AI Applications

NVIDIA has announced the availability of its new line of laptops, dubbed RTX Spark, which combine advanced graphics and artificial intelligence capabilities in a slim, portable package. The company claims that these devices can deliver up to 1 petaflop of AI compute power and up to 128GB of unified memory, making them suitable for demanding tasks such as gaming, content creation, and running local AI agents.

The RTX Spark laptops are available today from major manufacturers including ASUS, Dell, HP, Lenovo, Microsoft, and MSI. Additional systems will be released in the coming months, but those interested can already purchase these devices through various channels, with prices starting at $2,599. A limited-time promotion is also available for new Perplexity users.

NVIDIA’s RTX Spark laptops are powered by a custom-designed CPU from MediaTek, which provides best-in-class power efficiency, performance, and connectivity. This chip combines an NVIDIA Blackwell RTX GPU with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance, allowing for seamless execution of demanding tasks.

The shared memory architecture in these devices enables simultaneous execution of creative projects and local AI models without the need for cloud-based services. This means that users can work on complex tasks such as editing 8K video or rendering large 3D scenes up to 90GB while still having access to their device’s battery life, making it ideal for creators who often find themselves working in remote locations.

NVIDIA has also announced the availability of its OpenShell secure runtime and Microsoft Execution Containers, which provide users with control over agent permissions. Additionally, NVIDIA Skills is an open-source GitHub repository that teaches agents how to use NVIDIA CUDA-X tools and libraries without requiring expertise in CUDA programming.

RTX Spark laptops are designed for creators who need a powerful device that can handle demanding tasks such as video editing, 3D modeling, and AI development. The devices come with native support for Adobe Photoshop, Premiere, and Substance 3D through beta releases available at launch, with further updates expected in the coming months.

Adobe has also announced its plans to enhance its industry-leading creative applications with NVIDIA TensorRT acceleration in AI-editing features. This will enable creators to work faster across various aspects of post-production, including playback, rendering, effects, color grading, and more.

NVIDIA Broadcast 2.3 is another feature that allows users to remove background noise from existing recordings and shape their voice using Studio Voice Mic Profiles. Beta RTX Video features also provide up to 4K super resolution and 4x frame generation capabilities without requiring cloud-based services.

The NVIDIA OpenShell secure runtime provides a safe environment for running local AI agents, while Microsoft Execution Containers offer users control over agent permissions. This ensures that sensitive data remains protected within the device’s ecosystem.

NVIDIA has also readied developer tools for RTX Spark, including the first PyTorch-CUDA build for Windows on Arm and the first Python build native by default. Additionally, support is available for 97% of top 15,000 Python packages, making it easier for developers to create applications that can run locally.

RTX Spark laptops are also designed with gamers in mind, offering immersive gaming experiences through NVIDIA Blackwell RTX graphics. The devices come with launch support for hundreds of games, including Cyberpunk 2077, Fortnite, Gears of War: E-Day, Marvel Rivals, World of Warcraft, and NBA 2K27.

NBA 2K27 showcases the capabilities of RTX Spark laptops in gaming, using NVIDIA DLSS 5 technology to enhance lighting and material details for greater realism. A new AI model available today boosts DLSS 5 frame rates by up to 1.4x, providing a smoother gaming experience.

The availability of RTX Spark laptops marks an important milestone for the industry, as it brings together advanced graphics and artificial intelligence capabilities in a single device. This will enable creators, developers, and gamers to work on complex tasks without requiring cloud-based services or sacrificing performance.

NVIDIA’s Jensen Huang has stated that ‘RTX Spark brings the power of NVIDIA AI, RTX graphics, and CUDA together with Microsoft Windows in one PC for creating, gaming, and running personal AI agents.’ This vision is shared by Yusuf Mehdi from Microsoft, who believes that ‘Windows PCs powered by RTX Spark unlock powerful new local compute for creators and developers, expanding what they can create, build, and run on Windows’.

The future of computing looks bright with the introduction of NVIDIA’s RTX Spark laptops. With their advanced graphics capabilities and AI-powered performance, these devices are poised to revolutionize the way we work and play.

OpenAI has released an update to its ChatGPT extension for Google’s Chrome browser, granting the AI assistant more capabilities within the browser. The latest upgrade allows ChatGPT to access and analyze open tabs in Chrome, enabling it to provide more personalized and relevant responses to user queries.

The company had previously developed a standalone browser, but retired it on August 9th of this year. However, OpenAI continued to improve its Chrome extension, making ChatGPT a viable alternative to other AI-powered browsers like Gemini within the browser environment. With these latest updates, ChatGPT can now perform even more tasks directly in Chrome.

The update enables users to ask ChatGPT to analyze and compare information across multiple tabs, such as comparing different couch models based on dimensions, prices, delivery time, and return policies. This feature is particularly useful for users who need to make informed decisions quickly, without having to manually switch between tabs or windows.

For more complex tasks, the updated Chrome extension also allows users to utilize ChatGPT Work, a specialized agent designed for multi-step tasks. With this tool, users can ask ChatGPT to send messages, fill out forms, organize tabs, and even draft emails based on their needs. For instance, users can instruct ChatGPT to fill out a form for a new dentist appointment and then schedule it in the calendar.

Munich, Germany - Mercedes-Benz and NVIDIA have announced the expansion of their strategic partnership to advance safe highly automated driving in the next decade. The collaboration combines Mercedes-Benz’s automotive engineering expertise with NVIDIA’s accelerated computing and AI technologies.

The partnership brings together two industry leaders in a bid to accelerate the development of automated vehicles. Mercedes-Benz’s MB.OS operating system will be integrated with NVIDIA Hyperion’s compute and sensor architecture, Alpamayo Stack software, and Halos safety system to support advanced driver assistance systems (ADAS) and future SAE-Level 4 applications.

According to Jörg Burzer, member of the board of management at Mercedes-Benz Group AG and chief technology officer for development and procurement, NVIDIA is their most important strategic partner in automated driving. He stated that together they have developed the first Mercedes-Benz assistance system for urban point-to-point driving for the western world, marking a significant milestone in their partnership.

