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LeetCode

leetcode

10 LeetCode Patterns to Solve 1000 LeetCode Problems

Testing LLMs on Solving Leetcode Problems

Fundamentals of Graph Theory A graph is a mathematical structure used to model pairwise relationships between objects. A graph consists of a set of vertices (or nodes) and a set of edges, each of which connects a pair of vertices. This allows the representation of various data as networks, including road networks, social connections, relationships between different business processes, and more. Graphs can be directed or undirected. In a directed graph, edges have a direction indicating from one vertex to another, whereas in an undirected graph, edges have no directionality. 0 ------ 1
     / \       | \
    /   \      |  \
   2 --- 3     |   4
    \   / \    |  /
     \ /   \   | /
      5 ------ 6 What is a tree in the context of graph theory? In the context of graph theory, a tree is a special case of a graph. It is an undirected graph that is connected and acyclic. Trees exhibit several interesting properties, such as there being exactly one path between any two vertices. This makes them ideal for use in data structures such as search trees, parsing trees, process management structures, and more. 1
       / \
      2   3
     / \   \
    4   5   6
       /
      7 In problem-solving, we may also encounter binary trees. A binary tree is a hierarchical data structure in the form of a tree, consisting of nodes (vertices), where each node has at most two children: a left child and a right child. Each node contains some data (value) and references (pointers) to its child nodes. Key characteristics of a binary tree: : This is the topmost node of the tree, from which the entire structure originates. A binary tree has exactly one root. Root : Children Each node can have a maximum of two children: a left child and a right child. : The node positioned to the left of the current node. Left Child : The node positioned to the right of the current node. Right Child : Leaves These are nodes in the binary tree that do not have any children (i.e., their and pointers are ). left right null : Height of the Tree The height of a tree is defined as the maximum number of levels from the root to the furthest leaf node. An empty tree (with no nodes) has a height of 0, and a tree with only one node (just the root) has a height of 1. : Level of a Node The level of a node is determined by the number of edges on the path from the root to that node. The root node is at level 0. A binary tree is a fundamental data structure used in computer science and programming for a variety of applications, including binary search trees, expression trees, binary heaps, and more. Understanding the properties and operations of binary trees is essential for implementing efficient algorithms and solving various computational problems. It's time to look at some examples. Graph To begin with, I'd like to suggest implementing a basic graph construction first, and then we'll see how we can use it in problems. We'll create a simple representation of a graph using adjacency lists. import java.util.*;

class Graph {

    private int V; // Number of vertices
    private LinkedList<Integer>[] adj; // Adjacency list representation

    Graph(int v) {
        V = v;
        adj = new LinkedList[v];
        for (int i = 0; i < v; ++i) {
            adj[i] = new LinkedList();
        }
    }

    void addEdge(int v, int w) {
        adj[v].add(w); // Add w to v's adjacency list
        adj[w].add(v); // Add v to w's adjacency list (for undirected graph)
    }

    public static void main(String[] args) {
        Graph graph = new Graph(5);
        graph.addEdge(0, 1);
        graph.addEdge(0, 4);
        graph.addEdge(1, 2);

    }
} To work with graphs, we use just two important algorithms: DFS and BFS. Let's look at them and go through the theory. Depth-First Search (DFS) DFS (Depth-First Search) is an algorithm for traversing or searching in a graph or tree. It starts by choosing an initial vertex and then moves to its adjacent vertices. Once a vertex is reached that has no unvisited neighbours, the algorithm backtracks and continues exploring other branches. The main idea of DFS is to go as deep as possible into the data structure before backtracking. Key steps of DFS: Visit the current vertex. Recursively call DFS for each adjacent unvisited vertex. DFS can be used for various tasks such as finding paths between vertices, determining graph connectivity, topological sorting, and others. Let’s check the problem: .  We realized that we can solve this using a graph and DFS, but how? Instead of simple rooms, we have a graph here. We can start at position 0 without any keys, but we can move further. In our problem, we need to track the rooms we have visited. Therefore, we create a boolean array and mark room zero as visited. https://leetcode.com/problems/keys-and-rooms/ boolean[] seen = new boolean[rooms.size()];
 seen[0] = true; In Java, we can use the class to help maintain the order of our elements (Last-In-First-Out, or LIFO). We can push our nodes onto the stack. Stack while (!stack.isEmpty()) {
   int curruentNode = stack.pop(); 
   for (int n: rooms.get(curruentNode))
       if (!seen[n]) { 
           seen[n] = true; 
           stack.push(n); 
        }
   }
} The final step is to check if there are any unvisited rooms left. If we find an unvisited room, we return , indicating that not all nodes have been visited. Otherwise, we return , indicating that we have visited all nodes. false true class Solution {

    public boolean canVisitAllRooms(List<List<Integer>> rooms) {
        boolean[] seen = new boolean[rooms.size()];
        seen[0] = true;

