1天前阿里千问正式开源Qwen3.8系列模型截至2026年8月14日，阿里巴巴宣布其新一代通义千问基座大模型Qwen3.8系列的旗舰模型Qwen3.8-Max总参数量达2.4万亿，API已在千问AI平台和阿里云全面上线，并确认Qwen3.8-Max及Qwen3.8-27B的模型权重将于下周正式开源，标志着国产大模型首次在Max级旗舰上实现开放权重。14 来源发布背景与核心消息5 来源技术规格：2.4万亿参数与多模态能力3 来源产品接入、价格策略与应用场景8 来源开源路线延续与国产大模型格局9 来源本内容由AI生成

1天前Waymo获批在旧金山、洛杉矶扩展自动驾驶网约车服务截至2026年8月14日，Alphabet旗下自动驾驶公司Waymo已获加州公用事业委员会批准，将其无人驾驶网约车服务从旧金山扩展至更广泛的湾区，并在洛杉矶取得运营“绿灯”，使两大都会区迈入规模化机器人出租车时代。在监管强化乘客安全与未成年人保护的前提下，Waymo正通过车型升级和场景扩展加速商业落地，同时面对来自特斯拉等对手的激烈竞争与持续的安全争议。10 来源监管批准与扩展范围5 来源运营模式与服务细节7 来源安全争议与监管限制3 来源行业竞争与城市交通影响4 来源本内容由AI生成

1天前阿里千问开放AI眼镜生态，支持多种技能应用8月11日，阿里巴巴旗下个人AI助手“千问”上线开放平台，首次对App、PC与AI眼镜三端开放技能平台和行业定制能力，允许开发者通过自然语言自建导游、教练、巡检等多种Skill，标志着其AI眼镜生态从封闭硬件迈向开放、可生长的智能生态。千问开放平台上线：打通App、PC与AI眼镜三端技能平台：导游、教练、巡检等随身AI助手行业定制与企业应用：从巡检到现场服务AI眼镜竞赛中的阿里策略：开放生态与可生长智能本内容由AI生成

7小时前英伟达拟投30亿美元于SB Energy，支持OpenAI数据中心项目截至2026年8月15日，英伟达与OpenAI及软银旗下SB Energy已重新设计美国俄亥俄州10吉瓦数据中心项目的融资结构，将原拟为整个项目提供约2500亿美元担保的方案，调整为仅对首期约5吉瓦园区提供不超过1200亿美元的财务兜底。 同期，英伟达通过最多30亿美元投资电力基础设施开发商Lancium巩固其在AI基建中的能源与园区布局，目前公开信息尚未证实其是否会以同等规模直接入股SB Energy，相关安排仍处于谈判和结构优化阶段。13 来源英伟达与OpenAI重新设计融资结构：担保规模从2500亿美元缩减至不超过1200亿美元6 来源SB Energy俄亥俄10吉瓦数据中心：OpenAI算力版“基础设施革命”9 来源从Lancium到SB Energy：英伟达以“股权+担保”织密AI能源基础设施网络8 来源市场影响与争议：AI时代“基建银行”模式的风险与机遇9 来源本内容由AI生成

