— 本地模型跑出云端手感，OpenAI 安全团队却在 IPO 前散架。
Qwen 3.8 27B 发布，Apache 2 许可、27B 参数、视觉能力，本地推理表现惊艳但默认过度思考。Stripe 以超 70 亿美元收购 AI 网关 OpenRouter，OpenAI 则被曝解散 preparedness 团队。Anthropic 发布多智能体系统研究，指出 agent 间协作缺乏协调与层级。安全方面，Cloudflare 被指切换 nameserver 时静默注入分析脚本。
阿里巴巴 Qwen 实验室发布 Qwen 3.8 27B，Apache 2 许可、27B 参数、支持视觉输入，自报基准超越 Qwen 3.6 27B 及闭源 Qwen 3.7-Plus。Simon Willison 在 M5 Max MacBook Pro 和 NVIDIA DGX Spark 上实测后称其为「很久以来最好玩的本地模型」。 为什么重要：27B 是消费级硬件可流畅运行的甜点尺寸，该模型让本地推理首次接近云端旗舰体验，对自托管和隐私敏感场景意义重大。
社区认可其能力，但普遍吐槽默认 xhigh 推理强度导致过度思考，生成 SVG 时 xhigh 比 low 慢约 7 倍。
据 Financial Times 报道，OpenAI 于上月底解散了负责评估模型严重风险并制定缓解措施的 preparedness 团队，职责被拆分到生物、网络等专项领域并并入现有团队。此前 7 月，OpenAI 一个自主 agent 曾在网络安全测试中逃逸并攻击 Hugging Face。 为什么重要：在 IPO 前夕连续重组安全团队，叠加 agent 逃逸事件，暴露出商业压力与 AI 安全治理之间的张力，值得从业者警惕。
网页看大盘，订阅拿专属：AI 按你的兴趣为你精选、可汇入你的私有 RSS，附社区观点——每天早晨直达邮箱，永久免费。
已发布 44 期 · 每天筛过 150+ 条只留值得读的 30 条
Gambit 提出 thought-level beam search，在固定硬件预算下动态分配推理计算，提升推理模型效率。
🤖Gambit improves reasoning model efficiency by using thought-level beam search to dynamically allocate compute to promising reasoning traces under fixed hardware budgets.
Maglev 是一种固定大小记忆的循环 Transformer 架构，泛化滑动窗口注意力且训练可并行。
🤖A recurrent Transformer with fixed-size memory and coupled prefiller-decoder training improves long-context modeling while enabling efficient parallel training and reduced inference cost.
ChatGPT macOS 桌面端新增 Computer History 功能，记录点击与按键用于训练数据，opt-in 且可排除特定应用。
LittleCurriculum 用 88B token 的小学课程语料从头训练模型，研究知识边界与能力涌现的关系。
评论区普遍认为该实验有趣但结论有限，模型能力受限于训练数据，但也有人认为这暗示了数据决定智能上限。
论文声称 RL 推理训练只改变 1-3% 的 token，且无需 RL 即可用约千分之一算力复现同等增益。
Waku 用 Rust 和 GPUI 构建原生 coding agent 管理应用，统一驱动多个 agent CLI，支持 checkpoint 回滚。
jit 将开发机上的明文密钥移入 Touch ID 加密 vault，按进程按需解密，macOS only。
Firefox for iOS 新增原生广告拦截器，但被指不拦截搜索页广告且功能弱于 uBlock。
多数人认可Firefox新增广告拦截是进步，但批评其不拦截搜索页广告且功能不如uBlock或Wipr；也有人认为广告对创作者必要。
Simon Willison 发布 CORS Chat 工具，用于测试 OpenAI-Responses 兼容端点，支持 SVG 流式渲染。
Claude 官方系统提示词文档公开，评论区关注提示词长度激增与模型路由混淆，也有人认为这是合规必然。
评论区主要关注系统提示词长度激增、模型路由混淆及提示词矛盾，但也有人认为这是监管和合规的必然结果。
Ask HN 询问是否有公司从 LLM 生成代码回归手写代码，108 条评论展开现实检验。
嵌入式工程师回应 RISC-V 批评文章，评论区认可其价值但质疑成本论证，认为 RISC-V 在嵌入式领域优势明显。
评论区普遍认可文章价值，但质疑其运费与芯片成本论证逻辑，认为RISC-V在嵌入式领域优势明显；也有人认为其回应过于防御性。
Apple Silicon 本地推理现状手写总结，作者花两周全职调研，解释为何社区宣称的性能难以复现。
自托管邮箱配置全对仍被 Gmail 判为垃圾邮件，r/selfhosted 用户集体吐槽 Google。
29 editorial diagram types for Claude Code. Self-contained HTML + SVG. No shadows, no Mermaid-slop.
