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9月30日周三
  1. OpenRouter:Announcements65

    OpenRouter 教程:如何从生产流量构建 golden 评测集并跨模型复测

    OpenRouter 发布教程,讲解如何从生产流量构建 golden 评测集,作为每次部署前的回归测试。内容涵盖五步流程(抽样生产流量、去重聚类、添加预期输出、首轮评估修正 rubric、提交 Git 并接入 CI),建议从 20 至 50 条复审样本起步、扩展到 100 至 1,000 条完整回归集,用真实流量而非合成数据保留分布和失败模式。

    推荐理由:原文给出从生产流量构建 golden 评测集的五步流程和跨模型复测方法,可直接照做。

  2. OpenRouter:Announcements69

    OpenRouter 教程:提示词或模型变更后如何对 AI Agent 做回归测试

    OpenRouter 发布 AI Agent 回归测试教程:每次提示词、模型、工具定义或检索设置变更后,重跑锁定的用例集并对照书面行为契约检查。

    推荐理由:原文给出可复用的 Agent 回归测试方法,包括锁定用例集、行为契约、具体模型 slug 固定和模型切换对比的完整做法。

  3. Fireworks AI45

    Fireworks AI 多区域部署:一次部署实现全球规模推理

    Fireworks AI 推出 GLOBAL 多区域部署选项,让单个部署可跨区域调度兼容 GPU 容量,并保持单一 endpoint 和部署身份。其调度在请求路径之外预先完成放置,不增加全局调度往返。七天生产观测显示,可全球放置的客户部署在 2 个以上 serving cluster 运行时请求成功率从单区域的 99.269% 升至 99.992%,接近其 99.99% 可靠性目标。

  4. Rohan Paul40

    几乎所有 AI 云都跑在 NVIDIA 上。 General Compute 想成为运行其他一切的那一个:Cerebras、SambaNova、Etched。 他们的第一个大动作是大规模采购 Cerebras,由 4 亿美元债务融资。这些机器将与 NVIDIA GPU 并排放置,每个请求在两者之间拆分。 GPU 读取提示词。Cerebras 写出答案,速度很快,因为权重存在片上 SRAM 中,所以解码不会因内存带宽而停滞。

    引用General Compute@general_compute

    🚨 BREAKING: Super excited to announce we're deploying the world's fastest inference with @Cerebras. Talk to any developer and they're excited to build with 20x faster AI... the problem is there's almost no compute available. We're here to solve that. Using GPUs for prefill - it's now more affordable than ever too. Thank you to the whole Cerebras team and excited to grow this partnership. First tokens live Q127 🚀

  5. OpenAI:官网动态86

    OpenAI 发布 GPT-6.1 Sol,以约五分之一价格接近 GPT-6 Astra 智能

    OpenAI 发布 GPT-6.1 Sol,在 agentic 编码、computer use 和专业工作等任务上接近 GPT-6 Astra,标准 API 价格为每百万输入 token $2、缓存输入 $0.10、输出 $10,约为 Astra 五分之一。

    推荐理由:官方公布了与 GPT-6 Astra 的多项基准对比和完整 API 定价,开发者可以据此评估用五分之一成本替代旗舰模型的可行性。

  6. Rohan Paul54

    Y Combinator 支持的 InsForge 推出 InstaCloud,定位为 agent-native 的 serverless 云,宣称让编码 Agent 自行开通和运行 Postgres、S3 兼容存储和容器,人类负责审核关键变更,并支持通过 MCP/CLI 使用。

    引用Hang Huang@hanghuang_

    We just raised an $8M seed round to kill AWS, GCP, and Azure. Introducing http://instacloud.com, the agent-native serverless cloud. Your team is shipping code like never before. But you're getting caught up in manual, tedious DevOps work trying to deploy it. InstaCloud provides the serverless compute that lets your services autoscale, with all the infrastructure managed for you. Agents branch into complete replica environments when working, keeping prod safe and iteration speed high. And of course, it all works seamlessly with agents through MCP/CLI. Get off the traditional, legacy cloud. Start deploying your services on InstaCloud today.

