Still tending to your island in Animal Crossing: New Horizons? Then it might be worth picking up the Animal Crossing: New Horizons Official Complete Guide. The hardcover guide is still available --and it’s even seeing a slight discount right now. Best of all, this is the updated version published in 2023, meaning it includes details for all the major updates and the Happy Home Paradise expansion. Animal Crossing: New Horizons Official Complete GuidePublished by Future Press, this comprehensive 668-page guide covers everything you need to know about the game, including information on all the islanders, all the craftable items, and every collectible from seasonal events, updates, and DLC. There’s also a section covering unique island designs--so if you need inspiration for your next big project, you’ll find plenty of examples in this official guidebook.The original version of this guide was published in 2020. While the 2020 edition is still available, we recommend the updated 2023 edition, as it includes information on all the additional content released between 2020 and 2023--such as Happy Home Paradise--and is a much better fit for anyone playing New Horizons in 2025. See Future Press is also responsible for the new Metaphor: ReFantazio strategy guide and the popular Elden Ring strategy guides, along with dozens of other titles. If you’re interested in rounding out your bookcase with premium video game books, be sure to check out the full collection.Folks who haven’t yet purchased Animal Crossing: New Horizons will find it on sale for justat Woot--an Amazon company. If that deal sells out, it’s also discounted to .Continue Reading at GameSpot #animal #crossing #complete #strategy #guide

Telecom companies last year spent nearly billion in capital expenditures and over trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance, designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA. #calling #llms #new #nvidia #blueprint

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

In April 2025, at the ITER Private Sector Fusion Workshop in Cadarache, something remarkable unfolded. In a room filled with scientists, engineers and software visionaries, the line between big science and commercial innovation began to blur.   Three organisations – Microsoft Research, Arena and Brigantium Engineering – shared how artificial intelligence, already transforming everything from language models to logistics, is now stepping into a new role: helping humanity to unlock the power of nuclear fusion.  Each presenter addressed a different part of the puzzle, but the message was the same: AI isn’t just a buzzword anymore. It’s becoming a real tool – practical, powerful and indispensable – for big science and engineering projects, including fusion.  “If we think of the agricultural revolution and the industrial revolution, the AI revolution is next – and it’s coming at a pace which is unprecedented,” said Kenji Takeda, director of research incubations at Microsoft Research.  Microsoft’s collaboration with ITER is already in motion. Just a month before the workshop, the two teams signed a Memorandum of Understandingto explore how AI can accelerate research and development. This follows ITER’s initial use of Microsoft technology to empower their teams. A chatbot in Azure OpenAI service was developed to help staff navigate technical knowledge, on more than a million ITER documents, using natural conversation. GitHub Copilot assists with coding, while AI helps to resolve IT support tickets – those everyday but essential tasks that keep the lights on.  But Microsoft’s vision goes deeper. Fusion demands materials that can survive extreme conditions – heat, radiation, pressure – and that’s where AI shows a different kind of potential. MatterGen, a Microsoft Research generative AI model for materials, designs entirely new materials based on specific properties. “It’s like ChatGPT,” said Takeda, “but instead of ‘Write me a poem’, we ask it to design a material that can survive as the first wall of a fusion reactor.”  The next step? MatterSim – a simulation tool that predicts how these imagined materials will behave in the real world. By combining generation and simulation, Microsoft hopes to uncover materials that don’t yet exist in any catalogue.  While Microsoft tackles the atomic scale, Arena is focused on a different challenge: speeding up hardware development. As general manager Michael Frei put it: “Software innovation happens in seconds. In hardware, that loop can take months – or years.”  Arena’s answer is Atlas, a multimodal AI platform that acts as an extra set of hands – and eyes – for engineers. It can read data sheets, interpret lab results, analyse circuit diagrams and even interact with lab equipment through software interfaces. “Instead of adjusting an oscilloscope manually,” said Frei, “you can just say, ‘Verify the I2Cprotocol’, and Atlas gets it done.”  