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github · 2026-07-31 · GitHub / Claude Code stars:>50 / Python
语言:Python；stars:7929；更新:2026-07-31T01:29:48Z
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ithome · 2026-07-31 · 科技产品 / rss
IT之家 7 月 31 日消息，据彭博社今日援引知情人士消息，DeepSeek 正计划在内蒙古建设一座大型 AI 数据中心。这一雄心勃勃的基础设施项目显示，这家中国人工智能初创公司正在扩大 AI 算力布局。 据不愿透露姓名的知情人士透露， DeepSeek 计划在内蒙古乌兰察布增加 1GW（吉瓦）规模算力 。该公司除了自建数据中心计划外， 也有意从其他公司租赁额外算力 。 其中两位知情人士表示， DeepSeek 数据中心的部分算力将在明年年底或 2028 年初投入运行 ，目前尚不清楚该项目使用何种芯片。作为参考，英伟达目前是 AI 数据中心的行业标准，而华为则是中国数据中心领域的代表企业。 据IT之家此前报道 ，DeepSeek 已于 4 月放出新一批招聘信息，除了杭州和北京外，首次出现了工作地为内蒙古乌兰察布的岗位。两个相关岗位分别为数据中心高级交付经理、数据中心高级运维工程师，薪资为 15-30K · 14 薪。 乌兰察布年平均气温约为 4°C，天然的低温环境能够降低高功耗 AI 服务器的散热需求， 因此成为各大企业建设数据中心的理想地点 。 英伟达 CEO 黄仁勋曾预计，一座配备最先进 AI 加速器的 1GW 级数据中心，投资成本可能达到 500 亿美元 （IT之家注：现汇率约合 3386.82 亿元人民币） 。不过这种成本会因为建设地点、AI 芯片类别产生巨大差异。众所周知， 中国 AI 数据中心的建设成本通常低于美国 。 由于工作时间缘故，DeepSeek 并未立即回应彭博社的置评请求。
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github · 2026-07-31 · GitHub / Claude Code stars:>50 / Python
语言:Python；stars:406；更新:2026-07-31T01:28:44Z
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github · 2026-07-31 · GitHub / Claude Code stars:>50 / HTML
语言:HTML；stars:151；更新:2026-07-31T01:23:20Z
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github · 2026-07-31 · GitHub / Claude Code stars:>50 / JavaScript
语言:JavaScript；stars:5731；更新:2026-07-31T01:22:08Z
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github · 2026-07-31 · GitHub / LLM workflow stars:>300 / TypeScript
语言:TypeScript；stars:150842；更新:2026-07-31T01:25:32Z
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techcrunch-ai · 2026-07-29 · AI行业 / rss
At TechCrunch Disrupt 2026, the AI Stage is back to dig into the single hottest topic in the community for the past few years, presented by Google for Startups.
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techcrunch-ai · 2026-07-30 · AI行业 / rss
The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.
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techcrunch-ai · 2026-07-29 · AI行业 / rss
On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.
