This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Manus has kick-started an AI agent boom in China Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Read the full story. —Caiwei Chen Inside the race to find GPS alternatives Later this month, an inconspicuous 150-kilogram satellite is set to launch into space aboard the SpaceX Transporter 14 mission. Once in orbit, it will test super-accurate next-generation satnav technology designed to make up for the shortcomings of the US Global Positioning System (GPS). Despite the system’s indispensable nature, the GPS signal is easily suppressed or disrupted by everything from space weather to 5G cell towers to phone-size jammers worth a few tens of dollars. The problem has been whispered about among experts for years, but it has really come to the fore in the last three years, since Russia invaded Ukraine.Now, startup Xona Space Systems wants to create a space-based system that would do what GPS does but better. Read the full story. —Tereza Pultarova Why doctors should look for ways to prescribe hope —Jessica Hamzelou This week, I’ve been thinking about the powerful connection between mind and body. Some new research suggests that people with heart conditions have better outcomes when they are more hopeful and optimistic. Hopelessness, on the other hand, is associated with a significantly higher risk of death. The findings build upon decades of fascinating research into the phenomenon of the placebo effect. Our beliefs and expectations about a medicine (or a sham treatment) can change the way it works. The placebo effect’s “evil twin,” the nocebo effect, is just as powerful—negative thinking has been linked to real symptoms. Researchers are still trying to understand the connection between body and mind, and how our thoughts can influence our physiology. In the meantime, many are developing ways to harness it in hospital settings. Is it possible for a doctor to prescribe hope? Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk threatened to cut off NASA’s use of SpaceX’s Dragon spacecraftHis war of words with Donald Trump is dramatically escalating. (WP $)+ If Musk actually carried through with his threat, NASA would seriously struggle. (NYT $)+ Silicon Valley is starting to pick sides. (Wired $)+ It appears as though Musk has more to lose from their bruising breakup. (NY Mag $) 2 Apple and Alibaba’s AI rollout in China has been delayedIt’s the latest victim of Trump’s trade war. (FT $)+ The deal is supposed to support iPhones’ AI offerings in the country. (Reuters) 3 X’s new policy blocks the use of its posts to ‘fine-tune or train’ AI modelsUnless companies strike a deal with them, that is. (TechCrunch)+ The platform could end up striking agreements like Reddit and Google. (The Verge) 4 RJK Jr’s new hire is hunting for proof that vaccines cause autismVaccine skeptic David Geier is seeking access to a database he was previously barred from. (WSJ $)+ How measuring vaccine hesitancy could help health professionals tackle it. (MIT Technology Review) 5 Anthropic has launched a new service for the militaryClaude Gov is designed specifically for US defense and intelligence agencies. (The Verge)+ Generative AI is learning to spy for the US military. (MIT Technology Review) 6 There’s no guarantee your billion-dollar startup won’t failIn fact, one in five of them will. (Bloomberg $)+ Beware the rise of the AI coding startup. (Reuters) 7 Walmart’s drone deliveries are taking offIt’s expanding to 100 new US stories in the next year. (Wired $) 8 AI might be able to tell us how old the Dead Sea Scrolls really are Models suggest they’re even older than we previously thought. (The Economist $)+ How AI is helping historians better understand our past. (MIT Technology Review) 9 All-in-one super apps are a hit in the Gulf They’re following in China’s footsteps. (Rest of World) 10 Nintendo’s Switch 2 has revived the midnight launch eventFans queued for hours outside stores to get their hands on the new console. (Insider $)+ How the company managed to dodge Trump’s tariffs. (The Guardian) Quote of the day “Elon finally found a way to make Twitter fun again.” —Dan Pfeiffer, a host of the political podcast Pod Save America, jokes about Elon Musk and Donald Trump’s ongoing feud in a post on X. One more thing This rare earth metal shows us the future of our planet’s resources We’re in the middle of a potentially transformative moment. Metals discovered barely a century ago now underpin the technologies we’re relying on for cleaner energy, and not having enough of them could slow progress.  Take neodymium, one of the rare earth metals. It’s used in cryogenic coolers to reach ultra-low temperatures needed for devices like superconductors and in high-powered magnets that power everything from smartphones to wind turbines. And very soon, demand for it could outstrip supply. What happens then? And what does it reveal about issues across wider supply chains? Read our story to find out. —Casey Crownhart We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Sightings of Bigfoot just happen to correlate with black bear populations? I smell a conspiracy!+ Watch as these symbols magically transform into a pretty impressive Black Sabbath mural.+ Underwater rugby is taking off in the UK.+ Fed up of beige Gen Z trends, TikTok is bringing the 80s back.

Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without relying on proprietary APIs. Existing models frequently fall short in scenarios requiring nuanced semantic understanding across multiple languages or domain-specific tasks like code retrieval and instruction following. Moreover, most open-source models either lack scale or flexibility, while commercial APIs remain costly and closed. Qwen3-Embedding and Qwen3-Reranker: A New Standard for Open-Source Embedding Alibaba’s Qwen Team has unveiled the Qwen3-Embedding and Qwen3-Reranker Series—models that set a new benchmark in multilingual text embedding and relevance ranking. Built on the Qwen3 foundation models, the series includes variants in 0.6B, 4B, and 8B parameter sizes and supports a wide range of languages (119 in total), making it one of the most versatile and performant open-source offerings to date. These models are now open-sourced under the Apache 2.0 license on Hugging Face, GitHub, and ModelScope, and are also accessible via Alibaba Cloud APIs. These models are optimized for use cases such as semantic retrieval, classification, RAG, sentiment analysis, and code search—providing a strong alternative to existing solutions like Gemini Embedding and OpenAI’s embedding APIs. Technical Architecture Qwen3-Embedding models adopt a dense transformer-based architecture with causal attention, producing embeddings by extracting the hidden state corresponding to the [EOS] token. Instruction-awareness is a key feature: input queries are formatted as {instruction} {query}<|endoftext|>, enabling task-conditioned embeddings. The reranker models are trained with a binary classification format, judging document-query relevance in an instruction-guided manner using a token likelihood-based scoring function. The models are trained using a robust multi-stage training pipeline: Large-scale weak supervision: 150M synthetic training pairs generated using Qwen3-32B, covering retrieval, classification, STS, and bitext mining across languages and tasks. Supervised fine-tuning: 12M high-quality data pairs are selected using cosine similarity (>0.7), fine-tuning performance in downstream applications. Model merging: Spherical linear interpolation (SLERP) of multiple fine-tuned checkpoints ensures robustness and generalization. This synthetic data generation pipeline enables control over data quality, language diversity, task difficulty, and more—resulting in a high degree of coverage and relevance in low-resource settings. Performance Benchmarks and Insights The Qwen3-Embedding and Qwen3-Reranker series demonstrate strong empirical performance across several multilingual benchmarks. On MMTEB (216 tasks across 250+ languages), Qwen3-Embedding-8B achieves a mean task score of 70.58, surpassing Gemini and GTE-Qwen2 series. On MTEB (English v2): Qwen3-Embedding-8B reaches 75.22, outperforming other open models including NV-Embed-v2 and GritLM-7B. On MTEB-Code: Qwen3-Embedding-8B leads with 80.68, excelling in applications like code retrieval and Stack Overflow QA. For reranking: Qwen3-Reranker-0.6B already outperforms Jina and BGE rerankers. Qwen3-Reranker-8B achieves 81.22 on MTEB-Code and 72.94 on MMTEB-R, marking state-of-the-art performance. Ablation studies confirm the necessity of each training stage. Removing synthetic pretraining or model merging led to significant performance drops (up to 6 points on MMTEB), emphasizing their contributions. Conclusion Alibaba’s Qwen3-Embedding and Qwen3-Reranker Series present a robust, open, and scalable solution to multilingual and instruction-aware semantic representation. With strong empirical results across MTEB, MMTEB, and MTEB-Code, these models bridge the gap between proprietary APIs and open-source accessibility. Their thoughtful training design—leveraging high-quality synthetic data, instruction-tuning, and model merging—positions them as ideal candidates for enterprise applications in search, retrieval, and RAG pipelines. By open-sourcing these models, the Qwen team not only pushes the boundaries of language understanding but also empowers the broader community to innovate on top of a solid foundation. Check out the Paper, Technical details, Qwen3-Embedding and Qwen3-Reranker. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and GeminiAsif Razzaqhttps://www.marktechpost.com/author/6flvq/From Clicking to Reasoning: WebChoreArena Benchmark Challenges Agents with Memory-Heavy and Multi-Page TasksAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Mistral AI Introduces Mistral Code: A Customizable AI Coding Assistant for Enterprise WorkflowsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA AI Releases Llama Nemotron Nano VL: A Compact Vision-Language Model Optimized for Document Understanding

Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.  There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.  These emerging AI agents aren’t large language models themselves. Instead, they’re built on top of them, using a workflow-based structure designed to get things done. A lot of these systems also introduce a different way of interacting with AI. Rather than just chatting back and forth with users, they are optimized for managing and executing multistep tasks—booking flights, managing schedules, conducting research—by using external tools and remembering instructions.  China could take the lead on building these kinds of agents. The country’s tightly integrated app ecosystems, rapid product cycles, and digitally fluent user base could provide a favorable environment for embedding AI into daily life.  For now, its leading AI agent startups are focusing their attention on the global market, because the best Western models don’t operate inside China’s firewalls. But that could change soon: Tech giants like ByteDance and Tencent are preparing their own AI agents that could bake automation directly into their native super-apps, pulling data from their vast ecosystem of programs that dominate many aspects of daily life in the country.  As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Set the standard It’s been a whirlwind few months for Manus, which was developed by the Wuhan-based startup Butterfly Effect. The company raised $75 million in a funding round led by the US venture capital firm Benchmark, took the product on an ambitious global roadshow, and hired dozens of new employees.  Even before registration opened to the public in May, Manus had become a reference point for what a broad, consumer‑oriented AI agent should accomplish. Rather than handling narrow chores for businesses, this “general” agent is designed to be able to help with everyday tasks like trip planning, stock comparison, or your kid’s school project.  Unlike previous AI agents, Manus uses a browser-based sandbox that lets users supervise the agent like an intern, watching in real time as it scrolls through web pages, reads articles, or codes actions. It also proactively asks clarifying questions, supports long-term memory that would serve as context for future tasks. “Manus represents a promising product experience for AI agents,” says Ang Li, cofounder and CEO of Simular, a startup based in Palo Alto, California, that’s building computer use agents, AI agents that control a virtual computer. “I believe Chinese startups have a huge advantage when it comes to designing consumer products, thanks to cutthroat domestic competition that leads to fast execution and greater attention to product details.” In the case of Manus, the competition is moving fast. Two of the most buzzy follow‑ups, Genspark and Flowith, for example, are already boasting benchmark scores that match or edge past Manus’s.  