It doesn’t stop there. Atlas can write and adapt firmware on the fly, responding to real-time conditions. That means tighter feedback loops, faster prototyping and fewer late nights in the lab. Arena aims to make building hardware feel a little more like writing software – fluid, fast and assisted by smart tools.  Fusion, of course, isn’t just about atoms and code – it’s also about construction. Gigantic, one-of-a-kind machines don’t build themselves. That’s where Brigantium Engineering comes in. Founder Lynton Sutton explained how his team uses “4D planning” – a marriage of 3D CAD models and detailed construction schedules – to visualise how everything comes together over time. “Gantt charts are hard to interpret. 3D models are static. Our job is to bring those together,” he said.  The result is a time-lapse-style animation that shows the construction process step by step. It’s proven invaluable for safety reviews and stakeholder meetings. Rather than poring over spreadsheets, teams can simply watch the plan come to life.  And there’s more. Brigantium is bringing these models into virtual reality using Unreal Engine – the same one behind many video games. One recent model recreated ITER’s tokamak pit using drone footage and photogrammetry. The experience is fully interactive and can even run in a web browser. “We’ve really improved the quality of the visualisation,” said Sutton. “It’s a lot smoother; the textures look a lot better. Eventually, we’ll have this running through a web browser, so anybody on the team can just click on a web link to navigate this 4D model.”  Looking forward, Sutton believes AI could help automate the painstaking work of syncing schedules with 3D models. One day, these simulations could reach all the way down to individual bolts and fasteners – not just with impressive visuals, but with critical tools for preventing delays.  Despite the different approaches, one theme ran through all three presentations: AI isn’t just a tool for office productivity. It’s becoming a partner in creativity, problem-solving and even scientific discovery.  Takeda mentioned that Microsoft is experimenting with “world models” inspired by how video games simulate physics. These models learn about the physical world by watching pixels in the form of videos of real phenomena such as plasma behaviour. “Our thesis is that if you showed this AI videos of plasma, it might learn the physics of plasmas,” he said.  It sounds futuristic, but the logic holds. The more AI can learn from the world, the more it can help us understand it – and perhaps even master it. At its heart, the message from the workshop was simple: AI isn’t here to replace the scientist, the engineer or the planner; it’s here to help, and to make their work faster, more flexible and maybe a little more fun. As Takeda put it: “Those are just a few examples of how AI is starting to be used at ITER. And it’s just the start of that journey.”  If these early steps are any indication, that journey won’t just be faster – it might also be more inspired.  #fusion #how #private #sector #tech

html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "; 19 shortlisted projects for the 2025 Award cycle were revealed by the Aga Khan Award for Architecture. A portion of the million prize, one of the biggest in architecture, will be awarded to the winning proposals. Out of the 369 projects nominated for the 16th Award Cycle, an independent Master Jury chose the 19 shortlisted projects from 15 countries.The nine members of the Master Jury for the 16th Award cycle include Azra Akšamija, Noura Al-Sayeh Holtrop, Lucia Allais, David Basulto, Yvonne Farrell, Kabage Karanja, Yacouba Konaté, Hassan Radoine, and Mun Summ Wong.His Late Highness Prince Karim Aga Khan IV created the Aga Khan Award for Architecture in 1977 to recognize and promote architectural ideas that effectively meet the needs and goals of communities where Muslims are a major population. Nearly 10,000 construction projects have been documented since the award's inception 48 years ago, and 128 projects have been granted it. The AKAA's selection method places a strong emphasis on architecture that stimulates and responds to people's cultural ambitions in addition to meeting their physical, social, and economic demands.The Aga Khan Award for Architecture is governed by a Steering Committee chaired by His Highness the Aga Khan. The other members of the Steering Committee are Meisa Batayneh, Principal Architect, Founder, maisam architects and engineers, Amman, Jordan; Souleymane Bachir Diagne, Professor of Philosophy and Francophone Studies, Columbia University, New York, United States of America; Lesley Lokko, Founder & Director, African Futures Institute, Accra, Ghana; Gülru Necipoğlu, Director and Professor, Aga Khan Program for Islamic Architecture, Harvard