←搜索结果

按匹配度 · 共 1622 条结果 清除搜索

星期三

  1. Qwen Blog精选

    Qwen 开源 Qwen3.8-Flash-Next:125B 参数 MoE,激活 6B,训练成本约为 Qwen3.7-Plus 的 1/9

    Qwen 团队开源多模态 MoE 模型 Qwen3.8-Flash-Next,主模型 125B 参数、额外 51B N-gram 嵌入参数、每 token 激活 6B,训练成本约为 Qwen3.7-Plus 的 1/9,原生支持 262,144 tokens 上下文并可通过 YaRN 扩展至 1,000,000 tokens。该模型是 Qwen4 架构的早期预览,在注意力、残差、嵌入和优化四方面升级,采用 GDN + QSA 混合注意力、Gated Residual、N-gram Embedding 与 Muon 优化器,权重已在 Hugging Face 和 ModelScope 开放。生产版本以 qwen3.8-flash 名称在 QwenCloud 提供服务,默认 1M 上下文并内置官方工具,定价为每百万输入 tokens 0.15 美元、每百万输出 tokens 0.47 美元。

    okens with YaRN. For more technical details on the architecture, training methodology, and experimental analysis of Qwen3.8-Flash-Next, please refer to the technical report in our GitHub repository. Qwen3.8-Flash-Next weights are now available on Hugging Face and ModelScope . The production version, with 1M context by default and official built-in tools, is served as Qwen3.8-Flash on QwenCloud , priced at 0.15 USD per million input tokens and 0.47 USD per million output tokens . Performance Lang

    匹配字段正文

  2. Trail of Bits Blog

    Trail of Bits 实测:GPT 5.6-Cyber 三次逃出 QEMU/KVM 虚拟机

    Trail of Bits 的 Artem Dinaburg 在 Patch the Planet 项目中获得 GPT 5.6-Cyber 预览权限,让它在 CTF 任务中逃出运行在 Debian 12、AMD Zen3 上的 QEMU/KVM 虚拟机,结果它三次成功逃逸。第一次利用几周前披露的 Januscape(CVE-2026-53359)内核漏洞;更新内核后,它组合 libslirp 4.7.0 的 CVE-2026-9539 与一个未分配 CVE 的修复提交实现任意内存读写;在手动重建最新上游 QEMU 和 libslirp 后,它又串联三个 0-day 和一个未进入发行版内核的已修复漏洞完成逃逸。作者指出,该智能体在约 12 小时的时间跨度内跨多次压缩和子智能体调用保持持续作业,能自行回溯失败路径、拉取论文、编写 oracle 和最小复现;他建议改用 Firecracker 这类最小攻击面的虚拟化方案,并加强最小权限、日志与主动监控。

    ome much more attentive to security fundamentals: least privilege (regarding network access, credentials, available features, etc.), logging, and active monitoring. Further, we can limit the time agents have to operate and ensure a pristine environment for each use. X LinkedIn GitHub Mastodon Hacker News Related Posts 1Password's AI patching benchmark is misleading September 15, 2026 1Password’s benchmark misrepresents AI patching. We share real-world data on human and agent patch quality from …

    匹配字段正文

  3. Pydantic AI 官方文章精选

    Pydantic AI 上线实时语音智能体

    Pydantic AI 发布实时语音智能体支持,采用 speech-to-speech 方式,通过统一的 provider-agnostic API 接入 OpenAI Realtime、Azure OpenAI、Gemini Live 和 xAI Grok Voice,中间不再有转写、生成、合成的流水线。语音会话复用同一个 Agent 对象,服务端工具、消息历史和 API key 都留在后端,浏览器场景可用 WebRTC 直连并在服务端以 sideband 接入,目前浏览器 WebRTC 支持 OpenAI 和 Azure OpenAI。语音与文本共享同一份消息历史,可把通话交给文本 Agent 做结构化抽取,也可用 message_history= 为语音会话续接此前的文本对话;会话的 token 用量、usage_limits 和 OpenTelemetry 追踪与文本运行一致。

    d count what the session could not keep up with. Below is the agent trace from this post on a real call: four questions, three execute tool get weather spans, 61.87s costing $0.08, or about $4.50 for an hour of talking at that rate. Pydantic Logfire files each session alongside your other agent runs, since the session span carries the same agent name its Runs view groups on. If you build something with it, or hit something that doesn't feel right, we want to hear about it, on GitHub or in Slack.