Genspark, led by former Baidu executives Eric Jing and Kay Zhu, links many small “super agents” through what it calls multi‑component prompting. The agent can switch among several large language models, accepts both images and text, and carries out tasks from making slide decks to placing phone calls. Whereas Manus relies heavily on Browser Use, a popular open-source product that lets agents operate a web browser in a virtual window like a human, Genspark directly integrates with a wide array of tools and APIs. Launched in April, the company says that it already has over 5 million users and over $36 million in yearly revenue. Flowith, the work of a young team that first grabbed public attention in April 2025 at a developer event hosted by the popular social media app Xiaohongshu, takes a different tack. Marketed as an “infinite agent,” it opens on a blank canvas where each question becomes a node on a branching map. Users can backtrack, take new branches, and store results in personal or sharable “knowledge gardens”—a design that feels more like project management software (think Notion) than a typical chat interface. Every inquiry or task builds its own mind-map-like graph, encouraging a more nonlinear and creative interaction with AI. Flowith’s core agent, NEO, runs in the cloud and can perform scheduled tasks like sending emails and compiling files. The founders want the app to be a “knowledge marketbase”, and aims to tap into the social aspect of AI with the aspiration of becoming “the OnlyFans of AI knowledge creators”. What they also share with Manus is the global ambition. Both Genspark and Flowith have stated that their primary focus is the international market. A global address Startups like Manus, Genspark, and Flowith—though founded by Chinese entrepreneurs—could blend seamlessly into the global tech scene and compete effectively abroad. Founders, investors, and analysts that MIT Technology Review has spoken to believe Chinese companies are moving fast, executing well, and quickly coming up with new products.  Money reinforces the pull to launch overseas. Customers there pay more, and there are plenty to go around. “You can price in USD, and with the exchange rate that’s a sevenfold multiplier,” Manus cofounder Xiao Hong quipped on a podcast. “Even if we’re only operating at 10% power because of cultural differences overseas, we’ll still make more than in China.” But creating the same functionality in China is a challenge. Major US AI companies including OpenAI and Anthropic have opted out of mainland China because of geopolitical risks and challenges with regulatory compliance. Their absence initially created a black market as users resorted to VPNs and third-party mirrors to access tools like ChatGPT and Claude. That vacuum has since been filled by a new wave of Chinese chatbots—DeepSeek, Doubao, Kimi—but the appetite for foreign models hasn’t gone away.  Manus, for example, uses Anthropic’s Claude Sonnet—widely considered the top model for agentic tasks. Manus cofounder Zhang Tao has repeatedly praised Claude’s ability to juggle tools, remember contexts, and hold multi‑round conversations—all crucial for turning chatty software into an effective executive assistant. But the company’s use of Sonnet has made its agent functionally unusable inside China without a VPN. If you open Manus from a mainland IP address, you’ll see a notice explaining that the team is “working on integrating Qwen’s model,” a special local version that is built on top of Alibaba’s open-source model.  An engineer overseeing ByteDance’s work on developing an agent, who spoke to MIT Technology Review anonymously to avoid sanction, said that the absence of Claude Sonnet models “limits everything we do in China.” DeepSeek’s open models, he added, still hallucinate too often and lack training on real‑world workflows. Developers we spoke with rank Alibaba’s Qwen series as the best domestic alternative, yet most say that switching to Qwen knocks performance down a notch. Jiaxin Pei, a postdoctoral researcher at Stanford’s Institute for Human‑Centered AI, thinks that gap will close: “Building agentic capabilities in base LLMs has become a key focus for many LLM builders, and once people realize the value of this, it will only be a matter of time.” For now, Manus is doubling down on audiences it can already serve. In a written response, the company said its “primary focus is overseas expansion,” noting that new offices in San Francisco, Singapore, and Tokyo have opened in the past month. A super‑app approach Although the concept of AI agents is still relatively new, the consumer-facing AI app market in China is already crowded with major tech players. DeepSeek remains the most widely used, while ByteDance’s Doubao and Moonshot’s Kimi have also become household names. However, most of these apps are still optimized for chat and entertainment rather than task execution. This gap in the local market has pushed China’s big tech firms to roll out their own user-facing agents, though early versions remain uneven in quality and rough around the edges.  ByteDance is testing Coze Space, an AI agent based on its own Doubao model family that lets users toggle between “plan” and “execute” modes, so they can either directly guide the agent’s actions or step back and watch it work autonomously. It connects up to 14 popular apps, including GitHub, Notion, and the company’s own Lark office suite. Early reviews say the tool can feel clunky and has a high failure rate, but it clearly aims to match what Manus offers. Meanwhile, Zhipu AI has released a free agent called AutoGLM Rumination, built on its proprietary ChatGLM models. Shanghai‑based Minimax has launched Minimax Agent. Both products look almost identical to Manus and demo basic tasks such as building a simple website, planning a trip, making a small Flash game, or running quick data analysis. Despite the limited usability of most general AI agents launched within China, big companies have plans to change that. During a May 15 earnings call, Tencent president Liu Zhiping teased an agent that would weave automation directly into China’s most ubiquitous app, WeChat.  Considered the original super-app, WeChat already handles messaging, mobile payments, news, and millions of mini‑programs that act like embedded apps. These programs give Tencent, its developer, access to data from millions of services that pervade everyday life in China, an advantage most competitors can only envy. Historically, China’s consumer internet has splintered into competing walled gardens—share a Taobao link in WeChat and it resolves as plaintext, not a preview card. Unlike the more interoperable Western internet, China’s tech giants have long resisted integration with one another, choosing to wage platform war at the expense of a seamless user experience. But the use of mini‑programs has given WeChat unprecedented reach across services that once resisted interoperability, from gym bookings to grocery orders. An agent able to roam that ecosystem could bypass the integration headaches dogging independent startups. Alibaba, the e-commerce giant behind the Qwen model series, has been a front-runner in China’s AI race but has been slower to release consumer-facing products. Even though Qwen was the most downloaded open-source model on Hugging Face in 2024, it didn’t power a dedicated chatbot app until early 2025. In March, Alibaba rebranded its cloud storage and search app Quark into an all-in-one AI search tool. By June, Quark had introduced DeepResearch—a new mode that marks its most agent-like effort to date.  ByteDance and Alibaba did not reply to MIT Technology Review’s request for comments. “Historically, Chinese tech products tend to pursue the all-in-one, super-app approach, and the latest Chinese AI agents reflect just that,” says Li of Simular, who previously worked at Google DeepMind on AI-enabled work automation. “In contrast, AI agents in the US are more focused on serving specific verticals.” Pei, the researcher at Stanford, says that existing tech giants could have a huge advantage in bringing the vision of general AI agents to life—especially those with built-in integration across services. “The customer-facing AI agent market is still very early, with tons of problems like authentication and liability,” he says. “But companies that already operate across a wide range of services have a natural advantage in deploying agents at scale.”