University, Cambridge, United States of America; Hashim Sarkis, Founder & Principal, Hashim Sarkis Studios; Dean, School of Architecture and Planning, Massachusetts Institute of Technology, Cambridge, United States of America; and Sarah M. Whiting, Partner, WW Architecture; Dean and Josep Lluís Sert Professor of Architecture, Graduate School of Design, Harvard University, Cambridge, United States of America. Farrokh Derakhshani is the Director of the Award.Examples of outstanding architecture in the areas of modern design, social housing, community development and enhancement, historic preservation, reuse and area conservation, landscape design, and environmental enhancement are recognized by the Aga Khan Award for Architecture.Building plans that creatively utilize local resources and relevant technologies, as well as initiatives that could spur such initiatives abroad, are given special consideration. It should be mentioned that in addition to honoring architects, the Award also recognizes towns, builders, clients, master craftspeople, and engineers who have contributed significantly to the project.Projects had to be completed between January 1, 2018, and December 31, 2023, and they had to have been operational for a minimum of one year in order to be eligible for consideration in the 2025 Award cycle. The Award is not available for projects that His Highness the Aga Khan or any of the Aga Khan Development Networkinstitutions have commissioned.See the 19 shortlisted projects with their short project descriptions competing for the 2025 Award Cycle:Khudi Bari. Image © Aga Khan Trust for Culture / City SyntaxBangladeshKhudi Bari, in various locations, by Marina Tabassum ArchitectsMarina Tabassum Architects' Khudi Bari, which can be readily disassembled and reassembled to suit the needs of the users, is a replicable solution for displaced communities impacted by geographic and climatic changes.West Wusutu Village Community Centre. Image © Aga Khan Trust for Culture / Dou YujunChinaWest Wusutu Village Community Centre, Hohhot, Inner Mongolia, by Zhang PengjuIn addition to meeting the religious demands of the local Hui Muslims, Zhang Pengju's West Wusutu Village Community Centre in Hohhot, Inner Mongolia, offers social and cultural spaces for locals and artists. Constructed from recycled bricks, it features multipurpose indoor and outdoor areas that promote communal harmony.Revitalisation of Historic Esna. Image © Aga Khan Trust for Culture / Ahmed SalemEgyptRevitalisation of Historic Esna, by Takween Integrated Community DevelopmentBy using physical interventions, socioeconomic projects, and creative urban planning techniques, Takween Integrated Community Development's Revitalization of Historic Esna tackles the issues of cultural tourism in Upper Egypt and turns the once-forgotten area around the Temple of Khnum into a thriving historic city.The Arc at Green School. Image © Aga Khan Trust for Culture / Andreas Perbowo WidityawanIndonesiaThe Arc at Green School, in Bali, by IBUKU / Elora HardyAfter 15 years of bamboo experimenting at the Green School Bali, IBUKU/Elora Hardy created The Arc at Green School. The Arc is a brand-new community wellness facility built on the foundations of a temporary gym. High-precision engineering and regional handicraft are combined in this construction.Islamic Centre Nurul Yaqin Mosque. Image © Aga Khan Trust for Culture / Andreas Perbowo WidityawanIndonesiaIslamic Centre Nurul Yaqin Mosque, in Palu, Central Sulawesi, by Dave Orlando and Fandy GunawanDave Orlando and Fandy Gunawan built the Islamic Center Nurul Yaqin Mosque in Palu, Central Sulawesi, on the location of a previous mosque that was damaged by a 2018 tsunami. There is a place for worship and assembly at the new Islamic Center. Surrounded by a shallow reflecting pool that may be drained to make room for more guests, it is open to the countryside.Microlibrary Warak Kayu. Image © Aga Khan Trust for Culture / Andreas Perbowo WidityawanIndonesiaMicrolibraries in various cities, by SHAU / Daliana Suryawinata, Florian HeinzelmannFlorian Heinzelmann, the project's initiator, works with stakeholders at all levels to provide high-quality public spaces in a number of Indonesian parks and kampungs through microlibraries in different towns run by SHAU/Daliana Suryawinata. So far, six have been constructed, and by 2045, 100 are planned.Majara Residence. Image © Aga Khan Trust for Culture / Deed StudioIranMajara Complex and Community Redevelopment, in Hormuz Island by ZAV Architects / Mohamadreza GhodousiThe Majara Complex and Community Redevelopment on Hormuz Island, designed by ZAV Architects and Mohamadreza Ghodousi, is well-known for its vibrant domes that offer eco-friendly lodging for visitors visiting Hormuz's distinctive scenery. In addition to providing new amenities for the islanders who visit to socialize, pray, or utilize the library, it