全部RAG大模型AI Agent模型评测提示词工程多模态AI 安全AI 编程具身智能云计算数据库开源网络安全低代码DevOpsAPI架构设计边缘计算物联网产品设计用户增长商业化A/B 测试用户研究会员体系私域运营留存转化率增长实验降本增效组织调整裁员招聘绩效考核薪酬远程办公企业文化管理变革战略转型融资上市并购财报估值股价投资消费趋势市场竞争商业模式监管反垄断数据安全隐私保护劳动法知识产权税务政策产业政策医保政策教育政策求职跳槽转行晋升副业职业规划简历面试职场沟通领导力技能提升出海跨境电商本地化海外市场全球供应链国际贸易海外监管汇率关税地缘风险数字化转型智能化自动化可持续发展碳中和新消费下沉市场国产替代供应链重构价格战区块链智能合约加密资产交易所钱包DeFiNFTDAO链游RWAAI AgentsLLM PlatformAI Policy
AWS Machine Learning Blog发布于 08/25 02:59
Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS
（翻译）普及机构知识：使用 AWS 构建 AI 驱动的知识管理系统
Learn how to build a customizable, smart-caching knowledge management system on AWS that captures and delivers institutional (tribal) knowledge through a voice-first AI avatar. The accelerator uses Amazon Bedrock Knowledge Bases for retrieval-augmented generation and deploys in hours with AWS CloudF
Microsoft AI Blog发布于 05/19 07:06
At aged care provider Regis, AI takes on paperwork so staff can focus on residents
（翻译）在养老服务机构Regis，AI接手文书工作，让员工专注于居民护理
Regis Aged Care uses AI with Microsoft Foundry and Copilot Studio to reduce admin work and help staff focus on resident care.
Hugging Face Blog发布于 08/19 02:09
How Much Memory Does Your Agent Actually Need?
（翻译）你的智能体到底需要多少记忆？
A Blog post by IBM Research on Hugging Face
Hugging Face Blog发布于 08/18 08:00
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
（翻译）使用 Sentence Transformers 的多向量（后期交互）嵌入模型
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
AWS Machine Learning Blog发布于 08/22 00:59
Reduce RAG costs on Amazon Bedrock with query-aware compression
（翻译）通过查询感知压缩降低 Amazon Bedrock 上的 RAG 成本
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers
AWS Machine Learning Blog发布于 08/21 00:06
AWS vector solutions: Build agentic AI where your data lives
（翻译）AWS 向量解决方案：在数据所在之处构建智能体 AI
AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof poi
AWS Machine Learning Blog发布于 08/20 06:13
Domain and publish date filters for Web Search on AgentCore
（翻译）AgentCore 上 Web 搜索的域名和发布日期筛选
Web Search on Amazon Bedrock AgentCore now supports runtime domain and published-date filtering. New per-request filters give developers per-call control over which web sources their agents consult and how fresh those sources must be, all enforced server-side. This release also expands Web Search to
AWS Machine Learning Blog发布于 08/26 00:35
Governed reports with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP
（翻译）使用Amazon Quick Desktop和Amazon FSx for NetApp ONTAP实现受治理的报告
Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill drafts cited weekly reports and Slack summaries with human review before anything is shared.
Hugging Face Blog发布于 08/21 08:00
How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code
（翻译）Hugging Face Inference Endpoints、Jobs 与 Buckets 如何为 Papers with Code 搜索提供支持
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
开源中国发布于 08/25 11:43
从 Memory 到 Skill：长周期 AI Agent 如何实现持续进化
历史记录能帮 Agent 想起过去，却不能保证它下一次真的会按正确方法做。MSCE 想补上的，正是“记得”和“会做”之间的这段空白。 01 Agent 能记住，不等于下次会做 Agent 已经不只是在对话框里给出一个答案，它们会进代码库找文件、打开浏览器查资料、调用工具继续往下做。但随着任务越拉越长，问题也跟着冒出来：隔几天...
人人都是产品经理发布于 08/26 09:52
数据字典为什么总在建完以后失效？
数据字典上线即过时？当系统迭代与文档维护脱节，字段定义逐渐失真，甚至被AI放大为错误答案。本文剖析字典失效的深层原因，提出按结构、语义、责任三只钟分层治理，并给出嵌入变更流程的实操方案，让字典真正活起来。
arXiv cs.AI发布于 08/26 12:00
RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation
（翻译）RENDER：在LLM记忆评估中控制面向读者的证据
Abstract page for arXiv paper 2608.23568: RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation
arXiv cs.AI发布于 08/26 12:00
Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes
（翻译）审计合成回忆录：对照其所描述生活的文档记录度量LLM生成自传中的场景级虚构
Abstract page for arXiv paper 2608.23640: Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes
arXiv cs.AI发布于 08/26 12:00
Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Search
（翻译）利用强化学习增强的智能体搜索生成生物医学事实核查报告
Abstract page for arXiv paper 2608.23811: Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Search