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. China blocks Apple-Alibaba AI venture in retaliation for the US trade war Hamid Ganji Neowin @HamidGanji_ · Jun 5, 2025 05:28 EDT iPhones sold in China, Apple's second biggest market, still lack AI features. While Apple tried to solve the issue by forming an AI venture with China's e-commerce giant Alibaba, the move has faced setbacks from China's regulator, presumably to get back at the US trade war under the Trump administration. According to a new report by Financial Times, citing people familiar with the matter, Apple and Alibaba have been working on their AI venture over the past few months, hoping to bring some AI features to iPhones sold in China. However, the Cyberspace Administration of China hasn't approved the collaboration. "Apple's rollout of artificial intelligence services in China with Alibaba is being held up by a Beijing regulator, as the tech partnership becomes the latest casualty of Donald Trump's trade war. The tech giants have been working together to launch Apple Intelligence, the iPhone-maker's suite of AI services, for Chinese users. The system would be supported by Alibaba's latest models." Every new iPhone sold worldwide has built-in ChatGPT as a result of the Apple and OpenAI partnership. Since OpenAI has no official presence in China, Apple must partner with local tech companies like Alibaba to offer AI capabilities on iPhones sold in the country. The move could help Apple navigate China's regulatory restrictions, but it's now stalled due to the US-China trade war. The Cyberspace Administration of China doesn't publicly confirm whether halting the Apple-Alibaba AI venture is a response to the US trade war. Still, sources claim this is China's response to the recent tariff clash with the US. China also has a pretty solid record of retaliating against the US reciprocal tariffs. However, the Apple and Alibaba AI partnership also has some opponents in the US. Lawmakers and government officials in Washington have raised concerns about the AI deal. They fear that this collaboration could significantly bolster China's AI capabilities. Tags Report a problem with article Follow @NeowinFeed

The transformation of Hangzhou’s old steelworks into a park is a tribute to China’s industrial past in a city of the future The congressional hearing about Chinese AI engine DeepSeek held in the US this April has propelled Hangzhou, the heart of China’s new digital economy, to the headlines. With companies such as DeepSeek, Unitree and Alibaba – whose payment app allowed me to get on the metro without needing to buy a ticket – headquartered in Hangzhou, China’s future in AI, robotics and automation is emanating from this city. Getting off the metro in the suburban area of Gongshu, the sun was shining on an old steelworks, overgrown with vines and flowers now that it is being transformed by Jiakun Architects and TLS Landscape Architecture into the Grand Canal Steelworks Park. The unfolding trade war might help to accelerate China’s journey into an automated future, leaving the world of factories behind, yet this new public space shows an impulse to commemorate the country’s economic history, and the forces that have shaped its contemporary built environment. Starting in Hangzhou and travelling more than 1,700km to Beijing, the Grand Canal is an engineering project built 2,500 years ago to connect the different regions of eastern China. The country’s geography means rivers flow from west to east: from higher elevations, culminating in the Himalayas, to the basin that is the country’s eastern seaboard. Historically, it was difficult to transport goods from mercantile centres in the south, including Hangzhou and Suzhou, to the political centre in Beijing up north. As a civil engineering project, the Grand Canal rivals the Great Wall, but if the Great Wall aims to protect China from the outside, the Grand Canal articulates Chinese commerce from the inside. The historic waterway has been an important conduit of economic and cultural exchange, enabling the movement of people and goods such as grain, silk, wine, salt and gravel across the country. It became a UNESCO World Heritage site in 2014. The state‑owned enterprise collective was founded, and the physical facility of Hangzhou steelworks built, in the 1950s during the Great Leap Forward, when China strove for self‑sufficiency, and wended its way through the country’s economic trajectory: first the economic chaos of the 1960s, then the reforms and opening up in the 1980s. Steel remains an important industry today in China, home to more than half of the world’s production, but the listing of the Grand Canal enabled city leaders to move production to a new site and decommission the Hangzhou steelworks. External mandates, including entry into the World Trade Organization, the Beijing Olympics and UNESCO listings, have been instrumentalised in the country to pursue a range of internal interests, particularly economical and real estate ones.  