was constructed by highly trained local laborers.Jahad Metro Plaza. Image © Aga Khan Trust for Culture / Deed StudioIranJahad Metro Plaza in Tehran, by KA Architecture StudioKA Architecture Studio's Jahad Metro Plaza in Tehran was constructed to replace the dilapidated old buildings. It turned the location into a beloved pedestrian-friendly landmark. The arched vaults, which are covered in locally manufactured brick, vary in height to let air and light into the area they are protecting.Khan Jaljulia Restoration. Image © Aga Khan Trust for Culture / Mikaela BurstowIsraelKhan Jaljulia Restoration in Jaljulia by Elias KhuriElias Khuri's Khan Jaljulia Restoration is a cost-effective intervention set amidst the remnants of a 14th-century Khan in Jaljulia. By converting the abandoned historical location into a bustling public area for social gatherings, it helps the locals rediscover their cultural history.Campus Startup Lions. Image © Aga Khan Trust for Culture / Christopher Wilton-SteerKenyaCampus Startup Lions, in Turkana by Kéré ArchitectsKéré Architecture's Campus Startup Lions in Turkana is an educational and entrepreneurial center that offers a venue for community involvement, business incubation, and technology-driven education. The design incorporates solar energy, rainwater harvesting, and tall ventilation towers that resemble the nearby termite mounds, and it was constructed using local volcanic stone.Lalla Yeddouna Square. Image © Aga Khan Trust for Culture / Amine HouariMoroccoRevitalisation of Lalla Yeddouna Square in the medina of Fez, by Mossessian Architecture and Yassir Khalil StudioMossessian Architecture and Yassir Khalil Studio's revitalization of Lalla Yeddouna Square in the Fez medina aims to improve pedestrian circulation and reestablish a connection to the waterfront. For the benefit of locals, craftspeople, and tourists from around the globe, existing buildings were maintained and new areas created.Vision Pakistan. Image © Aga Khan Trust for Culture / Usman Saqib ZuberiPakistanVision Pakistan, in Islamabad by DB Studios / Mohammad Saifullah SiddiquiA tailoring training center run by Vision Pakistan, a nonprofit organization dedicated to empowering underprivileged adolescents, is located in Islamabad by DB Studios/Mohammad Saifullah Siddiqui. Situated in a crowded neighborhood, this multi-story building features flashy jaalis influenced by Arab and Pakistani crafts, echoing the city's 1960s design.Denso Hall Rahguzar Project. Image © Aga Khan Trust for Culture / Usman Saqib ZuberiPakistanDenso Hall Rahguzar Project, in Karachi by Heritage Foundation Pakistan / Yasmeen LariThe Heritage Foundation of Pakistan/Yasmeen Lari's Denso Hall Rahguzar Project in Karachi is a heritage-led eco-urban enclave that was built with low-carbon materials in response to the city's severe climate, which is prone to heat waves and floods. The freshly planted "forests" are irrigated by the handcrafted terracotta cobbles, which absorb rainfall and cool and purify the air.Wonder Cabinet. Image © Aga Khan Trust for Culture / Mikaela BurstowPalestineWonder Cabinet, in Bethlehem by AAU AnastasThe architects at AAU Anastas established Wonder Cabinet, a multifunctional, nonprofit exhibition and production venue in Bethlehem. The three-story concrete building was constructed with the help of regional contractors and artisans, and it is quickly emerging as a major center for learning, design, craft, and innovation.The Ned. Image © Aga Khan Trust for Culture / Cemal EmdenQatarThe Ned Hotel, in Doha by David Chipperfield ArchitectsThe Ministry of Interior was housed in the Ned Hotel in Doha, which was designed by David Chipperfield Architects. Its Middle Eastern brutalist building was meticulously transformed into a 90-room boutique hotel, thereby promoting architectural revitalization in the region.Shamalat Cultural Centre. Image © Aga Khan Trust for Culture / Hassan Al ShattiSaudi ArabiaShamalat Cultural Centre, in Riyadh, by Syn Architects / Sara Alissa, Nojoud AlsudairiOn the outskirts of Diriyah, the Shamalat Cultural Centre in Riyadh was created by Syn Architects/Sara Alissa, Nojoud Alsudairi. It was created from an old mud home that artist Maha Malluh had renovated. The center, which aims to incorporate historic places into daily life, provides a sensitive viewpoint on heritage conservation in the area by contrasting the old and the contemporary.Rehabilitation and Extension of Dakar Railway Station. Image © Aga Khan Trust for Culture / Sylvain CherkaouiSenegalRehabilitation and Extension of Dakar Railway Station, in Dakar by Ga2DIn order to accommodate the passengers of a new express train line, Ga2D extended and renovated Dakar train Station, which purposefully contrasts the old and modern buildings. The forecourt was once again open to pedestrian traffic after vehicular traffic was limited to the rear of the