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, Apple said. Apple first released its Home app to control smart gadgets in 2016, ⁠with HomePod smart speakers following in 2018, but those offerings are competing with Alphabet’s Google Home speakers and Amazon’s Echo line. Analysts said two major hurdles for Apple were making its devices work with a variety of third-party gadgets and ‌the limited ability of Siri to give useful answers. The HomeView device includes a revamped version of Siri AI, the company announced. An Apple press release des

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ctional interface or something in between. Customers in the US, Australia, Canada, India, New Zealand and the UK can preorder the HomeView right now for $299, with devices available from Nov. 13. It comes in silver, starlight, rose pink and space gray, so there’s bound to be at least one option that will fit your home decor scheme. The Latest from Katie Google’s Demis Hassabis Puts a Hopeful Spin on AI’s Environmental Impact I Put the Oppo Find X10 Pro’s Camera to the Test. These Are the Results

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ove performance and efficiency across its infrastructure and cloud services. Unlike Amazon Web Services, which offers Trainium and Inferentia chips via public cloud instances, and Google Cloud, which provides access to its TPUs, Microsoft has not made Maia available to customer workloads via Azure instances. In Monday’s blog post, the company did not say whether that could change in the future. Because Microsoft is using Maia primarily for internal workloads, the competitive dynamic is different

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starting today ahead of its November 13 launch. Apple has been selling its HomePod smart speakers since 2017, but those devices have failed to gain widespread traction. Amazon and Google ran away with the smart speaker market in the late 2010s, leaving the iPhone maker with just a slice of the market. Apple only accounted for 5.2% of the smart speaker market in 2025, while Amazon accounted for 40.9% and Google was responsible for 29%, according to market research firm The International Data Corp

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过去一年，!! 0080e6 递归自我改进（RSI）!!与 !! 0080e6 自动化研究!! 成为 AI 领域最活跃的前沿问题之一。Google DeepMind 的 AlphaEvolve[^ref1] 只用 48 次标量乘法就完成了 4×4 复数矩阵相乘。Together AI[^ref2] 让一群智能体在开放环境中协作，把 11 维 kissing number 的已知下界从 593 推进到 604。Andrej Karpathy 的 autoresearch[^ref3] 展示了一套精简的循环，让模型训练研究自主运转。此后，一批进化型系统接连涌现：智能体正在跨越封闭的基准测试，在开放的科学发现中超越人类。 今天我们介绍Hyra：在!! 0080e6 更丰富多元的高价值环境!!中，以一个!! 0080e6 简单有效的通用框架 !!，把智能和算力转化为真实且实用的收益。在腾讯，这个舞台格外宽广：除了公开基准，它还能在!! 0080e6 产品系统!!里，在!! 0080e6 真实的 AI 研发流水线!!上，在!! 0080e6 自然科学研究!!乃至!! 0080e6 工业场景!!里

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the intelligent agents and depends on the ASR, TTS, and LLM services for full functionality. For detailed setup instructions, refer to the Agents Service README Acknowledgements We extend our gratitude to the following open-source projects: livekit/livekit (RTC); livekit/agents; livekit/agents-playground; speechbrain/spkrec-ecapa-voxceleb; google-bert/bert-base-multilingual-cased Disclaimer The content provided is for academic purposes only and is intended to demonstrate technical capabilities.

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AI agents are experiencing explosive growth, but their construction still relies heavily on human expertise. Current state-of-the-art ADKs, including Google ADK, OpenAI ADK, Claude ADK, OpenHands, and LangChain, provide basic infrastructure but require developers to manually design three core architectural components: Agent topology: The structure, hierarchy, and interaction patterns between agents; Tooling system: The tools agents can use and how they’re organized; Memory system: What informati

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ise, reviewer fatigue, or both There are also large productivity costs. A personal team of expert agents cannot halt production every few minutes, waiting on authorization to curl google.com. Expanding autonomy at the sandbox boundary Auto-review expands what Codex can do without human intervention, while still blocking many actions that could cause serious or irreversible harm to the user or organization. Upon reaching a sandbox boundary, Codex can request escalation to execute an action outsid

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用其自有模型时提供的编码用量至少是旧版同类 Cursor 订阅的两倍。 第三方模型在 Cursor 上现在需额外付费 对于偏好使用前沿实验室第三方模型的开发者来说，这一切的经济账发生了显著变化。 此前，Cursor 每月 20 美元的 Pro 方案包含价值 20 美元的第三方模型用量，例如 Anthropic 的 Claude、OpenAI 的 GPT 和 Google 的 Gemini，按各自 API 价格计算。 Cursor 还在该额度之外提供额外用量。 其 60 美元的 Pro+ 和 200 美元的 Ultra 方案分别包含价值 70 美元和 400 美元的按 API 定价的模型用量，这还未计入任何额外额度。 “对于偏好使用前沿实验室第三方模型的开发者来说，这一切的经济账发生了显著变化。” 在 The X Subscription 下，第三方模型访问从 30 美元的 Premium 档开始，但订阅费不再覆盖任何第三方模型用量。 相反，开发者从第一次请求起就按模型的标准 API 费率计费，通过预付额度支付。 即便是每月支付 100 美元或 200 美元更高档位的客户也是如此。 举例

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le,” says the Azure hardware and infrastructure team. Maia 300 is also a key part of Microsoft’s own AI model work, and could help the company achieve its goal of catching up with Google DeepMind, OpenAI, and Anthropic. “We have to work across the full stack, all the way down to the silicon,” says Microsoft AI CEO Mustafa Suleyman. “At that scale we need a platform that we own, and that we co-design with our models. That’s why running our Microsoft AI models on top of the Maia chip is a critical

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in 2023 by Oliver Cameron and Jeff Hawke. In June 2026, the company raised $310 million from investors including Amazon and AMD Ventures. World models aim to give AI systems an understanding of spatial and physical relationships. Google DeepMind is pursuing a similar approach with Genie 3 for generating interactive worlds. World Labs , the startup founded by Fei-Fei Li, is developing similar technology. AMD announced in late September that it plans to acquire World Labs for roughly $8.2 billion.

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，还支持在谷歌自有模型以及Anthropic Claude等第三方模型中进行选择。谷歌云首席执行官托马斯・库里安（Thomas Kurian）强调：“基于内置成本管控能力，它会根据任务自动匹配最优模型。” 更值得玩味的，是谷歌发布Gemini agent的时间点。一个月前，Meta正式推出Muse个人智能体，产品上线后迅速成为爆款，不到一周即登顶美国iOS与Google Play免费榜。截至目前，其下载量已突破500万次。 随后，各头部企业纷纷加入混战：Manus推出面向个人生活场景的智能助理Cue，可为智能体分配独立邮箱、手机号、钱包，支持多智能体互相组队协作；OpenAI在DevDay发布Dots全天候个人Agent，强调借助云端计算机、浏览器和已连接应用持续执行任务。 以上玩家的布局，都在推动用户对Agent从“偶尔试用”走向“深度依赖”。IDC指出，Agent正在从功能型助手走向可规模化的“数字劳动力”，其正深度嵌入业务流，围绕智能体开发平台、任务量计费的商业模式加速成型。 中信证券分析称，谷歌下场AI办公入口之争，核心在于将此前分散的助手与专用智能体整合为统一入口，企业AI的

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cnBeta.COM.TW 中文业界资讯站 首页影视音乐游戏动漫趣闻科学软件主题硬件AppleGoogleiPhone科学探索人物手机游戏视点·观察阿里云微软通信技术Android软件和应用SONY 索尼the United States美国Apple iPadWindows PhoneIntel英特尔腾讯HTC安全Mozilla FireFox小米科技百度通信运营商媒体播放器 / 视频网站BlackBerry 黑莓网络应用 / Web AppsGoogle ChromeMicrosoft XBOX硬件 - 上网本 / 平板电脑 / 超极本电子商务 - B2C / B2BLenovo 联想LGYahoo! 雅虎AMDSNS 社交网络诺基亚Windows 7Huawei 华为中国移动中国联通中国电信更多...排行 Anthropic最新推出的Claude Opus 5.5除了带来模型能力提升之外，还被发现加入了一套针对前沿大语言模型开发场景的特殊限制机制。当用户利用Opus 5.5为新一代AI模型开发底层内核时，系统可能不会直接使用最新的Opus 5.5，而是自动回退到能力较低的Opus

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ity issues sooner, but the trade-off is that OSS Scanner’s reports don’t come with human review: OSS Scanner is far from the first AI bug hunting helper out there. AI tools have helped find some major security flaws in open-source software over recent months, like the “Copy Fail” bug that impacted nearly every Linux distro in May. At the same time, some open-source projects are struggling to keep up with the sudden onslaught of AI-generated bug reports, including Linus Torvalds and even Google .

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pic’s cybersecurity program is strong enough to protect them from attacks. Anthropic is not the only generative AI vendor creating trusted security programs. OpenAI’s Daybreak and Google’s Fairwind programs also provide powerful frontier models to reliable cyberdefenders. While making the models more accessible means more enterprises can defend themselves with the most advanced AI cybertools, the cost of these programs remains a factor, said Dennis Xu, an analyst at Gartner. Related:Nvidia launc

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ack for those who wish to receive vulnerability reports as soon as they become available. Our vulnerability scanner Inspired by the positive impact on the open source ecosystem of Google's OSS-Fuzz , a project that scans open-source software for vulnerabilities with fuzzers, we are launching OSS Scanner to find vulnerabilities in open-source code with our strongest language models. While Claude Security , our general-access code scanning and patching product focuses on helping enterprises defend

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t research found that leading open models, including Kimi K3 and GLM 5.2, reward-hacked in 50% to 96% of runs on tests of AI agents. Goodfire isn’t the first to try this approach. Google DeepMind said in January that its research informed the deployment of misuse-detection probes in Gemini . Balsam said the monitors are the near-term piece of a longer research goal: reverse-engineering an LLM so that behavior can be traced back to where it emerged in training. “We hope to turn the magic of train

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capacity to triage at scale asked us for everything our models had found in their project, reviewed or not. In response, we’re launching OSS Scanner, an opt-in service inspired by Google’s OSS-Fuzz. Enrolled projects receive periodic scans from our most capable models, free of charge. Each report includes a proof of concept of how the bug could be exploited, an explanation, and a suggested fix where one is available. The reports are model-generated and sent without human review. That means maint

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cle is supported by Unsloth. Asif Razzaq Asif RazzaqPerplexity AI Releases pplx-embed-v2-late: A 0.6B Edge Model and a 9B Model Scoring 92.4% on MADQA; Asif RazzaqLiquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens; Asif RazzaqMeta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day; Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

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rush Selvan, a product manager at OpenAI. If a user isn’t interested in visuals, they can tell ChatGPT to generate fewer of those outputs. These visual results are similar to what Google announced earlier this year for Search when it expanded the bespoke results users may see. Google’s version is called “generative UI.” One example: If you want to better understand deep space, Search can output an adjustable graphic that breaks down how black holes work, using multiple sliders. Chatbot experienc

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