        Stack<Integer> stack = new Stack();
        stack.push(0);

        while (!stack.isEmpty()) {
            int curruentNode = stack.pop(); 

            for (int n: rooms.get(curruentNode))
                if (!seen[n]) { 
                    seen[n] = true; 
                    stack.push(n); 
                }
        }

        for (boolean v: seen) {
            if (!v) {
                return false;
            }
        }
         
        return true;
    }
} Breadth-First Search (BFS) BFS (Breadth-First Search) is an algorithm for traversing or searching in a graph or tree. It starts by choosing an initial vertex and explores all of its neighbours at the current level before moving to neighbours of neighbours in the next level. In other words, it processes all adjacent vertices of the current vertex before moving on to adjacent vertices of those vertices and so on. Key steps of BFS: Enqueue the initial vertex into a queue. While the queue is not empty, dequeue a vertex from the front of the queue. Visit the current vertex and enqueue all its unvisited adjacent vertices to the back of the queue. Repeat steps 2-3 until the queue is empty. BFS is often used for finding the shortest path in unweighted graphs, checking graph connectivity, level-order traversal in trees, and other applications where it's necessary to process all vertices of a specific level before moving to the next level. Let’s check this one: As we can see, we already have a class TreeNode (we are near the topic, yes). https://leetcode.com/problems/same-tree/ public class TreeNode {
    int val;
    TreeNode left;
    TreeNode right;
    TreeNode() {}
    TreeNode(int val) { this.val = val; }
    TreeNode(int val, TreeNode left, TreeNode right) {
       this.val = val;
       this.left = left;
       this.right = right;
   }
} And what do we need to do? We should use recursion and check all nodes. Let’s start with checking nodes: boolean left = isSameTree(p.left, q.left);
boolean right = isSameTree(p.right, q.right);

boolean result = left && right; But we need to stop the recursion call and for the case, we should add base cases: all nodes are null if (p == null && q == null) return true; One node isn’t null if (p == null || q == null) return false; Values aren’t equels if (p.val != q.val) return false; And finally, we’ll add the return statement: /**
 * Definition for a binary tree node.
 * public class TreeNode {
 *     int val;
 *     TreeNode left;
 *     TreeNode right;
 *     TreeNode() {}
 *     TreeNode(int val) { this.val = val; }
 *     TreeNode(int val, TreeNode left, TreeNode right) {
 *         this.val = val;
 *         this.left = left;
 *         this.right = right;
 *     }
 * }
 */
class Solution {
    
    public boolean isSameTree(TreeNode p, TreeNode q) {
        if (p == null && q == null) return true;
        if (p == null || q == null) return false;
        if (p.val != q.val) return false;

        boolean left = isSameTree(p.left, q.left);
        boolean right = isSameTree(p.right, q.right);
        return left && right;
    }
} Conclusion Graphs and Trees: Graphs and trees are important data structures in computer science used for modelling and analyzing complex relationships between objects. They find wide applications in various fields including algorithms, network modelling, databases, bioinformatics, and more. Key Characteristics of Graphs and Trees: Graphs can be directed or undirected, contain cycles or be acyclic. Trees are a special case of graphs that are connected and acyclic. In graphs, each element is called a vertex, and the connections between vertices are called edges. DFS (Depth-First Search): Allows traversal of a graph or tree in a depth-first manner. Uses a stack for the recursive traversal of vertices. Applied for pathfinding, connectivity checking, topological sorting, and other tasks. BFS (Breadth-First Search): Allows traversal of a graph or tree in a breadth-first manner. Uses a queue for systematic exploration of neighbours at each level. Applied for shortest path finding, connectivity checking, level-order traversal, and other tasks.

Solving LeetCode Problems Using Graph Theory

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LLMs vs. Leetcode (Teil 1 und 2): Transformer-Lösungen für algorithmische Probleme verstehen

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LLM vs Leetcode (parties 1 et 2) : comprendre les solutions des transformateurs aux problèmes algorithmiques

एलएलएम बनाम लीटकोड (भाग 1 और 2): एल्गोरिदमिक समस्याओं के लिए ट्रांसफॉर्मर्स के समाधान को समझना

LLM 対 Leetcode (パート 1 と 2): アルゴリズムの問題に対する Transformer のソリューションを理解する

LLM과 Leetcode(1부 및 2부): 알고리즘 문제에 대한 변환기의 솔루션 이해

LLMs vs Leetcode (Parte 1 e 2): Compreendendo as soluções dos transformadores para problemas algorítmicos

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Yüksek Lisans ve Leetcode (Bölüm 1 ve 2): Transformers'ın Algoritmik Sorunlara Çözümlerini Anlamak

