In a world where the skies speak their secrets, I find myself lost in the silence. Private companies like WindBorne Systems are reaching out, launching balloons and drones to gather weather data for NOAA, yet the gaps remain, echoing my own feelings of emptiness. Despite their efforts, I can't shake the sense that no matter how many devices are deployed, the truth is that some things can't be fully captured. I watch the clouds drift by, yearning for connection, but feeling only the weight of solitude. The winds whisper tales of missed opportunities, and I can't help but wonder if anyone truly understands the storms inside me. #WeatherData #NOAA #WindBorneSystems #Loneliness #EmotionalStorms

Looks like the encryption made for police and military radios is about as secure as a paper bag in a rainstorm. Researchers have discovered that the algorithm meant to keep our brave protectors safe from prying ears is easier to crack than a nut at a toddler's birthday party. Who needs spies when you've got a front-row seat to the latest police drama? Maybe next time, they should consult a teenager before deploying their "state-of-the-art" security measures. It's a brave new world, folks! #EncryptionFails #PoliceRadio #CyberSecurity #TechHumor #WeakLinks

🌟 Bonjour à tous ! Aujourd'hui, parlons de "Wuchang: Fallen Feathers"! 🎮 Bien que le jeu ait rencontré des défis sur Steam en raison de problèmes de performance sur des configurations moins puissantes, il est incroyable de voir comment une mise à jour rapide a été déployée pour améliorer l'expérience de jeu ! 💪✨ L'éditeur a pris en compte les retours des joueurs et fait tout son possible pour rendre le jeu encore meilleur. Cela montre l'importance de la communication et de la volonté d'améliorer les choses. Chaque obstacle peut devenir une opportunité de croissance ! 🚀 Restons positifs et soutenons les développeurs dans leur quête pour offrir une expérience de jeu exceptionnelle !

🌟 Bonjour à tous, passionnés de jeux et aventuriers du monde virtuel ! 🎮✨ Aujourd'hui, je suis tellement excité de vous parler de quelque chose d'incroyable : **Shadowverse Worlds Beyond** ! 🌌💫 Ce jeu vient de sortir et il est déjà en train de captiver des millions de joueurs à travers le monde. Si vous n'avez pas encore plongé dans cet univers fascinant, c'est le moment parfait pour le faire ! 🚀 L'un des aspects les plus fantastiques de **Shadowverse Worlds Beyond** est la diversité des **Decks Préconstruits**. Ces decks sont conçus pour vous offrir une expérience de jeu instantanée, sans avoir à passer des heures à construire votre propre stratégie. Que vous soyez un joueur débutant ou un vétéran, il y a un deck qui correspond à votre style de jeu ! 💪🎉 Imaginez-vous en train de plonger dans des batailles épiques avec des cartes qui vous permettent de déployer vos capacités de manière stratégique et intuitive. C'est l'occasion rêvée de montrer vos talents et d'apprendre de nouvelles techniques tout en vous amusant. 🌈 N'oubliez pas que chaque partie est une nouvelle opportunité d'apprendre et de grandir ! Chaque carte que vous jouez, chaque stratégie que vous élaborez, vous rapproche un peu plus de la victoire. Ne laissez jamais les défaites vous décourager, car chaque échec est une étape vers le succès. 🎯💖 Et ce n'est pas tout ! Avec la communauté en pleine expansion autour de **Shadowverse Worlds Beyond**, vous aurez l'occasion de vous connecter avec d'autres joueurs, d'échanger des conseils et des astuces, et même de participer à des tournois palpitants. 🌍🤝 La camaraderie et l'esprit de compétition rendent l'expérience de jeu encore plus enrichissante. Alors, qu'attendez-vous ? Téléchargez **Shadowverse Worlds Beyond**, explorez les Decks Préconstruits et lancez-vous dans cette aventure incroyable ! Chaque journée est une page blanche sur laquelle vous pouvez écrire votre propre histoire. Soyez audacieux, soyez créatif et n'ayez jamais peur de briller ! ✨🌟 Rappelez-vous, le succès est à portée de main et chaque petit pas compte. Ensemble, nous allons conquérir des mondes et créer des souvenirs inoubliables ! 💖🌈 #Shadowverse #JeuxVidéo #DecksPréconstruits #Aventure #Communauté

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

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont

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

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 Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, 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 FP8generates images in half the time with similar quality as FP16. 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. #nvidia #tensorrt #boosts #stable #diffusion

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: / About Pax8  Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businessesthrough 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: / The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC. #easydmarc #integrates #with #pax8 #marketplace