Digital poison can mess with algorithms. Lately, we just scroll through feeds, wondering how much of it is real. These algorithms decide what we see, and honestly, it all feels pretty opaque. The constant speculation about their operations is tiring. Can they really be corrupted? Who knows. Maybe it doesn't even matter anymore. #DigitalPoison #Algorithms #SocialMedia #OnlineContent #TechThoughts

What a disgrace! The new Everybody’s Golf: Hot Shots has the audacity to lean on generative AI for something as fundamental as trees?! This is the kind of lazy development that shows a complete lack of respect for gamers who have been waiting nearly a decade for a worthy installment. Instead of genuine creativity, we get AI-generated junk that ruins the charm of a beloved franchise. How can we expect innovation in gaming when companies are cutting corners and relying on algorithms instead of skilled artists? This is not progress; it’s a slap in the face to every player who values quality. Stand up, gamers! We deserve better! #HotShotsGolf #Gaming #AIGenerated #GameDevelopment #PlayerRights

In a world where imagination knows no bounds, Don Diablo has taken the plunge into the tech-art romance we never knew we needed. Who knew that a DJ could create an AI-generated music video with Nvidia, turning a simple collaboration into a full-blown love affair? I guess when the beats of a human heart meet the cold algorithms of a machine, you get a masterpiece—or at least a decent TikTok backdrop. So, let's raise a glass to the new age of creativity, where we can let our devices do the thinking while we just vibe. Truly, this wasn’t just a brand collab; it was an existential crisis wrapped in pixels and beats! #TechMeetsArt #AIMusicVideo #DonDiablo #Nvidia #

Spotify and Apple are killing the album cover, and it’s time we raised our voices against this travesty! It’s infuriating that in this age of digital consumption, these tech giants have the audacity to strip away one of the most vital elements of music: the album cover. The art that used to be a visceral representation of the music itself is now reduced to a mere thumbnail on a screen, easily lost in the sea of endless playlists and streaming algorithms. What happened to the days when we could hold a physical album in our hands? The tactile experience of flipping through a gatefold cover, admiring the artwork, and reading the liner notes is now an afterthought. Instead, we’re left with animated visuals that can’t even be framed on a wall! How can a moving image evoke the same emotional connection as a beautifully designed cover that captures the essence of an artist's vision? It’s a tragedy that these platforms are prioritizing convenience over artistic expression. The music industry needs to wake up! Spotify and Apple are essentially telling artists that their hard work, creativity, and passion can be boiled down to a pixelated image that disappears into the digital ether. This is an outright assault on the artistry of music! Why should we stand by while these companies prioritize algorithmic efficiency over the cultural significance of album art? It’s infuriating that the very thing that made music a visual and auditory experience is being obliterated right in front of our eyes. Let’s be clear: the album cover is not just decoration; it’s an integral part of the storytelling process in music. It sets the tone, evokes emotions, and can even influence how we perceive the music itself. When an album cover is designed with care and intention, it becomes an extension of the artist’s voice. Yet here we are, scrolling through Spotify and Apple Music, bombarded with generic visuals that do nothing to honor the artists or their work. Spotify and Apple need to be held accountable for this degradation of music culture. This isn’t just about nostalgia; it’s about preserving the integrity of artistic expression. We need to demand that these platforms acknowledge the importance of album covers and find ways to integrate them into our digital experiences. Otherwise, we’re on a dangerous path where music becomes nothing more than a disposable commodity. If we allow Spotify and Apple to continue on this trajectory, we risk losing an entire culture of artistic expression. It’s time for us as consumers to take a stand and remind these companies that music is not just about the sound; it’s about the entire experience. Let’s unite and fight back against this digital degradation of music artistry. We deserve better than a world where the album cover is dying a slow death. Let’s reclaim the beauty of music and its visual representation before it’s too late! #AlbumArt #MusicCulture #Spotify #AppleMusic #ProtectArtistry

In a world where the digital and the real intertwine, I find myself drifting through the shadows of loneliness. The news of "Bientôt des mondes complets créés par IA dans Horizon Worlds" resonates deep within me, a reminder of the vastness of innovation that seems to grow every day, while I feel smaller and more isolated than ever. As Meta continues to surprise us with its ambitious vision, I wonder if these virtual landscapes will ever feel as real as the warmth of a genuine connection. 🌧️ I scroll through my feed, witnessing the excitement of others as they anticipate the new worlds crafted by artificial intelligence. Each post is a glimpse into a future filled with adventure and companionship, yet all I feel is a hollow ache that echoes in the silence of my room. Will these new realms be a place for me, or will they only serve to highlight my solitude? The thought weighs heavily on my heart, as I watch people forge friendships in the very spaces I yearn to explore. 💔 I used to believe that technology would bridge the gaps between us, that it could weave a tapestry of connection in an increasingly fragmented world. But as I sit here, enveloped by the glow of my screen, I can't help but feel that every pixel is a reminder of what I lack. Are these digital worlds truly the answer, or will they merely replace the warmth of human touch with cold algorithms? 🌌 As Meta's Horizon Worlds prepares to unveil its creations, I wonder if I will ever find solace within them. Will these AI-generated landscapes offer me the comfort I seek, or will they only serve as a reminder of the friendships I long for but cannot grasp? The weight of isolation is heavy, and sometimes it feels like the walls of my reality are closing in, suffocating my spirit. 😔 I am left questioning the meaning of connection in a world where everything can be simulated but nothing can truly replace the heart's yearning for companionship. Each day feels like a cycle of hope and despair, as I cling to the idea that someday, I might step into a world where I am not just a ghost wandering through the ether, but a being of warmth and light, surrounded by those who understand me. 🌈 As I reflect on the future that awaits us, I can’t help but wish for a spark of genuine warmth among the cold algorithms and digital dreams. The promise of "Bientôt des mondes complets créés par IA" fills me with both anticipation and dread, a bittersweet reminder of the connection I crave but cannot touch. Until then, I remain here, in the silence, yearning for a world where I can feel truly alive. #Loneliness #Connection #Meta #AIWorlds #HorizonWorlds