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LLM 与 Leetcode（第 1 部分和第 2 部分）：了解 Transformers 对算法问题的解决方案

Leetcode is famous for being a perfect platform for practicing coding problems and mastering coding interviews, unlike others, it is not for competitive programming, this guide will help you to get started with Leetcode without losing hope too early. Tip 1: Follow a list The biggest reason why people don't find DSA interesting is because they are unable to discover good problems which are easier to solve, simply solving popular questions with no track of questions will bore you very quickly. Even worse, you might try solving a really hard problem and lose motivation when it wasn't that hard, you just had to get a grip on it. That's why it's necessary to follow a list of questions, that way you won't have issues discovering new questions. Solving problems in the right order is very important You might see a question marked easy which isn't actually easy. The solution will be small, but sometimes, it isn't easy to come up with that solution if you haven't done a simpler version of it, thus, it will be demotivating. Blind 75 Leetcode Questions Above is an awesome list that is asked in interviews and is ordered by actual level of difficulty with prerequisites coming before harder questions. If you follow this, you'll feel more engaged. Once you have done most of this, do problems in the “similar questions" section below. Solve each problem till you master that category. Once you feel confident, you can use these - Leetcode Patterns Solve problems by category. This will help you master a data structure or some algorithm. Don't get afraid of “hard" questions as there is no hard problem that can't be broken up. Try to break every ‘difficult’ challenge into smaller clumps. You might not be able to solve it but you'll convert it to a much shorter set of problems that can be solved with some practice. Thinking abstract and looking at the bigger picture is very important. Leetcode is addictive if you improve gradually, try it. Tip 2: Avoid looking at solutions too quickly It's not bad to look at solutions. After all, you can't know everything and learning is necessary. However, looking at solution just after a few minutes of brainstorming is bad. You have to give your absolute best and try every possible "inefficient" solution you can come up with. The first phase is to figure out what Algorithm and data structure will be used. If you can determine what data structure will be used, you can check the section to verify if your assumption was correct, and if after a few minutes, you can't figure it out, you should still check the data structure that will be used and then try to figure out how and where it will be used in the given problem. Related Topics If you can come up with a solution that works correctly but isn't the best one, that's still a success as coming up with a brute-force solution is a bare minimum in an interview. You can try improving the brute force solution by using some optimization techniques. You might find the optimal solution immediately, but improving a solution is a great skill. After spending an hour, if you can't solve the problem, you should understand that you just aren't well versed with the given algorithm and should try solving related problems with that data structure and understand how it works. You should avoid looking at solutions as a solution you made yourself will help you much more. You should abandon the question and maybe revisit it in the future when you have some experience with that data structure. That way you can also track if you made some progress with that technique and if you could solve a new problem given to you in an interview, Tip 3: Interview Questions don't come with hints One thing to remember is that Interview questions won't tell you what data structure will be used for the problem. That's something you can only master with practice. The patterns and requirements of problems determine what's going to be used. There is no substitute for practice! Reading about algorithms will surely improve your range of thinking, but practice is what will help you master it. Tip 4: Be Consistent This goes without saying that practice needs consistency, simply overdoing it once and abandoning it for months will be destructive. It doesn't take much to take out some time every day for Leetcode, as far as discovering questions is concerned, you can use Daily Challenges to keep the consistency and maybe also earn Leetcode coins which might buy you a Leetcode T-Shirt one day. Tip 5: Turn Demotivation into a learning opportunity There will be times when you can't solve a problem despite all efforts, that's very common and bound to happen, but some questions being too hard is not something that should demotivate you. Every question is a learning opportunity and you have what it takes to learn it. Demotivation should be avoided and that's only possible if you have confidence in yourself and are willing to learn as much as you can. Tip 6: Participate in contests Even though Leetcode isn't a competitive programming platform, some contests allow you to try out brand-new problems and even compete with others. They have categories of 1 easy, 2 Medium, and 1 Hard, and solving 3 is more than enough. Once you have enough confidence in your problem-solving ability, these contests will help you gain interview experience as they don't have any hints, and solutions aren't available during the contest. This is pretty close to a real interview experience where time is limited. Tip 7: Keep Hustling Leetcode is an addiction and soon you'll fall in love with it, all you need to do is start, there is only one good time to start anything great, NOW. Just do it and you'll be satisfied with your decision and be proud of yourself. That's all, It's never too early and never too late.

7 Tips for Grinding Leetcode

How I Tackled Over 600 LeetCode Problems, and Here Are My Recommendations. I want to tell you a story about how I started from the level of "can't solve even 1 easy problem out of 10" to the level of "can solve every other medium problem" and went through several coding sessions at companies like Meta, Booking, Careem, Avito... It all started at the end of 2022 when I firmly decided that I wanted company. However, as you probably know, this requires knowledge of algorithms and data structures, which I lacked because, in regular work, you don't need , , , and other data structures. So, I had to start almost from scratch. to land a job at a FAANG heap tree disjoint set It was very challenging since I didn't know where to begin, how to approach problem-solving, what was important, and what wasn't. I also had questions like how many problems to solve to feel confident and if I could even progress to medium-level problems. Solving even easy problems was puzzling, and below, I'll try to help you with this. The first thing that comes to mind when you want to start solving LeetCode problems is to open the problems page and start solving them sequentially. https://leetcode.com/problemset/all/ However, it's not a great idea, even if you filter for problems, because many tasks labeled as . This can be demotivating and even lower your self-esteem. easy easy are quite challenging Tips: - pay attention to the rate; the higher, the better. This indicates that many people manage to solve the problem, increasing the likelihood that you can too. Additionally, you can explore the study plan and try solving problems from there: First tip Acceptance : A list of highly liked problems. If there are many likes, there's a chance it's not a convoluted problem that could lower your assessment. Top 100 Liked : A list of the top 150 problems for interview preparation. You're likely to encounter these in interviews, but some might be challenging, especially early on. Top Interview 150 : Described as a "Must-do problem list for interview prep." These problems are categorized into different topics; solve them following the next piece of advice. LeetCode 75 - start with the most fundamental data structures like (one-dimensional/two-dimensional), , ; you'll encounter them frequently. For instance, in many problems, execution time can be improved by adding a set or hashmap (though keep in mind this may increase memory consumption). Only after mastering these should you delve into other structures like , , , , and . The second tip arrays sets hashmaps, etc. stacks queues linked lists trees graphs On LeetCode, there are cards providing explanations of data structures and algorithms, along with problems to reinforce these concepts - . As mentioned earlier, , gradually increasing difficulty. LeetCode Explore begin with simple cards like arrays and strings I revisited these cards several times because some concepts weren't clear initially. Don't think there's anything wrong if something is unclear; return later, and you might find you now understand it. Here are some additional resources: : Cards with explanations and problems. LeetCode Learn : A roadmap with topics and problems; highly recommended. Neetcode Roadmap : Create a weekly plan using various filters. Tech Interview Handbook : A list of 150 popular problems categorized by topics, difficulty, and companies. LeetCode Patterns - if you can't come up with an optimal solution, start with a "brute-force" solution. Once you have that, think about how to improve it. Third tip Can you use a specific data structure? Can the array be sorted? What if you use a set or hashmap? Is there a template you can apply? If nothing helps and you've already spent 20-40 minutes, confidently open the solution and study it. After doing so, close the solution, and try to solve it again (this may take several iterations, which is normal). If you're still unable to solve it after multiple attempts, consider typing out the solution (I've done this several times). However, don't forget to revisit the problem later, and try solving it again. It's entirely possible that you might not succeed initially, and that's okay. Just repeat the steps until you're confident. - go through these , especially focusing on , , and , as they are often encountered. Take a pattern, find problems under that pattern (on the LeetCode problems page, like "Two pointers" or "Sliding window"), and solve enough problems to solidify your understanding of that topic. The fourth tip 14 patterns here two pointers sliding windows fast & slow pointers use the filter for tags - practice, practice, and practice again. In the beginning, I often felt like crying from frustration because I couldn't even solve simple problems. This is not an exaggeration. The thought of "I'm so dumb" lingered in my mind for the first few months. The fifth and most important tip Even after solving 600 problems, there are still moments when self-esteem drops, especially if I can't solve a seemingly easy problem. What's important is that with each solved problem, you'll become better relative to yourself. Solving LeetCode problems is now addressed on the platform , where problems are presented one after another, starting with easy ones and revisiting previously solved problems for reinforcement. There are explanations for various topics, and after solving a problem, you can see how others approached it, which is very helpful for understanding different solution strategies. Firecode FAQ: What Should I Do if I Can't Solve a Problem? If you can't solve a problem after 30-40 minutes, look at the solution, and try to find a video solution for that problem. After reviewing and understanding, attempt to solve it again. If unsuccessful, repeat the process. Periodically revisit old problems, especially those that were challenging. I've sometimes solved the same problem 10-15 times or more. How Many Problems Should I Solve Before Applying to Jobs? It varies individually. I spent six months solving a few problems every day, around 300 problems, before feeling a solid foundation. Confidence came after solving around 400-500 problems. Some may only need to solve 100-200 problems, but unfortunately, I'm not among them. Which Programming Language Is Best for Problem-Solving? In short - Python. I started with PHP, switched to Go, and then Java. After watching a , I tried Python and have been using it since. Python's concise code, built-in functions (like Counter, defaultdict, divmod, lambda, etc.), and speed are advantageous during interviews. video How to Find Time for Problem-Solving? I aimed to solve two problems daily, one before work and one after. On weekends, I dedicated more time to watching videos, reading articles, solving more problems, and revisiting old ones for reinforcement. Is It Worth Buying LeetCode Premium? The most useful feature of premium is seeing which companies gave a particular problem. If preparing for a specific company, it's recommended; otherwise, the free version is sufficient. Video explanations can be found on channels like . Neetcode Is It Worth Buying Access to ? educative.io While there are many recommendations for the Grokking Coding Interview Patterns course on educative, I didn't find it very useful. Visual explanations were the only positive aspect. In my opinion, watching YouTube videos is a better alternative. Is It Worth Buying Access to ? algoexpert.io In my view, is a very useful resource. Each problem comes with detailed explanations in videos lasting 30-50 minutes. They often have promotions, and you can sometimes get all courses for a year for $99. It's reasonably priced, has a pleasant website, numerous test cases, and a user-friendly editor. algoexpert.io Results: Achieved my initial goal for solving algorithms - passing coding interviews, a crucial step in FAANG companies. Successfully went through several interviews, and most importantly, no longer fear this stage. Deepened understanding of data structures. From not knowing what a heap is, I now have a comprehensive understanding of this structure and others. Changed my perspective on code during work. Now, I can identify areas to use more memory efficiently or reduce time complexity. Last but not least - brain fitness. Transitioned from "don't want to do it" to "want to solve problems every day." Useful Links: - Possibly the most helpful channel where concepts are explained in an accessible way. NeetCode YouTube Channel - Many useful videos explaining various data structures. William Fiset's YouTube Channel - Explains data structures and problems on a whiteboard. BackToBackSWE YouTube Channel Inside Code YouTube Channel Geekific YouTube Channel Michael Muinos YouTube Channel Nick White YouTube Channel TechLead YouTube Channel - Many recommendations; I haven't personally taken it. Coursera - Algorithms Part 1 (Russian)

