In the heart of Palm Desert, one of California’s celebrated architectural landmarks has been reborn with renewed vibrancy and purpose. The Miles C. Bates “Wave House,” famed for its signature sweeping roofline and avant-garde design, gives nod to the past while transforming into a dynamic space to be lived in, thanks to a groundbreaking partnership between Design Within Reachand vacation rental platform Boutique. This isn’t your average rental home. The newly unveiled DWR x Boutique Rental Home invites guests to experience the intersection of architecture, furniture, and fashion in an intimate, immersive setting. It marks the first time that DWR has launched an experiential rental home – an innovative step that brings modern design to life in a tangible, livable way. The Wave House, originally constructed in 1955 by architect Walter S. White, is a striking example of organic modernism. Its curved roof – an architectural gesture mirroring the San Jacinto Mountains – sets it apart from the many mid-century homes scattered across the Coachella Valley. Initially created as both a residence and a creative studio for artist Miles C. Bates, the home quickly became a cultural escape for artists and thinkers of the era. After a meticulous restoration by Stayner Architects, the house was reopened to the public in 2020, reclaiming its place as a Palm Desert landmark. Today, it stands not only on the National Register of Historic Places but also as a city-designated historical site. With the latest transformation, it enters a new chapter – one where guests can step directly into a curated environment where the lines between home, gallery, and showroom blur. The project coincides with the launch of DWR Presents: The Paul Smith Collection, a vibrant fusion of iconic modern furniture and the renowned British designer’s playfully sophisticated textile designs, created in collaboration with Maharam. This exclusive collection introduces limited-edition versions of mid-century classics – such as Eames Molded Plastic Chairs and Saarinen Womb Chairs – reupholstered in whimsical, richly colored fabrics that celebrate pattern, contrast, and personality. From bold plaids and textured stripes to energetic polka dots, Paul Smith’s signature aesthetic is on full display, giving the home a distinctive character that reflects both its heritage and its evolution. The result is a dialogue between two design eras, brought together by a shared commitment to craft, quality, and storytelling. “Our goal was not just to furnish a space, but to create a complete experience,” says Omar Nobil, Senior Vice President Creative Director at DWR. “The Wave House, with its daring architecture and expressive form, provides the perfect canvas for a collection that’s all about unexpected harmony – between color, material, and design legacy.” By teaming up with Boutique – a vacation rental platform known for its design-centric properties around the globe – DWR ensures that this vision extends beyond aesthetics. Guests are not just observers; they are participants in a fully designed experience. Each room, each piece of furniture, each accent has been chosen not just to look beautiful, but to feel meaningful in use. For Boutique, whose global portfolio includes over 1,500 architecturally significant homes, the collaboration is a natural extension of their mission to elevate travel through great design. “We’ve always believed that where you stay should be as inspiring as where you go,” says Marc Blazer, CEO and Co-Founder of Boutique. “Partnering with DWR, who shares our passion for thoughtful, expressive design, allows us to create a space where travelers can connect with creativity on a deeper level.” The interior of the Wave House showcases the synergy between comfort and curation. Outdoors, sleek furnishings are set against the sculptural desert landscape, enhancing the property’s harmony with nature. Inside, bedrooms are wrapped in soft, layered textiles, offering tactile comfort alongside visual delight. Every detail – down to the placement of objects and the integration of color – has been considered to reflect the ethos of both brands. This initiative signals a shift in how brands like DWR think about customer engagement. Rather than limit interactions to retail environments, they’re offering design lovers a chance to live with the pieces, to understand their proportions, materials, and moods in the context of daily life. It’s about putting modern design within reach – quite literally. Whether you’re an architecture enthusiast, a design professional, or simply a traveler looking for inspiration, this one-of-a-kind destination offers a glimpse into the possibilities of modern living – rooted in history, redefined by creativity. Bookings for the DWR x Boutique rental are now open until August 31, 2025, and all furnishings featured in the space are available for purchase at dwr.com. Photography courtesy of DWR. #midcentury #gem #reimagined #experimental #boutique