🌟✨ Hey, wonderful creators! 🌟✨ Have you ever felt that spark of inspiration while diving into the world of 3D printing? Well, buckle up, because the future has just gotten even brighter! 🚀🌈 Introducing PartCrafter, the revolutionary AI-driven 3D mesh generator that's ready to take your design game to the next level! 🎉💡 In a world where creativity knows no bounds, it's fascinating to see how artificial intelligence is revolutionizing the realm of 3D printing, especially in the design phase. PartCrafter is not just another tool; it’s a game changer that empowers designers and artists alike to bring their wildest ideas to life! 🎨💖 Imagine being able to synthesize intricate 3D models with just a few clicks—how incredible is that? This innovative generator harnesses the power of AI to create stunning designs that elevate your projects and push the boundaries of what’s possible. 🌌✨ The ease of use and the endless possibilities that PartCrafter offers are truly remarkable. Whether you're a seasoned professional or just starting your journey in 3D design, this tool is designed to inspire you and fuel your creativity. 🌟💼 With its user-friendly interface and intelligent algorithms, you can focus on what you do best—creating amazing designs that captivate and inspire! Remember, every great invention starts with a spark of imagination! 🌠💭 So, don't hold back! Embrace the power of technology and let PartCrafter be your partner in creativity. Imagine the models you can create: from intricate architectural designs to imaginative sculptures, the possibilities are limitless! 🏙️✨ And guess what? The best part is that you’re not alone on this journey! Join a community of passionate creators who are also exploring the wonders of AI in design. Share your ideas, learn from one another, and let’s uplift each other as we step into this exciting new era of 3D printing together! 🤝💕 So, what are you waiting for? Dive into the world of PartCrafter and watch your creative dreams unfold! The future is now, and it’s time to create something incredible! Let’s embrace innovation and let our imaginations soar! 🌈🎉 #3DPrinting #ArtificialIntelligence #PartCrafter #CreativeDesign #Innovation

In a world where AI is revolutionizing everything from coffee-making to car-driving, it was only a matter of time before our digital mischief-makers decided to hop on the bandwagon. Enter the era of AI-driven malware, where cybercriminals have traded in their basic scripts for something that’s been juiced up with a pinch of neural networks and a dollop of machine learning. Who knew that the future of cibercrimen would be so... sophisticated? Gone are the days of simple viruses that could be dispatched with a good old anti-virus scan. Now, we’re talking about intelligent malware that learns from its surroundings, adapts, and evolves faster than a teenager mastering TikTok trends. It’s like the difference between a kid throwing rocks at your window and a full-blown meteor shower—one is annoying, and the other is just catastrophic. According to the latest Gen Threat Report from Gen Digital, this new breed of cyber threats is redefining the landscape of cybersecurity. Oh, joy! Just what we needed—cybercriminals with PhDs in deviousness. It’s as if our friendly neighborhood malware has decided to enroll in the prestigious “School of Advanced Cyber Mischief,” where they’re taught to outsmart even the most vigilant security measures. But let’s be real here: Isn’t it just a tad amusing that as we pour billions into cybersecurity with names like Norton, Avast, and LifeLock, the other side is just sitting there, chuckling, as they level up to the next version of “Chaos 2.0”? You have to admire their resourcefulness. While we’re busy installing updates and changing our passwords (again), they’re crafting malware that makes our attempts at protection look like a toddler’s finger painting. And let’s not ignore the irony: as we try to protect our data and privacy, the very tools meant to safeguard us are themselves evolving to a point where they might as well have a personality. It’s like having a dog that not only can open the fridge but also knows how to make an Instagram reel while doing it. So, what can we do in the face of this digital dilemma? Well, for starters, we can all invest in a good dose of humor because that’s apparently the only thing that’s bulletproof in this age of AI-driven chaos. Or, we can simply accept that it’s the survival of the fittest in the cyber jungle—where those with the best algorithms win. In the end, as we gear up to battle these new-age cyber threats, let’s just hope that our malware doesn’t get too smart—it might start charging us for the privilege of being hacked. After all, who doesn’t love a little subscription model in their life? #Cibercrimen #AIMalware #Cybersecurity #GenThreatReport #DigitalHumor