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ithome · 2026-07-31 · 科技产品 / rss
IT之家 7 月 31 日消息，科技媒体 9to5Mac 昨日（7 月 30 日）发布博文，梳理彭博社马克 · 古尔曼（Mark Gurman）等消息源此前爆料内容， 指出目前苹果正测试的 iOS 27 系统中，仍有 5 项传闻功能尚未现身。 一、接入更多聊天机器人，以及扩展 Siri 体验 iOS 27 的搜索（Search）和询问（Ask）界面已提供入口，可以切换调用的 AI 模型，不过现阶段除了 Siri 之外，用户只能选择 OpenAI 的 ChatGPT 聊天机器人。 IT之家援引博文报道，苹果正测试接入 Claude（Anthropic 的人工智能助手）和 Gemini（谷歌的人工智能模型与助手），但尚不清楚何时开放接入。 二、升级健康应用 苹果曾计划推出内部代号为“Mulberry”的综合健康教练服务，后续缩减了该计划。保留下来的部分功能包括改进血糖相关追踪，以及允许用户借助设备相机监测运动情况。古尔曼称，这些更新预计不会随 iOS 27 首个正式版本推出。 三、调用第三方 AI 模型用于生成图片和文本 图乐园（Image Playground）和写作工具（Writing Tools）目前可以调用 ChatGPT 生成图片和文本，不过古尔曼透露在未来 iOS 27 版本中，将会开放接入第三方模型，进一步扩展 AI 生图和处理文本能力。 四、自定义相机应用界面 报道称，用户可自行选择位于相机界面顶部的一组控件，苹果将这类控件称为“widgets”。现有 iOS 27 已为相机应用新增 Siri 模式，但尚未开放上述自定义能力。该功能可能配合 iPhone 18 Pro 发布，但该时间点同属推测。 五、面向 iPhone Ultra 的并排应用多任务处理 iOS 27 中已有多款苹果应用支持横屏模式，报道将其视为适配宽屏折叠设备的准备。古尔曼称，待 iPhone Ultra 推出后，苹果还将支持 2 个应用并排显示与操作。iPhone Ultra 的产品名称及上市计划均未获苹果确认。
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ai-news · 2026-06-06 · finance_news / 股市 / 财报
戴尔最新的 AI 服务器积压订单爆表，高达 510 亿美元。庞大的需求侧数据再次印证了算力军备竞赛的火热程度。 启发:典型的“增收不增利”隐患重灾区。硬件厂商在 AI 浪潮里卖水确实赚钱，但如果由于极度依赖英伟达导致利润率起不来，最终也只是在给上游打高级黑工。
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hf-papers · 2026-07-26 · AI论文 / hf-daily-papers
Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval. Existing provenance work records what happened - execution traces, tool calls, evidence links - and source-reliability estimation is long established (truth discovery, reputation systems). What is missing is an operational framework that attaches graded, per-domain transmitter reliability to claim-level transmission chains, with completeness semantics, transformation-typed aggregation, decoupled content criticism, and serve/review/quarantine routing. Classical Islamic hadith science confronted a structurally similar problem: deciding whether knowledge transmitted through chains of human narrators should be accepted. Over centuries it developed a rigorous methodology - isnad (a complete transmission chain attached to every claim), rijal (systematic grading of each narrator's integrity and precision), weakest-link chain evaluation, corroboration through independent chains, and matn criticism (content evaluated independently of chain quality). This paper transfers that methodology to AI system design. We contribute a formal mapping from hadith-science concepts to multi-agent pipelines, a relational schema implementing claim chains and a graded narrator registry, a decision matrix combining chain grade with content criticism, and an evaluation on 20,000 claims from real physics textbooks. The evaluation validates weakest-link quarantine and independent-chain corroboration; reports a partial failure of the grade-recovery loop, which missed the highest-fault narrator; and reports two analyses as inconclusive, including a matched-coverage comparison the framework could not reach with the reference content critic. The paper is explicit throughout about which claims the evidence does and does not yet support.
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hf-papers · 2026-07-28 · AI论文 / hf-daily-papers / [object Object]
Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities. However, existing cybersecurity benchmarks focus on pre-compromise settings where agents are placed in a clean and idealized environment before an attack occurs. This leaves the post-compromise setting underexplored. To address this gap, we introduce SecRespond, the first benchmark for evaluating LLM agents on the post-compromise incident-response workflow. Given a forensic disk snapshot of a compromised host together with the alerts, vulnerability scans, and baseline checks reported by a host security product, agents are required to produce forensic reports on intrusions, baseline risks, and vulnerability risks, together with a remediation plan. We instantiate this task across 10 cyber ranges, each constructed from a distinct compromised cloud host, spanning 4 entry-point types, 21 ATT&CK techniques, and 5 operating systems. We evaluate 23 frontier LLMs on the OpenCode agent harness. Experimental results show that although current agents can reliably uncover the problems exposed by alerts, they struggle to proactively investigate the disk for silent intrusions and to produce comprehensive, verified remediation plans, with no model achieving complete detection and remediation on any single range. This reveals a fundamental bottleneck in building agents for real-world incident response. The benchmark is publicly available at https://github.com/Alibaba-NLP/qqr/tree/main/data/secrespond.