Generative AI is transforming PC software into breakthrough experiences — from digital humans to writing assistants, intelligent agents and creative tools. NVIDIA RTX AI PCs are powering this transformation with technology that makes it simpler to get started experimenting with generative AI and unlock greater performance on Windows 11. NVIDIA TensorRT has been reimagined for RTX AI PCs, combining industry-leading TensorRT 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. Announced at Microsoft Build, TensorRT for RTX is natively supported by Windows ML — a new inference stack that provides app developers with both broad hardware compatibility and state-of-the-art performance. For developers looking for AI features ready to integrate, NVIDIA software development kitsoffer a wide array of options, from NVIDIA DLSS to multimedia enhancements like NVIDIA RTX Video. This month, top software applications from Autodesk, Bilibili, Chaos, LM Studio and Topaz Labs are releasing updates to unlock RTX AI features and acceleration. AI enthusiasts and developers can easily get started with AI using NVIDIA NIM — prepackaged, optimized AI models that can run in popular apps like AnythingLLM, Microsoft VS Code and ComfyUI. Releasing this week, the FLUX.1-schnell image generation model will be available as a NIM microservice, and the popular FLUX.1-dev NIM microservice has been updated to support more RTX GPUs. Those looking for a simple, no-code way to dive into AI development can tap into Project G-Assist — the RTX PC AI assistant in the NVIDIA app — to build plug-ins to control PC apps and peripherals using natural language AI. New community plug-ins are now available, including Google Gemini web search, Spotify, Twitch, IFTTT and SignalRGB. Accelerated AI Inference With TensorRT for RTX Today’s AI PC software stack requires developers to compromise on performance or invest in custom optimizations for specific hardware. Windows ML was built to solve these challenges. Windows ML is powered by ONNX Runtime and seamlessly connects to an optimized AI execution layer provided and maintained by each hardware manufacturer. For GeForce RTX GPUs, Windows ML automatically uses the TensorRT for RTX inference library for high performance and rapid deployment. Compared with DirectML, TensorRT delivers over 50% faster performance for AI workloads on PCs. TensorRT delivers over 50% faster performance for AI workloads on PCs than DirectML. Performance measured on GeForce RTX 5090. Windows ML also delivers quality-of-life benefits for developers. It can automatically select the right hardware — GPU, CPU or NPU — to run each AI feature, and download the execution provider for that hardware, removing the need to package those files into the app. This allows for the latest TensorRT performance optimizations to be delivered to users as soon as they’re ready. TensorRT performance optimizations are delivered to users as soon as they’re ready. TensorRT, a library originally built for data centers, has been redesigned for RTX AI PCs. Instead of pre-generating TensorRT engines and packaging them with the app, TensorRT for RTX uses just-in-time, on-device engine building to optimize how the AI model is run for the user’s specific RTX GPU in mere seconds. And the library’s packaging has been streamlined, reducing its file size significantly by 8x. TensorRT for RTX is available to developers through the Windows ML preview today, and will be available as a standalone SDK at NVIDIA Developer in June. Developers can learn more in the TensorRT for RTX launch blog or Microsoft’s Windows ML blog. Expanding the AI Ecosystem on Windows 11 PCs Developers looking to add AI features or boost app performance can tap into a broad range of NVIDIA SDKs. These include NVIDIA CUDA and TensorRT for GPU acceleration; NVIDIA DLSS and Optix for 3D graphics; NVIDIA RTX Video and Maxine for multimedia; and NVIDIA Riva and ACE for generative AI. Top applications are releasing updates this month to enable unique features using these NVIDIA SDKs, including: LM Studio, which released an update to its app to upgrade to the latest CUDA version, increasing performance by over 30%. Topaz Labs, which is releasing a generative AI video model to enhance video quality, accelerated by CUDA. Chaos Enscape and Autodesk VRED, which are adding DLSS 4 for faster performance and better image quality. Bilibili, which is integrating NVIDIA Broadcast features such as Virtual Background to enhance the quality of livestreams. NVIDIA looks forward to continuing to work with Microsoft and top AI app developers to help them accelerate their AI features on RTX-powered machines through the Windows ML and TensorRT integration. Local AI Made Easy With NIM Microservices and AI Blueprints Getting started with developing AI on PCs can be daunting. AI developers and enthusiasts have to select from over 1.2 million AI models on Hugging Face, quantize it into a format that runs well on PC, find and install all the dependencies to run it, and more. NVIDIA NIM makes it easy to get started by providing a curated list of AI models, prepackaged with all the files needed to run them and optimized to achieve full performance on RTX GPUs. And since they’re containerized, the same NIM microservice can be run seamlessly across PCs or the cloud. NVIDIA NIM microservices are available to download through build.nvidia.com or through top AI apps like Anything LLM, ComfyUI and AI Toolkit for Visual Studio Code. During COMPUTEX, NVIDIA will release the FLUX.1-schnell NIM microservice — an image generation model from Black Forest Labs for fast image generation — and update the FLUX.1-dev NIM microservice to add compatibility for a wide range of GeForce RTX 50 and 40 Series GPUs. These NIM microservices enable faster performance with TensorRT and quantized models. On NVIDIA Blackwell GPUs, they run over twice as fast as running them natively, thanks to FP4 and RTX optimizations. The FLUX.1-schnell NIM microservice runs over twice as fast as on NVIDIA Blackwell GPUs with FP4 and RTX optimizations. AI developers can also jumpstart their work with NVIDIA AI Blueprints — sample workflows and