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今日 8 条
9月28日周一
  1. OpenAI News12

    你是 Codex Original 吗?

    OpenAI 正在征集使用 Codex 完成构建、研究或创作的真实用户故事,以推进 Codex Originals 项目。参与者需通过表单提交个人背景、项目介绍及相关链接,提交即表示同意 OpenAI 依据隐私政策使用材料并与其联系。

  2. OpenAI News34

    Basis 借助 GPT-6 Astra 以 2 倍速度完成税务工作簿

    Basis 测试显示,GPT-6 Astra 完成含 50 个标签页的复杂税务工作簿耗时比 GPT-5.6 Sol 少 50%,内部评测分数提升约 20%。该模型能按任务难度动态调整推理强度并保留 cache,从而降低成本与响应时间,同时更准确理解会计用户意图、减少纠错和 token 消耗。Basis 通过 OpenAI API 构建面向会计工作的 AI 智能体。

  3. Claude Platform:开发者版本说明(RSS)80

    Anthropic 发布 Claude Sonnet 5.5 并说明 API 迁移变化

    Anthropic 于 9 月 28 日发布 Claude Sonnet 5.5(claude-sonnet-5-5),已在 Claude API、Amazon Bedrock、Claude Platform on AWS、Google Cloud 和 Microsoft Foundry 提供。

    推荐理由:官方同时列出多平台可用性和从 Claude Sonnet 5 迁移时的五类兼容性变化,便于开发者提前调整请求与工具调用。

9月26日周六
  1. Replit ⠕52

    Replit 转发 Atta 创始人 Amin 的消息,确认已收购 Atta,且 Atta 的 charts 和 reports 已在 Replit 上线。Amin 与联合创始人 Omar Shaik 创立 Atta,最初的 canvas POC Fastview 就是在 Replit 中编写;公告链接为 https://replit.com/blog/replit-acquires-atta。Replit 官方仅配文称 This is just the beginning。

    引用abk@aminert

    I’m excited to share that @attaapp has been acquired by @Replit ! I started Atta with my co-founder, @omarshaik , because we thought AI would change how people explore and build with data. I wrote the first lines of the canvas POC for Fastview, which later became Atta, in @Replit. Now Atta’s charts and reports are live in @Replit . It’s been great working with @pirroh and the team, and I’m excited to be part of what @amasad and the @Replit team are building. A special thank you to @mejoff and @shawnvc and the @MenloVentures Labs team for believing in us and supporting us along the way. I’m incredibly grateful to our team, customers, investors, advisors, friends, family, and everyone who believed in us early or gave us feedback along the way. Read more here: https://replit.com/blog/replit-acquires-atta ❤️

  2. AWS Machine Learning Blog65

    NarrateAI 如何在 Amazon Bedrock 上实现生产级 LLM 质量保障

    AWS 介绍 NarrateAI 在 Amazon Bedrock 上用五项工程机制实现生产级 LLM 质量保障,服务超过 4,000 名 AWS 高管用户。系统通过自适应流水线让约 90% 查询走单次 LLM 调用,混合成本约为每次查询 1.4 次 LLM 调用;跨账号多模型故障转移把模型与账号组合成独立配额空间,负载测试支持超过 100 名用户并发流式请求。

    推荐理由:文章拆解了自适应路由、跨账号故障转移、流式评估和数值校验的工程组合,适合构建生产级 LLM 应用时参考。

  3. Replit ⠕54

    Replit 宣布收购 AI 数据分析公司 Atta,并把其分析体验引入 Replit 的构建流程。Atta 主打无需 SQL、数据清洗或手动选择分析方法,用户连接数据后即可生成图表和数据应用;相关新体验已在 Replit 上线,介绍链接为 https://replit.com/blog/replit-acquires-atta。