The expansion of the partnership reflects the mutual trust built between the two companies over the years. Ali Kani, vice president of automotive at NVIDIA, emphasized the importance of accelerated computing in enabling vehicles to understand and reason through complex driving situations. He noted that Mercedes-Benz brings the engineering excellence and commitment to safety required for this transformation.

The integration of MB.OS with NVIDIA Hyperion’s architecture will enable seamless over-the-air updates and support new driving functions. The Alpamayo Stack software provides full-stack driving capabilities, including AI reasoning, while Halos adds a parallel safety stack that enhances redundancy and guardrails. Together, these technologies aim to make autonomous driving an everyday reality.

Beyond automated driving, Mercedes-Benz also relies on NVIDIA’s technologies for vehicle engineering simulation within its digital production ecosystem, MO360. The partnership aims to enhance efficiency, flexibility, and quality across the global production network.

As part of its efforts to shape the future of commerce, Google has been working on two key fronts: developing agentic capabilities that can act on behalf of users and designing practical tools that give shoppers confidence and control. The Universal Cart is a prime example of this second track, mirroring how people actually shop today across various platforms without losing track of their items or savings.

The Universal Cart was first previewed earlier this year as an intelligent shopping cart and the new hub for shopping on Google. This innovative tool puts users in control by helping them organize their shopping, keep tabs on products they love from multiple retailers, stay informed about promotions, and enjoy effortless checkout powered by the Universal Commerce Protocol (UCP).

One of the key features of the Universal Cart is its ability to save products from various retailers across Google. This eliminates the need for users to start over each time they shop or rely on tabs and screenshots to keep track of their items. For instance, if a user saves a handbag from Steve Madden in AI Mode within Search and later adds a sweater from Quince and lip gloss from Ulta Beauty in the Gemini app, those products will be readily available in their cart when they return.

The Universal Cart also allows users to keep tabs on pricing or inventory changes for all items on their list. This is made possible by its integration with Google’s Shopping Graph, which contains over 60 billion continually refreshed product listings. As a result, the cart can proactively work behind the scenes, sending notifications about price drops or when an item comes back in stock.

Another significant feature of the Universal Cart is its ability to identify hidden savings or points from users’ carts. The cart’s intelligent reasoning can anticipate users’ needs and provide relevant promo codes right within the cart. Additionally, it understands users’ card perks through integration with Google Wallet and can intelligently help them choose between payment methods.

Google has launched its Universal Cart shopping tool in the United States, allowing users to collect and monitor products they find across search and within the Gemini app. The company first previewed this feature at Google I/O in May, with the goal of making online shopping easier to manage.

The idea behind Universal Cart is to provide a central hub for browsing and keeping track of potential purchases. Users can save items from multiple retailers, eliminating the need to hunt down products or start over when returning later. This tool also helps shoppers stay on top of deals and availability by sending notifications when prices drop or out-of-stock items become available again.

Universal Cart is designed to offer more tailored help during the shopping process. Google says it takes credit card perks into account when assisting users with payment choices, and surfaces relevant promo codes. The checkout process is tied to Google’s Universal Commerce Protocol (UCP), an open standard introduced earlier this year that enables AI agents and retailers to work together across different parts of the shopping journey.

Google has been investing heavily in AI-powered shopping features, and Universal Cart is its latest effort to make online shopping more convenient. This tool joins other features such as conversational product discovery in Search’s AI Mode and Google’s ‘Let Google Call’ tool, which uses an AI agent to contact nearby stores and check whether an item is in stock.

Bee, the personal AI wearable maker acquired by Amazon last year, has introduced its first new products since the acquisition. The company has launched two new devices: the Bee Frontier and the Bee Eclipse. These devices are designed to take notes from everyday conversations and provide users with actionable insights, summaries, and to-dos.

The Bee Frontier is a wrist-worn or clip-on device that comes in three colors: Onyx, Lavender, and Sage. It has a price tag of $74.99, making it more affordable than similar devices like the Plaud Note ($159) or Pocket AI device ($129). The Bee Eclipse, on the other hand, is a credit-card-sized device with magnetic attachment to smartphones, available in four colors: Silver, Midnight, Honey, and Blush, priced at $99.99.

The new devices use artificial intelligence to process conversations in real-time, creating summaries and recaps for free. Users can also upgrade to Bee Pro subscription ($11.99/month) for unlimited conversation summaries, integrations with Amazon.com, Alexa+, and third-party applications. With this subscription, users receive suggested to-dos, daily memories, a personal chat assistant, and conversation templates.

Bee devices employ a private computing security architecture that doesn’t store raw audio from conversations. Instead, audio is processed in real-time and then deleted. Transcripts of the conversation are encrypted using keys created and controlled by the device itself, ensuring no one else can access those files unless manually shared by the user. This includes Amazon employees.

The Bee devices also offer optional integration with Alexa, allowing users to connect their Echo device for voice recognition through Voice ID. When connected, the Echo device can record conversations to provide insights from the Bee device. Users have full control over deleting transcripts and summaries at any time. Additionally, they can make purchases on Amazon or ask Alexa for hands-free access to Bee’s insights, summaries, or to-dos.

The new devices come with a companion mobile app that has been redesigned, along with a desktop app. The Bee Frontier features IP67 water resistance, up to seven days of battery life, USB-C charging, and dual microphones with advanced noise filtering. Both devices have a single button for turning them on and off, accompanied by an indicator light when summarizing conversations.

Nordic countries have emerged as the European leader in unicorn startup density, producing a record number of companies valued at over $1 billion each. This achievement has set them on course to develop one of Europe’s most significant technology ecosystems.

The foundation for this growth is already being laid down, with an advantage built into their system: abundant renewable energy sources, strict data governance regulations, and a research tradition that bridges academia and industry. These factors have created an environment where innovation can thrive and grow.

One key factor driving this progress is the rapid expansion of AI clouds across the region. By 2027, these cloud services are expected to reach a massive capacity of 1 gigawatt. Companies like Bitdeer, CoreWeave, Crusoe, Hyperstack, Nscale, Verda, and Volta are at the forefront of this development.