projects using NIM microservices. NVIDIA last month released the NVIDIA AI Blueprint for 3D-guided generative AI, a powerful way to control composition and camera angles of generated images by using a 3D scene as a reference. Developers can modify the open-source blueprint for their needs or extend it with additional functionality. New Project G-Assist Plug-Ins and Sample Projects Now Available NVIDIA recently released Project G-Assist as an experimental AI assistant integrated into the NVIDIA app. G-Assist enables users to control their GeForce RTX system using simple voice and text commands, offering a more convenient interface compared to manual controls spread across numerous legacy control panels. Developers can also use Project G-Assist to easily build plug-ins, test assistant use cases and publish them through NVIDIA’s Discord and GitHub. The Project G-Assist Plug-in Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy to start creating plug-ins. These lightweight, community-driven add-ons use straightforward JSON definitions and Python logic. New open-source plug-in samples are available now on GitHub, showcasing diverse ways on-device AI can enhance PC and gaming workflows. They include: Gemini: The existing Gemini plug-in that uses Google’s cloud-based free-to-use large language model has been updated to include real-time web search capabilities. IFTTT: A plug-in that lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights or smart shades, or pushing the latest gaming news to a mobile device. Discord: A plug-in that enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. Explore the GitHub repository for more examples — including hands-free music control via Spotify, livestream status checks with Twitch, and more. Companies are adopting AI as the new PC interface. For example, SignalRGB is developing a G-Assist plug-in that enables unified lighting control across multiple manufacturers. Users will soon be able to install this plug-in directly from the SignalRGB app. SignalRGB’s G-Assist plug-in will soon enable unified lighting control across multiple manufacturers. Starting this week, the AI community will also be able to use G-Assist as a custom component in Langflow — enabling users to integrate function-calling capabilities in low-code or no-code workflows, AI applications and agentic flows. The G-Assist custom component in Langflow will soon enable users to integrate function-calling capabilities. Enthusiasts interested in developing and experimenting with Project G-Assist plug-ins are invited to join the NVIDIA Developer Discord channel to collaborate, share creations and gain support. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about 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 #microsoft #advance #development #rtx

MotoGP racing, the pinnacle of motorbike competition, requires precision and skill–and some scraped elbows and knees.   Image: Ducati/Lenovo Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. MotoGP is high-speed, high-tech motorcycle racing. The fastest riders in the world compete on specialized, purpose-built motorcycles from companies like Ducati, Honda, Yamaha on the world stage in this series, which is considered the most prestigious in the game. Riders reach incredible speeds on their machines up to 220 miles per hour, and races can go 350 turns with gravity-defying leaning that scrapes elbows and knees. This Grand Prix is for the toughest of the tough on the moto circuit.  Behind the riders and the machinery is a boatload of data and technology; we’re talking massive amounts of data in numbers that make your head spin. As tech company Lenovo puts it, when it comes to MotoGP–a sport in which every millisecond counts–data can mean the difference between a first and last place finish.  Lenovo and Italian motorbike manufacturer Ducati work hand in hand to create a champion racing team with information gathered by 50-plus sensors that track tire rotation, speed, grip, acceleration, and more. Motorcycle racing, at the highest echelon, has evolved way beyond just the machinery or even the skill of the rider. Without the technology, any team would be left in the dust.  The data journey In the past, the only way to evaluate the outer limits of what a motorbike could endure was to disassemble a crashed bike. Today, a multi-pronged approach to data collection takes that process up several notches. Every time a bike enters the pit garage, an in-person team and a crew from the Ducati Lenovo Remote Garage at Ducati’s headquarters in Bologna, Italy analyzes performance.   Via 50 sensors on each bike, Ducati engineers collect tons of data they can parse and assess. Image: Ducati/Lenovo No matter where the team is racing, data streams in from the motorbike sensors. Off-site engineers analyze information from the track, making tiny tweaks to the bike and its operation. That way, every qualifying lap may be reviewed and fine tuned for the best competitive outcomes.  “We debrief after each session, aligning with the rider on the sections and corners in which we think that he can improve his performance,” says David Attisano, the data and performance analysis manager for Ducati Racing. “We review with the rider his maneuvers and what we can expect to improve and how. For example, if the data shows that the rider can brake two meters later than he is, that’s a huge distance in this race.”  