Empowering Newbies: Building Confidence Through 600+ LeetCode Solutions – A Guide for Beginners

Introduction This is my consistent coding for the past year 👨🏻‍💻 https://twitter.com/Dhanush_Nehru/status/1741475660978397453?embedable=true Additionally, I discovered that I was one among 0.4% of consistent coders on Leetcode 🧠 https://twitter.com/Dhanush_Nehru/status/1735666289027227690?embedable=true Mastering Consistency How was I so consistent? Here’s how I did it, I just enjoy coding and problem solving, so I thought of doing something better than my 100 Days Of Code Challenge, so I chose to do a 365 Day of Leetcode Challenge. “Choose a job you love, and you will never have to work a day in your life.” — Confucius . I love to solve problems, and my interest and passion for problem-solving made it a habit to face a new problem and solve it without any motivation. “Every man is divine and strong in his real nature. What are weak and evil are his habits, his desires, and thoughts, but not himself.” — Swami Vivekananda. Similar to how we do our daily tasks, problem-solving has become a habit for me without much pressure or inspiration. “First we make our habits, and then our habits make us.” — John Dryden. Similar to how you go to the gym and exercise and maintain your health, it is necessary to train your brain as well as the ability to code. “Programming is a journey, not a destination. The code you write today is not the code you will write tomorrow.” — Unknown. Consistency is an art. It was hard for me to be consistent a few years back, so in order to master consistency as a goal, I did this challenge. It’s not what we do once in a while that shapes our lives. It’s what we do consistently.” — Tony Robbins . Mistakes to Avoid Here are a few mistakes that you can avoid while doing leetcode, We generally force ourselves to get the solution anyway, and once we see the solution, we feel bad about ourselves, and all the negative self-talk comes into play. But, most people do not understand that you must know the basics first before solving that particular problem. How can you expect yourself to play chess well if you don’t know how the coins move? Clear your fundamentals/syntax of whatever programming language, then push yourself to solve the problem. Build your foundation first: You should start valuing your efforts rather than getting the solution. Problem-solving skills are developed in those 30 minutes of fighting with the problem to get the solution. Sometimes, when you are not able to get the solution on the initial days, it is okay to look into the solutions by others, but it should not be a regular practice. Getting to the solution is not everything: Pick up a pen and paper, try to think of all the possible solutions, and focus on building up the solution. Start with the smallest example, try to come up with a solution, and then optimize it with edge case scenarios, etc. Don’t code in your head: There are more than 2000+ problems on Leetcode, and you don’t need to solve all of them. I just coded it as a matter of consistency. However, you need not solve every single problem every day if you want to improve your problem-solving. Concepts are limited. Problems are infinite: Even if I could solve the problem on my own, I always go to the comment section and quickly scan over superior solutions. You may not be aware of how many more effective approaches and viewpoints there are to solving the same issue. Also, I have done many open sources, so it has helped me to view others co’ code in a better way. Read other’s code: Establish a timer for each task; the breakdown is as follows: easy tasks take around 15 minutes, medium tasks take about 30 minutes, and hard tasks take about 45 minutes. Try not to enjoy the moment of finding the answer right away. Always time yourself: No matter how many questions you solve in the contest, show up every time. Once you show up, you have completed the hardest part. Don’t care about ratings: After the contest try to solve the questions without any time limit, if you don’t get it then check other’s solutions understand what they have done, read about that particular topic, and solve it back again using the specific knowledge. Up-solve the contest questions again: I have answered many more questions about my journey in the video below. Do watch, like, to show your support 🙏🏻 subscribe to my channel “The important thing is not to stop questioning. Curiosity has its own reason for existing.” — Albert Einstein. https://www.youtube.com/watch?v=J-hDaEU8smE&embedable=true In the end, it doesn’t matter how smart or dumb or lucky you are, if you are consistent enough you can outwork all the people you think are talented or smarter than you. Also, life is not something that happens on weekends. It is something that happens every single day. A Big Thank You 🙏🏻 If you’ve made it this far, thank you! If you love this post, please give a like as a sort of encouragement and also share this post on social media, which would benefit others. Connect ⬇️ / / / / Twitter Instagram Github Youtube Newsletter Also published . here

