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星期三

  1. The Register

    Atrophy CLI 工具发布:用 Elo 式评分对抗 vibe coding 技能退化

    开发者 Ashutosh Rath 推出命令行工具 Atrophy,用 Elo 式评分追踪开发者在语法回忆、调试、代码阅读、API 记忆和设计拆解五个技能领域的水平,每项独立评分、初始 1200 分。用户先做约 25 分钟的基线测试,之后每周 2-3 次、每次 5-10 分钟练习,工具会自动挑选最久未练的技能。练习覆盖 Python 和 JavaScript,分三个难度等级。

    stdlib call, and decomposition tests a coder’s ability to outline a design. Exercises test Python and JavaScript skills and come in three difficulty levels, Rath explained in the GitHub readme, with seeded generation for fresh variants of the different exercises. “If AI assistance is quietly eroding your ability to code unaided, the chart shows you – before an interview, an outage, or a day without wifi does,” Rath wrote in Atrophy's readme. Users take a baseline exam with one exercise in each

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  2. Cognition 模型 / Devin 博客

    Cognition 发布 FrontierCode 1.1:改进公平联网评测方法

    Cognition 发布 FrontierCode 1.1,改进评测方法以区分合法联网(如查文档)与不公平联网(如查找任务答案),并审计 1000+ 条评分标准、放宽 75 条过严标准。新版加入提示词与程序化检测两道防线,使各模型不公平联网率降至 1% 以下,同时公布 Sonnet 5 分数并更新 Fable 5 分数,不再报告 Diamond 子集。

    settling on these safeguards, we also considered enforcing fair internet use with a blocklist or allowlist of particular sites. Unfair internet use falls into two main categories: GitHub (and its many mirrors) and package registries (where agents will often just run npm install to pull the latest release that already contains the solution). Blocklisting turned out to be impractical. Over several iterations of blocking domains and inspecting agent trajectories, our blocklist grew to roughly 1,200

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星期二

  1. Hugging Face精选

    Microsoft Foundry 托管计算上的 Hugging Face 模型

    Microsoft 与 Hugging Face 将经过许可与安全审查的数千个开放模型引入 Foundry Model Catalog,目前以预览版提供,并每周更新。模型可通过统一端点部署到配备 NVIDIA A100、H100 或 AMD MI300X 的 Managed Compute,运行时升级和 CVE 修补由平台自动完成;后续将扩展模型与加速器覆盖,并支持 Bring Your Own Weights。

    00,000 organizations, and over 3 million open models published , with new frontier capabilities — agentic coding, video segmentation, speech, embeddings — landing weekly. It's the GitHub of open models, where the community publishes weights, writes model cards, compares evaluations, and pulls models for experimentation. Open models have closed the gap with proprietary models on benchmark after benchmark, and they unlock things proprietary endpoints can't: State-of-the-art is now open. Leading op

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星期一

  1. Hugging Face精选

    PRX 第4部分:我们的数据策略

    Photoroom 为 7B 文生图模型 PRX 构建了兼顾规模与多样性的预训练数据管线,以 Lance 完成探索和整理,再转换为 MDS 供分布式训练。团队用 Qwen3-VL-8B 重新生成详细图像描述,并采用 quality 92 的 JPEG、分辨率与宽高比分桶。文本分类和感知哈希通过跳过列表过滤文本密集、NSFW 及近重复样本,无需重写数据集。

    nce alignment as well as the data tooling we use for these in a future post. Build something with PRX, or find a bug in our pipeline? PRX is Apache 2.0 and the model code lives at github.com/Photoroom/PRX . We integrated PRX into the diffusers library and there is a Hugging Face Space to try out our latest version PRX Pixel . Come talk diffusion models and data with us on Discord . Qwen2.5-VL-7B-Captioner-Relaxed CMMD, "Rethinking FID" (Jayasumana et al., 2024) DINOv2 (Oquab et al., 2023), the f