In Sweden, a consortium led by Ericsson has launched Sferical One – a supercomputer powered by two NVIDIA DGX SuperPODs with 16 racks of NVIDIA DGX GB300 systems and 1,152 GPUs. This collaboration will enable researchers to build services using NVIDIA’s Nemotron open models, allowing for customization and secure deployment.

Some leading technology companies in the Nordics are now building proprietary AI solutions on top of NVIDIA infrastructure. Spotify uses NVIDIA TensorRT-LLM inference software to power fast and cost-effective podcast recommendations at scale. This is a case study demonstrating how efficient AI can be used to deliver the same results faster and cheaper than ever before.

ABB Robotics has developed RobotStudio HyperReality using NVIDIA Omniverse libraries, bridging the gap between robot simulations and real-world performance. By combining advanced simulation capabilities with ABB’s virtual controller – running the exact same firmware as its physical robots – engineers can now train, test, and validate robots virtually while continuously improving the digital model.

Finnish telecom company Elisa is launching a beta of Moontalk AIRI – an enterprise AI assistant that retrieves information, captures insights, and documents decisions during meetings and calls. Built on NVIDIA’s Nemotron open models and distributed infrastructure, it uses company data and low-latency networking to deliver fast context-aware responses.

Sweden’s startup ecosystem raised $2.8 billion in the first half of 2026 alone, projected to reach a staggering $5 billion for the year. The next generation is building quickly – and on top of open models like NVIDIA Nemotron. Stilta, based in Stockholm, is revolutionizing how companies protect their inventions with intellectual property by post-training these open models to scale patent analysis from single patents to entire portfolios.

ConfidentialMind’s SampoTron delivers more than 20% improvement in cross-lingual retrieval accuracy across English, Finnish, and Swedish. Agaton uses NVIDIA Nemotron Lightning 3.5 to speed up its Revenue Mining solution on live sales calls.

In Sweden, Klang fine-tuned the NVIDIA Parakeet v3 model to create Pianissimo – a transcription model that reduces errors by 75% while being 64 times faster than any other alternative available today. Built on over 50,000 hours of Swedish speech using the open source NeMo framework from NVIDIA, this model is freely available on Hugging Face.

Embedl, founded in Gothenburg and part of the NVIDIA Inception program for cutting-edge startups, has developed an onboard prototype that monitors protein crystallization, flags anomalies, and recommends actions – all done on a single NVIDIA Jetson Orin NX module without continuous ground connectivity. This innovation is set to revolutionize biotechnology.

Sweden AI Factory announced its collaboration with NVIDIA to open up its infrastructure and expertise for Inception startups and small- to medium-sized enterprises across various industries including life sciences, material science, autonomous systems, and gaming. This partnership will provide access to the latest technology and expertise for companies looking to innovate in these areas.

EINRIDE is developing its next-generation Einride Driver on NVIDIA Hyperion for autonomous freight trucks using the Halos safety system and Cosmos open world models. Meanwhile, Toyota Material Handling Europe – the leading manufacturer of forklifts worldwide – has adopted a simulation-first approach to deploying autonomous material handling solutions, cutting deployment time by up to 50% across its global engineering teams.

The Norrsken Foundation is partnering with NVIDIA to establish the Stockholm AI Lab – a physical hub where AI-native founders working on global challenges can access state-of-the-art infrastructure and networking opportunities. This lab will support startups looking to innovate in artificial intelligence, further pushing boundaries of what’s possible.

Sweden-based companies are leading quantum research using NVIDIA’s quantum computing platform. FirstQFM trained a model that reduces the execution time of quantum processors by 33% without sacrificing performance – an achievement with significant implications for the development and application of quantum technology.

Globally available today, Gemini 4 Argon is a highly capable model designed to tackle complex tasks across various domains. It can be accessed via the API in AI Studio and the Gemini platform, as well as through Google Antigravity for Google AI Ultra subscribers. Additionally, it’s available in the Gemini app and AI Mode in Search for Google AI Ultra and Pro subscribers.

Gemini 4 Argon excels at deep work in complex tasks across domains such as coding, finance, legal, cybersecurity, and science. Its depth and precision across multi-step tasks enable it to outperform other frontier models on benchmarks across domains, ranking #1 on the Vals Index, which measures model performance across critical economic sectors like finance, coding, legal, and tax work.

The model’s introductory price is $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off). This pricing structure makes it an attractive option for developers and enterprises looking to leverage its capabilities. Teams at Google have already been using Argon to achieve significant impact across various fields.

At Google, we’ve been using Argon on our hardest engineering problems. Over the past few weeks, software engineers have been shipping high-quality code faster than ever. Argon in Antigravity has helped teams increase agentic code submissions by more than 35% while reducing code rollbacks by nearly a third.

One notable example of Argon’s capabilities is its work on untangling browser feature code in Chrome. The model helped the Chrome team separate core webpage handling from a ~6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. This achievement will unlock the ability for live webpages to move fluidly between tabs, side panels, and floating windows without reloading.

Argon also demonstrated its capabilities in optimizing production Python codebases. In one instance, it helped a team behind Google’s open-source Agent Development Kit (ADK) push their performance engineering further by cutting in-memory session prep time by 40% in long sessions and speeding up cold imports by 21%. This achievement is particularly notable given the complexity of the task.

Quantum chemistry researchers have also been leveraging Argon to optimize quantum chemistry packages. Using the model, they were able to make xTB (Semiempirical Extended Tight-Binding) up to 2.3x faster at predicting molecular structures and 1.8x faster at conformer search. These improvements will enable scientists to accelerate their research and discovery.

Argon’s capabilities extend beyond engineering tasks. It has also been used in the field of quantum computing, where it helped researchers accelerate progress across both quantum error correction and algorithm design. The model translated an internal version of AlphaQubit into a single, highly optimized hardware kernel – nearly doubling its inference throughput.

In nuclear fusion research, Argon was used to optimize 3D magnetic fields for stellarators. A team at Google alongside Professor Matt Landreman discovered a broader class of counterexamples to Grad’s conjecture, providing reliable benchmarks for future designs. This achievement has significant implications for the development of limitless clean fusion energy.