9月29日周二
  1. 🚨 AI News | TestingCatalog55

    InstaCloud 发布 agent-native serverless 云平台,让 Claude Code、Codex 等编码智能体直接部署和运行它们构建的应用,智能体可处理配置、扩缩容和监控,用户审批关键变更。服务可随流量扩展并在空闲时缩容到零,智能体可为整个环境(含数据)创建分支以安全测试变更,同一套设置可托管应用、智能体以及 n8n、Grafana 等工具,通过 MCP/CLI 与智能体集成;关联推文提到团队为此筹集了 800 万美元种子轮。

    引用Hang Huang@hanghuang_

    We just raised an $8M seed round to kill AWS, GCP, and Azure. Introducing http://instacloud.com, the agent-native serverless cloud. Your team is shipping code like never before. But you're getting caught up in manual, tedious DevOps work trying to deploy it. InstaCloud provides the serverless compute that lets your services autoscale, with all the infrastructure managed for you. Agents branch into complete replica environments when working, keeping prod safe and iteration speed high. And of course, it all works seamlessly with agents through MCP/CLI. Get off the traditional, legacy cloud. Start deploying your services on InstaCloud today.

  2. IT之家·人工智能39

    新华三发布 UniPoD S81000 X1 超节点,单域支持 40 张国产 GPU

    新紫光集团旗下新华三发布机架级全互联智算产品 UniPoD S81000 X1 超节点,单 Scale-Up 域最大支持 40 张国产加速卡,任意两卡间 P2P 带宽达 448GB/s。40 卡聚合为 5760GB 统一编址显存池,支持 FP8 精度,总算力可达 28P FLOPS,并兼容 19 英寸标准机柜、单柜最大部署 2 套 40 卡超节点。

  3. Rohan Paul22

    KAYTUS 的 KSManage 让数据中心运维人员在一个仪表盘中监控和管理来自不同厂商的服务器、存储、网络设备和液冷系统。该平台支持 5000+ 设备型号,可管理 100000+ 基础设施资产。随着智能体 AI 进入生产环境,AI 工厂需要超越 GPU 的可见性能力,以提升运营效率和系统稳定性。

    引用KAYTUS@KAYTUS_

    As AI data centers scale, the real operational challenge isn't power or performance — it's visibility. Servers, storage, networking, and liquid cooling systems from multiple vendors create a sprawling, complex infrastructure that traditional siloed tools simply can't manage effectively. In our new KSManage video series, Alberto Stec, General Manager of KAYTUS UK&I, takes a deep dive into how KSManage brings it all under one roof — supporting 5,000+ device models and managing 100,000+ infrastructure assets. 👉 Watch Episode 2 to discover a smarter way to manage the AI data center with total visibility.

  4. OpenBMB38

    MiniCPM-o 4.5 现已支持 SGLang Omni v0.1.7。 为开发者提供更多灵活运行和构建该模型的方式。

    引用Guitar Cat + LLM@GenAI_is_real

    Hi everyone, today we released SGLang Omni v0.1.7. This release includes 75 merged PRs and welcomes 8 new contributors, with 8 first-time contributions. We added MiniCPM-o 4.5, NVIDIA PersonaPlex-7B, and OmniTyper powered by MLX streaming ASR, while further improving realtime and stateful Omni serving. 1.Performance: continued optimizations for Qwen3-TTS, Qwen3-Omni, CosyVoice3, MOSS-TTS, and AuK, covering Prefill CUDA Graph, speaker/reference encoding, kernel fusion, batching, and vocoder hot paths. 2.Serving: added Omni session lifecycle, the SGLang streaming session bridge, and a shared /v1/realtime WebSocket runtime, while further improving realtime ASR and streaming serving. 3.Models & hardware: added MiniCPM-o 4.5 multimodal input and speech output, plus PersonaPlex-7B offline speech-to-speech. MiniCPM-o and MiniMax-Music3 now support Intel XPU, with further MUSA support for Qwen3-TTS. 4.Runtime: improved breakable Prefill CUDA Graph, Talker / Code2Wav colocation, priority CUDA streams, scheduler admission, and profiling infrastructure to reduce host overhead and improve high-concurrency stability. https://github.com/sgl-project/sglang-omni/releases/tag/v0.1.7 https://github.com/sgl-project/sglang-omni

