Category: news

T-Mobile 5G Home Internet uses signals from nearby 5G base stations, rather than physical cables or fiber, to provide an Internet connection to your home. If you live in an area where your local Internet service provider has limited cable network buildout, T-Mobile 5G Home Internet is a great alternative to cable or fiber. Plus, T-Mobile 5G Home Internet doesn’t require a long-term contract, has simple equipment, and is fast enough for light to moderate use. We like its unlimited data and variety of pricing plans, but if you’re a heavy gamer or regularly watch a lot of HD video, a cable provider (if you have network) may be a better choice. Availability, Plans, and RatesT-Mobile 5G Home Internet isn’t available in all areas, but it has nationwide coverage with more than 6 million subscribers, according to the company’s most recent earnings report. Just enter your street address on the T-Mobile website to see if the service is available in the area you need.

Generally, it’s available in many of the markets where T-Mobile offers 5G service. T-Mobile’s most expensive All-In Home plan costs $75 per month ($70 with autopay and $55 with voice) and offers some nice perks. The plan includes the same 5G gateway and speeds as the Amplified plan, plus an additional Mesh Wi-Fi access point to help with coverage. AT&T and Verizon both offer 5G home internet service, and while coverage isn’t as extensive as T-Mobile’s, their plans and pricing are similar. Verizon ranks second with 4.2 million home internet subscribers. Like T-Mobile, you’ll need to enter your address on the Verizon website to see if the service is available in your area.

Verizon generally doesn’t offer5G home internet service in areas where it offers wired Fios service. Its plans include a modem and router combo, unlimited data, and a two-year price lock guarantee. If you live in an area without wired or wireless coverage, Starlink may be your only option. Starlink relies on satellites and covers most of the U.S., but costs more and offers lower quality service.

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Microsoft has enhanced the capabilities of its Copilot  AI chatbot to make it more helpful and autonomous for users. With increased memory, the AI ​​is now more personalized, can analyze its surroundings in real time to answer complex prompts, and can take actions on behalf of users. Microsoft has made improvements to its Copilot AI chatbot with new features that improve its ability to answer questions, entertain users, and remember all the information spoken.
When users want to be entertained or learn through audio, Copilot can now create customized podcasts based on personal interests and topics. The  AI ​​has gained the ability to conduct deep research, which means it will handle complex prompts like a human by solving problems step by step, using information from the web, and combining answers to create useful reports.
For mobile users, the  AIcan view your surroundings in real time to assist in answering questions. For Windows users, the AI ​​can view your desktop and work with you to change settings, manipulate files, search for information, and interact with content to assist users with tasks and projects.
Copilot now remembers every interaction and chat (with authorization) and all the information in it. This allows the AI​​to create pages summarizing a person’s thoughts and notes on various conversations and projects. Users can customize their avatars for more personalization.
The chatbot can automatically provide reminders and suggestions based on what it knows about your life. This also includes finding deals on items the user wants to buy. The new Actions feature allows the  AI​​to complete tasks such as booking flights and dinner reservations on the user’s behalf.

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While other big tech companies invested heavily in AI data centers, Apple (AAPL) chose to sit on the sidelines and avoid a surge in capital spending. But that seems to have changed, and Apple realized it needed to get in on theAI  ​​data center game.
Apple is ordering about $1 billion worth of Nvidia (NVDA) GB300 NVL72 systems, Loop Capital analyst Ananda Baruah said late Monday. That equates to about 250 servers, each worth $3.7 million to $4 million, he said in a client note. Apple is working with server makers Dell Technologies (DELL) and Super Micro Computer (SMCI) to develop large server clusters to support generative AI applications.
“AAPL is officially in the large server cluster GenAI game… and SMCI and DELL are the primary server partners,” he said. “While we are still gathering more complete information, it seems likely that this is a Gen AI LLM (large language model) cluster.”
Barua believes that Apple’s change of strategy is due to the trouble it has encountered in bringing its AI-powered Siri digital assistant to market. Apple has delayed the release of the new Siri indefinitely. The company had hoped to launch the AI ​​features earlier this year, after previewing them at the Worldwide Developers Conference last June. Apple has reportedly reorganized its executive team to deal with the company’s difficulties in releasing AI features. According to Bloomberg, an executive called the delays and missteps “unpleasant” and “embarrassing” because the company has been promoting the AI  ​​features in TV ads.

