For years, crypto firms complained about being “debanked” in the US. Under the Trump administration, a group of fintechs is rolling out the red carpet.

Posted on : June 13, 2025 By Tech World Times Development and Testing  Rate this post Sorting helps organize data in a specific order. It is used in search, reports, and efficient storage. Different sorting algorithms offer different performance. In this article, we will explain the Time Complexity of Sorting Algorithms in simple words. We will cover Python, Java, and C++ examples. 1. What Is Time Complexity? Time complexity tells how fast an algorithm runs. It measures the number of steps as input grows. It is written in Big-O notation. For example, O(n²) means steps grow with the square of inputs. 2. Types of Time Complexity Here are common types: O(1): Constant time O(n): Linear time O(n log n): Log-linear time O(n²): Quadratic time We will now apply these to sorting. 3. Bubble Sort Bubble Sort compares two numbers and swaps them if needed. It repeats until the list is sorted. Time Complexity: Best Case: O(n) (if already sorted) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef bubble_sort(arr): n = len(arr) for i in range(n): for j in range(n - i - 1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] Java Example: javaCopyEditvoid bubbleSort(int arr[]) { int n = arr.length; for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) { int temp = arr[j]; arr[j] = arr[j+1]; arr[j+1] = temp; } } C++ Example: cppCopyEditvoid bubbleSort(int arr[], int n) { for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) swap(arr[j], arr[j+1]); } 4. Selection Sort This sort picks the smallest number and places it at the front. Time Complexity: Best Case: O(n²) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef selection_sort(arr): for i in range(len(arr)): min_idx = i for j in range(i+1, len(arr)): if arr[j] < arr[min_idx]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] 5. Insertion Sort This algorithm builds the final list one item at a time. Time Complexity: Best Case: O(n) Average Case: O(n²) Worst Case: O(n²) Java Example: javaCopyEditvoid insertionSort(int arr[]) { for (int i = 1; i < arr.length; i++) { int key = arr[i]; int j = i - 1; while (j >= 0 && arr[j] > key) { arr[j + 1] = arr[j]; j = j - 1; } arr[j + 1] = key; } } 6. Merge Sort Merge Sort splits the array into halves and merges them back in order. Time Complexity of Sorting Algorithms like Merge Sort is usually better. Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n log n) Python Example: pythonCopyEditdef merge_sort(arr): if len(arr) > 1: mid = len(arr) // 2 left = arr[:mid] right = arr[mid:] merge_sort(left) merge_sort(right) i = j = k = 0 while i < len(left) and j < len(right): if left[i] < right[j]: arr[k] = left[i] i += 1 else: arr[k] = right[j] j += 1 k += 1 arr[k:] = left[i:] + right[j:] 7. Quick Sort Quick Sort picks a pivot and places smaller numbers before it. Time Complexity: Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n²) C++ Example: cppCopyEditint partition(int arr[], int low, int high) { int pivot = arr[high]; int i = low - 1; for (int j = low; j < high; j++) { if (arr[j] < pivot) { i++; swap(arr[i], arr[j]); } } swap(arr[i+1], arr[high]); return i + 1; } void quickSort(int arr[], int low, int high) { if (low < high) { int pi = partition(arr, low, high); quickSort(arr, low, pi - 1); quickSort(arr, pi + 1, high); } } 8. Built-in Sort Methods Languages have built-in sort functions. These are well-optimized. Python: sorted() or list.sort() uses TimSort Time Complexity: O(n log n) Java: Arrays.sort() uses Dual-Pivot QuickSort Time Complexity: O(n log n) C++: std::sort() uses IntroSort Time Complexity: O(n log n) These are better for most real-world tasks. 9. Time Complexity Comparison Table AlgorithmBestAverageWorstStableBubble SortO(n)O(n²)O(n²)YesSelection SortO(n²)O(n²)O(n²)NoInsertion SortO(n)O(n²)O(n²)YesMerge SortO(n log n)O(n log n)O(n log n)YesQuick SortO(n log n)O(n log n)O(n²)NoTimSort (Python)O(n)O(n log n)O(n log n)YesIntroSort (C++)O(n log n)O(n log n)O(n log n)No 10. How to Choose the Right Algorithm? Use Merge Sort for large stable data. Use Quick Sort for faster average speed. Use Insertion Sort for small or nearly sorted lists. Use built-in sort functions unless you need control. Conclusion The Time Complexity of Sorting Algorithms helps us pick the right tool. Bubble, Selection, and Insertion Sort are simple but slow. Merge and Quick Sort are faster and used often. Built-in functions are highly optimized. Python, Java, and C++ each have their strengths. Understand your problem and input size. Then pick the sorting method. This ensures better speed and performance in your code. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

One stock recently impacted by a whirlwind of volatility is Block—the fintech powerhouse behind Square, Cash App, Tidal Music, and more. The company’s COO and CFO, Amrita Ahuja, shares how her team is using new AI tools to find opportunity amid disruption and reach customers left behind by traditional financial systems. Ahuja also shares lessons from the video game industry and discusses Gen Z’s surprising approach to money management.   This is an abridged transcript of an interview from Rapid Response, hosted by Robert Safian, former editor-in-chief of Fast Company. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. As a leader, when you’re looking at all of this volatility—the tariffs, consumer sentiment’s been unclear, the stock market’s been all over the place. You guys had a huge one-day drop in early May, and it quickly bounced back. How do you make sense of all these external factors? Yeah, our focus is on what we can control. And ultimately, the thing that we are laser-focused on for our business is product velocity. How quickly can we start small with something, launch something for our customers, and then test and iterate and learn so that ultimately, that something that we’ve launched scales into an important product? I’ll give you an example. Cash App Borrow, which is a product where our customers can get access to a line of credit, often $100, $200, that bridges them from paycheck to paycheck. We know so many Americans are living paycheck to paycheck. That’s a product that we launched about three years ago and have now scaled to serve 9 million actives with $15 billion in credit supply to our customers in a span of a couple short years. The more we can be out testing and launching product at a pace, the more we know we are ultimately delivering value to our customers, and the right things will happen from a stock perspective. Block is a financial services provider. You have Square, the point-of-sale system; the digital wallet Cash App, which you mentioned, which competes with Venmo and Robinhood; and a bunch of others. Then you’ve got the buy-now, pay-later leader Afterpay. You chair Square Financial Services, which is Block’s chartered bank. But you’ve said that in the fintech world, Block is only a little bit fin—that comparatively, it’s more tech. Can you explain what you mean by that? What we think is unique about us is our ability as a technology company to completely change innovation in the space, such that we can help solve systemic issues across credit, payments, commerce, and banking. What that means ultimately is we use technologies like AI and machine learning and data science, and we use these technologies in a unique way, in a way that’s different from a traditional bank. We are able to underwrite those who are often frankly forgotten by the traditional financial ecosystems. Our Square Loans product has almost triple the rate of women-owned businesses that we underwrite. Fifty-eight percent of our loans go to women-owned businesses versus 20% for the industry average. For that Cash App Borrow product I was talking about, 70% of those actives, the 9 million actives that we underwrote, fell below 580 as a FICO score. That’s considered a poor FICO score, and yet 97% of repayments are made on time. And this is because we have unique access to data and these technology and tools which can help us uniquely underwrite this often forgotten customer base. Yeah. I mean, credit—sometimes it’s been blamed for financial excesses. But access to credit is also, as you say, an advantage that’s not available to everyone. Do you have a philosophy between those poles—between risk and opportunity? Or is what you’re saying is that the tech you have allows you to avoid that risk? That’s right. Let’s start with how do the current systems work? It works using inferior data, frankly. It’s more limited data. It’s outdated. Sometimes it’s inaccurate. And it ignores things like someone’s cash flows, the stability of your income, your savings rate, how money moves through your accounts, or how you use alternative forms of credit—like buy now, pay later, which we have in our ecosystem through Afterpay. We have a lot of these signals for our 57 million monthly actives on the Cash App side and for the 4 million small businesses on the Square side, and those, frankly, billions of transaction data points that we have on any given day paired with new technologies. And we intend to continue to be on the forefront of AI, machine learning, and data science to be able to empower more people into the economy. The combination of the superior data and the technologies is what we believe ultimately helps expand access. You have a financial background, but not in the financial services industry. Before Block, you were a video game developer at Activision. Are financial businesses and video games similar? Are there things that are similar about them? There are. There actually are some things that are similar, I will say. There are many things that are unique to each industry. Each industry is incredibly complex. You find that when big technology companies try to do gaming. They’ve taken over the world in many different ways, but they can’t always crack the nut on putting out a great game. Similarly, some of the largest technology companies have dabbled in fintech but haven’t been able to go as deep, so they’re both very nuanced and complex industries. I would say another similarity is that design really matters. Industrial design, the design of products, the interface of products, is absolutely mission-critical to a great game, and it’s absolutely mission-critical to the simplicity and accessibility of our products, be it on Square or Cash App. And then maybe the third thing that I would say is that when I was in gaming, at least the business models were rapidly changing from an intermediary distribution mechanism, like releasing a game once and then selling it through a retailer, to an always-on, direct-to-consumer connection. And similarly with banking, people don’t want to bank from 9 to 5, six days a week. They want 24/7 access to their money and the ability to, again, grow their financial livelihood, move their money around seamlessly. So, some similarities are there in that shift to an intermediary model or a slower model to an always-on, direct-to-consumer connection. Part of your target audience or your target customer base at Block are Gen Z folks. Did you learn things at Activision about Gen Z that has been useful? Are there things that businesses misunderstand about younger generations still? What we’ve learned is that Gen Z, millennial customers, aren’t going to do things the way their parents did. Some of our stats show that 63% of Gen Z customers have moved away from traditional credit cards, and over 80% are skeptical of them. Which means they’re not using a credit card to manage expenses; they’re using a debit card, but then layering on on a transaction-by-transaction basis. Or again, using tools like buy now, pay later, or Cash App Borrow, the means in which they’re managing their consistent cash flows. So that’s an example of how things are changing, and you’ve got to get up to speed with how the next generation of customers expects to manage their money.