property.Rami Library. Image © Aga Khan Trust for Culture / Cemal EmdenTürkiyeRami Library, by Han Tümertekin Design & ConsultancyThe largest library in Istanbul is the Rami Library, designed by Han Tümertekin Design & Consultancy. It occupied the former Rami Barracks, a sizable, single-story building with enormous volumes that was constructed in the eighteenth century. In order to accommodate new library operations while maintaining the structure's original spatial features, a minimal intervention method was used.Morocco Pavilion Expo Dubai 2020. Image © Aga Khan Trust for Culture / Deed StudioUnited Arab EmiratesMorocco Pavilion Expo Dubai 2020, by Oualalou + ChoiOualalou + Choi's Morocco Pavilion Expo Dubai 2020 is intended to last beyond Expo 2020 and be transformed into a cultural center. The pavilion is a trailblazer in the development of large-scale rammed earth building techniques. Its use of passive cooling techniques, which minimize the need for mechanical air conditioning, earned it the gold LEED accreditation.At each project location, independent professionals such as architects, conservation specialists, planners, and structural engineers have conducted thorough evaluations of the nominated projects. This summer, the Master Jury convenes once more to analyze the on-site evaluations and choose the ultimate Award winners.The top image in the article: The Arc at Green School. Image © Aga Khan Trust for Culture / Andreas Perbowo Widityawan.> via Aga Khan Award for Architecture #aga #khan #award #architecture #announces

Jun 16, 2025Ravie LakshmananMalware / DevOps Cybersecurity researchers have discovered a malicious package on the Python Package Indexrepository that's capable of harvesting sensitive developer-related information, such as credentials, configuration data, and environment variables, among others. The package, named chimera-sandbox-extensions, attracted 143 downloads and likely targets users of a service called Chimera Sandbox, which was released by Singaporean tech company Grab last August to facilitate "experimentation and development ofsolutions." The package masquerades as a helper module for Chimera Sandbox, but "aims to steal credentials and other sensitive information such as Jamf configuration, CI/CD environment variables, AWS tokens, and more," JFrog security researcher Guy Korolevski said in a report published last week. Once installed, it attempts to connect to an external domain whose domain name is generated using a domain generation algorithmin order to download and execute a next-stage payload. Specifically, the malware acquires from the domain an authentication token, which is then used to send a request to the same domain and retrieve the Python-based information stealer. The stealer malware is equipped to siphon a wide range of data from infected machines. This includes - JAMF receipts, which are records of software packages installed by Jamf Pro on managed computers Pod sandbox environment authentication tokens and git information CI/CD information from environment variables Zscaler host configuration Amazon Web Services account information and tokens Public IP address General platform, user, and host information The kind of data gathered by the malware shows that it's mainly geared towards corporate and cloud infrastructure. In addition, the extraction of JAMF receipts indicates that it's also capable of targeting Apple macOS systems. The collected information is sent via a POST request back to the same domain, after which the server assesses if the machine is a worthy target for further exploitation. However, JFrog said it was unable to obtain the payload at the time of analysis. "The targeted approach employed by this malware, along with the complexity of its multi-stage targeted payload, distinguishes it from the more generic open-source malware threats we have encountered thus far, highlighting the advancements that malicious packages have made recently," Jonathan Sar Shalom, director of threat research at JFrog Security Research team, said. "This new sophistication of malware underscores why development teams remain vigilant with updates—alongside proactive security research – to defend against emerging threats and maintain software integrity." The disclosure comes as SafeDep and Veracode detailed a number of malware-laced npm packages that are designed to execute remote code and download additional payloads. The packages in question are listed below - eslint-config-airbnb-compatts-runtime-compat-checksolders@mediawave/libAll the identified npm packages have since been taken down from npm, but not before they were downloaded hundreds of times from the package registry. SafeDep's analysis of eslint-config-airbnb-compat found that the JavaScript library has ts-runtime-compat-check listed as a dependency, which, in turn, contacts an external server defined in the former