Argon is also being used in rare disease research, where it helped researchers analyze de-identified genomes from newborns with rare developmental lung disorders. In one case, Argon pinpointed a chromosomal rearrangement that routine software missed for years, and explained why it went undetected.

The model’s capabilities extend to enzyme design as well. Using Argon, scientists were able to design serine esterases in just hours – a process that would normally take months of bespoke model tuning and candidate screening. This achievement demonstrates the potential for rapid acceleration in enzyme engineering across fields such as biomanufacturing, green chemistry, and drug development.

Argon is also being used in robotics research, where it’s helping to accelerate progress toward physical AGI. The model outperforms all other frontier models on the Embodied Reasoning (ERQA) benchmark, which tests a model’s ability to understand and solve physical, spatial problems in 3D environments.

To demonstrate Argon’s capabilities in action, researchers tested its ability to learn an out-of-distribution physical skill simply by watching. In an internal test, Argon watched a raw video of a human playing cornhole, mapped the spatial coordinates of the environment, and calculated the required whole-body joint movements. It then autonomously built the simulation code.

Argon’s capabilities are not limited to these areas alone. The model is also being used in cybersecurity research, where it helped cyber defenders test hypotheses, audit critical codebases, and catch subtle zero-day vulnerabilities and logic flaws. This achievement has significant implications for the development of more effective cybersecurity defenses.

The safety and security stack for Argon represents a comprehensive approach to ensuring the responsible use of this highly capable model. The team collaborated with third-party partners on measurement of certain model capabilities and safeguards, deploying a multi-layer defense stack combining jailbreak resistance, monitoring, and enforcement.

Argon’s enhanced capabilities across several domains have greatly improved its robustness – for instance, external bio red teaming could only unlock less than 1% of bio sub-tasks we test for. Additionally, the model is significantly better at not fulfilling harmful requests compared to previous models.

The development of Argon was guided by a Frontier Safety Framework that prioritizes safety and security. This framework ensures that the model’s capabilities are aligned with human values and goals, while also providing robust safeguards against malicious actors.

Argon works longer and harder to solve highly complex scientific, coding, and agentic problems. Its industry-leading 1M unified input-output token limit via the Gemini API enables it to tackle tasks that would be impossible for other models. The model is rolling out today globally – available through various channels including AI Studio, Android Studio, Google Antigravity, and the Gemini app.

Developers can access Argon’s capabilities by building with the Gemini API paid tier in AI Studio and Android Studio. Enterprises can try it in Gemini for business, the API, and in Google Antigravity. Everyday users can start using it today in the Gemini app and AI Mode in Search if they’re a Google AI Ultra or Pro subscriber.

Researchers and scientists interested in leveraging Argon’s capabilities can apply to get access to Google Antigravity’s highest usage tier free of charge for 6 months (onboarded on a rolling basis), subject to eligibility, capacity, and selection. Applications are now open.

Companies in France are harnessing the power of agentic computing across various industries, including space exploration, financial services, public sector initiatives, and scientific research. This achievement is a testament to the country’s ecosystem having transitioned from ambition to practical application.

The foundation for this progress was laid through deliberate groundwork. Key events such as the AI Action Summit in February 2025 and the Choose France Summits in 2025 and 2026 played a crucial role in anchoring major compute commitments on French soil.

One of the key developments is the expansion of agentic infrastructure by France-based companies using NVIDIA technology. Bull, for instance, is extending its European AI server production to include NVIDIA Vera Rubin Ultra NVL72 systems, which are planned for deployment in 2028. The trays will be produced in Europe and rack integration will take place at Bull’s Angers factory.

The launch of Altair-1, the first satellite in a $1 billion constellation with on-board autonomous capabilities, marks another significant milestone. Integrated by Loft Orbital and built in Abu Dhabi by Orbitworks, this satellite is flying NVIDIA Jetson AGX Orin edge modules for real-time Earth observation and autonomous operations in orbit.

NVIDIA Nemotron open models are being used across various French organizations that require sovereign, trustworthy data. LINAGORA and Docaposte have secured the CANUT framework agreement, making Twake.ai, built on NVIDIA Nemotron, the sole referenced sovereign digital workplace for more than 2,500 French public organizations.

In financial services, SBS is launching a sovereign platform enabling banks to deploy governed generative and agentic services within their own environments in collaboration with NVIDIA. This move aims to provide greater control over data management and processing.

Mistral introduced its Large 4 open model under private preview last week, boasting state-of-the-art performance in cyber defense, manufacturing, finance, and multimodal tasks. The model offers a solid foundation for customizing models across key industries.

Across various sectors such as retail, public services, telecommunications, and science, the pattern is consistent: open models and NVIDIA infrastructure are driving results in France. For instance, L’Oréal is integrating NVIDIA ALCHEMI into its research workflows to explore formulation possibilities in silico and guide laboratory research more effectively.

Artificial intelligence researchers have long sought to create voice assistants that can engage in natural-sounding conversations, but the current state of technology often falls short. The biggest annoyance is waiting for a machine to finish speaking before responding, which disrupts the flow of conversation and makes interactions feel stilted. Nvidia has released PersonaPlex-7B, an open-source voice model designed to address this issue by allowing simultaneous listening and speaking.

Nvidia’s PersonaPlex-7B uses a dual-stream design, where one stream continuously processes incoming audio from the user while another generates the agent’s speech and text in real-time. This approach eliminates the need for handoffs between separate systems, reducing latency and enabling full-duplex communication like phone calls or walkie-talkies.

The model is built on top of Moshi’s dual-stream Transformer architecture, which was developed by Kyutai, a French lab. Nvidia fine-tuned and extended this architecture to create PersonaPlex-7B, resulting in a 90.8% turn-taking rate and interruption response latency as low as 240 milliseconds.

PersonaPlex-7B is not the first full-duplex speech AI model, but it’s one of the few that have been made available for public use. The company reports impressive benchmarking results, including a time-to-first-token of roughly 170 milliseconds and a turn-taking rate of 90.8%. These numbers indicate that PersonaPlex-7B can respond quickly to interruptions and adapt to changing conversation dynamics.