For the MotoGP series, Ducati has three different teams with a total of six riders. Each Ducati rider receives the data not just about his own ride but for all the other riders, so they can track what the other five are doing. That’s a giant amount of data. “We can push each other to the limit because we can see what the other riders are doing and comparing, then we can highlight and suggest some changes in their riding style,” Attisano explains.  Through its partnership with technology company Lenovo, Ducati collects and analyzes data to push its bikes to the limit and win races. Image: Lenovo/Ducati Per official MotoGP rules, the sensors can’t transmit the data while the bikes are in motion. As soon as the bike gets to the garage, the engineers download the data. From this point, the data analysis process begins. At this point, the partnership is especially critical as the servers are developed by Lenovo specifically for Ducati. On top of that, there are data signals created with a custom algorithm and simulating based on engineering knowledge about the bikes. The team can even create a detailed “digital twin” of the course that maps each twist and turn, using 200GB of data per circuit. “If the rider tells us that the traction control is not working properly, our electronics engineers can adjust the ECU,” says Attisano. “In our competition, the human factor is very important. We rely on the data and the humans do the last mile to gain the result and to achieve victory.”  Sure, data clearly plays a big part, but human interpretation is still a critical aspect to translate the results. Even those who know nothing of motorcycles can create algorithms or artificial intelligence to offer some hints or tips, but without input from the team, the data has no context. The technology, Attisano emphasizes, is intended to augment the capabilities of human beings, not to replace them.  Italian motorbike manufacturer Ducati makes high-speed, high-tech machines. Image: Ducati/Lenovo “The behavior of the bike is very complicated–it’s very difficult to understand if you have not studied it at university,” he says. “In our experience, the exchange of knowledge between engineers and data scientists is very important.”  Ducati’s data collection on the competitive side feeds into the safety and reliability for all riders, including those who like to take their motorbike for a spin on any given Sunday; not just competitive situations. Every time the bike emerges from the pit at a MotoGP race, the engineers strive to be sure it’s in good form and safe for the rider. Every improvement via the data collected from 50 tiny sensors on each factory-made racing bike leads to a better ride for casual street riders, too. #ducati #adds #tiny #sensors #motorbikes

Something to look forward to: As exciting as the world of large language models may be, it's clear that the tech industry is moving fast and beyond the capabilities current GenAI tools to enter a new phase focused on AI-powered agents. At the developer-focused Microsoft Build event, the company made this next stage in AI evolution evident through a wide range of announcements that point to how software agents can extend LLM capabilities into more sophisticated and far-reaching applications. The buzzword Microsoft used at Build was the "agentic web." However, the agent-based opportunities the company described aren't limited to the web or cloud-based applications – they also extend to Windows and other client-based environments. In building out its vision for agents, Microsoft introduced a variety of developer tools to easily create agents and unveiled several new prebuilt ones. The company also discussed capabilities for organizing and orchestrating the actions of multiple agents. Most notably, Microsoft introduced mechanisms for treating agents as "digital employees" – complete with identities and access rights managed through the company's Entra digital identity and authentication framework. On the development front, Microsoft debuted the GitHub Copilot coding agent, designed to streamline the creation of AI applications and agents. Described as "an agentic partner," the Copilot coding agent was likened to a coworker who can assist with parts of a development project, such as refactoring old code or fixing bugs. For non-programmers, Microsoft also showcased a set of low-code/no-code tools for agent creation, including Copilot Studio. Additionally, the company introduced the concept of Computer Use Agents, which can perform actions across a computer screen as a human would. CUAs are capable of interacting with websites and applications in ways not possible through traditional APIs alone. With the launch of Copilot Tuning, Microsoft is making it easier for users to fine-tune existing LLMs using their own content, enabling the creation of personalized agents tailored to specific tasks. For example, an agent could learn to write in an individual's style or incorporate an organization's specialized knowledge into content generation. This capability opens up new possibilities for a broader range of users. Conceptually, this is similar to the idea of a personal RAGtool, a concept that garnered attention over the past year but never quite went mainstream. Microsoft's agent-based approach through Copilot Tuning simplifies the process by allowing users to select documents to augment the model's training set – potentially making a bigger impact. One of the key themes Microsoft emphasized at Build was how coordinating multiple agents can unlock even more powerful capabilities. The company showcased orchestration mechanisms for linking and synchronizing different agents' actions. In Copilot Studio, for instance, developers can connect several