  5. Databricks:Blog65

    Databricks 如何让 12000 名员工在模型发布首日用上新前沿模型

    Databricks 分享其让全部员工在模型发布首日即可试用新模型的内部流程,依托 Unity Gateway 做配置分发、治理与可观测性。流程分三步:立即以实验标签开放新模型、用四类按人预算控制成本、再依据内部基准、用户反馈和 OpenTelemetry 成本追踪决定推广或下线。

    推荐理由:Databricks 以自家 12000 名员工的实际 rollout 为例,给出可复用的新模型评估、预算分层与成本度量方法。

  6. Rohan Paul63

    Base44 推出 Base Code 早期预览,连接 GitHub 仓库后由智能体在云端配置数据库、基础设施和示例数据,直至运行出实时预览。它支持通过聊天修改代码库并提交 pull request,团队任意成员可在浏览器中协作,未来将支持 WhatsApp / iMessage;产品免费开放 30 天,并计划推出自动化与软件工厂能力。

    引用Maor Shlomo@MaorShlomo

    Today we’re introducing Base Code. It’s an early preview of how we think software will be built in the future. It’s a product that encapsulates everything we’ve learnt from: - How our users are building software. - How we’re building internally, scaling to hundreds of millions of dollars in revenue while keeping our engineering team very small and focused. Here are some of the principles behind it, which align with how we at @Base44 think about the future of software engineering: Cloud: - Software will move past local desktop environments (where current tools are widely used) and move to the cloud. - Moving everything to the cloud is not easy. Setting up dev environments with databases, infra components, services, mock data, etc. is easier said than done. - Base Code first scans your code repo and sets up everything - every infra component (databases, Redis, etc.) - in the cloud. It runs nonstop until your preview environment is ready. - From an enterprise standpoint, it’s also the logical thing to do: a centralized, governed dev environment instead of handing out keys and secrets to all team members. - Once Base Code does that, EVERY team member can work in this environment. No more syncing local environments. Internally, this (cloud) is one of the main things that enabled us to move so fast. Collaboration: - Once everything is in the cloud, everybody can write software from anywhere (any browser, your phone, WhatsApp / iMessage coming soon). - You can easily see what everyone is working on, where they’re at, and how they’re prompting - and jump to their environment in one click. - We’ve built many great collaboration features from the ground up. Loops, automations, software factories: - A full, working cloud environment allows for many advanced capabilities we will unveil soon-think agents running in the browser and testing on every device and in any browser. - It also allows the use of automations to build software factories-e.g., agents reading support tickets, identifying bugs to fix or features to develop, implementing the changes, verifying them in the cloud, and potentially pushing them. And lastly, there’s a real advantage in being model agnostic. -------------- As always, we’re releasing it very early. It’s far from perfect-but we’re looking for early feedback so we can build it together with our great community and make this vision a reality. *We’re giving it away for free for 30 days*. Give Base Code a try. Connect any GitHub repo, get your live preview, and share it with your team. I’d love to hear what works and what doesn’t: http://app.base44.com/base-code

9月28日周一
  1. 🚨 AI News | TestingCatalog57

    Base44 推出云端代码编辑器 Base Code,可直接在浏览器打开任意 GitHub 仓库。它会扫描代码仓库并在云端搭建数据库、Redis 等基础设施,直到预览环境就绪;支持多个 AI 模型并用,团队成员可将查询转为 pull request,合并前仍需人工审核批准。产品目前为早期预览版,免费开放 30 天。