Reflecting on My Year of Consistent Leetcode Practice and Mistakes to Avoid

Hi, colleagues! As we approach the end of 2023, LeetCode is honoring active participants with badges. I have consistently tackled problems for over 300 days, earning myself this badge. An important note to emphasize is that this article is not about boasting about my achievement; rather, it focuses on cultivating a habit of daily practice, be it solving problems or revisiting those we tend to forget. Personally, concepts like dynamic programming and sliding windows took a while to sink in, and problems involving diagrams left me baffled. Immersing myself in a continuous cycle of tackling challenges has proven effective in keeping my mind sharp. In this article, I want to share an excellent life hack on how to always stay afloat in the IT market and keep yourself in shape by spending just 10-30 minutes a day in the morning. To start, simply allocate 10 minutes or more before work. I began my workday with a straightforward task. Instead of having coffee and reading articles, I started by reading problems. Later, I shifted this activity after stretching and before breakfast – a brief period to wake up, gather myself, and engage my brain. As a result, coffee became unnecessary😌 In This Brief Article, We Will Explore: Two valuable resources, one of which you are already familiar with – LeetCode, and the other where you can find an excellent free roadmap for studying patterns, along with problem breakdowns on YouTube. Strategies for finding time and determining the optimal time investment. The ultimate process we will achieve in the end. For me, solving algorithmic problems has always seemed like something akin to an Olympiad, and I long failed to grasp its value. Many argue that it is necessary solely for passing interviews, and that knowledge of how to invert trees or traverse them in depth will never be useful in real life. Partially, this is true, but when we start looking not only at high-level API libraries but delve into the logic, we begin to be amazed. Working with time, we can merge intervals; dealing with dictionary search, we can work with prefixes; working with a hash map, we can apply hashing algorithms. This is just the tip of the iceberg. As soon as we start writing a lot of our own code, not limiting ourselves to using frameworks, we encounter the need to optimize code, check its complexity, and make it more efficient. https://leetcode.com/problemset/?embedable=true LeetCode is an excellent way to start tackling easy problems to understand what complexity is and how much it affects the result. Starting with simple problems, we devise solutions, becoming more and more efficient, and ultimately, identifying patterns. It is precisely these patterns that allow us to determine how to solve a problem and what its complexity is. In most cases, we can solve it in several ways. What does this give us? In short, at work on your project, you will learn to instantly assess the efficiency of your code, especially if your project is constantly under heavy load. If your code transitions from cubic complexity to linear complexity, your servers and administrators will thank you. It also saves the company significant funds, especially when using services like Amazon. It may seem trivial, but such optimizations can save several thousand dollars! My Way! So, where to start? You have LeetCode, and you have problems, but what to do next? 1. Start With Easy Problems. It is recommended to begin by solving several " " level problems. Don't rush into the difficult ones right away. Take a sheet of paper and a pen, and start analyzing the problem verbally, without writing code. easy 2. Work Through the Solution. After you have a general understanding of the problem and have considered edge cases, start writing code. 3. Turn It Into a Habit. Solve a couple of problems per week, but don't rush. Gradually, you will develop an understanding of patterns and approaches to solving problems. 4. Pay Attention to Patterns. At some point, you will notice that your solutions repeat. This is where the time for patterns begins. If possible, consider a premium course on algorithms and data structures on LeetCode. There, you will find analyses of problems, video tutorials, and detailed solutions. If you want to save money, turn to the resource neetcode, where free problem breakdowns are available. https://neetcode.io/?embedable=true Neetcode has effectively organized everything into a tree structure based on topics, as well as tasks that are commonly asked. Certainly, on LeetCode, the precision is much higher, as users can mark which problems were actually asked and frequently update the problem list. However, I highly recommend exploring this resource and channel, even if you have been solving problems for a long time. You can choose topics and tasks: 5. Create a Systematic Approach. Take a plan: Open Neetcodee, and choose problems from the tree. Open the corresponding problem on LeetCode. Try to solve it. If you have solved it or couldn't, look at the solution, and write it down in a notepad. Transfer the solution from the notepad to the code and submit. And remember, failures in the beginning are normal. The key to this process is to learn and memorize patterns. As soon as you recognize a familiar pattern, solving problems will become much easier. Whether tackling easy or medium-level problems, it doesn't matter — just solve them! If memorizing problems is what it takes, go for it! There's no need to compare yourself to others, as we're in the process of cultivating a new and excellent habit. This habit will undoubtedly lead to significant improvement in the long run! Good luck with your learning! If you have any further questions or need additional assistance, feel free to ask. Cheers! 🚀