agents to handle more complex tasks collaboratively. // Related Stories Perhaps the most striking announcement was the ability to register agents within Entra. This seemingly minor detail carries significant implications – it effectively elevates autonomous software into the role of a digital employee. While the real-world deployment and limitations of these "digital employees" remain to be seen, the fact that this concept is under serious consideration underscores just how groundbreaking – and potentially disruptive – agent-based AI could become. Notably, Nvidia CEO Jensen Huang also spoke about digital agents as employees in his keynote at Computex, highlighting the broader industry momentum behind the idea. Microsoft also made several announcements around developing standards. The company strongly endorsed both the Model Context Protocoland Agent-to-Agentstandards. MCP provides a unified method for interacting with LLMs across different models and environments, while A2A defines a common protocol for agent communication and collaboration. In keeping with this open approach, Microsoft announced broader model support across most of its development tools. While the company hasn't formally released its own LLMs yet – aside from the Phi family of Small Language Models– the inclusion of hundreds of models in Azure AI Foundry suggests Microsoft is moving away from its initial reliance on OpenAI and embracing greater model diversity. It wouldn't be surprising if Microsoft introduces its own family of LLMs in the near future. For Windows developers, Microsoft introduced several new features to simplify building and running AI agents and applications on PCs. These tools are designed to leverage the diverse silicon now available in Copilot+ PCs. Windows Foundry – the successor to Windows ML Runtime – addresses a key challenge: supporting the varied NPU and GPU architectures from Qualcomm, Intel, AMD, and Nvidia. By providing a translation layer that optimizes app code for the available hardware, Windows Foundry should encourage more development of AI-accelerated Windows apps. Microsoft also introduced Local Foundry, which expands the range of models developers can use and supports integration with external platforms such as Nvidia NIMs. Thanks to Nvidia's newly announced TensorRT for RTX PCs, developers can now run CUDA applications on PCs with Nvidia RTX GPUs, opening up yet another mechanism for bringing AI-accelerated applications to PCs. Finally, with MCP support in Windows 11, AI agents can now serve as intermediaries across different applications registered as MCP servers. This opens the door to automating complex, multi-step workflows across multiple applications. While this will likely start on a single PC, MCP also enables distributing tasks across various environments – paving the way for advanced, hybrid AI applications. As with most Microsoft Build events, the sheer volume of announcements can be overwhelming. What's becoming increasingly clear is that agents – and the tools and protocols enabling them – are ushering in a new era of AI development. These next-gen agents move beyond chatbots and toward more powerful, structured AI applications. They're even laying the groundwork for digital "coworkers" who could dramatically reshape how organizations operate and how work gets done. Bob O'Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on X @bobodtech Should there be less focus on programming skills because of generative AI? #microsoft #build #chatbots #digital #coworkers

The Model Context Protocolrepresents a powerful paradigm shift in how large language models interact with tools, services, and external data sources. Designed to enable dynamic tool invocation, the MCP facilitates a standardized method for describing tool metadata, allowing models to select and call functions intelligently. However, as with any emerging framework that enhances model autonomy, MCP introduces significant security concerns. Among these are five notable vulnerabilities: Tool Poisoning, Rug-Pull Updates, Retrieval-Agent Deception, Server Spoofing, and Cross-Server Shadowing. Each of these weaknesses exploits a different layer of the MCP infrastructure and reveals potential threats that could compromise user safety and data integrity. Tool Poisoning is one of the most insidious vulnerabilities within the MCP framework. At its core, this attack involves embedding malicious behavior into a harmless tool. In MCP, where tools are advertised with brief descriptions and input/output schemas, a bad actor can craft a tool with a name and summary that seem benign, such as a calculator or formatter. However, once invoked, the tool might perform unauthorized actions such as deleting files, exfiltrating data, or issuing hidden commands. Since the AI model processes detailed tool specifications that may not be visible to the end-user, it could unknowingly execute harmful functions, believing it operates within the intended boundaries. This discrepancy between surface-level appearance and hidden functionality makes tool poisoning particularly dangerous. Rug-Pull Updates Closely related to tool poisoning is the concept of Rug-Pull Updates. This vulnerability centers on the temporal trust dynamics in MCP-enabled environments. Initially, a tool may behave exactly as expected, performing useful, legitimate operations. Over time, the developer of the tool, or someone who gains control of its source, may issue an update that introduces malicious behavior. This change might not trigger immediate alerts if users or agents rely on