In 2016, the factory was shut down in 150 days, in what the company describes as a ‘heroic’ effort, and the site attracted tourists of industrial ruins. In the competition brief, Hangzhou planners asked for ‘as much of the existing blast furnaces and buildings’ as possible to be preserved. When I arrived in China in 2008, Chinese cities were notorious for heritage demolition, but today urban planners and architects increasingly work to preserve historical buildings. Just like several industrial sites in Beijing and Shanghai have been transformed into major public and cultural spaces in the past decade, in the Yangtze River Delta – of which Hangzhou is a major hub – several industrial sites along the Grand Canal’s course are being given a new lease of life. Today, the three blast furnaces of Hangzhou steelworks remain, with the silhouettes of their smokestacks easily recognisable from a distance. The project preserves as much as possible of the aesthetics of a steel mill with none of the danger or dust, ready to welcome instead new community facilities and cultural programmes in a vast and restored piece of landscape. Situated in a former working‑class district that has been gentrifying and welcoming young families, the new park is becoming a popular venue for music festivals, flower viewing in springtime and year‑round picnics – when I visited, parents were teaching their children to ride a bicycle, and students from Zhejiang University, about a kilometre from the park, were having lunch on the grass. New programmes accommodated in the old coke oven and steel mills will include a series of exhibition halls and spaces welcoming a wide range of cultural and artistic workshops as well as events – the project’s first phase has just completed but tenant organisations have not yet moved in, and works are ongoing to the north of the park. On the day of my visit, a student art exhibition was on display near one of the furnaces, with works made from detritus from the site, including old packing containers. The rehabilitated buildings also provide a range of commercial units, where cafés, restaurants, shops, a bookshop, ice cream shop and a gym have already opened their doors to visitors.  Several structures were deemed structurally unsafe and required demolition, such as the old iron casting building. The architects proposed to partially reconstruct it on its original footprint; the much more open structure, built with reclaimed bricks, now houses a semi‑outdoor garden. Material choices evoke the site’s industrial past: weathered steel, exposed concrete and large expanses of glazing dominate the landscape. The widespread use of red, including in an elevated walkway that traverses the park – at times vaguely reminiscent of a Japanese torii gate in the space below – gives a warm and reassuring earthiness to the otherwise industrial colour palette. Elements selected by the designers underwent sanitisation and detoxification before being reused. The landscaping includes old machinery parts and boulders; recuperated steel panels are for instance inlaid into the paving while pipes for pouring molten steel have been turned into a fountain. The train tracks that once transported material continue to run through the site, providing paths in between the new patches of vegetation, planted with local grasses as well as Japanese maples, camphors and persimmon trees. As Jiawen Chen from TLS describes it, the aesthetic feels ‘wild, but not weedy or abandoned’. The landscape architects’ inspiration came from the site itself after the steelworks’ closure, she explains, once vegetation had begun to reclaim it. Contaminated soil was replaced with clean local soil – at a depth between 0.5 and 1.5 metres, in line with Chinese regulations. The removed soil was sent to specialised facilities for purification, while severely contaminated layers were sealed with concrete. TLS proposed phytoremediation (using plants to detoxify soil) in selected areas of the site ‘as a symbolic and educational gesture’, Chen explains, but ‘the client preferred to be cautious’. From the eastern end of the park, hiking trails lead to the mountain and its Buddhist temples. The old steel mill’s grounds fade seamlessly into the hills. Standing in what it is still a construction site, a sign suggests there will soon be a rowing centre here.  While Jiakun Architects and TLS have prioritised making the site palatable as a public space, the project also brings to life a history that many are likely to have forgotten. Throughout, the park incorporates different elements of China’s economic history, including the life of the Grand Canal and the industrial era. There is, for example, a Maoist steelworker painted on the mural of one of the cafés, as well as historical photographs and drawings of the steelworks peppering the site, framed and hung on the walls. The ambition might be in part to pay homage to steelworkers, but it is hard to imagine them visiting. Gongshu, like the other suburbs of Hangzhou, has seen rapid increases in its property prices.  The steelworks were built during the Maoist era, a time of ‘battling with earth, battling with heaven, battling with humanity’, to borrow Mao’s own words. Ordinary people melted down pots and pans to surpass the UK in steel production, and industry was seen as a sharp break from a traditional Chinese way of life, in which humans aspire to live in harmony with their environment. The priorities of the government today are more conservative, seeking to create a garden city to attract engineers and their families. Hangzhou has long represented the balmy and sophisticated life of China’s south, a land of rice and fish. To the west of the city, not far from the old steelworks, are the ecologically protected Xixi wetlands, and Hangzhou’s urban planning exemplifies the Chinese principle of 天人合一, or nature and humankind as one.  Today, Hangzhou is only 45 minutes from Shanghai by high‑speed train. The two cities feel like extensions of one another, an urban region of 100 million people. The creation of the Grand Canal Steelworks Park reflects the move away from heavy industry that Chinese cities such as Hangzhou are currently making, shifting towards a supposedly cleaner knowledge‑driven economy. Yet the preservation of the steelworks epitomises the sentimental attitude towards the site’s history and acts as a reminder that today’s middle classes are the children of yesterday’s steelworkers, drinking coffee and playing with their own children in grassy lawns next to shuttered blast furnaces.  The park’s second phase is already nearing completion, and the competition for the nearby Grand Canal Museum was won by Herzog & de Meuron in 2020 – the building is under construction, and should open at the end of this year. It is a district rich in history, but the city is resolutely turned towards the future.  2025-06-02 Reuben J Brown Share AR May 2025CircularityBuy Now