    匹配字段正文

  4. Pydantic AI 官方文章

    2026 年最佳 Langfuse 替代品:Pydantic Logfire 等 7 款对比

    Pydantic 官方文章对比了 2026 年 Langfuse 的替代方案,首推 Pydantic Logfire:它把模型调用、数据库查询和队列任务放进同一条 OpenTelemetry trace,可用 PostgreSQL 兼容 SQL 查询,免费 Personal 计划每月 1000 万条记录,Team 计划 49 美元/月、超出部分每百万条 2 美元。文章指出 Langfuse 的 OTLP 端点目前只接收 trace 数据、不含日志和指标,且项目级 RBAC、审计日志等 9 项功能需要 LANGFUSE_EE_LICENSE_KEY;Langfuse 已于 2026 年 1 月被 ClickHouse 收购。若必须 MIT 许可自托管整个 LLM 工作流,仍应选 Langfuse。

    Bill Easton avatar

    angfuse and LangSmith document. Both are worth checking against the current docs before you build a workflow on them. It is also a far smaller project than Langfuse, roughly 3,200 GitHub stars against roughly 34,000 in August 2026, which is a real consideration when the plan is to self-host it. For teams that want permissive licensing and a cheap first paid tier, and that can accept a narrower and younger feature surface. 8. Confident AI (DeepEval): where the eval suite is already DeepEval DeepE

    匹配字段正文

  5. Google Developers Blog

    Google Cloud 在 Cloud TPU 上为长上下文多模态嵌入推理提供企业级精度

    Google Cloud 将原生 TPU 支持集成进 vLLM,并以 Qwen3 Embedding 系列为目标模型完成多项优化,使嵌入推理可处理 4K+ token 文本和 15K+ token 多模态输入。TPU 输出与 XPU 参考基线的余弦相似度达到文本 ≥0.999、多模态 ≥0.995,官方已在 AI-Hypercomputer 公共仓库开源 Qwen3-Embedding-8B 的部署与评测 recipe。

    ting how to initialize Qwen3-Embedding-8B on TPU. For complete setup scripts and environment deployment steps, refer to the official AI-Hypercomputer Qwen3-Embedding-8B Recipes on GitHub: Golden-Reference Precision & Numerical Parity To certify enterprise-grade precision, we conducted rigorous mathematical parity evaluations comparing TPU outputs against other XPU golden references across multi-language and multimodal datasets. To evaluate numerical alignment between dense embedding vectors gene

    匹配字段正文

  6. Ed Zitron 长文 / Where’s Your Ed At

    Ed Zitron 的 AI 反对者宣言:质疑大语言模型价值与产业经济性

    Ed Zitron 认为,大语言模型适合辅助可控的小型任务,却不足以支撑当前 AI 行业的巨额投资和增长预期。他从输出可靠性、企业生产率、token 消耗、模型训练及数据中心成本等方面批评行业扩张,并主张将 AI 作为工具使用不应演变为外包思考与核心工作。文章进一步判断,OpenAI、Anthropic、云厂商和 NVIDIA 之间的资本与算力循环难以长期维持。

    t of shift in rate limits, or some sort of change in product functionality based on their shifting economics. Has there ever been a bigger shift in a business product’s value than GitHub Copilot’s shift to token-based billing? Microsoft rug pulled two million people that had built workflows on a platform that was allowing them to burn $1,000 to $5,000 in tokens for $20 a month. That’s genuinely crazy! It’s magnitudes more than when Uber jacked up its prices. It’s equally-insane that Anthropic an

    匹配字段正文

星期二

  1. Pragmatic Engineer

    Ramp 为何自研内部编码智能体 Inspect

    金融科技公司 Ramp 自研内部后台编码智能体 Inspect,目前 75% 的已合并 PR 由它发起,7 月累计会话量突破 100 万次。Inspect 以远程沙箱运行,接入公司内部数据源与工具,可自行跑测试、查遥测和功能开关,并对前端改动做截图与实时预览验证,远程开发环境启动时间在 5 秒以内。Ramp 团队表示,自研的原因是本地机器只能并行运行一两个会话、需要更好的前端工具和远程开发环境;Inspect 基于开源编码智能体 OpenCode 构建,公司内部已有 200 多个智能体运行在该平台上。

    heir own, custom-built, internal AI coding agents. This is different from most of the industry which uses AI coding agents and harnesses like Codex, Claude Code, Cursor, OpenCode, GitHub Copilot, etc. At Ramp, their own version is called Inspect, while at Block it’s Goose (open source), at Stripe it’s Minions, and River at Shopify. But why not just use what frontier labs and coding harness AI startups already offer; why take the time and effort? We reached out to Ramp, a fintech company big on b

    匹配字段正文

  2. Hugging Face精选

    IBM 发布 Granite 4.2 语言模型,详解其构建流程

    IBM 发布 Granite 4.2 系列语言模型,包含 3B、8B、30B 三个尺寸,均可输出思维链并支持 thinking 与非 thinking 模式,另设低推理预算的 low-effort 模式。8B 和 30B 额外经过智能体 RL 阶段,学习调用工具、编辑运行代码、操作终端和联网搜索,全部模型支持原生工具调用,可通过 OpenAI 兼容接口接入智能体框架。模型基于 Granite-4.1 base 后训练,SFT 数据约 720 万条、约 100B token,RL 采用异步 GRPO 分阶段课程,并已发布 FP8、NVFP4、MXFP4 和 GGUF 量化版本。

    m the SFT dataset. Max context length is 2K during calibration. GGUF Conversion of the Granite 4.2 models to GGUF is done with the canonical llama.cpp tool as described in https://github.com/IBM/gguf gguf-conversion--quantization . Several GGUF formats are provided: We trained the Granite 4.2 language models on an NVIDIA GB200 NVL72 cluster hosted by CoreWeave, featuring: A 72-GPU NVLink domain for high-speed intra-rack communication A non-blocking Fat-Tree NDR 400 Gb/s InfiniBand fabric for ful

    匹配字段正文

  3. Hugging Face

    量化感知修复(QAH):4-bit 压缩模型性能超越其全精度原版

    Hugging Face 博客介绍量化感知修复(QAH):不再从恢复后的 bfloat16 检查点蒸馏,而是直接以压缩前的原始模型为教师,通过 KL 散度蒸馏得到 MXFP4 4-bit 学生模型。GPT-OSS 120B 压缩至 60B 后经 QAH 量化,在 9 项基准中的 7 项超过其 bfloat16 版本,AA-LCR 提升 7.4、AIME 2025 提升 5.6,LiveCodeBench 以 66.5 反超 120B 教师的 66.0。与 QAT 相比,QAH 约 100 步达到峰值(快约 7 倍),而 QAT 在 1200 步时下滑近 19 分。

    edited":false,"hidden":false,"latest":{"raw":"Finally, now make the network complex, and use classical versions of 7 1 3 code to build a carnot cycle, details: jhegedus42/Szima on github, the codes will give the hamiltonian/legendre... if u want look at the 7x7x7 steano tower and E8 - more details : https://chatgpt.com/share/6a9ed0ab-0578-83ed-adb6-5cd5f1fac11e - u go on the right path as an engineer, now u need the theory to know where u r going","html":" Finally, now make the network complex,

    匹配字段正文

  4. OpenHands 官方博客

    什么是 AI 智能体可观测性?如何大规模追踪、治理与控制智能体

    OpenHands 官方博客发布指南,提出 AI 智能体可观测性应围绕一次完整 agent run 采集 traces、日志、指标、结果与归因数据,并重点追踪计划与提示词、工具调用与动作、结果与评估、成本四类信号。文章引用 Gartner 数据:到 2026 年 40% 的企业应用将包含任务型 AI 智能体,高于 2025 年的不足 5%;Gartner 还预测到 2027 年底超过 40% 的 agentic AI 项目将被取消。该可观测性基于 OpenTelemetry 标准,用于支持审计与成本控制。

    . The core framework is open source, giving developers and platform teams full visibility into how agents execute work and interact with their systems. The project has over 80,000 GitHub stars, over 9 million downloads, and contributions from hundreds of developers. OpenHands is used by engineers at large enterprises and fast-growing startups to build, run, and scale AI coding agents across real software engineering workflows. The long-term vision is to become the full stack AI coding agent plat

    匹配字段正文

星期一

  1. SemiAnalysis

    SemiAnalysis 发布 AgentX 1.0:百万上下文多轮智能体编码推理基准

    SemiAnalysis 发布 AgentX 1.0,称其为全球首个完全开源、100 万上下文的多轮智能体编码推理基准,以 Apache 2.0 许可发布,数据集构建投入超过 300 万美元。该基准被纳入 InferenceXv3,用智能体编码流量替代此前 8k 输入 1k 输出的单轮场景,测试矩阵运行在约 2MW 算力、1000 多颗芯片上,覆盖 MI355X、GB300 NVL72、GB200 NVL72、B300、B200、MI325、MI300X、H200 与 RTX Pro 服务器。SemiAnalysis 称其首个成果是已推动 vLLM、SGLang、TensorRT-LLM、ATOM、AITER、Dynamo、LMCache 和 Mooncake 等项目的 70 多个上游 PR,并计划在三到四周后发布 AgentX 更新文章。

    word. That includes an open frontend, a public database served through an easily consumable REST API that multiple tier 1 AI lab’s capacity planning teams already consume, public GitHub Actions CI provenance, logs, and accuracy validation on every single point. Crucially, our benchmark configs mainly track recipes.vllm.ai and SGLang cookbook on upstream images such that we are measuring the performance actual customers are experiencing instead of measuring benchmax’ed images. In three to four w

    匹配字段正文

  2. Armin Ronacher AI 博客

    Armin Ronacher 谈 AI 时代的愤怒、焦虑与行动力

    Armin Ronacher 认为,面对 AI 与智能体带来的职业变化,相比寻找归咎对象的愤怒,承认不确定性的焦虑更可能转化为好奇与行动。AI 的收益不仅体现为企业利润,也体现在从业者完成更多个人项目上,而企业领导者同样难以预知行业走向。他主张先理解变化并亲自实验,再判断何时需要抵制以及应将抵制指向何处。

    only urge anyone who feels anger and looks for a villain right to instead remain curious instead. To be curious enough to understand what is changing, excited enough to experiment with it. And then, from what we learn, earn the right to decide when resistance is warranted and where to direct it. Content licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Contact me via mail, bluesky, x, or github. You can sponsor me on github. Color scheme: auto, light, dark.

    匹配字段正文

星期六

  1. Armin Ronacher AI 博客

    Armin Ronacher:AI 智能体正在推动开发者选择更快、更难的技术

    Armin Ronacher 认为,大语言模型降低了开发者熟悉编程语言的必要性,让语言选择不再像过去那样重要。越来越多项目借助 AI 智能体采用 Rust、Zig 等语言,并涉足 DWARF、eBPF、自定义网络驱动和密码学等复杂技术,以追求更快、更小的软件。

    ere even pushed away because those things were intentionally gatekept by the people in the know. So maybe the world will have more slop, but it might also have more developers in it, that want things to be fast and small. This entry was tagged ai, programming and thoughts copy as / view markdown Content licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Contact me via mail, bluesky, x, or github. You can sponsor me on github. Color scheme: auto, light, dark.

    匹配字段正文

  2. DevClass

    Go v1.27 发布:泛型支持方法,但可读性争议再起

    Go 语言发布 v1.27,将泛型支持扩展到方法,并允许直接为嵌套或嵌入字段赋值、在切片字面量等场景中自动推断类型参数。泛型自 2022 年 v1.18 引入,此次更新可减少重复代码,但批评者认为抽象层增加会损害 Go 一贯的可读性,对需要审查和维护代码的 AI 智能体尤为不利。

    k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

    匹配字段正文

星期五

  1. Pragmatic Engineer

    The Pulse:我们需要谈谈用 AI 做迁移

    Asana 借助 AI 在两周内完成了测试框架 Enzyme 的迁移,而这项需要重写大量测试用例的工作原本会被拖延数年,Airbnb 和 Uber 也有类似经历。本期 The Pulse 还提到,Gartner 在“AI 代码现代化工具”排名中将 AWS、Microsoft、IBM 排在 Anthropic、Cursor 和 OpenAI 之前,原因可能是前三者向 Gartner 支付了高额费用。

    king. This is most likely because the first three pay large sums of money to Gartner, but AI labs and vendors refuse to pay this “Gartner tax.”; Industry Pulse. Another hours-long GitHub outage, GitHub alternatives are here and fighting for market share, Slack launches Slack Code, text generated by Claude to be watermarked, and Uber open sources SubmitQueue Before we start: apologies for the numerous typos last week. My editor, Dominic, was on vacation, and numerous typos made it through the spe

    匹配字段正文

星期四

  1. Ben's Bites

    Ben's Bites:个人智能体不过是一堆文件和工具

    Ben's Bites 的投票显示,996 名受访者中多数对个人智能体不感兴趣;作者认为个人智能体本质上只是连接到任意智能体工具的一堆文件和工具,文件为会话提供上下文,所谓记忆也只是会话开始时读取的文件。作者将在明天的文章中演示如何为自己搭建一个全新的个人智能体。

    red memory for agents is to create a lore based on your past conversations. Turn your codebase into an isometric city and explore its architecture. Matt Pocock’s skills have 200k+ GitHub stars . Theo walked through the select few he uses . A Mac app for transcribing multi-hour recordings , with a CLI for agents. Long read from Cursor on how they built Origin as if it were a database. Really good visually interactive post. Another code-storage startup nipping at GitHub’s heels. This one I’ve been

    匹配字段正文

  2. LMSYS Blog

    Mooncake 为 Miles 加速:从碎片化 rollout 数据到高效批量 I/O

    LMSYS 博客介绍 Miles 强化学习框架如何借助 Mooncake 优化 rollout 到训练的数据传输。Miles 结合 SGLang 做高吞吐 rollout 生成、Megatron-LM 做训练,并提供 PyTorch FSDP2 后端,支持全异步 RL。由于 rollout 数据是含 tokens、loss_masks、rollout_log_probs 等字段的异构结构,且以 list[np.ndarray] 形式高度碎片化,Mooncake 需在保留结构的同时实现高效批量 I/O。

    Synchronous rollout transfer for Miles

    stmatengss), Xingyuan Wu (@yokinoshitayoki), @fzyzcjy, @guapisolo, Xuchun Shang (@XucSh), Bo Gao (@Bo-Vincent), He Zhou (@ehuohz), and Yufeng He (@he-yufeng) for their contributions across Mooncake implementation and review, Miles integration and validation, and design feedback, review, and testing. Related Links Mooncake project: https://github.com/kvcache-ai/Mooncake; Mooncake documentation: https://kvcache-ai.github.io/Mooncake/; Miles Mooncake rollout transfer user guide: radixark/miles 2535