The model’s architecture allows it to be fine-tuned for specific applications, such as customer service or tutoring. Before a conversation starts, PersonaPlex takes two prompts: one sets vocal characteristics and speaking style based on audio tokens, while the other defines the persona itself, its role, background, and scenario. This enables the same underlying model to be used in different contexts without retraining.

Nvidia’s decision to release PersonaPlex-7B as an open-source project has significant implications for the industry. By making a working full-duplex speech AI available for free, Nvidia is setting a public benchmark that other companies must answer. The company’s move also commoditizes the layer just below its actual product, encouraging more developers to build on top of it and potentially leading to increased demand for Nvidia GPUs.

PersonaPlex-7B has been released under an MIT license, with weights available through Hugging Face and code hosted on GitHub. This allows anyone to use and modify the model, which is a significant departure from the typical approach of keeping full-duplex speech AI behind API paywalls or demo videos.

Nvidia’s investment in Reflection AI, a startup that developed the 501-billion-parameter Beam model, has been reported to be worth $800 million. The company also provides chip supply deals worth billions through 2029. This partnership highlights Nvidia’s commitment to advancing voice AI technology and its willingness to invest in companies working on related projects.

Nvidia’s move into open-source voice AI is not without precedent. Chip and software companies have long used this strategy to create a foundation for their products, encouraging more developers to build on top of it and increasing demand for their hardware. By releasing PersonaPlex-7B as an open-source project, Nvidia is following in the footsteps of these companies.

Nvidia’s release of PersonaPlex-7B marks a significant milestone in voice AI research, offering a working full-duplex speech model that can be used by developers and researchers worldwide. The company’s decision to make this technology available for free sets a new standard for the industry, challenging other companies to create similar models with comparable performance.

Local artificial intelligence (AI) is an emerging trend that allows users to run powerful models on their own devices, rather than relying on cloud services. This approach offers improved privacy and control over personal data, which can be a major draw for those hesitant to entrust sensitive information to the cloud.

The concept of local AI has gained significant attention in recent times, with tech giants like Apple highlighting its benefits for Mac desktops. The new RTX Spark Windows machines are also designed to leverage agentic AI capabilities, featuring up to 128GB of RAM and a range of specialized models.

Running local AI on personal devices can be both exciting and overwhelming due to the sheer number of available models and their varying parameters. Models with billions of parameters offer more advanced capabilities but require significant computational resources and storage space.

The author has embarked on an experiment using the M5 Ultra Mac Studio, which boasts 256GB of unified memory. This allows for running complex AI models locally without worrying about token costs associated with cloud services.

One such model is Hermes Agent, a self-hosted AI agent desktop app compatible with macOS, Windows, and Linux. It’s free to use when working with local LLMs (large language models), making it an attractive option for those seeking a more private approach to AI.

The author chose the Qwen 3.8 Flash Next model, which boasts 125 billion parameters and requires around 105GB of storage space. This decision was made possible by the M5 Ultra Mac Studio’s ample memory capacity.

Using Hermes’ straightforward onboarding interface and model picker, the author quickly got up and running with the chosen model. Controlling it via a Telegram bot also proved to be relatively simple.

One of the first tasks assigned to the local AI was generating a daily morning briefing using email and calendar data. While this initial attempt failed due to macOS being asleep during the scheduled job time, subsequent attempts were successful once the issue was addressed.

The author then used Hermes to reorganize their Steam library by genre, leveraging its ability to access installed games through the Steam client. This process required registering a Steam web API key and granting permissions for the AI to take actions on behalf of the user.

Data analysis is another area where local AI has proven useful, with the author using it to crunch financial records and create a spec comparison spreadsheet for a new laptop. These tasks are particularly appealing due to their sensitivity and the need for control over personal data.

The author’s current focus lies in automating benchmark tests for laptops, which can be an incredibly time-consuming process when done manually. While progress has been slow, the potential benefits of automation make this effort worthwhile.

Throughout this journey, the author remains cautious and vigilant about the capabilities and limitations of local AI. They emphasize that these models are not magical assistants but rather tools to augment human productivity and decision-making.

The author’s goal is to explore the full range of possibilities offered by local AI without becoming overly reliant on it or expecting too much from its capabilities. By doing so, they aim to provide a balanced perspective on this emerging technology.

Despite initial frustrations with model selection and task execution, the author remains optimistic about the potential benefits of local AI for personal productivity and data management.

The California-based AI company Odyssey has made its world model, Odyssey-3, publicly available in a research preview. This generative model can create interactive environments from text prompts in real-time, allowing users to explore and interact with them from different perspectives.

Odyssey-3 is designed not only to generate virtual worlds but also to serve as a foundation for controlling various systems, including robotic arms, drones, and characters in video games. The company aims to use the same world model across different tasks, streamlining development and reducing complexity.

The public research preview of Odyssey-3 offers interactive environment generation capabilities, with users able to generate environments from text descriptions and explore them in real-time. This includes choosing between first-person and third-person perspectives, moving through the generated world, triggering events, and watching the model respond in real-time.

Odyssey-3 is built on an autoregressive diffusion transformer that continuously generates new video frames based on previous frames and user actions. During training, the model learns physical relationships and cause-and-effect from visual observations, allowing it to simulate complex environments with realistic physics.

The base Odyssey-3 model has 14 billion parameters and generates video at a resolution of 832 × 480 pixels, according to official benchmark submissions. The more powerful Odyssey-3 Pro supports higher resolutions up to 1280 × 720 pixels.

Odyssey-3’s performance on physics benchmarks is impressive, with the company claiming top scores in several categories. However, there are caveats to these claims: the results come from a single test run where a selection method picked one of eight generated videos for each task, and do not meet the benchmark rules’ requirement for four test runs.

The WorldMark benchmark shows Odyssey-3 ranking first in three out of four categories based on its own evaluation. In First-Person Stylized, it achieved 77.2 points; in Third-Person Real, 79.0 points; and in Third-Person Stylized, 76.3 points. However, in First-Person Real, it placed third with 80.6 points.

Odyssey-3’s capabilities extend beyond generating virtual worlds to controlling robotic arms, drones, and characters in video games. The company has demonstrated an AI built on Odyssey-3 controlling multiple robotic arms, recovering from failed grasps without additional training data.

