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ActuGaming.net Endless Legend 2 : Notre interview de Jean-Maxime Moris, directeur créatif sur le 4X d’Amplitude Studios Officialisé en début d’année, Endless Legend 2 sortira en accès anticipé le 7 août prochain […] L'article Endle

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

Jun 16, 2025Ravie LakshmananMalware / DevOps Cybersecurity researchers have discovered a malicious package on the Python Package Index (PyPI) repository 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 of [machine learning] solutions." 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 algorithm (DGA) in 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-compat (676 Downloads) ts-runtime-compat-check (1,588 Downloads) solders (983 Downloads) @mediawave/lib (386 Downloads) All 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 package ("proxy.eslint-proxy[.]site") to 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 ("firewall[.]tel"). This second-stage PowerShell script, also obscured, is designed to fetch a Windows batch script from another domain ("cdn.audiowave[.]org") and 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 ("i.ibb[.]co"). "[The DLL] 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 control (UAC) using a combination of FodHelper.exe and programmatic identifiers (ProgIDs) to 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 (AI)-assisted coding, also called vibe coding, has unleashed another novel threat in the form of slopsquatting, where large language models (LLMs) can 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 (MCP)-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    

Insights How to Create a Successful Leadership Development Program At Harvard Business Impact, we partner with organizations to craft tailored learning experiences for leaders across all levels. Though each collaboration is unique, there is a proven process for designing and developing impactful learning initiatives. Leverage our checklist to help your organization develop a leadership development program that delivers results. View the infographic Leadership DevelopmentStrategic Alignment Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post How to Create a Successful Leadership Development Program appeared first on Harvard Business Impact.