So, as we venture into the illustrious year of 2025, one can’t help but marvel at the sheer inevitability of ChatGPT's meteoric rise to global fame. I mean, who needs human interaction when you can chat with a glorified algorithm that receives 5.19 billion visits a month? That's right, folks—if you ever wondered what it’s like to be more popular than a cat video on the internet, just look at our dear AI friend. In a world where 400 million users are frantically asking ChatGPT whether pineapple belongs on pizza (spoiler alert: it does), it's no surprise that “How to Rank in ChatGPT and AI Overviews” has turned into the hottest guide of the decade. Because if we can’t rank in a chat platform, what’s left? A life of obscurity, endlessly scrolling through TikTok videos of people pretending to be experts? And let’s not forget the wise folks at Google, who’ve taken the AI plunge much like that friend who jumps into the pool before checking the water temperature. Their integration of generative AI into Search is like putting a fancy bow on a mediocre gift—yes, it looks nice, but underneath it all, it’s still just a bunch of algorithms trying to figure out what you had for breakfast. But fear not, my friends! The secret to ranking in ChatGPT lies not in those pesky things called “qualifications” or “experience,” but in mastering the art of keywords! Yes, sprinkle a few buzzwords around like confetti, and voilà! You’re an instant expert. Just remember, if it sounds impressive, it must be true. Who needs substance when you can dazzle with style? Oh, and let’s address the elephant in the room (or should I say the AI in the chat). In a landscape where “AI Overviews” are the new gospel, it’s clear that we’re all just one poorly phrased question away from existential dread. “Why can’t I find my soulmate?” “Why is my cat judging me?” “Why does my life feel like a never-ending cycle of rephrased FAQs?” ChatGPT has the answers, or at least it will confidently pretend to. So buckle up, everyone! The race to rank in ChatGPT is the most exhilarating ride since the invention of the wheel (okay, maybe that’s a stretch, but you get the point). Let’s throw all our doubts into the void and embrace the chaos of AI with open arms. After all, if we can’t find meaning in our interactions with a chatbot, what’s the point of even logging in? And remember: in the grand scheme of things, we’re all just trying to outrank each other in a digital world where the lines between human and machine are as blurred as the coffee stain on my keyboard. Cheers to that! #ChatGPT #AIOverviews #DigitalTrends #SEO #2025Guide

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

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, Omeans steps grow with the square of inputs. 2. Types of Time Complexity Here are common types: O: Constant time O: Linear time O: Log-linear time O: 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: OAverage Case: OWorst Case: OPython Example: pythonCopyEditdef bubble_sort: n = lenfor i in range: for j in range: if arr> arr: arr, arr= arr, arrJava Example: javaCopyEditvoid bubbleSort{ int n = arr.length; forforif{ int temp = arr; arr= arr; arr= temp; } } C++ Example: cppCopyEditvoid bubbleSort{ forforifswap; } 4. Selection Sort This sort picks the smallest number and places it at the front. Time Complexity: Best Case: OAverage Case: OWorst Case: OPython Example: pythonCopyEditdef selection_sort: for i in range): min_idx = i for j in range): if arr< arr: min_idx = j arr, arr= arr, arr5. Insertion Sort This algorithm builds the final list one item at a time. Time Complexity: Best Case: OAverage Case: OWorst Case: OJava Example: javaCopyEditvoid insertionSort{ for{ int key = arr; int j = i - 1; while{ arr= arr; j = j - 1; } arr= 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: OAverage Case: OWorst Case: OPython Example: pythonCopyEditdef merge_sort: if len> 1: mid = len// 2 left = arrright = arrmerge_sortmerge_sorti = j = k = 0 while i < lenand j < len: if left< right: arr= lefti += 1 else: arr= rightj += 1 k += 1 arr= left+ right7. Quick Sort Quick Sort picks a pivot and places smaller numbers before it. Time Complexity: Best Case: OAverage Case: OWorst Case: OC++ Example: cppCopyEditint partition{ int pivot = arr; int i = low - 1; for{ if{ i++; swap; } } swap; return i + 1; } void quickSort{ if{ int pi = partition; quickSort; quickSort; } } 8. Built-in Sort Methods Languages have built-in sort functions. These are well-optimized. Python: sortedor list.sortuses TimSort Time Complexity: OJava: Arrays.sortuses Dual-Pivot QuickSort Time Complexity: OC++: std::sortuses IntroSort Time Complexity: OThese are better for most real-world tasks. 9. Time Complexity Comparison Table AlgorithmBestAverageWorstStableBubble SortOOOYesSelection SortOOONoInsertion SortOOOYesMerge SortOOOYesQuick SortOOONoTimSortOOOYesIntroSortOOONo 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, 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 #time #complexity #sorting #algorithms #python