The same world model is being used for different applications, including robotics, autonomous driving, and video games. For each application, the model gets paired with a specialized controller that translates its predictions into concrete commands. This approach aims to reduce development time and increase efficiency across various tasks.

Odyssey has partnered with Swiss robotics company Flexion to develop humanoid robot controllers using Odyssey-3. These controllers are said to perform more reliably than comparison models, even in changing conditions. The company also demonstrated a drone controller trained on simulated flight data, capable of dodging obstacles and flying to specific targets.

The public research preview offers interactive environment generation capabilities for now, with further work needed for robotics and autonomous systems. Odyssey’s goal is to use its world model as a foundation for controlling various systems, reducing complexity and increasing efficiency across different tasks.

Odyssey was founded in 2023 by Oliver Cameron and Jeff Hawke, who have since raised $310 million from investors including Amazon and AMD Ventures. The company aims to revolutionize the development of AI systems with its generative world model, making it easier for developers to create interactive environments and control various systems.

Anthropic is continuing to develop its AI chatbot, Claude, into a more comprehensive office suite. The company has launched two new beta tools: Claude Dashboards, which enables users to build live data dashboards, and Claude Motion, which allows for the creation of editable animations. Additionally, Anthropic has moved Claude’s Docs, Slides, and Design tools out of beta and made them available on every Claude plan.

The launch of Claude Dashboards is particularly significant for businesses, as it connects to business data platforms and customer-management systems. Users can build live dashboards and analyses by describing what they want in plain language, without needing to learn a BI tool or file a ticket with an analytics team. The goal, according to Anthropic, is to help people work with business data and produce visual content without writing code.

However, the success of Claude Dashboards will depend on its ability to handle complex tasks such as ambiguous metrics, permissions, and data governance. IT departments will be closely watching how well the tool integrates with various platforms, including which specific systems are supported at launch. Anthropic has not provided a detailed list of supported platforms, so this integration will be an important aspect to monitor.

Claude Motion is another new feature that allows users to create animations and keep them editable before exporting them as video files. This tool is currently limited to Team and Enterprise customers but could have various applications such as explainer clips, social content, or presentation inserts. The likely uses of Claude Motion are similar to those of a motion designer or template service.

The tech company Anthropic has announced its commitment to supporting American scientific research through a significant investment in the Genesis Mission. Over three years, Anthropic will provide $150 million to accelerate scientific and technological discovery using artificial intelligence (AI). This funding will enable more than 15 federal agencies participating in the mission, including NASA, the National Institutes of Health, and the National Science Foundation, to utilize Claude, a powerful AI tool developed by Anthropic.

Anthropic’s partnership with the US Department of Energy (DOE) and the Genesis Mission was first announced last December. Since then, the company has collaborated with DOE to bring Claude to scientists across national laboratories. Today, at the Science: A New Golden Age Summit hosted by the White House Office of Science and Technology Policy in Washington, DC, Anthropic reaffirmed its commitment to supporting American scientific research.

The Genesis Mission aims to harness AI’s potential for driving breakthroughs in various fields, including fusion energy and quantum computing. Through this initiative, researchers at federal agencies and national laboratories will be able to use Claude to tackle some of the world’s most complex scientific challenges. Over the next three years, Anthropic plans to provide significant support to Genesis Mission research projects by offering Claude, Claude Code, and API credits.

Anthropic is also committed to providing training, onboarding, and technical assistance to scientists working on Genesis Mission projects. Additionally, the company will help agencies new to the mission get their first projects up and running. This comprehensive approach aims to empower researchers with the tools they need to advance their work significantly.

As part of its broader effort to support scientific research, Anthropic has been actively expanding its AI capabilities for scientists. Earlier this year, the company launched Claude Science, an integrated AI workbench that streamlines access to commonly used tools and packages. Additionally, Anthropic opened 10,000 free and discounted seats for academic scientists using Claude, demonstrating its commitment to democratizing access to cutting-edge technology.

Anthropic’s support for scientific research extends beyond the Genesis Mission. The company has also expanded its AI for Science program, which provides credits to researchers on high-impact projects. Furthermore, Anthropic is working towards establishing a shared specification for AI agents to safely operate lab instruments through the Model Hardware Standard. This initiative aims to ensure that AI systems can be used in conjunction with laboratory equipment without compromising safety or accuracy.

The Genesis Mission and Anthropic’s commitment to it are part of a broader effort to accelerate scientific research using AI. By providing significant funding, support, and resources, Anthropic is helping researchers tackle complex challenges and drive breakthroughs in various fields. This collaboration between the private sector and government agencies has the potential to transform the landscape of scientific discovery.

Anthropic’s commitment to supporting American scientists aligns with the administration’s vision outlined in ‘Science: A New Golden Age.’ The report emphasizes AI’s potential for driving positive change in scientific research, and Anthropic is eager to contribute its expertise and resources towards this goal. By working together, researchers, government agencies, and private companies can unlock new possibilities for advancing human knowledge and addressing pressing global challenges.

Singapore’s disability community is set to benefit from a significant donation by Meta, which has pledged 1,000 of its AI-powered Ray-Ban glasses to four local organizations. The initiative aims to enhance the daily lives of people with visual impairments through improved accessibility.

The recipient organizations are the Singapore Association of the Visually Handicapped (SAVH), Guide Dogs Singapore, the Singapore Disability Sports Council (SDSC) and Gardens by the Bay. This collaboration is part of Meta’s broader efforts to promote digital inclusion in Singapore.

Joel Kaplan, Meta’s Chief Global Affairs Officer, announced the initiative at an AI glasses accessibility showcase co-hosted with SDSC. The event drew a diverse crowd, including Eric Chua, Senior Parliamentary Secretary at the Ministry of Law and Ministry of Social and Family Development, as well as athletes, disability advocates, and representatives from participating organizations.

The donated Ray-Ban Meta AI glasses are designed to assist people with visual impairments in everyday activities such as reading text, identifying objects, and understanding their surroundings. The eyewear can describe objects and environments, providing crucial information for individuals who are blind or have low vision.