“The best way to predict the future is to invent it,” the famed computer scientist Alan Kay once said. Uttered more out of exasperation than as inspiration, his remark has nevertheless attained gospel-like status among Silicon Valley entrepreneurs, in particular a handful of tech billionaires who fancy themselves the chief architects of humanity’s future.  Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals and ambitions in the near term, but their grand visions for the next decade and beyond are remarkably similar. Framed less as technological objectives and more as existential imperatives, they include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality (or something close to it); establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos. While there’s a sprawling patchwork of ideas and philosophies powering these visions, three features play a central role, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits. In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker calls this triumvirate of beliefs the “ideology of technological salvation” and warns that tech titans are using it to steer humanity in a dangerous direction.  “In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress.” “The credence that tech billionaires give to these specific science-fictional futures validates their pursuit of more—to portray the growth of their businesses as a moral imperative, to reduce the complex problems of the world to simple questions of technology, [and] to justify nearly any action they might want to take,” he writes. Becker argues that the only way to break free of these visions is to see them for what they are: a convenient excuse to continue destroying the environment, skirt regulations, amass more power and control, and dismiss the very real problems of today to focus on the imagined ones of tomorrow.  A lot of critics, academics, and journalists have tried to define or distill the Silicon Valley ethos over the years. There was the “Californian Ideology” in the mid-’90s, the “Move fast and break things” era of the early 2000s, and more recently the “Libertarianism for me, feudalism for thee”  or “techno-­authoritarian” views. How do you see the “ideology of technological salvation” fitting in?  I’d say it’s very much of a piece with those earlier attempts to describe the Silicon Valley mindset. I mean, you can draw a pretty straight line from Max More’s principles of transhumanism in the ’90s to the Californian Ideology [a mashup of countercultural, libertarian, and neoliberal values] and through to what I call the ideology of technological salvation. The fact is, many of the ideas that define or animate Silicon Valley thinking have never been much of a ­mystery—libertarianism, an antipathy toward the government and regulation, the boundless faith in technology, the obsession with optimization.  What can be difficult is to parse where all these ideas come from and how they fit together—or if they fit together at all. I came up with the ideology of technological salvation as a way to name and give shape to a group of interrelated concepts and philosophies that can seem sprawling and ill-defined at first, but that actually sit at the center of a worldview shared by venture capitalists, executives, and other thought leaders in the tech industry.  Readers will likely be familiar with the tech billionaires featured in your book and at least some of their ambitions. I’m guessing they’ll be less familiar with the various “isms” that you argue have influenced or guided their thinking. Effective altruism, rationalism, long­termism, extropianism, effective accelerationism, futurism, singularitarianism, ­transhumanism—there are a lot of them. Is there something that they all share?  They’re definitely connected. In a sense, you could say they’re all versions or instantiations of the ideology of technological salvation, but there are also some very deep historical connections between the people in these groups and their aims and beliefs. The Extropians in the late ’80s believed in self-­transformation through technology and freedom from limitations of any kind—ideas that Ray Kurzweil eventually helped popularize and legitimize for a larger audience with the Singularity.  In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress. I should say that AI researcher Timnit Gebru and philosopher Émile Torres have also done a lot of great work linking these ideologies to one another and showing how they all have ties to racism, misogyny, and eugenics. You argue that the Singularity is the purest expression of the ideology of technological salvation. How so? Well, for one thing, it’s just this very simple, straightforward idea—the Singularity is coming and will occur when we merge our brains with the cloud and expand our intelligence a millionfold. This will then deepen our awareness and consciousness and everything will be amazing. In many ways, it’s a fantastical vision of a perfect technological utopia. We’re all going to live as long as we want in an eternal paradise, watched over by machines of loving grace, and everything will just get exponentially better forever. The end. The other isms I talk about in the book have a little more … heft isn’t the right word—they just have more stuff going on. There’s more to them, right? The rationalists and the effective altruists and the longtermists—they think that something like a singularity will happen, or could happen, but that there’s this really big danger between where we are now and that potential event. We have to address the fact that an all-powerful AI might destroy humanity—the so-called alignment problem—before any singularity can happen.  Then you’ve got the effective accelerationists, who are more like Kurzweil, but they’ve got more of a tech-bro spin on things. They’ve taken some of the older transhumanist ideas from the Singularity and updated them for startup culture. Marc Andreessen’s “Techno-Optimist Manifesto” [from 2023] is a good example. You could argue that all of these other philosophies that have gained purchase in Silicon Valley are just twists on Kurzweil’s Singularity, each one building on top of the core ideas of transcendence, techno­-optimism, and exponential growth.  Early on in the book you take aim at that idea of exponential growth—specifically, Kurzweil’s “Law of Accelerating Returns.” Could you explain what that is and why you think it’s flawed? Kurzweil thinks there’s this immutable “Law of Accelerating Returns” at work in the affairs of the universe, especially when it comes to technology. It’s the idea that technological progress isn’t linear but exponential. Advancements in one technology fuel even more rapid advancements in the future, which in turn lead to greater complexity and greater technological power, and on and on. This is just a mistake. Kurzweil uses the Law of Accelerating Returns to explain why the Singularity is inevitable, but to be clear, he’s far from the only one who believes in this so-called law. “I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear.” My sense is that it’s an idea that comes from staring at Moore’s Law for too long. Moore’s Law is of course the famous prediction that the number of transistors on a chip will double roughly every two years, with a minimal increase in cost. Now, that has in fact happened for the last 50 years or so, but not because of some fundamental law in the universe. It’s because the tech industry made a choice and some very sizable investments to make it happen. Moore’s Law was ultimately this really interesting observation or projection of a historical trend, but even Gordon Moore [who first articulated it] knew that it wouldn’t and couldn’t last forever. In fact, some think it’s already over.  These ideologies take inspiration from some pretty unsavory characters. Transhumanism, you say, was first popularized by the eugenicist Julian Huxley in a speech in 1951. Marc Andreessen’s “Techno-Optimist Manifesto” name-checks the noted fascist Filippo Tommaso Marinetti and his futurist manifesto. Did you get the sense while researching the book that the tech titans who champion these ideas understand their dangerous origins? You’re assuming in the framing of that question that there’s any rigorous thought going on here at all. As I say in the book, Andreessen’s manifesto runs almost entirely on vibes, not logic. I think someone may have told him about the futurist manifesto at some point, and he just sort of liked the general vibe, which is why he paraphrases a part of it. Maybe he learned something about Marinetti and forgot it. Maybe he didn’t care.  I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear. For many of these billionaires, the vibes of fascism, authoritarianism, and colonialism are attractive because they’re fundamentally about creating a fantasy of control.  You argue that these visions of the future are being used to hasten environmental destruction, increase authoritarianism, and exacerbate inequalities. You also admit that they appeal to lots of people who aren’t billionaires. Why do you think that is?  I think a lot of us are also attracted to these ideas for the same reasons the tech billionaires are—they offer this fantasy of knowing what the future holds, of transcending death, and a sense that someone or something out there is in control. It’s hard to overstate how comforting a simple, coherent narrative can be in an increasingly complex and fast-moving world. This is of course what religion offers for many of us, and I don’t think it’s an accident that a sizable number of people in the rationalist and effective altruist communities are actually ex-evangelicals. More than any one specific technology, it seems like the most consequential thing these billionaires have invented is a sense of inevitability—that their visions for the future are somehow predestined. How does one fight against that? It’s a difficult question. For me, the answer was to write this book. I guess I’d also say this: Silicon Valley enjoyed well over a decade with little to no pushback on anything. That’s definitely a big part of how we ended up in this mess. There was no regulation, very little critical coverage in the press, and a lot of self-mythologizing going on. Things have started to change, especially as the social and environmental damage that tech companies and industry leaders have helped facilitate has become more clear. That understanding is an essential part of deflating the power of these tech billionaires and breaking free of their visions. When we understand that these dreams of the future are actually nightmares for the rest of us, I think you’ll see that senseof inevitability vanish pretty fast.  This interview was edited for length and clarity. Bryan Gardiner is a writer based in Oakland, California. 

Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion (SD) 3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kit (SDK) double performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time (JIT), on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8 (right) generates images in half the time with similar quality as FP16 (left). Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.

Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan. The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution. The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering.  Key features of the integration include: Centralized billing With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience. Automated provisioning  Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay. Bundled offerings The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform. Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said: “We’re thrilled to be working with Pax8  to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure.  As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.” Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said:  “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.” About EasyDMARC EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management. Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model. For more information on the EasyDMARC, visit: https://easydmarc.com/ About Pax8  Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businesses (SMBs) through AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem. Find out more: https://www.pax8.com/en-us/ The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC.

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