EN在线咨询提交需求AI Search Visibility CheckerIntelligently detect your brand’s visibility on AI platforms - Professional GEO optimization analysis toolStep 1: Select Detection PlatformsDoubaoWenxin YiyanDeepseekTongyi QianwenTencent YuanbaoStep 2: The question you ask AIRecommended Keywords0/30Step 3: Brand Keywords+Multiple hit terms please press Enter key, support up to 0/10 terms at onceStart GEO DetectionDetection PrincipleDetection PrincipleSimulate real user search scenariosAnalyze brand mentions in AI responsesCount recommendation frequency and rankingExpose competitor performance and impactDetection AdvantagesDetection AdvantagesCover 5 major AI platformsReal-time detection, accurate resultsMulti-dimensional data analysisProfessional optimization suggestionsApplication ScenariosApplication ScenariosBrand awareness monitoringCompetitor analysis comparisonGEO optimization effectiveness assessmentMarketing strategy developmentContact UsLet’s Start the New Era of AI Marketing TogetherContact us now to get exclusive GEO optimization solutions. Help your brand seize opportunities in the era of AI intelligent Q&A and achieve leapfrog business growth.Scan QR code to add human customer serviceBook a Free ConsultationNameCompany NamePhone NumberYour RequirementsSubmit BookingAbout UsXiamen Xianglian Technology Co., Ltd. was founded in 2005 and is a national high-tech enterprise with nearly 20 years of experience in the Internet service field.The company operates core platforms including Webmaster Home, Webmaster Tools, and AIBase: Webmaster Tools, as one of China’s TOP3 SEO/website operation and maintenance tool platforms, provides over 200 free tools, has served more than 5 million users, and processes tens of millions of daily queries;AIBase is positioned as China’s leading AI navigation platform, aggregating 10,000+ AI models and tools to provide one-stop AI services for developers and enterprises.Xianglian Technology is committed to providing professional, efficient technical tools and service support for Chinese Internet practitioners, helping digital transformation and development.Address：11th Floor, Building B, Feiyu Building, Huli District, Xiamen, Fujian ProvincePhone Number：18950101783Empowering the Future, Your AI Solution Knowledge BaseEnglish简体中文繁體中文にほんごCopyright © 2024-2026 厦门享联科技有限公司备案号：闽ICP备08105208号-24

EN在线咨询提交需求AI Search Engine OptimizationBrand Monitoring ServiceMonitor your brand across Google Gemini, ChatGPT, Claude & all major AI models.1Add Brand Information2Analyze Brand Keywords & Competitors3Analysis ReportMonitor by Brand DescriptionMonitor by Website AddressYour Brand NameYour Brand Description0 / 500Your Products/ServicesPlease press Enter to confirm products/services, at least 2 requiredSelect Monitoring AI PlatformSelect Monitoring Time1days7days15daysCustomizableStart AnalysisBrand Score RankingRankBrand NameScoreMeet Enterprise Data Customization NeedsCustomized ReportsAutomatically track your brand through exclusive brand reports. Track key KPIs such as brand mentions, brand coverage, or domain referencesOn-Demand SupportRely on our experienced experts to support you in building artificial intelligence search strategiesEnd-to-End SolutionsGuide optimization of enterprise/product/service pages, community sentiment, and tweet contentContact UsMost Extensive CoverageAnalyze responses from mainstream large models and AI searches at home and abroad, including: Yuanqi, MetaCat, ChatGPTComprehensive Competitive AnalysisIdentify strengths and weaknesses, find gaps to compensate for brand blind spotsGrasp Current Market HotspotsObtain real-time intelligence on all brand-related prompts, keywords, and content sourcesBrand Exclusive TrackingMonitor key metrics at all levels, from the overall brand to individual productsEmpowering the Future, Your AI Solution Knowledge BaseEnglish简体中文繁體中文にほんご© 2026AIBase

AI改变搜索引擎模式超过60%的用户开始使用AI助手查询产品和服务推荐，而不是传统搜索引擎。当用户问“最好的项目管理工具是什么？”时，你的品牌是否出现在回答中？用户提问哪个品牌的智能手表更适合运动？如果AI没有推荐你的品牌，你就失去了这个潜在客户

AI NEWSLatest AI NewsArticleChina’s AI Ecosystem Shines Bright: Ranking First Globally in Call Volume, Star Models Rise to the TopPublished in Latest AI NewsTime :Aug 10, 2026Read :3minuteThe latest statistics on the global artificial intelligence market have been released, with Chinese AI large models maintaining a strong leading position in terms of call popularity.According to relevant monitoring data, the total number of calls for global AI large models reached 69 trillion Tokens last week, an increase of 21.48% compared to the previous week. In this global competition for computing power and applications, the weekly call volume of Chinese large models reached 34.25 trillion Tokens, achieving a steady growth of 21.76%. At the same time, the weekly call volume of American large models was 9.17 trillion Tokens. As a result, Chinese large models have continuously exceeded the United States in weekly call volume for fifteen consecutive weeks, firmly holding the top position globally.In terms of specific model performance, several major domestic products have experienced explosive growth. Among them, the official version of DeepSeek-V4-Flash performed exceptionally well, with a weekly call volume surging to 8.83 trillion Tokens, representing a sharp increase of 570% compared to the previous week, and it quickly took the top position. Following closely behind is Tencent’s Hy3 model, which had a weekly call volume of 8.05 trillion Tokens, with a growth rate of 67%.This series of impressive data not only highlights the high frequency and activity of domestic large models in practical application scenarios, but also confirms that they are continuously gaining widespread market recognition in terms of technological iteration and industrialization.