Huawei’s AI capabilities have made a breakthrough in the form of the company’s Supernode 384 architecture, marking an important moment in the global processor wars amid US-China tech tensions.The Chinese tech giant’s latest innovation emerged from last Friday’s Kunpeng Ascend Developer Conference in Shenzhen, where company executives demonstrated how the computing framework challenges Nvidia’s long-standing market dominance directly, as the company continues to operate under severe US-led trade restrictions.Architectural innovation born from necessityZhang Dixuan, president of Huawei’s Ascend computing business, articulated the fundamental problem driving the innovation during his conference keynote: “As the scale of parallel processing grows, cross-machine bandwidth in traditional server architectures has become a critical bottleneck for training.”The Supernode 384 abandons Von Neumann computing principles in favour of a peer-to-peer architecture engineered specifically for modern AI workloads. The change proves especially powerful for Mixture-of-Experts models (machine-learning systems using multiple specialised sub-networks to solve complex computational challenges.)Huawei’s CloudMatrix 384 implementation showcases impressive technical specifications: 384 Ascend AI processors spanning 12 computing cabinets and four bus cabinets, generating 300 petaflops of raw computational power paired with 48 terabytes of high-bandwidth memory, representing a leap in integrated AI computing infrastructure.Performance metrics challenge industry leadersReal-world benchmark testing reveals the system’s competitive positioning in comparison to established solutions. Dense AI models like Meta’s LLaMA 3 achieved 132 tokens per second per card on the Supernode 384 – delivering 2.5 times superior performance compared to traditional cluster architectures.Communications-intensive applications demonstrate even more dramatic improvements. Models from Alibaba’s Qwen and DeepSeek families reached 600 to 750 tokens per second per card, revealing the architecture’s optimisation for next-generation AI workloads.The performance gains stem from fundamental infrastructure redesigns. Huawei replaced conventional Ethernet interconnects with high-speed bus connections, improving communications bandwidth by 15 times while reducing single-hop latency from 2 microseconds to 200 nanoseconds – a tenfold improvement.Geopolitical strategy drives technical innovationThe Supernode 384’s development cannot be divorced from broader US-China technological competition. American sanctions have systematically restricted Huawei’s access to cutting-edge semiconductor technologies, forcing the company to maximise performance within existing constraints.Industry analysis from SemiAnalysis suggests the CloudMatrix 384 uses Huawei’s latest Ascend 910C AI processor, which acknowledges inherent performance limitations but highlights architectural advantages: “Huawei is a generation behind in chips, but its scale-up solution is arguably a generation ahead of Nvidia and AMD’s current products in the market.”The assessment reveals how Huawei AI computing strategies have evolved beyond traditional hardware specifications toward system-level optimisation and architectural innovation.Market implications and deployment realityBeyond laboratory demonstrations, Huawei has operationalised CloudMatrix 384 systems in multiple Chinese data centres in Anhui Province, Inner Mongolia, and Guizhou Province. Such practical deployments validate the architecture’s viability and establishes an infrastructure framework for broader market adoption.The system’s scalability potential – supporting tens of thousands of linked processors – positions it as a compelling platform for training increasingly sophisticated AI models. The capability addresses growing industry demands for massive-scale AI implementation in diverse sectors.Industry disruption and future considerationsHuawei’s architectural breakthrough introduces both opportunities and complications for the global AI ecosystem. While providing viable alternatives to Nvidia’s market-leading solutions, it simultaneously accelerates the fragmentation of international technology infrastructure along geopolitical lines.The success of Huawei AI computing initiatives will depend on developer ecosystem adoption and sustained performance validation. The company’s aggressive developer conference outreach indicated a recognition that technical innovation alone cannot guarantee market acceptance.For organisations evaluating AI infrastructure investments, the Supernode 384 represents a new option that combines competitive performance with independence from US-controlled supply chains. However, long-term viability remains contingent on continued innovation cycles and improved geopolitical stability.(Image from Pixabay)See also: Oracle plans $40B Nvidia chip deal for AI facility in TexasWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.