packageto retrieve and execute a Base64-encoded string. The exact nature of the payload is unknown. "It implements a multi-stage remote code execution attack using a transitive dependency to hide the malicious code," SafeDep researcher Kunal Singh said. Solders, on the other hand, has been found to incorporate a post-install script in its package.json, causing the malicious code to be automatically executed as soon as the package is installed. "At first glance, it's hard to believe that this is actually valid JavaScript," the Veracode Threat Research team said. "It looks like a seemingly random collection of Japanese symbols. It turns out that this particular obfuscation scheme uses the Unicode characters as variable names and a sophisticated chain of dynamic code generation to work." Decoding the script reveals an extra layer of obfuscation, unpacking which reveals its main function: Check if the compromised machine is Windows, and if so, run a PowerShell command to retrieve a next-stage payload from a remote server. This second-stage PowerShell script, also obscured, is designed to fetch a Windows batch script from another domainand configures a Windows Defender Antivirus exclusion list to avoid detection. The batch script then paves the way for the execution of a .NET DLL that reaches out to a PNG image hosted on ImgBB. "is grabbing the last two pixels from this image and then looping through some data contained elsewhere in it," Veracode said. "It ultimately builds up in memory YET ANOTHER .NET DLL." Furthermore, the DLL is equipped to create task scheduler entries and features the ability to bypass user account controlusing a combination of FodHelper.exe and programmatic identifiersto evade defenses and avoid triggering any security alerts to the user. The newly-downloaded DLL is Pulsar RAT, a "free, open-source Remote Administration Tool for Windows" and a variant of the Quasar RAT. "From a wall of Japanese characters to a RAT hidden within the pixels of a PNG file, the attacker went to extraordinary lengths to conceal their payload, nesting it a dozen layers deep to evade detection," Veracode said. "While the attacker's ultimate objective for deploying the Pulsar RAT remains unclear, the sheer complexity of this delivery mechanism is a powerful indicator of malicious intent." Crypto Malware in the Open-Source Supply Chain The findings also coincide with a report from Socket that identified credential stealers, cryptocurrency drainers, cryptojackers, and clippers as the main types of threats targeting the cryptocurrency and blockchain development ecosystem. Some of the examples of these packages include - express-dompurify and pumptoolforvolumeandcomment, which are capable of harvesting browser credentials and cryptocurrency wallet keys bs58js, which drains a victim's wallet and uses multi-hop transfers to obscure theft and frustrate forensic tracing. lsjglsjdv, asyncaiosignal, and raydium-sdk-liquidity-init, which functions as a clipper to monitor the system clipboard for cryptocurrency wallet strings and replace them with threat actor‑controlled addresses to reroute transactions to the attackers "As Web3 development converges with mainstream software engineering, the attack surface for blockchain-focused projects is expanding in both scale and complexity," Socket security researcher Kirill Boychenko said. "Financially motivated threat actors and state-sponsored groups are rapidly evolving their tactics to exploit systemic weaknesses in the software supply chain. These campaigns are iterative, persistent, and increasingly tailored to high-value targets." AI and Slopsquatting The rise of artificial intelligence-assisted coding, also called vibe coding, has unleashed another novel threat in the form of slopsquatting, where large language modelscan hallucinate non-existent but plausible package names that bad actors can weaponize to conduct supply chain attacks. Trend Micro, in a report last week, said it observed an unnamed advanced agent "confidently" cooking up a phantom Python package named starlette-reverse-proxy, only for the build process to crash with the error "module not found." However, should an adversary upload a package with the same name on the repository, it can have serious security consequences. Furthermore, the cybersecurity company noted that advanced coding agents and workflows such as Claude Code CLI, OpenAI Codex CLI, and Cursor AI with Model Context Protocol-backed validation can help reduce, but not completely eliminate, the risk of slopsquatting. "When agents hallucinate dependencies or install unverified packages, they create an opportunity for slopsquatting attacks, in which malicious actors pre-register those same hallucinated names on public registries," security researcher Sean Park said. "While reasoning-enhanced agents can reduce the rate of phantom suggestions by approximately half, they do not eliminate them entirely. Even the vibe-coding workflow augmented with live MCP validations achieves the lowest rates of slip-through, but still misses edge cases." Found this article interesting? Follow us on Twitter  and LinkedIn to read more exclusive content we post. SHARE     #malicious #pypi #package #masquerades #chimera

