科技周报｜Vercel 发布 AI 基础组件库；OpenAI 发布 GPT-5 与开放模型；TypeScript AI Agent 框架

8.11最新【RVC变声器】旺仔小乔音色模型上线啦！免费无套路！支持PC+安卓+ios！无需声卡！0延迟！另有百款音色等你体验！人工智能AI变声，小白一键直装！

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Qwen Code 现在提供每日2000次免费调用；Claude Code新增后台任务处理能力【AI 早报 2025-08-09】

Intro: 00:00 ⬛️ Sam Altman 回应 GPT-5 相关问题: 00:09 🔗 https://x.com/sama/status/1953893841381273969 ⬛️ Qwen 开源模型再升级 支持100万token超长上下文: 00:33 🔗 https://x.com/Alibaba_Qwen/status/1953760230141309354 🔗 https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507 🔗 https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507 🔗 https://x.com/Alibaba_Qwen/status/1953760230141309354/photo/1 ⬛️ Qwen-Code 现在提供每日2000次免费调用: 00:45 🔗 https://x.com/Alibaba_Qwen/status/1953835877555151134 🔗 https://github.com/QwenLM/qwen-code ⬛️ Anthropic Claude Code新增后台任务处理能力: 00:57 🔗 https://x.com/_catwu/status/1953926541370630538 ⬛️ Google测试全新AI驱动的Google Finance: 01:13 🔗 https://blog.google/products/search/google-finance-ai/ ⬛️ Google DeepMind发布开源实时音乐生成模型Magenta RealTime: 01:35 🔗 https://huggingface.co/google/magenta-realtime 🔗 https://g.co/magenta/rt 🔗 https://arxiv.org/abs/2508.04651 ⬛️ Google研究称通过高保真标签可将训练数据量减少10000倍: 01:49 🔗 https://research.google/blog/achieving-10000x-training-data-reduction-with-high-fidelity-labels/ ⬛️ 微软Copilot发布多项功能更新: 02:08 🔗 https://x.com/Copilot/status/1953863782079639660 🔗 https://x.com/testingcatalog/status/1953917584371327059 🔗 https://github.blog/changelog/2025-08-07-github-copilot-in-vs-code-july-release-v1-103 ⬛️ 腾讯开源RAG框架WeKnora，专为复杂文档理解设计: 02:29 🔗 https://github.com/Tencent/WeKnora 🔗 https://weknora.weixin.qq.com/ ⬛️ R-Zero框架发布：让LLM从零数据开始自我进化推理能力: 02:50 🔗 https://github.com/Chengsong-Huang/R-Zero 🔗 https://arxiv.org/abs/2508.05004 ⬛️ Hugging Face推出开源工具AI Sheets，支持本地模型进行数据处理: 03:18 🔗 https://github.com/huggingface/aisheets 🔗 https://huggingface.co/blog/aisheets ⬛️ 传闻：OpenAI计划推出新的订阅等级: 03:36 🔗 https://x.com/scaling01/status/1953891522077753447 🔗 https://x.com/scaling01/status/1953892324318040079 ⬛️ 智谱AI GLM-4.5系列新模型即将开源: 03:43 ⬛️ 开源项目New API更新，兼容Claude Code并引入模型管理模块: 03:52 🔗 https://linux.do/t/topic/852752 🔗 https://github.com/QuantumNous/new-api/issues ⬛️ Outro: 04:12

赛博朋克2025：美国老百姓要和AI抢电用了

美国国家气象局近日对美国多个地区发布高温预警，受此影响，美国电力消耗将达到4年来的最高水平。但就在这个情况下，特朗普却宣布美国要发展“用电大户”——数据中心，以维持美国在人工智能领域的领先地位。美国想兴建数据中心来强化人工智能发展的愿望能实现吗？谭主通过美国电力数据，找到了美国AI被“卡脖子”的困境…

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多层高程地图驱动的四足机器人自主安全复杂地形穿越

Title:  Learning Autonomous and Safe Quadruped Traversal of Complex Terrains Using Multi-Layer Elevation Maps Abstract: Legged robots hold great promise for agile and flexible mobility across diverse and unstructured terrains, inspired by the remarkable adaptability of bipeds and quadrupeds in nature. However, achieving robust autonomous locomotion in cluttered and complex environments remains a significant challenge. In this work, we present a hierarchical control framework for quadrupedal robots that enables safe and autonomous traversal of cluttered terrains. Central to our approach is a novel multi-layer elevation map representation, which is generalized enough to capture a wide range of terrains. To further improve policy generalization and maneuverability, we incorporate terrain augmentation, knowledge distillation, and carefully designed reward functions. Extensive simulation experiments demonstrate that each component contributes to improved policy generalization, and that our terrain representation is more efficient and informative than existing alternatives. By training a terrain compressor in simulation, we successfully deploy our system on a low-cost quadrupedal robot in real-world environments, showcasing the practicality and robustness of our approach. 文章名： 多层高程地图驱动的四足机器人自主安全复杂地形穿越 简介： 四足机器人以自然界双足与四足动物的卓越适应力为灵感，具备在非结构化地形中灵活穿梭的潜力，然而在杂乱复杂环境中实现稳健自主运动仍极具挑战。本研究提出一种层级式控制框架，使低成本四足机器人可安全自主地穿越各类复杂地形。方法核心在于全新设计的多层高程地图表示，能以统一而简洁的形式刻画广泛地形特征；通过地形数据增强、知识蒸馏与精心设计的奖励函数，进一步提升策略的泛化能力与机动性。大量仿真实验表明，各组件均显著提升策略泛化性能，且本地形表示在效率与信息量上优于现有替代方案；通过在仿真中训练地形压缩器，我们成功将系统部署于真实低成本四足平台，并在现实复杂环境中验证了其鲁棒性与实用性。 文章刚被IEEE Robotics and Automation Letters（RA-L）接受。

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