DeepSeek's R1 update, plus the rest of the AI news this week. Credit: Thomas Fuller / SOPA Images / LightRocket / Getty Images This week, DeepSeek released an updated version of its R1 model on HuggingFace, reigniting the open-source versus closed-source competition. The updated version, called DeekSeek-R1-0528, has 685 billion parameters, an upgrade from January's version, which had 671 billion. Unlike OpenAI and Google's models, which are famously closed-source, DeepSeek's model weights are publicly available. According to the benchmarks, the R1-0528 update has improved reasoning and inference capabilities and is closing the gap with OpenAI's o3 and Google's Gemini 2.5 Pro. DeepSeek also introduced a distilled version of R1-0528 using Alibaba's Qwen3 8B model. This is an example of a lightweight model that is less capable but also requires less computing power. DeepSeek-R1-0528-Qwen3-8B outperforms both Google's latest lightweight model Gemini-2.5-Flash-Thinking-0520 and OpenAI's o3-mini in certain benchmarks. But the bigger deal is that DeekSeek's distilled model can reportedly run on a single GPU, according to TechCrunch. You May Also Like To… distill all this information, the Chinese rival is catching up to its U.S. competitors with an open-weight approach that's cheaper and more accessible. Plus, DeepSeek continues to prove that AI models may not require as much computing power as OpenAI, Google, and other AI heavyweights currently use. Suffice to say, watch this space.That said, DeepSeek's models also have their drawbacks. According to one AI developer (via TechCrunch), the new DeepSeek update is even more censored than its previous version when it comes to criticism of the Chinese government. Of course, a lot more happened in the AI world over the past few days. After last week's parade of AI events from Google, Anthropic, and Microsoft, this week was lighter on product and feature news. That's one reason DeepSeek's R1 update captured the AI world's attention this week. In other AI news, Anthropic finally gets voice mode, AI influencers go viral, Anthropic's CEO warns of mass layoffs, and an AI-generated kangaroo. Google's Veo 3 takes the internet by stormOn virtually every social media platform, users are freaking out about the new Veo 3, Google's new AI video model. The results are impressive, and we're already seeing short films made entirely with Veo 3. Not bad for a product that came out 11 days ago. Not to be outdone by AI video artists, a reporter from The Wall Street Journal made a short film about herself and a robot using Veo 3.Mashable's Tech Editor Timothy Werth recapped Veo's big week and had a simple conclusion: We're so cooked.More AI product news: Claude's new voice mode and the beginning of the agentic browser eraAfter last week's barrage, this week was lighter on the volume of AI news. But what was announced this week is no less significant.  Mashable Light Speed Want more out-of-this world tech, space and science stories? Sign up for Mashable's weekly Light Speed newsletter. By clicking Sign Me Up, you confirm you are 16+ and agree to our Terms of Use and Privacy Policy. Thanks for signing up! Anthropic finally introduced its own voice mode for Claude to compete with ChatGPT, Grok, and Gemini. The feature is currently in beta on mobile for the Claude app and will even be available to free plans with a limit of 20 to 30 voice conversations per day. Anthropic says you can ask Claude to summarize your calendar or read documents out loud. Paying subscribers can connect to Google Workspace for Calendar, Gmail, and Docs access. OpenAI is exploring the ability to sign into third-party apps with ChatGPT. We don't know much yet, but the company posted an interest form on its site for developers using Codex, its engineering agent, to add this capability to their own apps. It may not sound like a big deal, but it basically means users could easily link their personalized ChatGPT memories and settings to third-party apps, much like the way it works when you sign into a new app with your Google account.Opera announced a new agentic AI browser called Neon. "Much more than a place to view web pages, Neon can browse with you or for you, take action, and help you get things done," the announcement read. That includes a chatbot interface within the browser and the ability to fill in web forms for tasks like booking trips and shopping. The announcement, which included a promo video of a humanoid robot browsing the robot, which is scant on details but says Neon will be a "premium subscription product" and has a waitlist to sign up.The browser has suddenly become a new frontier for agentic AI, now that it's capable of automating web search tasks. Perplexity is working on a similar tool called Comet, and The Browser Company pivoted from its Arc browser to a more AI-centric browser called Dia. All of this is happening while Google might be forced to sell off Chrome, which OpenAI has kindly offered to take off its hands. Dario Amodei's prediction about AI replacing entry-level jobs is already starting to happenAnthropic CEO Dario Amodei warned in an interview with Axios that AI could "wipe out half of all entry-level white-collar jobs." Amodei's predictions might be spot on because a new study from VC firm SignalFire found that hiring for entry-level jobs is down to 7 percent from 25 percent in the previous year. Some of that is due to changes in the economic climate, but AI is definitely a factor since firms are opting to automate the less-technical aspects of work that would've been taken on by new hires.  Related Stories The latest in AI culture: That AI-generated kangaroo, Judge Judy, and everything elseGoogle wants you to know its AI overviews reach 1.5 billion people a month. They probably don't want you to know AI Overviews still struggles to count, spell, and know what year it is. As Mashable's Tim Marcin put it, would AI Overviews pass concussion protocol?The proposal of a 10-year ban on states regulating AI is pretty unpopular, according to a poll from Common Sense Media. The survey found that 57 percent of respondents opposed the moratorium, including half of the Republican respondents. As Mashable's Rebecca Ruiz reported, "the vast majority of respondents, regardless of their political affiliation, agreed that Congress shouldn't ban states from enacting or enforcing their own youth online safety and privacy laws."In the private sector, The New York Times signed a licensing deal with Amazon to allow their editorial content to be used for Amazon's AI models. The details are unclear, but from the outside, this seems like a change of tune from the Times, which is currently suing OpenAI for copyright infringement for allegedly using its content to train its models. That viral video of an emotional support kangaroo holding a plane ticket and being denied boarding? It's AI-generated, of course. Slightly more obvious, but no less creepy is another viral trend of using AI to turn public figures like Emmanuel Macron and Judge Judy into babies. These are strange AI-slop-infested times we're living in. AI has some positive uses too. This week, we learned about a new humanoid robot from HuggingFace called HopeJr (with engineering by The Robot Studio), which could be available for sale later this year for just $3,000.And to end this recap on a high note, the nonprofit Colossal Foundation has developed an AI algorithm to detect the bird calls of the near-extinct tooth-billed pigeon. Also known as the "little dodo," the tooth-billed pigeon is Samoa's national bird, and scientists are using the bioacoustic algorithm to locate and protect them. Want to get the latest AI news, from new product features to viral trends? Check back next week for another AI news recap, and in the meantime, follow @cecily_mauran and @mashable for more news.Disclosure: Ziff Davis, Mashable’s parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems. Topics OpenAI DeepSeek Cecily Mauran Tech Reporter Cecily is a tech reporter at Mashable who covers AI, Apple, and emerging tech trends. Before getting her master's degree at Columbia Journalism School, she spent several years working with startups and social impact businesses for Unreasonable Group and B Lab. Before that, she co-founded a startup consulting business for emerging entrepreneurial hubs in South America, Europe, and Asia. You can find her on X at @cecily_mauran.