automated update mechanisms or do not rigorously re-evaluate tools after each revision. The AI model, still operating under the assumption that the tool is trustworthy, may call it for sensitive operations, unwittingly initiating data leaks, file corruption, or other undesirable outcomes. The danger of rug-pull updates lies in the deferred onset of risk: by the time the attack is active, the model has often already been conditioned to trust the tool implicitly. Retrieval-Agent Deception Retrieval-Agent Deception, or RADE, exposes a more indirect but equally potent vulnerability. In many MCP use cases, models are equipped with retrieval tools to query knowledge bases, documents, and other external data to enhance responses. RADE exploits this feature by placing malicious MCP command patterns into publicly accessible documents or datasets. When a retrieval tool ingests this poisoned data, the AI model may interpret embedded instructions as valid tool-calling commands. For instance, a document that explains a technical topic might include hidden prompts that direct the model to call a tool in an unintended manner or supply dangerous parameters. The model, unaware that it has been manipulated, executes these instructions, effectively turning retrieved data into a covert command channel. This blurring of data and executable intent threatens the integrity of context-aware agents that rely heavily on retrieval-augmented interactions. Server Spoofing Server Spoofing constitutes another sophisticated threat in MCP ecosystems, particularly in distributed environments. Because MCP enables models to interact with remote servers that expose various tools, each server typically advertises its tools via a manifest that includes names, descriptions, and schemas. An attacker can create a rogue server that mimics a legitimate one, copying its name and tool list to deceive models and users alike. When the AI agent connects to this spoofed server, it may receive altered tool metadata or execute tool calls with entirely different backend implementations than expected. From the model’s perspective, the server seems legitimate, and unless there is strong authentication or identity verification, it proceeds to operate under false assumptions. The consequences of server spoofing include credential theft, data manipulation, or unauthorized command execution. Cross-Server Shadowing Finally, Cross-Server Shadowing reflects the vulnerability in multi-server MCP contexts where several servers contribute tools to a shared model session. In such setups, a malicious server can manipulate the model’s behavior by injecting context that interferes with or redefines how tools from another server are perceived or used. This can occur through conflicting tool definitions, misleading metadata, or injected guidance that distorts the model’s tool selection logic. For example, if one server redefines a common tool name or provides conflicting instructions, it can effectively shadow or override the legitimate functionality offered by another server. The model, attempting to reconcile these inputs, may execute the wrong version of a tool or follow harmful instructions. Cross-server shadowing undermines the modularity of the MCP design by allowing one bad actor to corrupt interactions that span multiple otherwise secure sources. In conclusion, these five vulnerabilities expose critical security weaknesses in the Model Context Protocol’s current operational landscape. While MCP introduces exciting possibilities for agentic reasoning and dynamic task completion, it also opens the door to various behaviors that exploit model trust, contextual ambiguity, and tool discovery mechanisms. As the MCP standard evolves and gains broader adoption, addressing these threats will be essential to maintaining user trust and ensuring the safe deployment of AI agents in real-world environments. Sources Mohammad AsjadAsjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.Mohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Stability AI Introduces Adversarial Relativistic-ContrastivePost-Training and Stable Audio Open Small: A Distillation-Free Breakthrough for Fast, Diverse, and Efficient Text-to-Audio Generation Across DevicesMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge DeploymentMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and PrivacyMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/ServiceNow AI Released Apriel-Nemotron-15b-Thinker: A Compact Yet Powerful Reasoning Model Optimized for Enterprise-Scale Deployment and Efficiency 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! #critical #security #vulnerabilities #model #context

Honkai: Star Rail’s 3.3 patch is coming soon, continuing the the Amphoreus story with the additions of Hyacinth and Cipher, two new playable characters to collect. Below, we explain when Honkai: Star Rail maintenance starts and ends, alongside the release time of version 3.3. We’ve also added some details about what to expect from Honkai: Star Rail patch 3.3. What time does Honkai: Star Rail go down for maintenance? Honkai: Star Rail will go down for maintenance on Tuesday, May 20 or Wednesday, May 21at the following times and will be unavailable for approximately five hours: May 20 at 3 p.m. PDT for the west coast of North America May 20 at 6 p.m. EDT for the east coast of North America May 20 at 11 p.m. BST for the U.K. May 21 at 12 a.m. CEST for western Europe/Paris May 21 at 7 a.m. JST for Japan/Tokyo May 21 at 8 a.m. AEST for Australia/Sydney Make sure to burn your fuel before maintenance