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github · 2026-07-27 · GitHub / AI agent language:Python stars:>500 / Python
语言:Python；stars:18217；更新:2026-07-27T01:24:18Z
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github · 2026-07-27 · GitHub / MCP server stars:>100 / Python
语言:Python；stars:1089；更新:2026-07-27T01:29:25Z
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github · 2026-07-27 · GitHub / Claude Code stars:>50 / Python
语言:Python；stars:3415；更新:2026-07-27T01:26:18Z
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github · 2026-07-26 · GitHub / AI 最新 趋势 stars:>50 / TypeScript
语言:TypeScript；stars:170；更新:2026-07-26T16:33:39Z
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github · 2026-07-27 · GitHub / AI agent language:Python stars:>500 / Python
语言:Python；stars:7781；更新:2026-07-27T01:20:39Z
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hf-papers · 2026-07-22 · AI论文 / hf-daily-papers / [object Object]
Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference. To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments. OpenForgeRL achieves this with a lightweight proxy that serves the harness's model calls while recording them as training data for a standard RL codebase (e.g., veRL), and a Kubernetes orchestrator that runs each rollout in its own remote container, together enabling training on any harness in any environment at scale. By decoupling training and inference, OpenForgeRL allows researchers to easily train, study, and improve agents directly in the real harnesses and environments they are deployed with. We validate our framework across diverse, complex harnesses and environments, spanning tool/claw-based agents and multimodal GUI browser- and computer-use agents. Using only hundreds to a few thousand tasks, OpenForgeClaw reaches 31.7 pass^3 and 55.9 pass@3 on ClawEval and 33.7 on QwenClawBench. OpenForgeGUI reaches 37.7 on OSWorld-Verified, 63.0 on Online-Mind2Web, and 72.3 on WebVoyager. Both outperform open baselines of similar size on nearly all benchmarks, and in the GUI setting match or surpass models several times larger. Beyond benchmarks, we analyze how harness choice (e.g., ZeroClaw, OpenClaw, Codex) and RL shape agent behavior. We find that some harnesses are substantially harder to learn than others, and that RL improves agentic reliability, such as self-verification, tool coverage, and completing multi-step plans, though critical abilities such as error recovery remain weak.
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techcrunch-ai · 2026-07-26 · AI行业 / rss
"The first autonomous agent cyberattack is an unprecedented event. It deserves an unprecedented response!"
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techcrunch-ai · 2026-07-23 · AI行业 / rss
AegisAI co-founders developed AI agents that quickly analyze each message as a human would, paying attention to small anomalies that even the most elaborate checklist wouldn’t catch.
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ithome · 2026-07-27 · 科技产品 / rss
IT之家 7 月 27 日消息，据《华尔街日报》昨日报道，英伟达正在与人工智能企业 OpenAI 谈判，拟为其提供约 2500 亿美元（IT之家注：现汇率约合 1.69 万亿元人民币）的融资担保。 据报道，英伟达提供的担保资金，将帮助 OpenAI 租用软银旗下能源公司在美国俄亥俄州南部的 10GW 级数据中心项目。如果算上芯片等成本，该数据中心的投资总额将超过 5000 亿美元（现汇率约合 3.39 万亿元人民币），成为全世界迄今为止规划规模最大的数据中心。 知情人士透露，该项目所需的电力由美国政府控制，并由日本方面提供资金支持。 OpenAI 已就该项目进行数周的深入谈判 ， 并对该数据中心表现出强烈兴趣 。 此外，Anthropic、微软和谷歌等美国企业近期也与美国商务部长卢特尼克进行沟通，希望获得该项目。截至发稿，英伟达和 OpenAI 并未回应置评请求。
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ithome · 2026-07-27 · 科技产品 / rss
IT之家 7 月 27 日消息，英伟达上周（7 月 24 日）宣布与韩国互联网巨头 Naver 达成 10 亿美元（IT之家注：现汇率约合 67.78 亿元人民币）投资协议，计划将此前部署的 NVIDIA DSX AI 工厂扩容至 200 兆瓦。 IT之家从官方新闻稿了解到，英伟达本次还将与布鲁克菲尔德资产管理（Brookfield）合作，计划提供最高 90 亿美元（现汇率约合 610 亿元人民币）的资金支持。扩建后的工厂将为韩国和美国 AI 创新企业提供 AI 算力，开发下一代 AI 大模型、智能体和 AI 服务。 NVIDIA DSX AI 工厂将采用 Vera Rubin、Blackwell 等英伟达最新平台， 落成后将推动韩国 AI 发展 。 据悉，布鲁克菲尔德资产管理自 2014 年进军韩国市场，一直是韩国的长期投资者， 目前管理 120 亿美元（现汇率约合 813.33 亿元人民币）韩国资产 ，覆盖基础设施、房地产和能源领域。 此外，Naver 还计划进一步扩展英伟达基础设施建设规模，最终将达到 1 吉瓦。该公司还计划在今年下半年推出 AI Agent 平台，基于英伟达 Agent Toolkit 打造。
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techcrunch-ai · 2026-07-24 · AI行业 / rss
ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.