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DeepSeek’s AI has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. Chinese AI startup DeepSeek has released an update to its V3 model that promises better programming capabilities.
The V3-0324 update, which was initially announced on Hugging Face this week but has not yet been officially released, claims to address real-world challenges while setting benchmarks for accuracy and efficiency. V3 is actually an older DeepSeek platform, and DeepSeek claims to have significant improvements in benchmark performance across multiple metrics.
It also claims to have improved the style and content quality of Chinese writing features, improved multi-round interactive rewrites, optimized translation quality and letter writing, enhanced reporting analysis requests and output more detailed output, and improved the accuracy of function calls, fixing issues in previous V3 versions.
The startup’s AI service has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. It also highlights the company’s intention to stay ahead of its competitors, especially those from Silicon Valley, such as OpenAI and Google.
Previously, DeepSeek surpassed OpenAI’s ChatGPT to become the most popular free app in Apple’s US App Store.
DeepSeek’s achievements also include the performance of the initial R1 model, which seems to be on par with OpenAI’s best model, but at a fraction of the cost. The cost part was particularly shocking to the industry and triggered a sell-off in  AI and technology-related stocks in the US market. This is because the best companies in Silicon Valley have invested huge amounts of money in their artificial intelligence projects, but have only achieved similar results

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Samsung Demonstrates Significant Advances in AI-RAN Technology and Ecosystem Development, Unleashing the Full Potential of Software-Based Networks with NVIDIA AI PlatformSamsung Electronics today announced a collaboration with NVIDIA to advance AI-RAN technology. The collaboration reflects Samsung’s commitment to fostering a strong ecosystem and diverse available computing platforms. The effort aims to support the smooth and easy application of AI in mobile networks by expanding the central processing unit (CPU) ecosystem and strengthening collaboration with graphics processing unit (GPU) companies.
To maximize the power of AI and incorporate it into the radio access network (RAN), Samsung has made significant technological advances since early 2024 by leveraging its internal expertise in AI and radio. One of the key milestones achieved was interoperability between Samsung’s O-RAN-enabled virtualized RAN (vRAN) and NVIDIA accelerated computing, which was achieved at Samsung Research Labs late last year. Samsung successfully demonstrated a proof of concept of how NVIDIA’s accelerated computing can be seamlessly integrated into software-based networks to help enhance AI capabilities.
This achievement further enhances Samsung’s advancement in unique innovations combining AI and RAN. Building on this, Samsung can integrate its vRAN (virtual distributed unit, vDU) with NVIDIA’s accelerated computing into commercial off-the-shelf (COTS) servers with Samsung vRAN software installed, enabling seamless delivery of AI-RAN.
In addition, the two companies will continue to explore the best combination of AI-RAN solutions, leveraging Samsung vRAN with NVIDIA Grace CPU and/or GPU-based AI platforms using Compute Unified Device Architecture (CUDA) technology. All of these options are optimal for every network deployment environment, from rural and suburban areas to densely populated cities.
At MWC 2025, Samsung demonstrated its leadership in AI-For-RAN innovation with two AI-RAN demonstrations. Both demonstrations were endorsed by the AI-RAN Alliance and developed in collaboration with multiple members, including NVIDIA. The demonstrations included AI-based physical uplink shared channel (PUSCH) estimation and non-uniform modulation, showcasing innovative ways to integrate AI into mobile networks.
“AI is changing the telecom landscape, and Samsung is helping operators build the network architecture and environment needed to enable AI with our proven AI-driven vRAN,” said June Moon, executive vice president of R&D for the Networks Business at Samsung Electronics. “This collaboration with NVIDIA reflects our ongoing efforts to expand the GPU and CPU ecosystem, and we look forward to exploring new opportunities in the future.” »
“AI-RAN is a critical technology that will significantly improve network utilization, efficiency, and performance while enabling new AI services,” said Ronnie Vasishta, senior vice president of telecommunications at NVIDIA. “Samsung is a leader in AI-RAN development. Its expertise and vRAN software will be invaluable to our customers.”
As a founding member of the AI-RAN Alliance, which was established in 2024, Samsung is actively collaborating with academic institutions and industry leaders such as NVIDIA to advance AI-RAN technology. As vice chair-elect of the board and Working Group 3 (AI-on-RAN), Samsung is leading the industry’s transformation to next-generation AI networks.
Samsung’s end-to-end software network architecture provides the best foundation for easy deployment and adoption of AI at every layer of the network. By doing so, Samsung will be able to support operators with flexible networks, enhance their competitive advantage, and maintain leadership in the  AI ​​era. This advancement paves the way for leveraging network infrastructure not only for mobile communications but also for general workloads, providing a data center-like network architecture that opens up new business opportunities