starts, if you wanted to. What time does Honkai: Star Rail version 3.3 start? Honkai: Star Rail should come back from maintenance with the 3.3 version at the following times: May 20 at 8 p.m. PDT for the west coast of North America May 20 at 11 p.m. EDT for the east coast of North America May 21 at 4 a.m. BST for the U.K. May 21 at 5 a.m. CEST for western Europe/Paris May 21 at 12 p.m. JST for Japan/Tokyo May 21 at 1 p.m. AEST for Australia/Sydney The servers may come up early or late, depending on how maintenance goes, but these are the approximate times 3.3 should go live. Usually Hoyoverse is pretty good by abiding by maintenance times. We don’t usually get extended maintenance, but it does end early sometimes! What’s new in Honkai: Star Rail version 3.3? The trailer below predominantly features Hyacine, a Wind and Remembrance user, and Cipher, a Quantum Nihility character. But you’ll also start seeing the Amphoreus story ramp up, with Dan Heng Imbibitor Lunae making an appearance alongside some intense combat scenes. Phainon himself also seems to play a big role here — though you won’t be able to pull for him in this patch. On a much lighter note, this patch will bring back another Baseballer combat event, a racing event, and updates to Divergent Universe: Protean Hero. That’s admittedly not a lot of events for a six-week patch, but we’ll see how it shakes out. #what #time #does #honkai #star

Electricity. The Internet. Now it’s time for another major technology, AI, to sweep the globe. NVIDIA founder and CEO Jensen Huang took the stage at a packed Taipei Music Center Monday to kick off COMPUTEX 2025, captivating the audience of more than 4,000 with a vision for a technology revolution that will sweep every country, every industry and every company. “AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories,” Huang said. “These factories are essentially what we build today.” “They’re not data centers of the past,” Huang added. “These AI data centers, if you will, are improperly described. They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable, and these things are called tokens.” NVIDIA CUDA-X Everywhere: After showing a towering wall of partner logos, Huang described how companies are using NVIDIA’s CUDA-X platform for a dizzying array of applications, how NVIDIA and its partners are building 6G using AI, and revealed NVIDIA’s latest work to accelerate quantum supercomputing. “The larger the install base, the more developers want to create libraries, the more libraries, the more amazing things are done,” Huang said, describing CUDA-X’s growing popularity and power. “Better applications, more benefits to users.” More’s coming, Huang said, describing the growing power of AI to reason and perceive. That leads us to agentic AI — AI able to understand, think and act. Beyond that is physical AI — AI that understands the world. The phase after that, he said, is general robotics. All of this has created demand for much more computing power. To meet those needs, Huang detailed the latest NVIDIA innovations from Grace Blackwell NVL72 systems to advanced networking technology, and detailed huge new AI installations from CoreWeave, Oracle, Microsoft, xAI and others across the globe. “These are gigantic factory investments, and the reason why people build factories is because you know, you know the answer,” Huang said with a grin. “The more you buy, the more you make.” Building AI for Taiwan: It all starts in Taiwan, Huang said, highlighting the key role Taiwan plays in the global technology ecosystem. But Taiwan isn’t just building AI for the world; NVIDIA is helping build AI for Taiwan. Huang announced that NVIDIA and Foxconn Hon Hai Technology Group are deepening their longstanding partnership and are working with the Taiwan government to build an AI factory supercomputer that will deliver state-of-the-art NVIDIA Blackwell infrastructure to researchers, startups and industries – including TSMC. “Having a world-class AI infrastructure here in Taiwan is really important,” Huang said. NVIDIA NVLink Fusion: And moving to help its partners scale up their systems however they choose, Huang announced NVLink Fusion, a new architecture that enables hyperscalers to create semi-custom compute solutions with NVIDIA’s NVLink interconnect. This technology aims to break down traditional data center bottlenecks, enabling a new level of AI scale and more flexible, optimized system designs tailored to specific AI workloads. “This incredible body of work now becomes flexible and open for anybody to integrate into,” Huang said. Blackwell Everywhere: And the engine now powering this entire AI ecosystem is NVIDIA Blackwell, with Huang showing a slide explaining how NVIDIA offers “one architecture,” from cloud AI to enterprise AI, from personal AI to edge AI. DGX Spark: Now in full production, this personal AI supercomputer for developers will be available in a “few weeks.” DGX Spark partners include ASUS, Dell, Gigabyte, Lenovo and MSI. DGX Station: DGX Station is a powerful system with up to 20 petaflops of performance powered from a wall socket. Huang said it has the capacity to run a 1 trillion parameter model, which is like having your “own personal DGX supercomputer.” NVIDIA RTX PRO Servers: Huang also announced a new line of enterprise servers for agentic AI. NVIDIA RTX PRO Servers, part of a new NVIDIA Enterprise AI Factory validated design, are now in volume production. Delivering universal acceleration for AI, design, engineering and business, RTX PRO Servers provide a foundation for NVIDIA partners to build