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  2. Hugging Face精选

    Hugging Face Kernels 重大更新:新增 Hub 仓库类型并强化安全机制

    Hugging Face 为 Kernels 新增 Hub 仓库类型,并通过可信发布者、可复现构建和 Sigstore cosign 代码签名强化安全;签名加载时自动验证尚未启用。更新还重构了 kernels 与 kernel-builder CLI,支持 Torch Stable ABI 和 Apache TVM FFI,并为智能体构建、基准测试和迭代优化 kernel 提供基础。

    se signing keys are only valid for a limited time, an attacker typically cannot use the private key, even when it is leaked. We also verify that the kernel was signed by a trusted GitHub workflow from a trusted GitHub repository. Kernel signing is already supported by kernel-builder and we have provided the kernels verify-signature to verify a kernel. Kernels does not verify the signature upon loading a kernel yet, since we would like to test this new functionality more before fully rolling it o

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星期六

  1. Lilian Weng:Lil'Log

    Lilian Weng 长文梳理 harness engineering 与自我改进

    Lilian Weng 在 Lil'Log 发布长文,围绕 harness engineering 如何推动递归自我改进,梳理了工作流自动化、文件系统持久记忆、子智能体与后端任务三类设计模式。文章进一步综述 ACE、MCE、Meta-Harness、ADAS、AFlow、STOP、Self-Harness、AHE、AlphaEvolve、Darwin Gödel Machine 等自改进与进化搜索工作,并指出评测器模糊、上下文与记忆生命周期、负面结果、多样性崩塌、奖励攻击等瓶颈,认为人类应在合适抽象层提供监督。

    ndustry. Pattern 1: Workflow Automation Defining a workflow in which the model can operate, test, and iterate is a key design for automation. Karpathy’s autoresearch repo (https://github.com/karpathy/autoresearch) is a clean example of how such a workflow can be constructed. A common workflow follows a goal-oriented loop of plan, execute, observe/test, improve, and execute again until the goal is achieved. The process may trigger proactive requests to users for clarity in task specification or e

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星期四

  1. Mistral AI

    Mistral AI 发布 Leanstral 1.5:6B 激活参数的开源形式化验证模型

    Mistral AI 发布 Leanstral 1.5,一个 Apache-2.0 许可、119B 总参数、6B 激活参数的开源模型,在形式化验证上大幅升级:miniF2F 达到 100%,PutnamBench 解出 587/672 题,FATE-H 达 87%、FATE-X 达 34%。该模型经 mid-training、监督微调和 CISPO 强化学习三阶段训练,在 57 个仓库中发现 5 个此前未知的 bug,并已通过 Hugging Face 和免费 API 开放使用。

    n instead, also with four attempts. Across 57 tested repositories, this process flagged 47 violated properties, with 11 pointing to genuine bugs—5 of them previously unreported on GitHub. One such bug was in the sign function for zigzag decoding of the datrs/varinteger library. On input Std.U64.MAX, the expression (value + 1) overflowed, causing crashes in debug mode and silent corruption in release mode—an edge case that testing and fuzzing would typically miss. Leanstral’s pipeline caught it a

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  2. Trail of Bits Blog

    GPT-5.5-Cyber 一天内为 zlib 搭建模糊测试实验室

    Trail of Bits 在 Patch the Planet 项目中用 Codex 的 /goal 命令驱动 GPT-5.5-Cyber,一天内为 zlib 搭建了覆盖十余个入口的模糊测试流程,包括 harness、sanitizer 构建和种子,并发现多个待协调披露的漏洞。模型自行判断静态审查价值有限,转而构建动态测试工具,并主动放弃一个在真实条件下难以触发的 inflateBack 空回调崩溃。作者认为,定制化模糊测试的专家门槛正在消失,攻防双方都能更快发现漏洞。

    ing work being done by Patch the Planet matters. Every new capability that helps us find bugs faster is just as available to an attacker, so the advantage goes to whoever finds the bugs and fixes them first. Patch the Planet If you enjoyed this post, share it: X LinkedIn GitHub Mastodon Hacker News Related Posts How we use /goal to find bugs in Patch the Planet July 28, 2026 Over the first few weeks of Patch the Planet, our engineers found three techniques for using /goal, which hands Codex an …

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  3. LMSYS Blog

    LMSYS 分享 SGLang 智能体辅助开发实践:从 SKILL 到 SOTA 循环

    LMSYS 发布博客,介绍 SGLang 智能体辅助开发的初步探索,把 CUDA 崩溃调试、内核集成、测试、CI、性能剖析、生产排障等仓库级流程写成可执行的 SKILL.md 与脚本。文中给出 llm-torch-profiler-analysis 与 llm-pipeline-analysis 两个剖析技能,前者输出 kernel、overlap、fuse 三类固定表格,后者按前向传播与层拆分 trace。基于 KDA-Pilot 的 10 个 B200 扩散内核任务中,多数在提取的生产行上取得 1.1341x 至 2.7499x 的 wall-geomean 加速,截至 2026 年 6 月 27 日已有三个优化合入 SGLang 上游。

    ihao Liu, Ligeng Zhu, Zijian Zhang, Zenus Zhang, shinan6, DYZhang, Chao Liu, Zhou Yaoyang, gyy0592, AcrossForest, Emin, Qiming Chu, jiaxiaoyu, tastynoob, and zhenwei. 8. References SGLang GitHub Repository; SGLang .claude/skills; SGLang diffusion .claude/skills; AI-Infra-Auto-Driven-SKILLS; AI-Infra-Auto-Driven-SKILLS prompts; Kernel Design Agents (KDA); KernelWiki skill; ncu-report-skill; KDA-Pilot; SGLang Diffusion Advanced Optimizations, LMSYS Blog; OpenAI Codex Prompting: Goal mode; Humanize

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  4. Cognition 模型 / Devin 博客精选

    Cognition 发布 Devin Security Swarm,自动验证漏洞并提交修复 PR

    Cognition 发布 Devin Security Swarm,由并行智能体分段排查代码库,组合出完整攻击路径并在隔离沙箱中复现验证可利用性,确认后自动编写补丁并提交 PR 供审查。官方称其在 50 个真实漏洞基准上比最接近的同类方案多发现漏洞且成本低 30%,并独有发现 PHP 模板注入沙箱绕过等三个关键漏洞。该产品即日起可用,支持从威胁模型文档生成扫描配置、按日/周/自定义计划扫描,首次全量扫描后仅处理变更代码。

    , read our blog post on the Agentic MapReduce architecture. Performance We evaluated Devin Security Swarm on a benchmark of 50 real-world vulnerabilities, each tied to a published GitHub Security Advisory (GHSA) across repositories in Go, Python, JavaScript, Rust, Ruby, C , Java, Swift, PHP, Elixir, Erlang, C, Kotlin, and Dart. Only Devin found three critical vulnerabilities that other tools missed: a PHP sandbox bypass via template injection, an argument injection through metadata value parsing

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星期三

  1. Berkeley AI Research (BAIR) Blog

    2026 BAIR 博士毕业生展示:从机器人到 LLM 推理的 AI 研究全景

    BAIR 公布 2026 届博士毕业生名单,其研究覆盖机器人、具身智能、大语言模型与推理、计算机视觉、生成模型、AI 安全、人机交互及 AI for Science 等方向。毕业生去向包括 OpenAI、Mistral AI、Physical Intelligence、Amazon 研究岗、普林斯顿 CITP 博士后以及自主创业。

    Baifeng Shi

    eley, and we can’t wait to see what they do next! Thank you to our friends at the Stanford AI Lab for this idea! Baifeng Shi Email: baifeng shi@berkeley.edu Website: https://bfshi.github.io/ Advisor(s): Trevor Darrell Research Blurb: I work on building generalist vision and robotic models. What's next: Member of Technical Staff at Physical Intelligence Charlie Snell Email: csnell22@berkeley.edu Website: https://sea-snell.github.io Advisor(s): Dan Klein Research Blurb: My work aims to understand

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星期二

  1. Dwarkesh Patel Podcast & Blog

    Grant Sanderson 谈 AI 与数学的未来

    Dwarkesh Patel 与 3Blue1Brown 作者 Grant Sanderson 对谈 AI 与数学的未来,讨论 AI 在数学上的进展为何快于其他领域,以及这种进展对其他行业意味着什么。Sanderson 认为 IMO 金牌只是又一个基准,数学内部同样存在参差,几何题可被暴力求解而组合题仍难;他还以 Galois 群论为例说明概念突破的验证回路可能长达百年,并谈到 AI 能否在领域之间建立连接、以及数学家的角色可能转向类似策展人的方向。

    e out on whether it’ll yield anything—is that with Lean, you could imagine having a basically endlessly running program that’s constantly trying to extend Mathlib. Mathlib is this GitHub repository that’s basically all of math written in code. It’s very far from all of math, but they want it to be all of math. It’s written in code where you can ask, “Is this proof correct?” It’s very labor-intensive to write these proofs. There’s a whole subcommunity around it. But you could imagine having an AI

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星期六

  1. Ahead of AI精选

    Sebastian Raschka:如何用开源工具搭建本地编码智能体

    Sebastian Raschka 演示了如何用 Ollama、本地开放权重 LLM 和 Qwen-Code 搭建可读写文件、运行命令并验证修改的本地编码智能体。文章还介绍了安全审计、性能测试及 Codex、Claude Code 的接入方式;其小规模评测中,Codex 使用更少 token,而 Claude Code 在部分模型组合上取得更高任务成功率。

    the increasingly popular Cline, but more on that later. The reason why I am primarily using Qwen-Code when working with Qwen models is that: it is open-source, like Codex (https://github.com/openai/codex) but unlike Claude Code; Qwen models have been specifically optimized for the Qwen-Code harness (more information below); I can run both Codex (with the latest GPT model) and Qwen-Code with a local Qwen model side by side on the same machine without having to switch manually back and forth betwe

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  2. LMSYS Blog

    Netpreme X-Mem™ MPU 加速 SGLang HiCache:TTFT 最高降低 6.7 倍

    Netpreme X-Mem™ MPU 作为专用 KV 内存层接入 SGLang HiCache,在前缀密集负载下将首 token 时延(TTFT)较基于 Host DRAM 的方案降低约 6.7 倍。单个内存节点提供最高 24 TB 内存、4 TB/s 带宽,通过 CUDA 与 PyTorch 兼容 API 集成。端到端测试中,中等负载下每用户 TPS 提升 33%,高负载下交互性提升 50%、系统 TPS 提升 30%。

    GLang Documentation: HiCache Design; SGLang Blog: HiCache: High-Performance KV Cache Storage with Hierarchical Caching for LLM Serving; Netpreme SGLang X-Mem™ Integration: https://github.com/netpreme/sglang xmem; LinkedIn Engineering Blog: Turbocharging LinkedIn’s Recommendation Systems with SGLang; Qwen3-235B-A22B-Thinking-2507: https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507; SWE-bench: https://github.com/SWE-bench/SWE-bench; Netpreme Coding Agents Experiments: https://github.com/net

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星期四

  1. DevClass

    CPU 在智能体 AI 基础设施中日益重要的角色

    Arm 认为,随着智能体 AI 部署在云和企业环境扩张,CPU 正成为整个系统的控制平面,负责数据在存储、内存与加速器之间的协调搬运、工作负载安全隔离以及分布式调度。Arm 与 Meta 合作打造的 Arm AGI CPU 基于 Arm Neoverse V3 平台,面向机架级密度和机密计算等需求。AWS Graviton、Google Axion、Azure Cobalt 以及 NVIDIA Grace Hopper、Grace Blackwell 和 Vera Rubin NVL72 均采用 Arm Neoverse CPU 设计。

    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

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  2. OpenHands 官方博客

    AI 编程基准测试解析:SWE-bench、LiveBench 等 9 项基准

    OpenHands 官方博客梳理了 2026 年值得关注的 9 项 AI 编程基准测试,包括 SWE-bench Verified、SWE-bench Pro 和 SWE-rebench。SWE-bench Verified 含 500 个人工验证实例,头部模型得分已超 80%,公开榜单显示最新模型在 90% 出头;SWE-bench Pro 覆盖 41 个仓库的 1865 道题,原论文中领先模型最高仅 23.3%。文章还区分了模型基准与智能体基准,并说明各基准的评分方式与污染风险。