Posted on : June 13, 2025 By Tech World Times Uncategorized  Rate this post Sorting is important in programming. It helps organize data. Sorting improves performance in searching, analysis, and reporting. There are many sorting algorithms. One of the simplest is Insertion Sort. In this article, we will learn how to implement Insertion Sort in Java. We will explain each step in simple words. You will see examples and understand how it works. What Is Insertion Sort? Insertion Sort is a simple sorting algorithm. It works like how you sort playing cards. You take one card at a time and place it in the right position. It compares the current element with those before it. If needed, it shifts elements to the right. Then, it inserts the current element at the correct place. How Insertion Sort Works Let’s understand with a small list: Example List: [8, 3, 5, 1] Steps: First element (8) is already sorted. Compare 3 with 8. Move 8 right. Insert 3 before it → [3, 8, 5, 1] Compare 5 with 8. Move 8 right. Insert 5 after 3 → [3, 5, 8, 1] Compare 1 with 8, 5, 3. Move them right. Insert 1 at start → [1, 3, 5, 8] Now the list is sorted! Why Use Insertion Sort? Insertion Sort is simple and easy to code. It works well for: Small datasets Nearly sorted lists Educational purposes and practice However, it is not good for large datasets. It has a time complexity of O(n²). Time Complexity of Insertion Sort Best Case (already sorted): O(n) Average Case: O(n²) Worst Case (reversed list): O(n²) It performs fewer steps in nearly sorted data. How to Implement Insertion Sort in Java Now let’s write the code for Insertion Sort in Java. We will explain each part. Step 1: Define a Class javaCopyEditpublic class InsertionSortExample { // Code goes here } We create a class named InsertionSortExample. Step 2: Create the Sorting Method javaCopyEditpublic static void insertionSort(int[] arr) { int n = arr.length; for (int i = 1; i < n; i++) { int key = arr[i]; int j = i - 1; while (j >= 0 && arr[j] > key) { arr[j + 1] = arr[j]; j = j - 1; } arr[j + 1] = key; } } Let’s break it down: arr[i] is the current value (called key). j starts from the previous index. While arr[j] > key, shift arr[j] to the right. Insert the key at the correct position. This logic sorts the array step by step. Step 3: Create the Main Method Now we test the code. javaCopyEditpublic static void main(String[] args) { int[] numbers = {9, 5, 1, 4, 3}; System.out.println("Before sorting:"); printArray(numbers); insertionSort(numbers); System.out.println("After sorting:"); printArray(numbers); } This method: Creates an array of numbers Prints the array before sorting Calls the sort method Prints the array after sorting Step 4: Print the Array Let’s add a helper method to print the array. javaCopyEditpublic static void printArray(int[] arr) { for (int number : arr) { System.out.print(number + " "); } System.out.println(); } Now you can see how the array changes before and after sorting. Full Code Example javaCopyEditpublic class InsertionSortExample { public static void insertionSort(int[] arr) { int n = arr.length; for (int i = 1; i < n; i++) { int key = arr[i]; int j = i - 1; while (j >= 0 && arr[j] > key) { arr[j + 1] = arr[j]; j = j - 1; } arr[j + 1] = key; } } public static void printArray(int[] arr) { for (int number : arr) { System.out.print(number + " "); } System.out.println(); } public static void main(String[] args) { int[] numbers = {9, 5, 1, 4, 3}; System.out.println("Before sorting:"); printArray(numbers); insertionSort(numbers); System.out.println("After sorting:"); printArray(numbers); } } Sample Output yamlCopyEditBefore sorting: 9 5 1 4 3 After sorting: 1 3 4 5 9 This confirms that the sorting works correctly. Advantages of Insertion Sort in Java Easy to implement Works well with small inputs Stable sort (keeps equal items in order) Good for educational use When Not to Use Insertion Sort Avoid Insertion Sort when: The dataset is large Performance is critical Better algorithms like Merge Sort or Quick Sort are available Real-World Uses Sorting small records in a database Teaching algorithm basics Handling partially sorted arrays Even though it is not the fastest, it is useful in many simple tasks. Final Tips Practice with different inputs Add print statements to see how it works Try sorting strings or objects Use Java’s built-in sort methods for large arrays Conclusion Insertion Sort in Java is a great way to learn sorting. It is simple and easy to understand. In this guide, we showed how to implement it step-by-step. We covered the logic, code, and output. We also explained when to use it. Now you can try it yourself. Understanding sorting helps in coding interviews and software development. Keep practicing and exploring other sorting methods too. The more you practice, the better you understand algorithms. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Development and Testing  Rate this post Sorting is a basic task in programming. It arranges data in order. There are many sorting algorithms. Selection Sort is one of the simplest sorting methods. It is easy to understand and code. But it is not the fastest. In this guide, we will explain the Selection Sort Time Complexity. We will cover best, worst, and average cases. What Is Selection Sort? Selection Sort works by selecting the smallest element from the list. It places it in the correct position. It repeats this process for all elements. One by one, it moves the smallest values to the front. Let’s see an example: Input: [5, 3, 8, 2]Step 1: Smallest is 2 → swap with 5 → [2, 3, 8, 5]Step 2: Smallest in remaining is 3 → already correctStep 3: Smallest in remaining is 5 → swap with 8 → [2, 3, 5, 8] Now the list is sorted.How Selection Sort Works Selection Sort uses two loops. The outer loop moves one index at a time. The inner loop finds the smallest element. After each pass, the smallest value is moved to the front. The position is fixed. Selection Sort does not care if the list is sorted or not. It always does the same steps. Selection Sort Algorithm Here is the basic algorithm: Start from the first element Find the smallest in the rest of the list Swap it with the current element Repeat for each element This repeats until all elements are sorted. Selection Sort Code (Java Example) javaCopyEditpublic class SelectionSort { public static void sort(int[] arr) { int n = arr.length; for (int i = 0; i < n - 1; i++) { int min = i; for (int j = i + 1; j < n; j++) { if (arr[j] < arr[min]) { min = j; } } int temp = arr[min]; arr[min] = arr[i]; arr[i] = temp; } } } This code uses two loops. The outer loop runs n-1 times. The inner loop finds the minimum. Selection Sort Time Complexity Now let’s understand the main topic. Let’s analyze Selection Sort Time Complexity in three cases. 1. Best Case Even if the array is already sorted, Selection Sort checks all elements. It keeps comparing and swapping. Time Complexity: O(n²) Reason: Inner loop runs fully, regardless of the order Example Input: [1, 2, 3, 4, 5] Even here, every comparison still happens. Only fewer swaps occur, but comparisons remain the same. 2. Worst Case This happens when the array is in reverse order. But Selection Sort does not optimize for this. Time Complexity: O(n²) Reason: Still needs full comparisons Example Input: [5, 4, 3, 2, 1] Even in reverse, the steps are the same. It compares and finds the smallest element every time. 3. Average Case This is when elements are randomly placed. It is the most common scenario in real-world problems. Time Complexity: O(n²) Reason: Still compares each element in the inner loop Example Input: [3, 1, 4, 2, 5] Selection Sort does not change behavior based on input order. So the complexity remains the same. Why Is It Always O(n²)? Selection Sort compares all pairs of elements. The number of comparisons does not change. Total comparisons = n × (n – 1) / 2 That’s why the time complexity is always O(n²).It does not reduce steps in any case. It does not take advantage of sorted elements. Space Complexity Selection Sort does not need extra space. It sorts in place. Space Complexity: O(1) Only a few variables are used No extra arrays or memory needed This is one good point of the Selection Sort. Comparison with Other Algorithms Let’s compare Selection Sort with other basic sorts: AlgorithmBest CaseAverage CaseWorst CaseSpaceSelection SortO(n²)O(n²)O(n²)O(1)Bubble SortO(n)O(n²)O(n²)O(1)Insertion SortO(n)O(n²)O(n²)O(1)Merge SortO(n log n)O(n log n)O(n log n)O(n)Quick SortO(n log n)O(n log n)O(n²)O(log n) As you see, Selection Sort is slower than Merge Sort and Quick Sort. Advantages of Selection Sort Very simple and easy to understand Works well with small datasets Needs very little memory Good for learning purposes Disadvantages of Selection Sort Slow on large datasets Always takes the same time, even if sorted Not efficient for real-world use When to Use Selection Sort Use Selection Sort when: You are working with a very small dataset You want to teach or learn sorting logic You want stable, low-memory sorting Avoid it for: Large datasets Performance-sensitive programs Conclusion Selection Sort Time Complexity is simple to understand. But it is not efficient for big problems. It always takes O(n²) time, no matter the case. That is the same for best, worst, and average inputs. Still, it is useful in some cases. It’s great for learning sorting basics. It uses very little memory. If you’re working with small arrays, Selection Sort is fine. For large data, use better algorithms. Understanding its time complexity helps you choose the right algorithm. Always pick the tool that fits your task. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com