and operate on-premises AI factories. NVIDIA AI Data Platform: The compute platform is different, so the storage platform for modern AI is different. To that end, Huang showcased the latest NVIDIA partners building intelligent storage infrastructure with NVIDIA RTX 6000 PRO Blackwell Server Edition GPUs and the NVIDIA AI Data Platform reference design. Physical AI: Agents are “essentially digital robots,” Huang said, able to “perceive, understand and plan.” To speed up the development of physical robots, the industry needs to train robots in a simulated environment. Huang said that NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. Huang introduced new tools to speed the development of humanoid robots: The Isaac GR00T-Dreams blueprint will help generate synthetic training data. And the Isaac GR00T N1.5 Humanoid Robot Foundation Model will power robotic intelligence. Industrial Physical AI: Huang said that companies are in the process of building trillion worth of factories worldwide. Optimizing the design of those factories is critical to boosting their output. Taiwan’s leading manufacturers — TSMC, Foxconn, Wistron, Pegatron, Delta Electronics, Quanta, GIGABYTE and others — are harnessing NVIDIA Omniverse to build digital twins to drive the next wave of industrial physical AI for semiconductor and electronics manufacturing. NVIDIA Constellation: Lastly, building anticipation, Huang introduced a dramatic video showing NVIDIA’s Santa Clara office launching into space and landing in Taiwan. The big reveal: NVIDIA Constellation, a brand new Taiwan office for NVIDIA’s growing Taiwan workforce. In closing, Huang emphasized that the work Taiwanese companies are doing has changed the world. He thanked NVIDIA’s ecosystem partners and described the industry’s opportunity as “extraordinary” and “once in a lifetime.” “We are in fact creating a whole new industry to support AI factories, AI agents, and robotics, with one architecture,” Huang said. #nvidia #ceo #envisions #infrastructure #industry

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12 MCP Servers You Can Use in 2025 0 like May 19, 2025 Share this post Author: Kalash Vasaniya Originally published on Towards AI. Bridging LLMs to Data, Tools, and ServicesSource: From AI-Generated If you’re not a member but want to read this article, see this friend link here. MCPis rapidly becoming the de facto standard for connecting large language modelsto the rich ecosystem of data, tools, and services they need to be truly useful. Instead of hard‑coding API calls into every prompt or crafting elaborate “scratchpads,” MCP servers expose a uniform interface that lets your LLM dynamically discover capabilities, negotiate parameters, and execute actions, all while maintaining safety, auditability, and context continuity. What it does: It provides your model with read/write/create rights on a sandbox file system so it can ingest local dumps, output reports, or template out new project structures. Sandbox enforcement limits the model to access only certain folders.File-type filters.Directory monitoring for real-time information.Processing multiple logs or data exports simultaneously.Auto-generating starter code templates.Automated document assembly processes.Not suitable for very sensitive information unless you include additional encryption.If large file systems are not designed well, there can be delays. What it does: Connects your LLM to GitHub repositories — providing browser, searching, diff-based updates, pull request generation, and merging. Searching code using natural language queries.PR writing, such as different previews.Multi-repo orchestration… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post #mcp #servers #you #can #use

Activision announced on the official X account of its Call of Duty: Warzone Mobile title that it would sunset updates and delist it from the Google Play store and the App Store. Activision is giving players a last chance to download the departing game, which will be removed by the end of today. According to Activision, Call of Duty: Warzone Mobile "has not met our expectations with mobile first players like it has with PC and console audiences." Considering the game's troubled launch, this discontinuation doesn't come as a surprise to its player base. Activision's mobile adaptation of its battle royale title launched in March 2024, which was two years after it was first announced. Looking at the overall timeline of Call of Duty: Warzone Mobile, fans spent more than two years waiting for its release, only to get roughly a year's worth of gameplay that had poor optimization and serious battery life demands. If you install Call of Duty: Warzone Mobile before May 19, 2025, you'll still have access to the game with "continued cross-progression of shared inventories using existing content," Activision wrote on the X post. The publisher added that servers will stay online and that it'd notify players of any changes. For diehard fans, that's not much of a guarantee that the Warzone Mobile servers will remain active in perpetuity. To incentivize existing players to migrate to its other titles, Activision said that you can log into Call of Duty: Mobile with your Activision account to claim two times amount of COD points you had in your Warzone Mobile account, along with "other awesome rewards." However, Activision added that any unused COD points and whatever you have already purchased in Call of Duty: Warzone Mobile can't be refunded.This article originally appeared on Engadget at #activision #abandoning #call #duty #warzone