    Async coding agents download CTA

    neering tasks. Every benchmark, whatever its reputation, combines the same three parts: Task set: The work the system is asked to do, from short synthetic Python functions to real GitHub issues paired with the pull requests that fixed them; Evaluation setup: The conditions the system solves the task under, including the system prompt, tools, retrieval, execution environment, and retry logic; Scoring method: The definition of success, whether hidden tests, objective ground-truth answers, or blind

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星期三

  1. Mistral AI

    Mistral 为 Connectors 增加管理员权限控制与调试器

    Mistral 为 Connectors 推出多项新能力:管理员控制(GA)可按工作区或组织设置连接器访问并逐个开关工具,带连接器作用域的 API key(GA)防止自动化任务冒用身份,多账号连接器(GA)支持一个连接器绑定多个账号。Connectors Debugger 进入公开预览,可对 MCP 连接做 11 步端到端排查;Connectors in Vibe Code 已 GA,Connectors in Workflows 处于公开预览。目录现有 60 多个预置集成,并支持自定义 MCP 连接器。

    connector-Atlassian (black)

    ore than 60 integrations, and when the platform you need isn't listed, a custom MCP connector fills the gap. Analytics & Business Intelligence Developer Platforms & Infrastructure GitHub Cloudflare Developer Platform Research, Scientific & Public Data Enterprise controls, Connector Credentials, and Connectors in Vibe Code are generally available now. Connectors Playground and Connectors in Workflows are in public preview. All of it is live in Studio. Connectors documentation; Connectors in Vibe

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  2. Lilian Weng:Lil'Log

    Scaling Laws 详解:从 Kaplan 到 Chinchilla 的缩放定律

    Lilian Weng 在 Lil'Log 发文系统梳理深度学习缩放定律(Scaling Laws)的发展脉络,涵盖 Kaplan et al. (2020) 与 Chinchilla 缩放定律,以及拟合缩放定律的三种方法:固定模型规模变化 token 预算、IsoFLOP 曲线和参数化拟合。文章还讨论了数据无限与数据受限两种情形下的缩放规律,以及现实中拟合缩放定律的难点。

    , only medium models, or all models gives different apparent scaling laws Please cite this work as: Weng, Lilian. “Scaling Laws, Carefully”. Lil’Log (Jun 2026). https://lilianweng.github.io/posts/2026-06-24-scaling-laws/ Or use the BibTex citation: [1] S. Amari, N. Fujita, and S. Shinomoto. “Four Types of Learning Curves. Neural Computation.” 4(4):605–618, 1992. [2] Hestness et al. “Deep Learning Scaling is Predictable, Empirically.” arXiv preprint arXiv:1712.00409, 2017. [3] Rosenfeld et al. “A

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  3. Replit Blog

    Replit 如何评估并改进 Replit Agent:从单次打分到持续改进闭环

    Replit 为评估 Replit Agent 构建了离线基准与线上 A/B 测试、生产 trace 相结合的双支柱评估体系,并推出面向 vibe coding 的公开基准 ViBench,用自然语言 PRD 和测试计划检验智能体从零构建的应用是否符合需求。早期结果显示,前沿编码基准分数并不总能迁移到完整应用构建,且多数模型在扩展自己写的代码时表现变差。

    Closing the loop: Evaluating and improving Replit Agent at scale — cover image

    terested in working on autonomous software engineering agents, our team is hiring; reach out to [email protected]. References [1] SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [5] Quantifying infrastructure noise in agentic coding evals [9] How we made continuous trace intelligence possible at scale Follow @Replit Sep 29, 2026Free the models: Harness design at the frontierModel routers are everywhere right now, but they have a fundamental limitation. No matter if based on adva

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星期二

  1. DevClass

    Google 眼中的新数据库世界:不精确查询与无处不在的 AI

    Google Cloud 数据库高管在伦敦峰会上表示,未来三到五年人类将不再直接使用数据平台,而是由人类编排智能体、智能体完成实际工作,数据检索的重点也从“精确结果”转向“最佳结果”。Google SQL 已引入 AI.IF 等 AI 函数,由 Gemini LLM 评估自然语言条件并返回 true 或 false;其代理模型方案据论文称可减少约 400 倍 token 消耗,延迟降低 30 至 100 倍。

    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

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