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Manus is an advanced AI agent designed to think, plan, and perform real-world tasks independently. It can create websites, plan trips, analyze stocks, and more, all with a single user prompt. Just weeks after the launch of DeepSeek, China has unveiled another powerful artificial intelligence (AI) model, Manus, highlighting the country’s accelerating momentum to join the AI ​​race. Developed by Chinese startup Monica, Manus is comparable to top AIsystems created by OpenAI, Google, and Anthropic. The company claims the model is a general AI that can perform tasks autonomously without human supervision.
What is Manus?
Manus is an advanced AI agent designed to think, plan, and perform real-world tasks independently. It can create websites, plan trips, analyze stocks, and more — all with a single user prompt. Unlike standard AI chatbots that provide answers, Manus takes comprehensive actions to complete tasks. For example, if asked to create a report on climate change, it will conduct research, write a report, create charts, and compile everything into a final document without further instructions. Chinese startup Monica has just launched Manus, the world’s first general AIagent. #Manus achieves state-of-the-art (SOTA) performance on all three difficulty levels, outperforming #OpenAI’s DeepResearch on the GAIA real-world problem-solving benchmarkWhat makes Manus so unique?
Manus was launched on March 6 and has quickly gained global attention. According to its creators, it outperforms OpenAI’s DeepResearch on the GAIA benchmark, a measure of AI performance. The demo video released by Monica shows Manus interacting with the internet, collecting data, and performing complex tasks in real time. It can browse websites, take screenshots, record online activity, and generate reports, spreadsheets, or presentations. This level of automation has many calling it a major leap forward in AI technologyMain features of Manus
Manus runs independently in the cloud, and it continues to perform assigned tasks even if the user disconnects the device. This feature ensures that long-term projects can proceed uninterrupted.
Unlike many AI models, Manus actively browses the web, interacts with websites, and displays its workflow in real time. This helps users understand how AI collects and processes information.
It learns from user interactions to provide customized results. Over time, it adapts to user preferences, improving the relevance and quality of responses.
The AI ​​can access platforms like X (formerly Twitter), Telegram, and others to collect and process data. It can even manage multiple screens at once, as shown in its official video. Manus does more than just produce text-based results. It can create detailed reports, interactive presentations, and even code-based outputs like data visualizations and spreadsheets.How to use Manus AI
Manus functions similarly to AI chatbots like ChatGPT, but with greater autonomy. Users simply enter a task, such as “create a 7-day Bali itinerary within budget,” and Manus starts researching, collecting data, and formulating responses. The AI ​​compiles all the relevant information and provides a complete itinerary with links, maps, and travel suggestions. If the user loses connection, the AIcontinues working in the cloud and notifies them when the task is complete.
Availability and future plans
Currently, Manus is available through an invite-only web preview. Monica has not announced a public release date, but hinted that it may be available soon. The company also plans to open source the model in the coming months, allowing developers to integrate it into their own projects. This move could lead to rapid improvement and widespread adoption of the technology.

