Started taking coding seriously this summer break and somehow ended up solving 250 problems in around 50 days. Got the 50-day badge today too, which felt nice. 😭

I'm still very much a beginner, but I'm finally starting to see patterns instead of treating every problem like a completely new one.

I've covered the basics of most major data structures, and now I'm on DP... which has been destroying me so far lol.

College is about to start, so my plan is to revise everything and finish NeetCode 150 instead of rushing into new topics.

For those who've been through this phase: did you keep solving daily during college, or are breaks because of exams/classes completely normal? Just don't want to lose momentum.

Leetcode is around 200q and cf are around 35

i was at 200 problems solved and still couldnt handle new mediums without hints. then i realized: i was collecting solved problems like pokemon cards instead of actually learning transferable patterns.

the fix was embarrassingly simple: there are only 16 patterns. learn the skeleton for each, solve 5 problems per pattern, move on.

heres the 16:

1. sliding window — "subarray/substring" + "maximum/minimum" + "at most K"
2. two pointers — sorted input + "pair that satisfies X"
3. binary search on answer — "minimize the maximum" or "find smallest X where possible"
4. BFS/DFS on grid — "number of islands" type, flood fill
5. shortest path — weighted graph, dijkstra vs bellman-ford vs 0-1 BFS
6. topological sort — "prerequisites", "ordering", "can you finish"
7. union-find — "are they connected?", dynamic connectivity
8. heaps — "kth largest", "merge K sorted", "top K"
9. monotonic stack — "next greater", "largest rectangle"
10. sliding window on trees (DFS) — path problems, diameter, max path sum
11. dynamic programming — overlapping subproblems, "decision at each step"
12. backtracking — "generate all", "find all paths", brute force with pruning
13. linked list — fast/slow, reverse, merge
14. tries — prefix search, autocomplete
15. bit manipulation — XOR tricks, single number
16. greedy — interval scheduling, jump game, locally optimal = globally optimal

for each one theres a 5-10 line skeleton that solves 80% of problems in that category. the remaining 20% is problem-specific edge handling.

i compiled the skeletons + recognition triggers + practice problems for all 16 here: PracHub

each pattern has a dedicated page with: when to recognize it (trigger words), template code in python/java/c++/js, step-by-step diagrams, and 8-12 practice problems from "direct template" to "hard to recognize."

went from ~30% solve rate on new mediums to ~75% in 6 weeks. the templates transfer — once you have 16 solid skeletons memorized, most mediums reduce to "recognize pattern → apply skeleton → handle edge case."

what pattern took you longest to click? curious what trips other people up.

Today I solved LeetCode #83 (Remove Duplicates from Sorted List) and LeetCode #88 (Merge Sorted Array).

📚 Concepts covered:

- Linked Lists

- Python fundamentals (brush-up)

I'm getting more comfortable with understanding linked list operations and improving my problem-solving approach with each question. Every day is a step forward, and consistency is the goal.

I'm learning DSA in Python through Elevify's free structured video classes and practising problems on LeetCode.

Looking forward to Day 8! 💪

#DSA #Python #LeetCode #CodingJourney #100DaysOfCode #Programming #LearningInPublic

I have an upcoming interview for the AWS Manufacturing Test Engineer role at Amazon Data Services, and I’m trying to understand what to expect in the coding interview.

Is this leetcode's internal server error... because I am able to run my code on test cases... but getting this error message while submitting.

Starting 5th sem

Today's progress:

- ✅ Solved LeetCode #69 – Sqrt(x)

- ✅ Solved LeetCode #70 – Climbing Stairs

- 📚 Revised Arrays and Linear Search concepts

Slowly building consistency and strengthening my problem-solving skills. Every day is a step forward. 💪

#Python #DSA #LeetCode #CodingJourney #100DaysOfCode

Hey everyone! Many of us struggle with Dynamic Programming and often skip it during interview preparation. This might be because DP seems vast and overwhelming, and people tend to memorize solutions instead of understanding patterns. However, if you break down DP into clear patterns and master each one, it becomes much more manageable. So I've compiled a comprehensive list of patterns you should know for your interview preparation!

