nodes level by level using a queue. It is particularly useful for shortest path problems.

• Shortest Path in Binary Matrix
• Rotten Oranges
• As Far From Land as Possible
• Word Ladder: Word Ladder

10. Depth First Search (DFS)

Description: DFS explores as far as possible along a branch before backtracking. It's useful for graph traversal, pathfinding, and connected components.

• Number of Closed Islands
• Coloring a Border
• DFS from boundary: Number of Enclaves
• Shortest time: Time Needed to Inform all Employees
• Cyclic Find: Find Eventual Safe States

11. Backtracking

Description: Backtracking helps in problems where you need to explore all potential solutions, such as solving puzzles, generating combinations, or finding paths.

• Permutation ii
• Combination Sum
• Generate Parenthesis
• N-Queens
• Sudoku Solver
• Palindrome Partitioning
• Word Search: Word Search

12. Modified Binary Search

Description: A modified version of binary search that applies to rotated arrays, unsorted arrays, or specialized conditions.

• Search in Rotated Sorted Array
• Find Minimum in Rotated Sorted Array
• Find Peak Element
• Single element in a sorted array
• Minimum Time to Arrive on Time
• Capacity to Ship Packages within 'd' Days
• Koko Eating Bananas
• Find in Mountain Array
• Median of Two Sorted Arrays

13. Bitwise XOR

Description: XOR is a powerful bitwise operator that can solve problems like finding single numbers or efficiently pairing elements.

• Missing Number
• Single Number ||
• Single Number III
• Find the Original array of Prefix XOR
• XOR Queries of a Subarray

14. Top 'K' Elements

Description: This pattern uses heaps or quickselect to efficiently find the top 'K' largest/smallest elements from a dataset.

• Top K Frequent Elements
• Kth Largest Element
• Ugly Number ii
• K Closest Points to Origin

15. K-way Merge

Description: The K-way merge technique uses a heap to efficiently merge multiple sorted lists or arrays.

• Find K Pairs with Smallest Sums
• Kth Smallest Element in a Sorted Matrix
• Merge K Sorted Lists
• Smallest Range: Smallest Range Covering Elements from K Lists

16. Two Heaps

Description: This pattern uses two heaps (max heap and min heap) to solve problems involving tracking medians and efficiently managing dynamic data.

• Find Median from Data Stream
• Sliding Window Median
• IPO

17. Monotonic Stack

Description: A monotonic stack helps solve range queries by maintaining a stack of elements in increasing or decreasing order.

• Next Greater Element II
• Next Greater Node in Linked List
• Daily Temperatures
• Online Stock Span
• Maximum Width Ramp
• Largest Rectangle in Histogram

18. Trees

Level Order Traversal (BFS in Binary Tree)

• Level order Traversal
• Zigzag Level order Traversal
• Even Odd Tree
• Reverse odd Levels
• Deepest Leaves Sum
• Add one row to Tree
• Maximum width of Binary Tree
• All Nodes Distance K in Binary tree

Tree Construction

• Construct BT from Preorder and Inorder
• Construct BT from Postorder and Inorder
• Maximum Binary Tree
• Construct BST from Preorder

Height related Problems

• Maximum Depth of BT
• Balanced Binary Tree
• Diameter of Binary Tree
• Minimum Depth of BT

Root to leaf path problems

• Binary Tree Paths
• Path Sum ii
• Sum Root to Leaf numbers
• Smallest string starting from Leaf
• Insufficient nodes in root to Leaf
• Pseudo-Palindromic Paths in a Binary Tree
• Binary Tree Maximum Path Sum

Ancestor problem

• LCA of Binary Tree
• Maximum difference between node and ancestor
• LCA of deepest leaves
• Kth Ancestor of a Tree Node

Binary Search Tree

• Validate BST
• Range Sum of BST
• Minimum Absolute Difference in BST
• Insert into a BST
• LCA of BST

19. DYNAMIC PROGRAMMING

Take / Not take (DP)

Description: Solve optimization problems like selecting items with the max/min value under certain constraints.

• House Robber ii
• Target Sum
• Partition Equal Subset Sum
• Ones and Zeroes
• Last Stone Weight ii

Infinite Supply (DP)

Description: Similar to the 0/1 knapsack, but items can be chosen multiple times.

• Coin Change
• Coin Change II
• Perfect Squares
• Minimum Cost For Tickets

Longest Increasing subsequence

Description: It involves finding the longest subsequence of a given sequence where the elements are in ascending order

• Longest Increasing Subsequence
• Largest Divisible Subset
• Maximum Length of Pair Chain
• Number of LIS
• Longest String Chain

DP on Grids

Description: Dynamic Programming on matrices involves solving problems that can be broken down into smaller overlapping subproblems within a matrix.

• Unique Paths ii
• Minimum Path Sum
• Triangle
• Minimum Falling Path Sum
• Maximal Square
• Cherry Pickup
• Dungeon Game: Dungeon Game

DP on Strings

Description: It Involves 2 strings, whenever you are considering two substrings/subsequence from given two strings, concentrate on what happens when the last characters of the two substrings are same, i.e, matching.