As part of the initiative, a US$30,000 grant will support SAVH in developing and delivering free accessibility training programs for people with visual impairments. This comprehensive curriculum will cover safe and effective use of the devices in daily life, including functions to identify objects, read text, and issue voice commands.

Cheryl Yeo, Head of Rehabilitation Services at SAVH, emphasized the importance of ensuring that these devices are integrated safely and meaningfully into the lives of people with visual impairments. The organization will provide guidance on responsible use to help recipients become familiar with the technology and incorporate it into their routines.

The donation also extends to disability sports, where SDSC sees opportunities for AI glasses to support athletes in training and daily life. Dr Teo-Koh Sock Miang, President of SDSC, identified reviewing training activities and improving performance as possible applications, alongside helping athletes become more independent in everyday situations.

Gardens by the Bay will explore how AI-enabled eyewear could improve accessibility for visitors with different abilities. The attraction already provides sensory tours for people with visual impairments and has piloted a robotic guide dog to make it more accessible.

Zach Yadegari, the founder of Cal AI, has launched a new startup called Persona, which aims to revolutionize personal assistant technology. The company has already raised $10 million in funding from investors like Vine Ventures and Z Fellows founder Cory Levy.

Yadegari’s previous venture, Cal AI, was acquired by MyFitnessPal for an undisclosed sum after it gained significant traction in the market. The app had generated over $30 million in annual revenue in just two years before being sold to its new owner.

After leaving his role at MyFitnessPal, Yadegari began working on Persona, a personal AI assistant that can be controlled through a wearable band or via iMessage text messages. The startup has already attracted a few thousand beta users and generated five figures in preorder revenue from the sale of these bands.

Persona’s device is designed to provide users with a more private and secure experience compared to other consumer AI assistants on the market. Unlike some competitors, Persona doesn’t run its AI model or store user data on the device itself; instead, it relies on cloud-based processing and encrypts all data in transit and at rest.

The startup has also implemented safeguards against phishing and prompt-injection attempts, which can be a major concern for agents like OpenAI. Additionally, Persona’s agent is designed to present unbiased recommendations during shopping research by separating sponsored results from organic ones.

Yadegari envisions a future where users will have access to a single super app powered by an AI assistant that integrates various utility apps into one seamless experience. He hopes to make Persona even more proactive in the long run, offering help and solving problems before they become apparent to the user.

The wearable band for Persona is expected to be available in December, with an accompanying app also in development. The startup’s current business model relies on advertising revenue from sponsored results during shopping research, but Yadegari emphasizes that user data will not be sold to advertisers or data brokers.

Frontier AI models are teaming up with NVIDIA’s Omniverse libraries in a big way. Together, they’re making it easier than ever to turn simulation ideas into working applications.

When developers use these tools together, the process of building simulations for exploring scenarios, investigating failures, and improving designs becomes much more efficient.

In one project, developers are using AI agents to direct them through natural-language instructions. This helps them review results and guide changes as needed.

The Omniverse libraries provide GPU-accelerated physics, rendering, and sensor simulation capabilities that help carry out the work quickly.

One key project showcasing this collaboration is a humanoid simulator for a warehouse environment. Frank DeLise, an NVIDIA product manager, used Astra to turn a SimReady warehouse and humanoid robot into an interactive simulator with first- and third-person views.

DeLise directed Astra to connect various NVIDIA Omniverse libraries, including ovphysx for physics, ovstage for scene updates, ovrtx for rendering, and ovui for the user interface. He also used Astra with SimReady to create a physical scene in simulation.

The resulting simulator allows developers to explore task behavior and evaluate how work gets done before automating warehouse tasks. This interactive environment is essential for evaluating different approaches and making informed decisions.

Another project demonstrating this collaboration’s power is an autonomous-driving testing workflow. Doyub Kim, a manager on NVIDIA’s simulation technology team, used Astra to build Zero to Alpamayo – a reusable simulation environment based on San Francisco’s Market Street.

Kim directed Astra to map out the workflow and connect asset creation, traffic, Omniverse RTX sensor simulation, and Alpamayo driving in stages. The resulting prototype served as a testing ground for comparing models and tracing how scene or sensor changes affect downstream driving behavior.

This collaboration also enables developers to create digital twins by comparing recorded and simulated camera and lidar outputs. Ashley Reid directed Astra and Claude Fable 5 agents to compare ovrtx camera and raw LiDAR outputs with recorded data.

Reid guided an iterative workflow in which the agents measured differences, created or modified OpenUSD scenes, and checked the results. Changes addressed missing objects, geometry, and materials, with acceptance depending on camera and lidar metrics.

In this process, developers use measured discrepancies to guide scene creation and improvement. This helps them build better simulations more efficiently.

Tae Kim, who leads NVIDIA Omniverse engineering and product, used sports videos and natural-language instructions to guide Astra in building Robo Olympics – an experimental project that tests simulated Unitree G1 humanoids performing sports movements.

Under Kim’s direction, Astra built controllers and refined them through physics trials. Newton Physics Engine simulated behavior, the open-source NVIDIA Warp framework accelerated calculations, and ovrtx rendered scenes and virtual-camera images.

In one experiment, the robot cleared a single hurdle in 64 of 100 simulation trials. This shows how Astra can help developers refine their simulations more accurately.

This collaboration also enables developers to test robotic disassembly with computer-aided design and simulation. Jens Jebens directed Astra to model a car suspension in PTC Onshape and configure it in NVIDIA Isaac Sim.

With Astra’s help, Jebens explored computer-aided design and tooling revisions for robots informed by simulation. The agent measured the available space and designed a wrench that the robot could use to reach the suspension’s bolts.

Jebens reported successful removal of a suspension component in simulation. This kind of testing is crucial for ensuring robotic systems can disassemble objects safely and efficiently.

Nic Johns prompted Astra to assemble NASA assets into an OpenUSD International Space Station model with telemetry. Johns built the application with a single prompt, then used a follow-up prompt to shift the scene to Earth’s daytime side so that the planet was visible.