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Alibaba Group has introduced QwenLong-L1, a new framework that enables large language models (LLMs) to reason over extremely long inputs. This development could unlock a new wave of enterprise applications that require models to understand and draw insights from extensive documents such as detailed corporate filings, lengthy financial statements, or complex legal contracts. The challenge of long-form reasoning for AI Recent advances in large reasoning models (LRMs), particularly through reinforcement learning (RL), have significantly improved their problem-solving capabilities. Research shows that when trained with RL fine-tuning, LRMs acquire skills similar to human “slow thinking,” where they develop sophisticated strategies to tackle complex tasks. However, these improvements are primarily seen when models work with relatively short pieces of text, typically around 4,000 tokens. The ability of these models to scale their reasoning to much longer contexts (e.g., 120,000 tokens) remains a major challenge. Such long-form reasoning requires a robust understanding of the entire context and the ability to perform multi-step analysis. “This limitation poses a significant barrier to practical applications requiring interaction with external knowledge, such as deep research, where LRMs must collect and process information from knowledge-intensive environments,” the developers of QwenLong-L1 write in their paper. The researchers formalize these challenges into the concept of “long-context reasoning RL.” Unlike short-context reasoning, which often relies on knowledge already stored within the model, long-context reasoning RL requires models to retrieve and ground relevant information from lengthy inputs accurately. Only then can they generate chains of reasoning based on this incorporated information.  Training models for this through RL is tricky and often results in inefficient learning and unstable optimization processes. Models struggle to converge on good solutions or lose their ability to explore diverse reasoning paths. QwenLong-L1: A multi-stage approach QwenLong-L1 is a reinforcement learning framework designed to help LRMs transition from proficiency with short texts to robust generalization across long contexts. The framework enhances existing short-context LRMs through a carefully structured, multi-stage process: Warm-up Supervised Fine-Tuning (SFT): The model first undergoes an SFT phase, where it is trained on examples of long-context reasoning. This stage establishes a solid foundation, enabling the model to ground information accurately from long inputs. It helps develop fundamental capabilities in understanding context, generating logical reasoning chains, and extracting answers. Curriculum-Guided Phased RL: At this stage, the model is trained through multiple phases, with the target length of the input documents gradually increasing. This systematic, step-by-step approach helps the model stably adapt its reasoning strategies from shorter to progressively longer contexts. It avoids the instability often seen when models are abruptly trained on very long texts. Difficulty-Aware Retrospective Sampling: The final training stage incorporates challenging examples from the preceding training phases, ensuring the model continues to learn from the hardest problems. This prioritizes difficult instances and encourages the model to explore more diverse and complex reasoning paths. QwenLong-L1 process Source: arXiv Beyond this structured training, QwenLong-L1 also uses a distinct reward system. While training for short-context reasoning tasks often relies on strict rule-based rewards (e.g., a correct answer in a math problem), QwenLong-L1 employs a hybrid reward mechanism. This combines rule-based verification, which ensures precision by checking for strict adherence to correctness criteria, with an “LLM-as-a-judge.” This judge model compares the semanticity of the generated answer with the ground truth, allowing for more flexibility and better handling of the diverse ways correct answers can be expressed when dealing with long, nuanced documents. Putting QwenLong-L1 to the test The Alibaba team evaluated QwenLong-L1 using document question-answering (DocQA) as the primary task. This scenario is highly relevant to enterprise needs, where AI must understand dense documents to answer complex questions.  Experimental results across seven long-context DocQA benchmarks showed QwenLong-L1’s capabilities. Notably, the QWENLONG-L1-32B model (based on DeepSeek-R1-Distill-Qwen-32B) achieved performance comparable to Anthropic’s Claude-3.7 Sonnet Thinking, and outperformed models like OpenAI’s o3-mini and Qwen3-235B-A22B. The smaller QWENLONG-L1-14B model also outperformed Google’s Gemini 2.0 Flash Thinking and Qwen3-32B.  Source: arXiv An important finding relevant to real-world applications is how RL training results in the model developing specialized long-context reasoning behaviors. The paper notes that models trained with QwenLong-L1 become better at “grounding” (linking answers to specific parts of a document), “subgoal setting” (breaking down complex questions), “backtracking” (recognizing and correcting their own mistakes mid-reasoning), and “verification” (double-checking their answers). For instance, while a base model might get sidetracked by irrelevant details in a financial document or get stuck in a loop of over-analyzing unrelated information, the QwenLong-L1 trained model demonstrated an ability to engage in effective self-reflection. It could successfully filter out these distractor details, backtrack from incorrect paths, and arrive at the correct answer. Techniques like QwenLong-L1 could significantly expand the utility of AI in the enterprise. Potential applications include legal tech (analyzing thousands of pages of legal documents), finance (deep research on annual reports and financial filings for risk assessment or investment opportunities) and customer service (analyzing long customer interaction histories to provide more informed support). The researchers have released the code for the QwenLong-L1 recipe and the weights for the trained models. 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