    引用Omar Shaik@omarshaik

    I'm thrilled to announce that @Replit has acquired @attaapp, bringing our world-class AI analytics to everyone. Two years ago, I cold emailed @amasad asking him to invest in Atta. He and @pirroh saw an early demo of our charts and data apps on an infinite canvas and wrote a check the same day. @aminert and I founded Atta to democratize business analysis. We empowered the people closest to the business problems to understand their data, communicate their insights, and make better decisions. No SQL. No cleaning up AI slop. No picking the right analytical method, the right visualization, or even which model to use at what effort. You simply connected your data and we took it from there. Earlier this year, two public companies ran their Q1 QBRs on Atta. At one of them, the top 50 execs replaced their SaaS tools with decks and docs built in Atta, connected directly to their data warehouse. We're excited to continue our mission at Replit, where it will reach business leaders everywhere. Joining Replit means the world to me. I grew up in a low-income, immigrant community outside Chicago where nobody knew anything about computers, so I taught myself. I wrote my first line of code on Codecademy in 2012. My mom still remembers me running over to show her a Python function that passed all its tests. When Replit came out as a cloud-based terminal, it was the perfect place to practice coding challenges with my younger siblings, both software engineers now. (I later realized Amjad also built Codecademy before founding Replit!) Learning to code pulled my family out of generational poverty even though most people had already written us off. Thank you to our families for patiently enduring the joys of startup life. Thank you to our team and advisors who helped build Atta along the way. Thank you to our customers who trusted Atta with their highest-stakes decisions. Thank you to @MenloVentures (@mejoff, @shawnvc ), who supported us from inception to design partners to our first annual contracts. And thank you to Amjad and Michele for the opportunity to continue pursuing our dreams. After we pitched Atta, Amjad suggested we co-work from the Replit office. We're here now, and we're loving it. Try the new analytics experience in Replit today: https://replit.com/blog/replit-acquires-atta

9月25日周五
  1. OpenAI News39

    Lowe’s 借助 OpenAI 为数百万家装问题提供解答

    Lowe’s 基于 OpenAI 模型打造面向顾客的 Mylow 与面向员工的 Mylow Companion,自 2025 年春季上线以来已解答数百万个家装问题。Mylow Companion 已覆盖全美 50 个州约 1,700 家门店的约 300,000 名员工,近半数交互通过语音完成。使用 Mylow 的顾客转化率是未使用时的 3 倍。

  2. Claude:Blog(网页)72

    Anthropic 推出 Claude 插件开发者门户,支持提交审核与使用分析

    Anthropic 开放 Claude 插件目录提交门户,付费 Claude 方案开发者可提交单个 MCP connector 或托管在 GitHub 的插件包,审核通过后自行发布到 Claude directory。

    推荐理由:官方说明了插件提交、审核追踪、发布后分析和 MCP 2.0 扩展,开发者可据此判断如何接入 Claude 生态。

  3. GitHub Blog · AI & ML62

    GitHub Security Lab 推出基于 Taskflow Agent 的 AI 模糊测试流水线

    GitHub Security Lab 发布开源 Fuzzing Taskflow,用 LLM 智能体为 C/C++ 仓库自动完成入口识别、harness 编写、AFL++ 模糊测试、覆盖率改进、崩溃分诊和漏洞报告。

    推荐理由:文章给出从入口识别、覆盖率反馈到崩溃分诊的完整自动化链路,适合了解 LLM 智能体如何接管安全研究中的重复流程。

  4. Databricks:Blog(RSS)37

    在 Databricks 上用 SQL 运行 open-Jev

    Databricks 展示如何在其平台上部署开源决策模型 SemIf-OpenJev,并通过 SQL 中的 ai_query 直接对受治理数据运行结构化分类。该方案使用 Serverless GPU 与 AI Runtime,经 Notebook 自动下载模型、通过 Express Deployments 注册并创建 GPU Model Serving 端点,示例用于将酒店评论分类为好评或差评。

  5. Databricks:Blog(RSS)61

    Databricks 推出 Unity Gateway CLI,集中部署和管理编码智能体

    Databricks 推出 Unity Gateway CLI,让管理员集中配置编码智能体的默认模型、MCP 服务器、技能、Smart Routing 和支出策略,开发者可用 ug claude、ug codex 等命令启动已批准的智能体。

    推荐理由:文章说明了集中配置、预算策略、Smart Routing 和追踪如何统一治理多类编码智能体,便于评估企业级落地方式。

  6. AWS Machine Learning Blog42

    在 SageMaker AI 上使用 WhisperX 实现带说话人标签的语音转录

    AWS 推出 WhisperX 深度学习容器,可在 Amazon SageMaker AI 上部署带逐词时间戳和说话人标签的语音转录服务。该容器基于 OpenAI Whisper,集成 wav2vec2 强制对齐与说话人分离,支持 json、srt、vtt 等输出格式,无需自建镜像。实时端点适用于 60 秒内的短音频,异步端点适用于长音频与批量任务。