Who knows where branding will go in the future? However, for many of us working in the creative industries, it's our job to know. So it's something we need to start talking about, and Frontify Futures wants to be the platform where that conversation unfolds. This ambitious new thought leadership initiative from Frontify brings together an extraordinary coalition of voices—CMOs who've scaled global brands, creative leaders reimagining possibilities, strategy directors pioneering new approaches, and cultural forecasters mapping emerging opportunities—to explore how effectiveness, innovation, and scale will shape tomorrow's brand-building landscape. But Frontify Futures isn't just another content platform. Excitingly, from a design perspective, it's also a living experiment in what brand identity can become when technology meets craft, when systems embrace chaos, and when the future itself becomes a design material. Endless variation What makes Frontify Futures' typography unique isn't just its custom foundation: it's how that foundation enables endless variation and evolution. This was primarily achieved, reveals developer and digital art director Daniel Powell, by building bespoke tools for the project. "Rather than rely solely on streamlined tools built for speed and production, we started building our own," he explains. "The first was a node-based design tool that takes our custom Frame and Hairline fonts as a base and uses them as the foundations for our type generator. With it, we can generate unique type variations for each content strand—each article, even—and create both static and animated type, exportable as video or rendered live in the browser." Each of these tools included what Daniel calls a "chaos element: a small but intentional glitch in the system. A microstatement about the nature of the future: that it can be anticipated but never fully known. It's our way of keeping gesture alive inside the system." One of the clearest examples of this is the colour palette generator. "It samples from a dynamic photo grid tied to a rotating colour wheel that completes one full revolution per year," Daniel explains. "But here's the twist: wind speed and direction in St. Gallen, Switzerland—Frontify's HQ—nudges the wheel unpredictably off-centre. It's a subtle, living mechanic; each article contains a log of the wind data in its code as a kind of Easter Egg." Another favourite of Daniel's—yet to be released—is an expanded version of Conway's Game of Life. "It's been running continuously for over a month now, evolving patterns used in one of the content strand headers," he reveals. "The designer becomes a kind of photographer, capturing moments from a petri dish of generative motion." Core Philosophy In developing this unique identity, two phrases stood out to Daniel as guiding lights from the outset. The first was, 'We will show, not tell.' "This became the foundation for how we approached the identity," recalls Daniel. "It had to feel like a playground: open, experimental, and fluid. Not overly precious or prescriptive. A system the Frontify team could truly own, shape, and evolve. A platform, not a final product. A foundation, just as the future is always built on the past." The second guiding phrase, pulled directly from Frontify's rebrand materials, felt like "a call to action," says Daniel. "'Gestural and geometric. Human and machine. Art and science.' It's a tension that feels especially relevant in the creative industries today. As technology accelerates, we ask ourselves: how do we still hold onto our craft? What does it mean to be expressive in an increasingly systemised world?" Stripped back and skeletal typography The identity that Daniel and his team created reflects these themes through typography that literally embodies the platform's core philosophy. It really started from this idea of the past being built upon the 'foundations' of the past," he explains. "At the time Frontify Futures was being created, Frontify itself was going through a rebrand. With that, they'd started using a new variable typeface called Cranny, a custom cut of Azurio by Narrow Type." Daniel's team took Cranny and "pushed it into a stripped-back and almost skeletal take". The result was Crany-Frame and Crany-Hairline. "These fonts then served as our base scaffolding," he continues. "They were never seen in design, but instead, we applied decoration them to produce new typefaces for each content strand, giving the identity the space to grow and allow new ideas and shapes to form." As