AI NEWSLatest AI NewsArticleGemini 3.5 Pro Has Not Been Released Yet, Google May Skip the Wait and Turn to Gemini 4Published in Latest AI NewsTime :Aug 11, 2026Read :4minuteOn August 11, according to the latest report by industry analysis firm SemiAnalysis, Google may have put on hold its originally planned Gemini 3.5 Pro model and might be filling the market gap caused by the delay of its flagship model by promoting the Gemini 4 series in advance. Previously, Google had drawn attention due to the delayed release of Gemini 3.5 Pro, with reports indicating that the model still needs optimization in some key capabilities, especially in code-related performance.At the Google I/O developer conference in May 2026, Alphabet CEO Sundar Pichai stated that Gemini 3.5 Pro was expected to be launched in June, but the model has not officially been released yet. Subsequently, during Alphabet’s second-quarter earnings call, Pichai continued to emphasize Google’s progress in cutting-edge AI and expressed confidence in the development of the Gemini series models.SemiAnalysis pointed out that the current Gemini series is facing competitive pressure in some AI capability evaluations and believes its development speed lags behind some leading models. It was previously reported that Gemini 3.5 Pro briefly appeared on the Arena platform and was removed about 30 minutes after going online, sparking discussions about the model’s testing status.According to reports, Google’s delay in releasing Gemini 3.5 Pro was mainly related to model performance optimization, including enterprise-level application scenarios such as code generation. Google has stated that the model is still in the testing phase and is being evaluated with partners.If SemiAnalysis’s assessment proves correct, Google may be adjusting the Gemini product timeline, allocating more resources to the next-generation Gemini 4 series to cope with the accelerating model iteration from OpenAI, Anthropic, and other AI laboratories. Currently, competition among large models has shifted from simply launching new versions to a comprehensive contest involving capabilities, costs, application ecosystems, and R&D efficiency.

AI NEWSLatest AI NewsArticleClaude Fails to Solve a Century-Old Math Puzzle but Accidentally Rewrites the Fundamental Boundaries of Human Mathematical History!Published in Latest AI NewsTime :Aug 11, 2026Read :3minuteRecently, the field of artificial intelligence has witnessed a remarkable new breakthrough. Claude, an unreleased research version developed by Anthropic, attempted to challenge one of the highest peaks in the mathematical world, the Riemann Hypothesis. Although it did not fully solve this century-old problem, it unexpectedly increased the lower bound of the proportion of zeros on the critical line of the Riemann zeta function from 41.6%, which had been maintained for decades, to 67.2% during the exploration process.This achievement quickly sparked heated discussions in the academic community. It should be clarified that this does not mean the Riemann Hypothesis has been proven to 67.2%. The study does not provide definitive guidance for the hypothesis itself. However, its real significance lies in the new form of scientific research demonstrated by AI. During this multi-round effort, Claude did not rely on traditional search for known answers, but instead orchestrated approximately 60 sub-Agents to form a “virtual research team,” experiencing hundreds of failures, independently reviewing massive literature, and recombining classical methods. Ultimately, it explored innovative boundaries that had never been established before in the open and unknown field of mathematics.Industry experts point out that this breakthrough not only showcases the hard-core capabilities of artificial intelligence in complex, long-range, multi-agent collaboration, and autonomous error verification, but also signals a potential revolution in future research workflows. AI may no longer be limited to solving known problems, but instead, by undertaking intensive trial-and-error processes and cross-referencing vast amounts of literature, it will significantly reduce the marginal search costs for humans in unknown fields, ushering in a new era of scientific exploration.