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Tencent on Thursday unveiled a new artificial intelligence model that it says can answer queries faster than global hit DeepSeek’s R1, the latest sign that the startup’s success at home and abroad is putting pressure on its larger Chinese rivals.Tencent said in a statement that Hunyuan Turbo S can answer queries in under a second, differentiating it from “DeepSeek R1, Hunyuan T1 and other slow-thinking models that need to ‘think for a while before answering.’” Tencent added that in tests of knowledge, math and reasoning, Turbo S’s capabilities were comparable to DeepSeek-V3, which powers DeepSeek’s AI chatbot that has surpassed OpenAI’s ChatGPT in app store downloads.DeepSeek did not immediately respond to a request for comment.The success of DeepSeek’s R1 and V3 models, the first time a Chinese company has received widespread acclaim and adoption in Silicon Valley, has also prompted Chinese tech giants such as Tencent to scramble to launch new versions of the AI ​​models they began developing after OpenAI‘s ChatGPT came out in late 2022. Last month, just days after DeepSeek-R1 shook up the global tech order and triggered a sell-off in AI stocks outside of China, e-commerce giant Alibaba (9988.HK), opened in a new tab, released the Qwen 2.5-Max model, claiming that it outperformed DeepSeek-V3 in all aspects. Tencent also said that the new Turbo S is many times cheaper to use than previous generations, highlighting how DeepSeek’s open source and low-price strategy has forced other leading Chinese AI companies to charge users less.

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While the debut of the DeepSeek AI model earlier this week sparked a sharp sell-off in U.S. tech stocks, its gains in AI processing efficiency could have big implications for data centers.
Market darling Nvidia shares fell more than 12% and the Nasdaq fell 2.7%, with analysts saying the reaction reflected concerns about whether huge investments in AI and its infrastructure are justified.
Meanwhile, U.S. power and utility stocks fell sharply on reports that DeepSeek’s model raised questions about expectations of an AI-driven surge in data center power demand.
Any shift toward cheaper, more powerful and more energy-efficient algorithms has the potential to significantly expand the scope of AI applications, which could ultimately drive demand for large-scale and distributed data center infrastructure.
“If the reports about DeepSeek are true, this will only drive AI innovation forward,” said Mitch Lenzi, vice president of sales and operations at Baxtel, an online platform dedicated to directories and reviews of managed data centers around the world.
The new model and reduced deployment costs will enable competitors to optimize their own AI strategies, driving demand and adoption, he said.
Lenzi said he believes AI advances like DeepSeek will ultimately accelerate, not slow, data center growth.
“Innovation in AI doesn’t reduce demand, it drives it,” he said. “As AI becomes more pervasive and cost-effective, the industry will continue to expand, maintaining demand for high-performance data center infrastructure.”
Sean Farney, vice president of data center strategy at JLL, agreed that the introduction of more efficient AI models like DeepSeek could reshape the data center market.
“That’s great news for the industry,” Farney said. “If someone finds a cheaper, more efficient way to do AI, it lowers the barrier to entry and makes AI accessible to a wider audience.”
Over time, that will drive increased usage and create new opportunities for data center growth.
Farney noted that AI GPU-focused data centers are already the fastest-growing segment of the market, with a compound annual growth rate (CAGR) of 39%, nearly double the overall data center growth rate of about 20%.
“AI-focused facilities are growing much faster than traditional data centers,” Farney said. “With innovations like DeepSeek, we may see an acceleration in this space.”
The financial implications of this growth are huge: According to Farney, annual spending on infrastructure by major hyperscale data center operators has soared from $200 billion to $300 billion.
“The industry is booming,” he said. “If technologies like DeepSeek make AI applications faster and easier to deploy, we will need more data centers to support this adoption.”
John Dinsdale, chief analyst and research director at Synergy Research Group, noted that it is generative AI (GenAI) that has led to some data center rethinking and re-architecting.
“If technology emerges that can significantly reduce the required power density, this may mean a return to pre-GenAI designs with more traditional cooling and power distribution,” he said.
Dinsdale explained that there is currently considerable investment in GenAI technology and products across the IT ecosystem, and this situation will not change in the short term.
“Will some technology emerge that can reduce the power consumption and cost of training and running AI models? Absolutely,” he said. “It’s the nature of technology development and lifecycles.”
When costs go down and capabilities go up, that tends to spur big increases in adoption and usage.
“Take the growth of cloud computing services over the past 15 years,” Dinsdale said.
The role of modular and edge data centers
Farney also highlighted the growing importance of small, modular, and edge data centers in this evolving environment.
While training large AI models will still require large centralized facilities, the growing focus on AI inference (using trained models to provide real-time insights) is likely to drive demand for distributed, latency-sensitive edge data centers.
“As we move into the inference phase of AI, there is a growing need for localized compute power,” Farney said.
Inference typically requires low latency and proximity to users, which makes smaller edge facilities more practical.
“We may end up covering the globe with small 1- or 2-megawatt data centers dedicated to  AI  tasks,” he said.
Farney envisions a hybrid future where giant hub data centers and distributed edge facilities coexist to meet the diverse needs of AI workloads.
“This is not a zero-sum game,” he explained. “We will see continued growth in large facilities for batch AI training, and a surge in small data centers for inference and real-time applications.”
The case for data decentralization
Phil Mataras, founder and CEO of AR.IO, a decentralized permanent cloud network provider, said that the current centralized data center approach to storing data 

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What are people using AI models for? Despite the rapid growth in popularity of large language models, we still know very little about what they are used for.
This isn’t just out of curiosity, or even sociological research. Understanding how people actually use language models is important for security reasons: providers put a lot of effort into pre-deployment testing and use trust and safety systems to prevent misuse. But the scale and diversity of language models makes it hard to understand what they are used for (not to mention any kind of comprehensive security monitoring).
There’s another key factor that prevents us from having a clear understanding of how  AI models are used: privacy. At Anthropic, our Claude model is not trained on user conversations by default, and we take protecting our users’ data very seriously. So how do we study and observe how our systems are used while strictly protecting our users’ privacy?
Cl aude Insights and Observations (“Clio” for short) is our attempt to answer this question. Clio is an automated analytics tool that performs privacy-preserving analysis of real-world language model usage. It gives us insight into everyday usage at claude.ai in a similar way to tools like Google Trends. It’s already helping us improve our security measures. In this post (with the full research paper attached), we describe Clio and some of its initial results.
How Clio Works: Privacy-Preserving Analytics at Scale
Traditional top-down security approaches (such as assessments and red teams) rely on knowing what to look for in advance. Clio takes a different approach, enabling bottom-up pattern discovery by distilling conversations into abstract, understandable clusters of topics. It does this while protecting user privacy: data is automatically anonymized and aggregated, and only higher-level clusters are visible to human analysts.
All of our privacy protections are extensively tested, as described in our research paper.
How People Use Claude: Insights from ClioUsing Clio, we were able to gain insight into how people use claude.ai in practice. While public datasets such as WildChat and LMSYS-Chat-1M provide useful information about how people use language models, they only capture specific contexts and use cases. Clio gives us a glimpse into the full real-world usage of claude.ai (which may differ from other  AI systems due to differences in user base and model type).
Summary of Clio’s analysis steps, illustrated using fictional examples of conversations.
Here’s a brief overview of Clio’s multi-stage process:
Extracting Aspects: For each conversation, Clio extracts multiple “aspects” — specific properties or metadata, such as the topic of the conversation, the number of back-and-forths in the conversation, or the language used.
Semantic Clustering: Similar conversations are automatically grouped based on topics or general themes.
Cluster Descriptions: Each cluster receives a descriptive title and summary that captures common themes from the raw data while excluding private information.
Building Hierarchies: Clusters are organized into multi-level hierarchies for easier exploration. They can then be presented in an interactive interface that a human factors analyst can use to explore patterns along different dimensions (topics, language, etc.).
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DeepSeek shocked the tech world last month. There’s a reason for that, according to AI experts, who say we’re likely just seeing the beginning of the Chinese tech startup’s influence in the field.
In late January, DeepSeek made headlines with its R1 AI model, which the company said roughly matched the performance of Open AI’s O1 model but cost a fraction of the price. DeepSeek briefly replaced ChatGPT as the top app in Apple’s App Store, sending tech stocks tumbling.
The achievement prompted U.S. tech giants to question America’s place in the AI ​​race with China, and the billions of dollars behind those efforts. While Vice President JD Vance didn’t mention DeepSeek or China by name during his speech at the AI  ​​Action Summit in Paris on Tuesday, he did emphasize the importance of America’s lead in the field.