The workflow used Blender for asset preparation and Omniverse libraries for rendering (ovrtx), scene runtime (ovstage), and streaming (ovstream). The application brings 3D models and operational data into the browser, with Johns guiding its development through prompts and corrections.

Chirag Majithia directed Astra to turn stereo camera captures into an editable OpenUSD studio. The workflow combined PyCuSFM, FoundationStereo, and nvblox for reconstruction, with user review guiding object selection and placement.

Nvidia, the leading developer of graphics processing units (GPUs), has been investing heavily in physical artificial intelligence technologies. The company’s multitrillion-dollar market capitalization is largely driven by its dominance in the field of AI data centers, but Nvidia is also making significant bets on robotics and self-driving cars. A key part of this strategy involves developing a comprehensive safety system for these machines to prevent harm to nearby people.

Nvidia has already made progress in this area with the launch of Halos, a full-stack safety system that helps developers implement guardrails in autonomous vehicles. The company expanded its safety architecture to more physical AI technologies by announcing Nvidia Halos for Robotics in June 2026. This new offering aims to enable safe deployments of autonomous mobile robots in warehouses and factories.

Amit Goel, head of robotics ecosystem and edge computing at Nvidia, explained the importance of safety in physical AI systems: ‘Now that the AI models are getting capable, the robot hardware is getting capable, and a thing we thought would be the next bottleneck is safety.’ The company’s expanded safety offering for robotics comes as its CEO Jensen Huang highlighted the significant revenue generated by the physical AI business. During an appearance on the All-In Podcast in March 2026, Huang noted that physical AI already drives nearly $10 billion in annual revenue for Nvidia.

Nvidia has also invested directly in multiple robotics companies, including Agility Robotics and Figure AI, which specialize in humanoid robots. The company has partnered with Unitree, a Chinese humanoid robotics firm, to provide an ‘open humanoid robot reference design’ for researchers. This investment is part of the tech giant’s broader strategy to develop more advanced physical AI technologies.

Anthropic has unveiled its latest artificial intelligence (AI) model, Claude Haiku 5.5, which boasts impressive speed and affordability. The new model is designed for high-volume, low-cost work and runs approximately 75% cheaper than its predecessor, Claude Haiku 4.5.

The launch of Claude Haiku 5.5 marks a significant milestone in Anthropic’s efforts to provide developers with efficient and cost-effective AI solutions. This latest iteration is tailored for repetitive tasks that require speed and low costs, making it an ideal choice for applications such as summarizing long text, sorting items into groups, asking questions of a database, and shortening context.

Anthropic emphasizes that Claude Haiku 5.5 is particularly well-suited for jobs where the full power of a larger model is not necessary. It can run alongside other models like Claude Opus 5.5 and Sonnet 5.5 as a coding sub-agent, making it an attractive option for developers who need to streamline their workflows.

The new model also introduces an ‘effort’ setting that allows users to adjust the balance between cost and performance according to their specific needs. This feature enables developers to fine-tune the model’s behavior to suit various tasks and applications.

Pricing is another key aspect of Claude Haiku 5.5, with input costs at 10 cents per million tokens, output costs at 50 cents per million tokens, cache read costs at 1 cent per million tokens, and cache write costs at 12.5 cents per million tokens. For longer prompts exceeding the size limit, prices increase accordingly.

Anthropic highlights that 90% of requests to older Haiku models fell within this size range, making Claude Haiku 5.5 an attractive option for developers who need to process large volumes of data without breaking the bank. Additionally, the company is adding beta support for computer use and browser use to its Claude Python and TypeScript SDKs, further expanding the model’s potential applications.

The safety features of Claude Haiku 5.5 are also noteworthy, with the new model demonstrating better results in almost all alignment checks compared to its predecessor. Anthropic emphasizes that this improved performance is a direct result of the company’s ongoing efforts to enhance the model’s capabilities and reduce the risk of harm associated with AI-powered tasks.

Developers can access Claude Haiku 5.5 on Amazon Web Services, Google Cloud, Microsoft Azure, or directly through the Claude Platform under the name claude-haiku-5.5. With its impressive speed, affordability, and safety features, Claude Haiku 5.5 is poised to become a go-to solution for developers seeking efficient AI-powered applications.

In June, Nvidia announced agreements with Microsoft and other PC makers to develop AI- and agent-ready PCs for Windows based on its RTX Spark chip. However, details about these machines were scarce at the time. Now, Microsoft has revealed more information about its new Surface Laptop Ultra, which is powered by the RTX Spark chip.

The Surface Laptop Ultra comes in two base models: one starting at $2,600 and another with a more powerful chip that starts at $3,700. Prices increase from there, with options for additional memory and storage up to $5,900. Microsoft notes that its highest-end device is already out of stock.

Microsoft also introduced the Surface RTX Spark Dev Box, a workstation powered by the RTX Spark chip, priced at $6,000. This PC comes equipped with Microsoft development tools like VS Code, GitHub Copilot CLI, WSL, and PowerShell 7. The company emphasizes that these devices are designed to run AI models on-device locally, without requiring cloud computing or additional costs.

The revamped Windows 11 operating system includes a feature called ‘Execution Containers,’ which makes it easier for developers to sandbox AI agents. This feature will be available for all Windows 11 users, according to Microsoft CEO Satya Nadella. At the event in San Francisco during Tech Week, Nadella explained that having just an AI model is not enough; rather, one needs to orchestrate and integrate multiple models with context and memory.

Nadella highlighted the importance of a ‘harnessed layer’ that can handle multiple models, context, and action space. He emphasized that Microsoft’s goal is to enable this capability for all developers, not just those using Microsoft apps. This aligns with the company’s vision for the Windows platform going forward.

The new Surface PCs are clearly targeted at developers, which is why Microsoft is offering up to $1,000 off for customers who trade in a MacBook Pro. Macs have become popular among developers and AI enthusiasts, particularly in Silicon Valley. However, these powerful graphics processing units also enable content creation, video processing, and gaming.

Microsoft was not the only company announcing Nvidia-RTX Spark AI Windows PCs on Wednesday. Dell revealed details about its previously announced Dell XPS 16 Creator Edition PC, which is available for preorder now at Best Buy with a later October delivery date. The price of this device is $3,800.