Daniel saw it, the demands on the typeface were pretty simple. "It needed to set an atmosphere. We needed it needed to feel alive. We wanted it to be something shifting and repositioning. And so, while we have a bunch of static cuts of each base style, we rarely use them; the typefaces you see on the website and social only exist at the moment as a string of parameters to create a general style that we use to create live animating versions of the font generated on the fly." In addition to setting the atmosphere, it needed to be extremely flexible and feature live inputs, as a significant part of the branding is about the unpredictability of the future. "So Daniel's team built in those aforementioned "chaos moments where everything from user interaction to live windspeeds can affect the font." Design Process The process of creating the typefaces is a fascinating one. "We started by working with the custom cut of Azuriofrom Narrow Type. We then redrew it to take inspiration from how a frame and a hairline could be produced from this original cut. From there, we built a type generation tool that uses them as a base. "It's a custom node-based system that lets us really get in there and play with the overlays for everything from grid-sizing, shapes and timing for the animation," he outlines. "We used this tool to design the variants for different content strands. We weren't just designing letterforms; we were designing a comprehensive toolset that could evolve in tandem with the content. "That became a big part of the process: designing systems that designers could actually use, not just look at; again, it was a wider conversation and concept around the future and how designers and machines can work together." In short, the evolution of the typeface system reflects the platform's broader commitment to continuous growth and adaptation." The whole idea was to make something open enough to keep building on," Daniel stresses. "We've already got tools in place to generate new weights, shapes and animated variants, and the tool itself still has a ton of unused functionality. "I can see that growing as new content strands emerge; we'll keep adapting the type with them," he adds. "It's less about version numbers and more about ongoing movement. The system's alive; that's the point. A provocation for the industry In this context, the Frontify Futures identity represents more than smart visual branding; it's also a manifesto for how creative systems might evolve in an age of increasing automation and systematisation. By building unpredictability into their tools, embracing the tension between human craft and machine precision, and creating systems that grow and adapt rather than merely scale, Daniel and the Frontify team have created something that feels genuinely forward-looking. For creatives grappling with similar questions about the future of their craft, Frontify Futures offers both inspiration and practical demonstration. It shows how brands can remain human while embracing technological capability, how systems can be both consistent and surprising, and how the future itself can become a creative medium. This clever approach suggests that the future of branding lies not in choosing between human creativity and systematic efficiency but in finding new ways to make them work together, creating something neither could achieve alone. #inside #thinking #behind #frontify #futures039

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:Steps: First elementis already sorted. Compare 3 with 8. Move 8 right. Insert 3 before it →Compare 5 with 8. Move 8 right. Insert 5 after 3 →Compare 1 with 8, 5, 3. Move them right. Insert 1 at start →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. Time Complexity of Insertion Sort Best Case: OAverage Case: OWorst Case: OIt 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 n = arr.length; for{ int key = arr; int j = i - 1; while{ arr= arr; j = j - 1; } arr= key; } } Let’s break it down: arris the current value. j starts from the previous index. While arr> key, shift arrto 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{ intnumbers = {9, 5, 1, 4, 3}; System.out.println; printArray; insertionSort; System.out.println; printArray; } 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{ for{ System.out.print; } 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 n = arr.length; for{ int key = arr; int j = i - 1; while{ arr= arr; j = j - 1; } arr= key; } } public static void printArray{ for{ System.out.print; } System.out.println; } public static void main{ intnumbers = {9, 5, 1, 4, 3}; System.out.println; printArray; insertionSort; System.out.println; printArray; } } 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 sortGood 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, 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 #how #implement #insertion #sort #java