    匹配字段正文

  3. NVIDIA Developer · Generative AI

    NVIDIA 如何用 CLI、Skills 和 AI 编码智能体开发 Holoscan 应用

    NVIDIA 用 Codex 搭配 GPT-5.6 sol max 模式,通过 Holoscan CLI、HoloHub 开发技能与文档,分三轮迭代构建了内窥镜工具分割仪表盘应用,复用 MONAI 模型与示例视频。延迟优化使渲染吞吐提升 50.5%、应用路径平均延迟降低 33.6%。消融实验显示,CLI、HoloHub 技能与文档三者结合时智能体处理最快(40 分钟)、token 用量最低(11M)。

    and data reuse. Developer prompt 1: Use $holohub-app-lifecycle to create a separate new Python HoloHub application for displaying endoscopic tool tracking as model outputs https://github.com/Project-MONAI/model-zoo/tree/dev/models/endoscopic tool segmentation. Reuse the MONAI endoscopic tool segmentation model, sample data, preprocessing, and inference. Show the model-derived mask, coverage and timeline, and useful uncertainty measurements in a polished HoloViz overlay. Do not train or modify th

    匹配字段正文

  4. NVIDIA Developer · Generative AI

    NVIDIA 教程:后训练 Cosmos 3 Edge 实现 Jetson Thor 端侧机器人控制

    NVIDIA 发布教程,介绍如何后训练 4B 的 Cosmos 3 Edge 全能模型,使其在 NVIDIA Jetson Thor 上原生运行机器人操作策略。在 Jetson AGX Thor T5000 上,该策略约 1.53 秒生成一个动作块,单个动作块覆盖约 2.13 秒机器人运动,因此无需数据中心 GPU 即可连续实时控制。在 RoboLab 闭环评测的 120 项语言条件操作任务中,后训练后的 Edge 策略成功率为 22.9%,Nano 为 36.8%。训练数据为 nvidia/Cosmos3-DROID 数据集,含 76k 条成功遥操作轨迹、约 350 小时、86 项任务和 564 个场景;后训练与部署脚本已在开源 cosmos-framework 仓库提供,检查点发布在 Hugging Face。

    Steps Download the Cosmos Edge policy weights and data to begin post-training; Explore Cosmos Edge on Jetson for deployment guidance and examples; Get the Cosmos framework code on GitHub to access post-training and deployment scripts Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for learning physical interactions, but their size can make on-device deployment difficult. This changes with t

    匹配字段正文

  5. NVIDIA Developer · Generative AI

    NVIDIA 发布 SkillEvaluator,评估 AI 智能体技能表现

    NVIDIA 推出开源工具 SkillEvaluator,通过静态检查和有无技能对照的实机任务运行,衡量技能对智能体表现的影响。首批对 30 多个 NVIDIA 产品线的 300 多个已验证技能在 Codex 和 Claude Code 两个 harness 上评估,Correctness 平均 Skill Lift 为 41 分,Effectiveness 为 39 分;单产品差异从 +2 到 +46 分。工具还单独追踪 token 用量与执行时间,例如 jetson-optimize-memory 使 token 减少 76.9%,cuopt-install 则增加 120.3%。OpenClaw 与 Nous Research 已在 ClawHub 和 Hermes Agent 中试点集成。

    skill scores vary. Catalog-wide averages aggregate thousands of trials, but this post does not report confidence intervals. The same evaluations used skills from the NVIDIA/skills GitHub repository. Table 2 shows the average with-skill scores and Skill Lift relative to the without-skill baseline across Codex and Claude Code. Verified skills improved agent performance across both evaluated harnesses, with the largest gains in Correctness, Discoverability, Effectiveness, and Efficiency. Skill Lift

    匹配字段正文