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ai-news · 2026-06-06 · finance_news / AI 基础设施 / 能源
特锐德推出了专供智算中心的模块化供电站“算电岛”，不仅能直供 800V 直流机房，官方号称配合 AI 优化调度，能让 Token 的综合用电成本直接砍掉约 30%。 启发:算力的尽头是电力。国内这种卷底层基建的降本思路非常实在，毕竟现在大模型拼到最后，拼的就是显卡折旧和电费谁能省下来。
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hf-papers · 2026-07-22 · AI论文 / hf-daily-papers / [object Object]
We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and rewritten as a short, colloquial, role-played request, so that a task's prompt is not recoverable by web-searching the underlying issue, pull request, or commit thread. Because the dataset is released openly - task directories, environment images, evaluation harness, tests, and reference solutions - contamination resistance rests on this construction together with dataset versioning rather than on secrecy. The four subsets - repository-level engineering, front-end development, office and business workflows, and red-/blue-team security - probe complementary facets of real work, each with its own verification style. All are packaged in a uniform task-directory format and run, under a uniform and reproducible protocol, on two agent harnesses (CodeBuddy Code and Claude Code); the full open release makes the benchmark reproducible end to end and directly auditable, since any third party can re-run each task and inspect its content. Because each subset uses a different scoring instrument, scores are not comparable across subsets and the suite reports no suite-wide average. We report a cross-model leaderboard across several model families.
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hf-papers · 2026-07-21 · AI论文 / hf-daily-papers / [object Object]
Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its methods are the actions the model can take, fields are its state, docstrings are its prompts, and its type annotations are contracts. A method whose code body consists of "..." is completed at runtime by an LLM-driven agent loop, while methods with normal bodies remain standard deterministic Python. This gives developers and agents the same interface, so agent behavior can be tested, traced, refactored, and improved just like other software. This paper makes three contributions. (1) We present the agent-as-a-Python-object programming model and the design principles behind it. Where Python has existing abstractions, we adopt them directly. Agent-specific capabilities--context, events, state rendering, long-term memory, and validated LLM loops--are exposed through simple Pythonic APIs, so both developers and agents share one familiar programming model. (2) We identify six model-facing ideas that NOOA is, to our knowledge, the first to combine on a single surface: typed input/output, pass-by-reference over live objects, code as action, programmable loop engineering, explicit object state, and model-callable harness APIs for context and events. We find the community already converging on several of these ideas--often as experimental or partial features--and present the comparison to encourage further adoption. (3) We demonstrate that current models use this interface effectively, both in targeted capability tests and on agentic and reasoning benchmarks such as SWE-bench Verified and Terminal-Bench 2.0 and ARC-AGI-3.
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ifanr · 2026-07-27 · 明日产品 / rss
· OpenAI 签署支持 AI 开源模型发展公开信 · 马斯克：X 下月开放更多系统代码并接受审计 · 曝 Anthropic 向 SK 寻求自研芯片所需供应· #欢迎关注爱范儿官方微信公众号：爱范儿（微信号：ifanr），更多精彩内容第一时间为您奉上。
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techcrunch-ai · 2026-07-27 · AI行业 / rss
Forget YouTube videos—frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.
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ithome · 2026-07-26 · 科技产品 / rss
IT之家 7 月 26 日消息，据央视新闻今日报道，德国国家工程科学院院士、中国工程院外籍院士赫尔佐格荣获 2025 年度中华人民共和国国际科学技术合作奖。近日，赫尔佐格接受总台《高端访谈》栏目专访时谈到人工智能发展，他表示， 人工智能领域下一次重大突破绝非单一大型系统，而是众多小型的专业化智能体协同运作 。 总台记者何岩柯：随着智能体越来越普及，各界对此讨论热烈。您认为人工智能领域下一次重大突破，会诞生于性能极强的单一大模型，还是由多个智能体协同组成的系统？ 德国国家工程科学院院士、中国工程院外籍院士赫尔佐格：我一直主张发展小型智能体。未来的发展方向绝非单一大型系统，而是众多小型的专业化智能体协同运作。人类之间通过沟通、协作甚至适度竞争，共同创造成果，这是我们早已习惯的模式。智能体也可以复刻这套运行逻辑。这种架构的优势十分突出，适配性极强。我们可以随时移除、新增智能体，落地实操效果远胜于单一大系统。 总台记者何岩柯：您是否思考过这样一个问题：人工智能的终极目标是什么？我们应当研发具备人类思维能力的机器，还是创造一种全新的智能形态，用来拓展或是补足人类自身的能力边界？ 德国国家工程科学院院士、中国工程院外籍院士赫尔佐格：互联网其实已经在补足人类的能力。互联网汇集了全球各类知识， 人工智能还能进一步降低人们获取知识的门槛 。这会是未来发展目标之一，大语言模型已经初步实现了这一构想，但想要完全落地，还需要大量实践与科研工作。 IT之家注意到，2026 年 7 月 8 日，中国国家科学技术奖励大会在人民大会堂举行，德国科学家赫尔佐格荣获 2025 年度中国国际科学技术合作奖， 这是中国授予外国科学家的最高科学荣誉 。 据介绍，赫尔佐格是德国的人工智能专家。1985 年，他创立了首个德国人工智能研发中心， 领导了德国第一支人工智能团队 。1993 年开始，他在德国不来梅大学担任人工智能教授，其间创立欧洲首个将人工智能技术和 4G、可穿戴计算等先进通信技术结合并开发创新应用的科研机构。 2014 年，赫尔佐格受邀来到中国 ，投身于中国人工智能与城市规划交叉学科的创新建设。自 2015 年起，他担任长三角城市群智能规划协同创新中心首席科学家，为智能规划理论体系建设和中德交流合作作出了卓越贡献。
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techcrunch-ai · 2026-07-26 · AI行业 / rss
A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor.