Chinese President Xi Jinping's three-day visit to Egypt, coinciding with the 70th anniversary of diplomatic relations between the two nations, marked his first trip to the Middle East in four years and the first to Egypt in a decade. The trip caused debate among geopolitical observers over Egypt's diplomatic balancing act between China, one of its largest economic partners, and the United States, a major defence and strategic partner. Xi's visit came at a time when Chinese tech giant Huawei has submitted a tender to build Egypt's AI data centres. The US, meanwhile, is reportedly pulling together an offer to counter Huawei's bid. Whoever wins the contract to build the data centres will also gain an opportunity to strengthen their political and economic ties with Egypt.

Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Huawei's second tri-fold phone debuts September 7, but nearly 20 years of U.S. restrictions keep it off American shelves. Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . Huawei is gearing up to unveil its next triple-folding smartphone, the Mate XT 2, at an event in China on September 7.

Employees walk into Huawei's European-themed data center on July 24 in Guian New Area in southwestern China's Guizhou province. GUIAN NEW AREA, CHINA - In China's hilly Guizhou province, a cluster of European-style buildings complete with a clock tower and a multi-arched bridge emits a low, permanent hum -- a clue to its unexpected identity as tech giant Huawei's largest data center. The town-like complex is unique in design, but not in its placement. Dozens of data centers have been constructed in the southwestern province in recent years. They are part of a government strategy that has seen much of the digital infrastructure for breakneck artificial intelligence development across China's eastern seaboard established in the country's less populated, rural west. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

The Chinese company is adapting to the demise of Moore's Law, which guides chip production. It could complicate US chip dominance. Tingbo He, president of Huawei's chip-design subsidiary HiSilicon, says her company's engineers have developed a novel way to optimize semiconductors--and she believes it will close the performance gap between Chinese and Western chips over the next few years. Huawei's method, in short, focuses on speeding up computations across chips, circuits, and entire computing systems, rather than squeezing ever-more components onto a single piece of silicon. "We found a new path," He said at the IEEE International Symposium on Circuits and Systems in Shanghai last weekend.

The long-awaited V4 is more efficient and a win for Chinese chipmakers. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that helps it handle large amounts of text more efficiently. Like DeepSeek's previous models, V4 is open source, meaning it is available for anyone to download, use, and modify. V4 marks DeepSeek's most significant release since R1, the reasoning model it launched in January 2025. R1, which was trained on limited computing resources, stunned the global AI industry with its strong performance and efficiency, turning DeepSeek from a little-known research team into China's best-known AI company almost overnight.

Our supplementary includes author statement, licensing, and implementation details of benchmark results for reproducibility. We bear all responsibilities for licensing, distributing, and maintaining our dataset. The proposed dataset is under the CC BY -NC-SA 4.0 license, while the code in the repository is For what purpose was the dataset created? The dataset comprises various types of annotations, including instances and topology relationships. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset?

Nvidia CEO Jensen Huang speaks alongside U.S. President Donald Trump at the White House in Washington on April 30. U.S. President Donald Trump decided to let Nvidia sell its H200 artificial intelligence chips to China after concluding the move carried a lower security risk because the company's Chinese archrival, Huawei Technologies, already offers AI systems with comparable performance, according to a person familiar with the deliberations. Administration officials who weighed whether to clear Nvidia's H200 had considered multiple possible scenarios, factoring in the views of national security hawks in Washington, said the person. Options ranged from exporting zero AI chips to China to allowing exports of everything to flood the Chinese market and overwhelm Huawei. Ultimately the policy backed by Trump called for clearing H200s to China while holding back the latest Nvidia chips for American customers, the person said.

In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focused on weight sparsity, it often suffers from significant accuracy degradation. Activation sparsity, though promising, is typically training-dependent and faces challenges in generalization. To address these limitations, we introduce Amber Pruner, a training-free N:M activation sparsity method designed specifically for the prefill stage, targeting the acceleration of linear projection layers in LLMs. Extensive experiments across multiple models and sparsity ratios (2:4, 4:8, and 8:16) demonstrate that Amber Pruner can effectively sparsify and accelerate more than 55% of linear computations without requiring model retraining. To further enhance generality and efficiency, we propose Outstanding-sparse, a unified framework that integrates Amber Pruner with post-training W8A8 quantization. Our approach preserves strong performance across a range of downstream tasks, with notable advantages in generative tasks. This work pioneers a new frontier in activation sparsity, providing foundational insights that are poised to guide the co-evolution of algorithms and architectures in the design of next-generation AI systems.