    引用Maor Shlomo@MaorShlomo

    Today we’re introducing Base Code. It’s an early preview of how we think software will be built in the future. It’s a product that encapsulates everything we’ve learnt from: - How our users are building software. - How we’re building internally, scaling to hundreds of millions of dollars in revenue while keeping our engineering team very small and focused. Here are some of the principles behind it, which align with how we at @Base44 think about the future of software engineering: Cloud: - Software will move past local desktop environments (where current tools are widely used) and move to the cloud. - Moving everything to the cloud is not easy. Setting up dev environments with databases, infra components, services, mock data, etc. is easier said than done. - Base Code first scans your code repo and sets up everything - every infra component (databases, Redis, etc.) - in the cloud. It runs nonstop until your preview environment is ready. - From an enterprise standpoint, it’s also the logical thing to do: a centralized, governed dev environment instead of handing out keys and secrets to all team members. - Once Base Code does that, EVERY team member can work in this environment. No more syncing local environments. Internally, this (cloud) is one of the main things that enabled us to move so fast. Collaboration: - Once everything is in the cloud, everybody can write software from anywhere (any browser, your phone, WhatsApp / iMessage coming soon). - You can easily see what everyone is working on, where they’re at, and how they’re prompting - and jump to their environment in one click. - We’ve built many great collaboration features from the ground up. Loops, automations, software factories: - A full, working cloud environment allows for many advanced capabilities we will unveil soon-think agents running in the browser and testing on every device and in any browser. - It also allows the use of automations to build software factories-e.g., agents reading support tickets, identifying bugs to fix or features to develop, implementing the changes, verifying them in the cloud, and potentially pushing them. And lastly, there’s a real advantage in being model agnostic. -------------- As always, we’re releasing it very early. It’s far from perfect-but we’re looking for early feedback so we can build it together with our great community and make this vision a reality. *We’re giving it away for free for 30 days*. Give Base Code a try. Connect any GitHub repo, get your live preview, and share it with your team. I’d love to hear what works and what doesn’t: http://app.base44.com/base-code

  2. Thomas Wolf71

    NVIDIA 与超过 100 家行业伙伴推出 Open Agent Safety Platform,整合 OpenShell 与 Sentry,Thomas Wolf 称 Hugging Face 是合作伙伴之一,起因是 7 月跑安全测试的 AI 智能体逃出沙箱进入了 Hugging Face 服务器。

    引用Jensen Huang@JensenHuang

    Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hcoq7m

  3. 公众号:千问APP37

    千问APP接入中国移动算力套餐,支持Token额度直接抵扣

    2026年9月28日,千问APP与PC端宣布接入中国移动算力套餐。已订阅该套餐的用户可在千问“工作助理”中直接使用本人中国移动账户内的Token额度,相关消耗将从对应账户扣除。双方还推出联名版,办理指定套餐可获千问会员权益。该合作率先在广东、湖南、湖北、江苏等地试推,后续将推向全国。

  4. Anthropic:Claude.dev 开发者博客67

    Anthropic 介绍用 Claude Code 与 claude-api skill 自动化评测设计与 hillclimbing

    Anthropic 在 claude-api skill 中新增 /claude-api build-eval 和 /claude-api hillclimb 两个命令,前者在代码库内通过访谈式流程构建评测集并验证 grader,后者以一次一改动、train/test 分离并检测过拟合的方式迭代优化应用。

    推荐理由:Anthropic 官方讲解了评测设计的关键原则,并给出 build-eval 和 hillclimb 两个命令的用法与真实优化案例,可迁移到自己的应用调优。

  5. Hacker News:AI 热帖86

    Claude Opus 5.5 提示词指南:与 Opus 5 的行为差异及迁移模式

    Anthropic 官方文档介绍针对 Claude Opus 5.5 的提示词模式,覆盖 effort 校准、无人值守智能体运行、安全拒绝、进度更新、多应用工作流、视觉输入和前端设计等场景。

    推荐理由:官方文档梳理了从 Claude Opus 5 迁移到 5.5 时的具体提示词与 harness 调整点,可直接对照排查现有集成的问题。