InfoQ 中文发布于 08/23 17:10

事故频发并不意味着可靠性下降

IT 工程项目领导者最常见的假设之一,是报告的事故数量不断增加就意味着系统可靠性正在下降。然而,Great Circle 最近的一篇文章提出,实际情况往往恰恰相反:事故数量增加,反而可能意味着组织的事故管理文化正在改善。随着团队不断投入资源完善流程、工具、培训和运维规范,他们会更愿意正式地将那些过去大概只会被悄悄处理、甚至被隐瞒的事故报告上去。这样带来的结果就是组织对运营问题的可见性提高了,但却不一定意味着系统的健康状况恶化。

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InfoQ 中文发布于 08/23 01:17

Cloudflare 推出 Cache Response Rules,在源站响应后进一步控制缓存

Cloudflare 近日推出缓存响应规则(Cache Response Rules),这是一套新的规则引擎,运行在源站返回响应之后、内容写入 Cloudflare 缓存之前。此前,缓存规则(Cache Rules)只能根据请求属性进行判断;缓存响应规则则新增了一个响应处理阶段,可以在响应进入缓存之前检查源站返回的内容。

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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

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AWS Machine Learning Blog发布于 08/21 05:46

Introducing cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock

(翻译)在 Amazon Bedrock 上推出 OpenAI GPT-5.6 模型的跨区域推理

Amazon Bedrock now offers OpenAI GPT-5.6 models (Sol, Terra, and Luna) in more than 25 AWS Regions with cross-Region inference. Learn how US geographic and global inference profiles route requests for higher throughput, how to call the models with the OpenAI and Converse APIs, and how to configure I

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AWS Machine Learning Blog发布于 08/21 00:31

Authoring Dogwood policies from natural language in Amazon Bedrock AgentCore

(翻译)在 Amazon Bedrock AgentCore 中通过自然语言编写 Dogwood 策略

AI agents can take actions that do not match your organization's policies. Policy in Amazon Bedrock AgentCore lets teams enforce controls across agents, now including time-based constraints. This post shows how Policy Authoring turns natural-language policy documents into correct Dogwood policies, w

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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

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