Why Making LeetCode a Daily Habit Is the Key to Skill Development and Career Growth

Introduction As an individual who has delved into the world of programming and coding challenges, I have had the opportunity to explore several platforms, each with its own set of advantages and disadvantages. Among the platforms I have utilized are LeetCode, CodeWars, CodeSignal, and HackerRank. Allow me to share my experiences and insights regarding these platforms, highlighting their pros and cons. Leetcode LeetCode has been my go-to platform, primarily because I have successfully solved the largest number of problems here. One of the significant advantages of LeetCode is its vast collection of coding challenges. This extensive repertoire ensures that there is always a problem to suit your skill level or area of interest. Additionally, LeetCode provides more comprehensive and better test cases compared to other platforms. The vibrant and large community associated with LeetCode is another positive aspect, as it allows for interaction, discussions, and sharing of solutions with fellow coders. Furthermore, LeetCode offers a solution working at the backdrop, which provides the correct answer for any randomly generated test case. This feature greatly assists in identifying flaws in your solution. However, it is worth mentioning that some of the problems on LeetCode are occasionally poorly explained. Another drawback is that the output generation feature is not available for interviews conducted via LeetCode, which can be inconvenient. Codewars CodeWars is another platform I have found useful, having solved a considerable number of problems here. One notable aspect of CodeWars is its impressive user interface, which enhances the overall user experience. The platform also offers a good collection of coding challenges that cater to various skill levels. However, a downside of CodeWars is that it lacks the ability to generate correct output for random test cases. In other words, there is no readily available correct solution working at the backdrop, which can make it more challenging to validate your code. CodeSignal Although I did not solve many problems on CodeSignal, I did participate in random coding tests. What struck me as highly advantageous about CodeSignal is its ability to generate correct output for any random test case, even during coding tests. This feature proved to be extremely useful, as it allowed me to validate my code promptly. Unfortunately, due to limited usage, I cannot provide a detailed analysis of its drawbacks and must give it the benefit of the doubt. HackerrRank HackerRank is another platform where I tackled a few problems. It stands out for its abundant collection of beginner-friendly problems, making it an excellent choice for individuals who are new to programming and looking to build their skills. However, unlike the aforementioned coding platforms, HackerRank requires you to write the entire code yourself. Personally, I find this to be somewhat arduous and time-consuming. I encountered an instance where I struggled with a particular problem on HackerRank, attempting to solve it using Python. However, the output had to be formatted as a string with spaces between numbers, which proved to be a tricky task in Python. While HackerRank processed C/C++ solutions for this problem, it lacked the mechanism to process Python solutions. It was an exasperating experience that left me feeling frustrated. Can there be a perfect or a near-perfect coding site? In my opinion, the optimal solution lies in combining LeetCode and CodeSignal. LeetCode, with its extensive collection and robust test cases, serves as an excellent platform. However, it would greatly benefit from adding the output generation mechanism for the interviewing feature as well. This enhancement would eliminate the need to write a separate brute-force solution for stress testing during interviews. Stress testing is not always feasible, particularly for problems for which brute-force solutions are much harder to think of than trivial approaches. Additionally, the time constraints of interviews may make it impractical to carry out stress testing. If LeetCode incorporates this feature, it would become a nearly perfect online judge for programmers. Conclusion Confused about which one should you choose? Well, I encourage individuals interested in coding challenges and improving their programming skills to explore platforms like LeetCode, CodeWars, CodeSignal, and HackerRank. Each platform offers unique merits, such as extensive problem collections, comprehensive test cases, user-friendly interfaces, or beginner-friendly content. By engaging with these platforms, programmers can gain exposure to diverse challenges, enhance problem-solving abilities, and benefit from the supportive coding communities provided by each platform. Feel free to have a look at my profiles on these coding sites: : Leetcode https://leetcode.com/sadman022/ Hackerrank: https://www.hackerrank.com/sadman022 Codewars: https://www.codewars.com/users/Abedin022 CodeSignal: https://app.codesignal.com/profile/sadman022