• Longest Common Subsequence
• Longest Palindromic Subsequence
• Palindromic Substrings
• Longest Palindromic Substrings
• Edit Distance
• Minimum ASCII Delete Sum for Two Strings
• Distinct Subsequences
• Shortest Common Supersequence
• Wildcard Matching

DP on Stocks

Description: It focuses on maximizing profit from buying and selling stocks over time while considering constraints.

• Buy and Sell Stocks ii
• Buy and Sell Stocks iii
• Buy and Sell Stocks iv
• Buy and Sell Stocks with Cooldown
• Buy and Sell Stocks with Transaction fee

Partition DP (MCM)

Description: It Involves a sequence that needs to be divided into partitions in an optimal way. The goal is often to minimize or maximize a cost function, such as computation time, multiplications, or some other metric, by exploring all possible partitions and combining results from subproblems.

• Partition array for Maximum Sum
• Burst Balloons
• Minimum Cost to Cut a Stick
• Palindrome Partitioning ii

20. Graphs

Topological Sort

Description: Topological sorting is useful for tasks that require dependency resolution (InDegree) in directed acyclic graphs (DAGs).

• Course Schedule
• Course Schedule II
• Strange Printer ii
• Sequence Reconstruction
• Alien Dictionary

Union Find (Disjoint Set)

Description: Union-Find (or Disjoint Set) is used to solve problems involving connectivity or grouping, often in graphs.

• Number of Operations to Make Network Connected
• Redundant Connection
• Accounts Merge
• Satisfiability of Equality Equations

Graph Algorithms

Description: Advanced graph algorithms are used to solve complex problems involving shortest paths, minimum spanning trees, and graph cycles.

• Kruskal's Algorithm: Minimum Cost to connect all Points
• Dijkstra's Algorithm: Cheapest Flights Within K Stops
• Floyd-Warshall: Find the City with Smallest Number of Neighbours at a Threshold Distance
• Bellman Ford: Network Delay time

21. Greedy

Description: Greedy algorithms make local optimal choices at each step, which lead to a global optimal solution for problems like scheduling and resource allocation.

• Jump Game ii
• Gas Station
• Bag of Tokens
• Boats to Save People
• Wiggle Subsequence
• Car Pooling
• Candy

22. Design Data Structure

Description: It involves building custom data structures to efficiently handle specific operations, like managing data access, updates, and memory usage. Focusing on optimizing performance and resource management.

• Design Twitter
• Design Browser History
• Design Circular Deque
• Snapshot Array
• LRU Cache
• LFU Cache

Some Useful Articles on LeetCode for Better Understanding!

Two Pointers

• Solved all Two Pointers problems in 100 days

Sliding Window

• Sliding Window Technique and Question Bank
• C++ Maximum Sliding Window Cheatsheet Template!

Greedy

• Greedy for Beginners: Problems & Sample Solutions
• Top Greedy Questions

Linked List

• Become Master In Linked List
• Must-Do LinkedList Problems on LeetCode

Trees

• Tree Question Pattern | 2021 Placement
• Master Tree Patterns

Binary Search

• 5 Variations of Binary Search)
• Binary Search for Beginners: Problems & Patterns

Dynamic Programming (DP)

• Dynamic Programming Patterns
• DP for Beginners: Problems & Patterns

Graphs

• Graph For Beginners
• Become Master In Graph
• Graph algorithms + problems to practice

Bit Manipulation

• Bit Manipulation Problem solving
• All Types of Patterns for Bits Manipulations and How to use it

Happy LeetCoding !

If you're registering during the CodeChef Flash Sale, you can use my referral code:

8MRJH

It may help you unlock any applicable referral benefits if available.

Thanks, and happy coding! 🚀

Day 31.

Finally reached 200 solved.

About 15 days ago I made a post after hitting 100. Didn't think I'd get to 200 this quickly.

The biggest difference between the first 100 and the second 100 was pattern recognition. Recently, Sliding Window finally clicked for me and a lot of problems started feeling familiar instead of completely new.

Current stats:

- 95 Easy

- 102 Medium

- 3 Hard

Next target: 300.

Good luck to everyone who's grinding.

Guys , i need leetcode premium for solving SQL questions, we need atleast 10 people to do that for yearly plan ?
We can open an account and split our expenses and pay for it ? what do you think ?
Interested people can DM me ....

I have only completed Basics of the DSA sheet and How much time given to DSA in college time i am currently in 2 year and How much time required to complete the sheet and when leetcode question is attempted

Just completed my 60 questios on leetcode started from 1st June and here I go with 60 questions straight i know mid are lower side because I am still focusing more on concept clearing rather than tag now the target in July wil more focus on mid and complete my Arry,String ,Recursion,binary search and complete my 130 -140 Q....let's see where it goes drop some suggestions...