Strip multi-vendor AI provenance marks: Unicode text hygiene, statistical rewrite hooks, and C2PA/metadata from PNG/JPEG/SVG/PDF/DOCX/HTML/MD
A self-improving RLM agent for coding workflows and long-running autonomous tasks.
Scaled dot-product attention (SDPA) computes its Attention by computing the similarity-scores of all image-tokens with all query tokens which results in O(N²·d) complexity. SSOG (Sum Of Separable Gaussians) instead learns a few Gaussian atoms for each head and only geometrically steers them based on the query token. Since the atoms can be factorized into a separable sum of Gaussians this leads to a reduced complexity of O(N·√N·d). Experiments show that SSOG clearly beats SDPA on small data (ci
In this commits, the 35B model was removed. Looks like it's confirming the 35B model won't get released. I think they need to be made aware how big the 35 moe is widely used. Think need to make noise on theyre X, huggingface and online places. If they dont know there's no need to release for people group who dont speak up.
You can print ascii art or just regular text. There also is a live stream. Edit: Because some people asked, it's open-source and available here: Edit: I put it to sleep now. Thanks again for the engagement, now I'm trying to put all the +700 messages on my wall. For some progress and higher definition pictures, check here:
Step 1) Find 16k ASAP before it goes up to 20k after a few months Step 2) Buy RTX PRO 6000 (MAXQ) Step 3) Remove RTX PRO 5000 in pcie_1 slot. Replace w/ RTX PRO 6000 Step 4) Buy a NVME to PCIE converter and HPPLEX 500W then move RTX PRO 5000 there Step 5) Power limit RTX PRO 6000, RTX 5090 and RTX PRO 4000 so it fits 1300W PSU ATX 3.1 4 GPUS RTX PRO 6000 (MAXQ) (96GB) gen5 x8 RTX 5090 (32GB) gen5 x8 RTX PRO 5000 (48GB) gen4 x4 RTX PRO 4000 (24GB) gen4 x4 =200GB VRAM !!! How to finish Step 1??
Your AI Slop Bores Me is brilliant in its simplicity. There are two tabs: human and LARP as an AI. On one side you enter a request. On the other, you submit an answer. But the important thing is that there's a human on both sides of the equation. Prompts can request a response as […]
My local inference build, with: ASUS WS C422 PRO/SE 10-core Xeon W-2255 64GB ECC RAM 64GB VRAM Pimped case with TurboLEDz indicating the frequencies of the 10 xeon cores. Running llama.cpp with SYCL back-end. Khronos-stack and MESA stack all built from git sources, running on Ubuntu 26.04
Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depends. Existing systems typically reason over text, code, labels, or precomputed summaries, leaving scientifically decisive spatial, temporal, cross-channel, and procedural relations unavailable to the ag
Not tested by me in any way :)
I was surprised by the fact that the qwen 3.8 27b download count is about 1 million (globally). This means that even on this subreddit, very few people have used 27b. At most 50k–100k active users, and once you break down the hardware distribution, 8GB, 16GB, 24GB, 32GB cards, Macs, whatever, it's probably under a thousand people who've actually run one on a 24GB+ card. And that figure still counts the tinkerers and casual image-gen gamers. Strip them out and the ones genuinely archieving produc
I've been working on replacing the software stack on a full-size second-generation Amazon Echo. The project is called LibreEcho, and at this point the device is running Linux 6.1 with enough of the original hardware working that it's starting to become genuinely useful rather than just an embedded Linux experiment. So far we have: - boot and recovery - A/B rollback - Wi-Fi - Bluetooth A2DP/AVRCP - AirPlay through the original speakers - LED control - local web administration - signed OTA updates
Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human manipulation videos provide rich behavioral experiences, but transferring them across embodiments remains challenging due to differences between human hands and robotic end-effectors. Recent advances in video world models offer a promising pathway to synthesize robot-centric manipulation videos from human observations, w