Comparing Coding Platforms: LeetCode, CodeWars, CodeSignal, and HackerRank

While of ChatGPT successfully solving live coding interview problems (and supposedly ) have been quite enthusiastic, it's important to note a few characteristics of the model: reports passing a Google interview for L3 grade The output has a random element. The same input can yield both correct and incorrect results. ChatGPT is trained on data published online before September 2021. The task likely becomes easier if the problem description and its solution were available before this date. I was intrigued to find how much ChatGPT's performance was genuinely "problem-solving ability" versus sheer randomness or “memorization of the correct solution." You see the short answer in the title, but let's proceed with the details. A brief plan of my simple experiment I randomly picked several problems from based on two independent parameters: and . I selected 13 problems from each of the 4 categories, totaling 52 problems. Each problem description was inputted into with a prompt describing a "live coding interview" setting. I expected the output to be Python code to pass the given problem's tests. If the code didn't pass, I provided only one opportunity to fix it. Leetcode difficulty level (easy/hard) publication date (before/after September 2021) ChatGPT-4 Why Leetcode? Simply put, Leetcode's popularity has soared to the point where it's virtually a household name. Thousands of interview candidates use it for preparation and seem successful. Leetcode provides problem descriptions, solutions examples, and automatic tests for submitted code — just what we need. I decided to exclude problems that incorporated images in their descriptions (as I was uncertain how to input an image into the chat), those with more downvotes than upvotes (a sign of a lame problem), and all problems classified as medium difficulty. Python was chosen as it's not only widely popular in production applications but also sufficiently succinct to solve algorithmic problems. Also initially, I chose a smaller set of problems. Eventually, I added a few more to each category to enhance the statistical significance of the results. Results and their Interpretation In my experiment, I got the following Acceptance rate (essentially the percentage of correct solutions), taking into account one additional attempt: Easy Hard Before September 2021 100% 46% After September 2021 69% 0% All Time 85% 23% (A link to the complete list of problems, solutions, and all dialogues can be found at the end of the post) ChatGPT-4 solved all easy problems and nearly half of the hard problems it likely had access to, given they were published before its cut-off date. However, the model only solved 69% of the easy problems it hadn't seen before, as those were published after the cut-off date. It was unable to tackle any new hard problems. From this, we can deduce that while some memorization does occur, the model doesn't precisely recall all problems and their respective solutions. These results might be considered if we were discussing a human. Indeed, easy problems are easier to solve than hard ones ( ); and already-seen problems are easier than unfamiliar ones. Intuitively, this reasoning should also hold true for the results generated by the language model. expected duh Out of 25 incorrect submissions, ChatGPT successfully fixed its code only once. Interestingly, it's unclear why the correct answer wasn't produced in the first place. My request to improve the solution included no additional details except pointing out that the previously proposed code was broken. ChatGPT and live coding interviews How good is ChatGPT-4 in an interview context? Let's envision a scenario where an interview at a hypothetical company involves solving two easy Leetcode problems, typical for junior or trainee positions. Here, ChatGPT-4's odds of acing the interview hover around 72%. However, when faced with a mix of one easy and one hard problem, these chances plummet to a mere 20%. And if presented with two hard problems — an approximation of senior-level interviews in big tech — the success rate falls even further to approximately 5%. that ChatGPT "outperforms 90% of programmers" could be considered exaggerated, though the validity depends significantly on the programmer sample used. Claims With non-zero odds of passing an interview, it's plausible to speculate that big tech companies are already testing ChatGPT, assessing their problem databases for "cracking" by this AI model and drawing corresponding conclusions. It's time to reflect again on what traits we truly value in a candidate during live coding. After all, the sole ability to output code only covers some of the essential skills, and it's often not the most critical. Conclusion ChatGPT have the ability to solve algorithmic problems, which is not simply a result of luck or "remembering" solutions found online. Its proficiency in live coding interviews is currently likely limited to entry-level grades. However, it has non-zero chances of success even in interviews for senior positions. It's an impressive feat for a general-purpose language model that's not specifically trained to code. does It's reasonable to anticipate that future models will significantly outperform ChatGPT-4 in solving algorithmic problems. This will undoubtedly reshape how companies conduct live coding interviews overall. Links Summary table with the results of my experiment and all chat transcripts: https://docs.google.com/spreadsheets/d/1C2b9ai-DBD4AwgRNEJuiLaGPUvwNie_1e-0vb6N5ohc/edit?usp=sharing Cover image generated by DALL-E 2.

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