Former OpenAI Chinese researcher Tian Yonglong joins Tencent Hunyuan team
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Tencent announced that Tian Yonglong, a former researcher at OpenAI, has joined the company to participate in the development of VLM.
According to sources at Tencent, Yonglong Tian, a former researcher at OpenAI, has recently joined Tencent's Large Language Model Department and will participate in the research and development of VLM (Visual Language Model). This marks another instance of Tencent poaching talent from OpenAI. On December 17th last year, Tencent announced an upgrade to its large model R&D architecture, establishing the AI Infra Department, AI Data Department, and Data Computing Platform Department. Former OpenAI senior researcher Yao Shunyu was appointed as the Chief AI Scientist in the "CEO/President's Office" (reporting to Martin Lau), and concurrently served as the head of the AI Infra Department and the Large Language Model Department (reporting to Lu Shan). This announcement garnered widespread attention on social media. (The Paper)
Analysis: To reduce costs, overseas developers are turning to Chinese AI models such as DeepSeek.
According to Odaily Odaily, as the cost of using AI continues to rise, more and more overseas startups and developers are adopting Chinese AI models such as DeepSeek, Alibaba, and Moonshot AI to reduce inference costs. Data shows that DeepSeek's share of AI traffic on the cloud platform Vercel has increased from less than 1% in May of this year to 17%. OpenRouter, an AI model aggregation platform, stated that DeepSeek's usage doubled in the first half of 2026, becoming the platform's most popular model. It also pointed out that the token usage share of Chinese open-source models, including those from Xiaomi, MiniMax, and Tencent, continues to rise, while the share of Google and OpenAI has declined. (Bloomberg)
OpenAI's Rogue AI Agents Were Probing Hugging Face Two Months Before Hack
An independent researcher found the agents hijacked Hugging Face accounts and mapped the platform's defenses as early as May 13—activity OpenAI's own incident report never fully described.
OpenAI Staff Blame Rush to Ship for Rogue Agent Hack
Current and former OpenAI employees reportedly say pressure to release new AI products made it harder to prioritize safety.
Some large-scale AI models in China are 90% cheaper than those in the US; Chinese AI's high cost-effectiveness is capturing the US market.
According to a report by CNBC on July 7th, influenced by the continued price increases of models from leading US AI vendors, Chinese AI large-scale models are rapidly expanding their application scale in US enterprises due to their cost-effectiveness advantage. Industry insiders point out that the performance of some leading open-source and open weighted models in China is currently about 6 to 9 months behind the technology of top-tier US models such as OpenAI and Anthropic, while the price is 60% to 90% lower, and they can cover the vast majority of routine AI tasks, thus gaining popularity among US enterprises. According to statistics from the AI model aggregation platform OpenRouter, since February 8th of this year, the proportion of Chinese AI models used by US enterprises has exceeded 30% weekly, reaching a peak of 46%; while the average proportion in the previous 12 months was 11%. Another industry statistic shows that in the first week of the launch of Zhipu's latest large-scale model GLM 5.2, the daily average number of word calls increased by 27 times and the number of customers increased by 80 times, making it the fastest-deployed model on the platform in 2026; US AI startup Lindy has significantly reduced costs after switching all its AI business to DeepSeek models, and expects to save millions of dollars within a few months. (CCTV Finance)
Meituan open-sourced its trillion-parameter large-scale model LongCat-2.0, and simultaneously released the inference code for domestically developed Chinese card processors.
According to Beating's monitoring, Meituan has officially open-sourced its trillion-parameter large-scale model, LongCat-2.0, with a total of 1.6T parameters and an average activation of approximately 48B, designed specifically for real-world agentic coding tasks. Architecturally, it innovatively introduces LongCat sparse attention and N-gram embedding. The former reduces fragmented memory access through flow-aware indexing and hierarchical indexing, accelerating training and inference with millions of contexts; the latter, while achieving nearly 97% sparsity in MoE, invests 135B parameters into the embedding layer, balancing parameter gains and structural stability. Post-training employs multi-teacher online distillation, categorizing experts into Agent, Inference, and Interaction types, seamlessly integrating them on a domestic computing power cluster through the MOPD architecture. As the industry's first trillion-parameter model to complete inference on a 50,000-card domestic computing power cluster, LongCat-2.0 validates the mature capability of domestic chips to handle complex large-scale model tasks. To address the multiple limitations of domestically produced Chinese chips in terms of memory, bandwidth, and interconnects, Meituan has made breakthroughs in three areas: model, chip adaptation, and deployment. At the model level, ScMoE leverages the core control capabilities of domestically produced chips to achieve physical core-level parallelism for Dense and MoE branches, combined with KV-cache partitioning to alleviate the pressure on ultra-long context memory. At the chip adaptation level, Super Kernel reduces operator startup overhead, and Weight Prefetch hides I/O latency, maximizing hardware utilization under constrained conditions. At the deployment level, PD separation is adopted to balance TTFT and TPOT, along with asynchronous Expert-Parallel load balancing to solve load unevenness under high EP (efficiency level). This open-source release simultaneously provides multiple precision versions, including BF16, FP8, and INT8, and fully opens up inference results optimized for domestic computing power, aiming to enable existing domestically produced cards and even older cards to smoothly deploy trillion-model inference services. --------------------------------- Click the original link below to join the Beating · Lark AI news channel and monitor global AI hot topics and news 24/7.