“The United States is a leader in AI , and our government plans to keep it that way,” he said, but added that “the United States wants to work with other countries.”
But there’s more to DeepSeek’s efficiency and capabilities than that. Experts say DeepSeek R1’s ability to reason and “think” answers to deliver high-quality results, combined with the company’s decision to make key parts of its technology public, will drive growth in the field.
While AI has long been used in tech products, it has reached a tipping point in the past two years thanks to the rise of ChatGPT and other generative AI  services that have reshaped how people work, communicate and find information. It’s made companies like chipmaker Nvidia Wall Street darlings and upended the trajectory of Silicon Valley giants. So any development that helps build more powerful and efficient models is sure to be closely watched.
“This is definitely not hype,” said Oren Etzioni, former CEO of the Allen Institute for Artificial Intelligence. “But it’s also a world that’s changing very quickly.”
AI’s TikTok Moment
Tech leaders were quick to react to DeepSeek’s rise. Demis Hassabis, CEO of Google DeepMind, called the hype around DeepSeek “overblown,” but he also said the model was “probably the best work I’ve seen in China,” according to CNBC.
Microsoft CEO Satya Nadella said on the company’s quarterly earnings call in January that DeepSeek had some “real innovation,” while Apple CEO Tim Cook said on the iPhone maker’s earnings call that “innovation that drives efficiency is a good thing.”
But the attention isn’t all positive. Semiconductor research firm SemiAnalysis cast doubt on DeepSeek’s claim that it cost just $5.6 million to train. OpenAI told the Financial Times it found evidence that DeepSeek used the U.S. company’s models to train its own competitors.
“We are aware of and are reviewing indications that DeepSeek may have improperly improved our models, and we will share that information once we learn more,” an OpenAI spokesperson told CNN in a statement. DeepSeek was not immediately available for comment.
Two U.S. lawmakers called for a ban on the app on government devices after security researchers highlighted its possible links to the Chinese government, according to the Associated Press and ABC. Similar concerns have been raised about the popular social media app TikTok, which must be sold to a U.S. owner or risk being banned in the U.S.
“DeepSeek is the TikTok of (large language models),” Etzioni said.
How DeepSeek impressed the tech worldTech giants are already thinking about how DeepSeek’s technology will impact their products and services.
“DeepSeek basically gave us a solution in the form of a technical paper, but they didn’t provide the additional missing pieces,” said Lewis Tunstall, a senior research scientist at Hugging Face, an AI platform that provides tools for developers.
Tunstall is leading Hugging Face’s efforts to fully open source DeepSeek’s R1 model; while DeepSeek provided the research paper and model parameters, it did not reveal the code or training data.
Nadella said on Microsoft’s earnings call that Windows Copilot+ PCs (i.e. PCs built to specific specifications to support AI models) will be able to run AI models extracted from DeepSeek R1 locally. Mobile chip maker Qualcomm said on Tuesday that models extracted from DeepSeek R1 were running on smartphones and PCs equipped with its chips within a week.
AI researchers, academics and developers are still exploring what DeepSeek means for AI progress.
DeepSeek’s model isn’t the only open source model, nor is it the first that can reason about an answer before responding; OpenAI’s o1 model, which launched last year, can do that, too.
What makes DeepSeek so important is its ability to reason and learn from other models, and theAI community can see what’s going on behind the scenes. Those who use the R1 model in the DeepSeek app can also see how it “thinks” as it answers questions.
“You can see the wheels turning inside the machine,” Durga Malladi, senior vice president and general manager of technology planning and edge solutions at Qualcomm, told CNN.

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