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techcrunch-ai · 2026-07-30 · AI行业 / rss
As experts have warned for the last two years, some companies — like Microsoft and now Google — are finding and patching an exponential number of bugs in their products, thanks to the use of LLMs and AI tools.
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techcrunch-ai · 2026-07-30 · AI行业 / rss
Meta says AI is making it dramatically easier to build and launch new consumer apps, with CEO Mark Zuckerberg telling investors the company has more new consumer products on the way.
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hf-papers · 2026-07-28 · AI论文 / hf-daily-papers / [object Object]
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a behavioral oracle, even frontier models solve fewer than 1% of instances. Existing frameworks conflate documentation reading, behavioral exploration, and code synthesis into a single pass, causing agents to probe insufficiently, lose behavioral intent as context drifts, and propagate early misinterpretations into the final implementation. Inspired by classical requirements engineering, we argue that behavioral specification elicitation should be a first-class phase that precedes implementation. We present SpecFirst, a two-stage framework that forces the specification elicitation before code synthesis. A dedicated spec agent first probes the binary and combines observations with documentation into a structured specification. Next, a code synthesis agent then uses this specification to drive implementation. This decomposition resolves documentation ambiguities before coding begins and provides a stable behavioral reference throughout synthesis. We evaluate SpecFirst on all 200 ProgramBench instances across four models spanning two families and an order of magnitude of capability. SpecFirst consistently outperforms the single-loop baseline, improving test pass rates by 6.9%-21.3% and binary exploration coverage by 9.4%-18.5%, all statistically significant. Behavioral analysis on code synthesis further shows that a prior specification enables earlier and more sustained code construction. Our results demonstrate that an explicit requirements-engineering phase is an effective paradigm for from-scratch program construction.
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ithome · 2026-07-31 · 科技产品 / rss
IT之家 7 月 31 日消息，印度人工智能企业 Sarvam AI 在当地时间本周四开幕的首届 Sarvam Epoch 会议上宣布， 该公司将开发参数达到万亿量级的人工智能模型 。 Sarvam AI 表示，其现有的 105B 模型在诸多方面表现出色，语音模型则能提供显著优于全球竞争者的性价比。 该企业之所以要开发 T 级模型，是因为一种“印度应该生产自身消费的 Token（词元）”的信念， 印度应避免在金钱和数据上向外国人工智能企业“双重付费” 。这一设想中的新模型预计将在未来六个月内完成，可在编程、网络安全、科学研究等领域与领先模型展开竞争。 Sarvam AI 昨日宣布将在美国设立办事处， 吸引当地印度裔人工智能人才回流 。其还公布了两款模型产品，分别是 Bulbul V4 文本转语音模型和 Saaras V4 Multi-speaker 语音识别模型。
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hf-papers · 2026-07-28 · AI论文 / hf-daily-papers / [object Object]
We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file through bounded edits, accepting an edit only when it strictly improves a held-out score. Extended from classical ASR-LM framework, we refer this split the listener-thinker architecture; the two roles are coupled only through the memory, so no weights change and the learned skill stays auditable and portable. Restraint turns out to be the operative skill this loop discovers: unconstrained generative error correction (GER) over-corrects, breaking correct tokens on up to 64% of its edits on financial news, and Voice Memory, reduces this rate to 35%. Across ten HyPoradise domains with an open corrector, Voice Memory, lowers weighted word error rate from 8.36% to 7.52% (7.47% with three added in-context examples) without regressing any dataset below its 1-best baseline; gains concentrate where recoverable headroom is largest, including air-travel commands (8.40% to 3.40%) and noisy far-field speech (CHiME-4, 12.69% to 10.46%). The memory transfers across corrector families and adds zero parameters to the inference path. A demo and example code are provided for future studies.