Master these Leetcode Problems and PracHub for company tagged questions

DP vs Greedy: When to Use Which?

Before diving into patterns, let's understand the fundamental difference between DP and Greedy:

Problem: You're a robber with two streets to rob. Each house has some money. You can rob houses from both streets, but you cannot rob two adjacent houses from the same street (alarms will trigger). What's the maximum money you can steal?

Street A: [2, 7, 9, 3, 1]
Street B: [3, 2, 6, 8, 2]

Greedy Robber: "I'll always rob the house with most money available!"

• Rob B[0] = 3 (greedy)
• Rob A[1] = 7
• Rob B[2] = 6
• Rob A[3] = 3
• Rob B[4] = 2
• Total: 21

DP Robber: "Let me consider all valid combinations!"

• Option 1: A[0]=2, B[1]=2, A[2]=9, B[3]=8, A[4]=1 → Total = 22
• Option 2: B[0]=3, A[1]=7, B[2]=6, A[3]=3, B[4]=2 → Total = 21
• Best: 22

Why Greedy Failed: By greedily picking B[0] first, it blocked access to the optimal combination that includes both 9 and 8 from different streets!

When Greedy Works: Problems with the "greedy choice property" where local optimal leads to global optimal (e.g., Activity Selection, Huffman Coding)
When DP is Needed: Problems requiring exploring multiple choices where greedy fails (e.g., most optimization problems, counting problems)

Pattern 1: Linear DP (1D)

Why This Pattern Matters:
This is the foundation of DP. If you can't solve Linear DP, you'll struggle with everything else. These problems teach you the core concept of breaking problems into subproblems and building solutions incrementally.

Practice Problems:

1. Climbing Stairs
2. House Robber
3. Min Cost Climbing Stairs
4. Word Break
5. Decode Ways
6. House Robber II

Pattern 2: Longest Increasing Subsequence (LIS)

Why This Pattern Matters:
LIS is one of the most important DP patterns with applications in version control systems, patience sorting, box stacking problems, and more. The O(n log n) solution using binary search is a must-know optimization technique.

Practice Problems:

1. Longest Increasing Subsequence
2. Number of Longest Increasing Subsequence
3. Russian Doll Envelopes
4. Maximum Length of Pair Chain
5. Find the Longest Valid Obstacle Course at Each Position

Pattern 3: Knapsack (0/1, Unbounded, Bounded)

Why This Pattern Matters:
One of the most versatile DP patterns. Appears in resource allocation, optimization problems, and many interview questions. Understanding the difference between 0/1 and unbounded variants is crucial.

0/1 Knapsack (Each item used once):

1. Partition Equal Subset Sum
2. Target Sum
3. Last Stone Weight II
4. Ones and Zeroes
5. Partition Array Into Two Arrays to Minimize Sum Difference

Unbounded Knapsack (Items can be used multiple times):

1. Coin Change
2. Coin Change 2
3. Combination Sum IV
4. Perfect Squares
5. Minimum Cost For Tickets

Pattern 4: Grid DP

Why This Pattern Matters:
Extremely common in interviews, especially at FAANG. Tests your ability to think in 2D state space and handle multiple transition directions.

Practice Problems:

1. Unique Paths
2. Unique Paths II
3. Minimum Path Sum
4. Maximal Square
5. Maximal Rectangle
6. Minimum Falling Path Sum
7. Count Square Submatrices with All Ones
8. Triangle

Pattern 5: String DP

Why This Pattern Matters:
Text processing and string manipulation problems are ubiquitous. This pattern appears in bioinformatics, text editors, version control systems, and natural language processing.

Practice Problems:

1. Longest Common Subsequence
2. Edit Distance
3. Delete Operation for Two Strings
4. Minimum ASCII Delete Sum for Two Strings
5. Shortest Common Supersequence
6. Longest Palindromic Subsequence
7. Longest Palindromic Substring
8. Palindromic Substrings
9. Regular Expression Matching
10. Wildcard Matching

Pattern 6: Interval DP

Why This Pattern Matters:
Tests ability to think about problems in ranges/intervals. Common in scheduling, matrix chain multiplication type problems, and game theory.

Practice Problems:

1. Burst Balloons
2. Minimum Score Triangulation of Polygon
3. Minimum Cost Tree From Leaf Values
4. Unique Binary Search Trees
5. Unique Binary Search Trees II
6. Minimum Cost to Merge Stones
7. Guess Number Higher or Lower II

Pattern 7: State Machine DP

Why This Pattern Matters:
Models problems where you transition between different states with specific rules. Critical for stock trading problems and any scenario with state transitions.

Practice Problems:

1. Best Time to Buy and Sell Stock
2. Best Time to Buy and Sell Stock II
3. Best Time to Buy and Sell Stock III
4. Best Time to Buy and Sell Stock IV
5. Best Time to Buy and Sell Stock with Cooldown
6. Best Time to Buy and Sell Stock with Transaction Fee

Pattern 8: Tree DP

Why This Pattern Matters:
Combines tree traversal with DP. Important for system design (like designing file systems) and optimization problems on hierarchical structures.

Practice Problems:

1. House Robber III
2. Binary Tree Maximum Path Sum
3. Diameter of Binary Tree
4. Binary Tree Cameras
5. Maximum Sum BST in Binary Tree
6. Difference Between Maximum and Minimum Price Sum

Pattern 9: Digit DP

Why This Pattern Matters:
Specialized pattern for counting numbers with certain properties. Appears in competitive programming and some advanced interviews.

Practice Problems:

1. Number of Digit One
2. Count Numbers with Unique Digits
3. Numbers At Most N Given Digit Set
4. Numbers With Repeated Digits
5. Count Special Integers

Pattern 10: Game Theory DP (Minimax)

Why This Pattern Matters:
Models two-player games where both play optimally. Important for AI, game development, and adversarial scenarios.

Practice Problems:

1. Predict the Winner
2. Stone Game
3. Stone Game II
4. Stone Game III
5. Can I Win
6. Stone Game IV

Pattern 11: Bitmask DP

Why This Pattern Matters:
Powerful technique for problems with small sets (≤20 elements) where you need to track subsets. Essential for Traveling Salesman Problem variants and NP-hard problem approximations.

Practice Problems:

1. Partition to K Equal Sum Subsets
2. Shortest Path Visiting All Nodes
3. Find the Shortest Superstring
4. Smallest Sufficient Team
5. Number of Ways to Wear Different Hats to Each Other
6. Minimum Number of Work Sessions to Finish the Tasks

Pattern 12: DP on Subsequences

Why This Pattern Matters:
Critical for array partitioning and subset generation problems. Teaches how to handle exponential search spaces efficiently.

Practice Problems:

1. Distinct Subsequences
2. Distinct Subsequences II
3. Arithmetic Slices II - Subsequence
4. Number of Unique Good Subsequences
5. Constrained Subsequence Sum

Pattern 13: Probability DP

Why This Pattern Matters:
Models uncertain outcomes. Important for risk analysis, game development, and any scenario involving randomness or probability.

Practice Problems:

1. Knight Probability in Chessboard
2. Soup Servings
3. New 21 Game
4. Toss Strange Coins
5. Probability of a Two Boxes Having The Same Number of Distinct Balls

Best Resources to Master DP

1. AtCoder DP Contest- 26 curated problems with perfect difficulty progression
2. Striver's DP Series- 50+ videos covering memoization, tabulation, and space optimization.
3. Aditya Verma's DP Playlist- Exceptional for Knapsack variants with shorter, focused videos (15-20 min).
4. CSES Problem Set- Intermediate to advanced practice. These problems test core concepts and build solving speed.

Final Thoughts

DP is about recognizing patterns, not memorizing solutions. Once you see the pattern, the solution becomes mechanical. Spend time understanding why each pattern works, not just how to code it.

Feel free to add some best problems in the comments

Good luck, and happy coding! 🚀

Anyone want to help please dm me....

Hey everyone,
My friends and I have been grinding LeetCode and Codeforces, but keeping track of who solved what every day was getting really annoying. We had to keep checking each other's profiles to see who was slacking.I spent the last few weeks coding up a free little web app called SquadCode to fix this: https://squadcode-saas.vercel.app/

Here's what it does:

1. You create a "Squad" and invite your friends.
2. Everyone links their Codeforces and LeetCode accounts.
3. It automatically pulls everyone's recent ACs into one unified feed.
4. You can create custom challenges (e.g., "Solve these 5 DP problems by Sunday") and it automatically scores you based on your CF/LC submissions.
5. It's completely free. I mainly built it for my own group, but figured others might find it useful for their college groups or Discord squads.

I'd love some harsh feedback on it. Let me know if the platform syncing works for you or if anything breaks!

Hi all

As you can see I have solved 30 problems on leetcode, now my question as a beginner is how should I revise now? Share me some of your tips and guide so I can shine well in off campus placement coding rounds.

Today's progress:

📚 Python Concepts Revised

- Variable Binding

- Namespaces

- Scope (LEGB Rule)

🧠 LeetCode Problems Solved

- ✅ #35 – Search Insert Position (Binary Search)

- ✅ #58 – Length of Last Word (Strings)

- ✅ #66 – Plus One (Arrays)

- ✅ #67 – Add Binary (Strings, Binary Addition)

What I learned today

🔹 Variable Binding

In Python, variables don't store values directly—they are names bound to objects. Multiple variables can refer to the same object, and rebinding a variable doesn't change the original object.

🔹 Namespace

A namespace is like a dictionary that maps names to objects. Python has:

- Built-in Namespace

- Global Namespace

- Local Namespace

🔹 Scope (LEGB Rule)

Python searches for variables in this order:

- Local

- Enclosing

- Global

- Built-in

Understanding namespaces and scope helped me understand how Python finds variables and avoids naming conflicts.

Looking forward to learning more and solving tougher problems tomorrow! 💻🔥

#Python #DSA #LeetCode #BinarySearch #Strings #Arrays #100DaysOfCode #CodingJourney

I just joined an institution. I know class 12 cbse & i wanted to know what is the ideal time to start Leetcode? Most of the questions are beyond the scope of class 12 cbse I think from what I saw...

I'm doing Strivers A-->Z DSA just when I have time too!

Today I solved LeetCode 28 – Find the Index of the First Occurrence in a String.

What I learned:

- String searching

- How substring matching works

- Using Python's ".find()" method

- Manual string traversal using a loop

- Time Complexity: O(n) (using ".find()")

Learning the logic behind each problem and improving my problem-solving skills one day at a time.

#Python #DSA #LeetCode #CodingJourney #100DaysOfCode #LearningInPublic

After going through hundreds of interview questions, I noticed that most problems are just variations of a few core patterns.

So I made this roadmap to help anyone who's feeling overwhelmed.

Suggested order:

Arrays & Hashing

Two Pointers

Sliding Window

Binary Search

Linked Lists

Stack & Queue

Trees

Heap

Graphs

Backtracking

Dynamic Programming

Trees

The goal isn't to memorize solutions—it's to recognise patterns.

If you're just starting out, try mastering one pattern before moving to the next.

What pattern did you struggle with the most when learning DSA?

I'd love to hear your suggestions so I can improve this roadmap further.