知鸦日报2024-12-20

本文将详细介绍 B 站装机系统的演进过程，以新交付装机和复杂网络装机两个装机场景为例，重点探讨我们在装机实践中面临的挑战和提出的解决方案。

在鸿蒙生态系统中，虽然原生应用通常基于 ArkTS 实现，但在实际研发过程中发现，使用 C++ 可以显著提升应用框架和业务的性能表现。

光大银行数据中台以统一规范、统一语义构建业务模型，通过企业级、业务领域级数据资产加工实现数据资产持续积累，为光大银行数字化转型提供数据支撑，本文将对数据中台模型内容及设计方法做系统说明。

本文将基于我们的思考，探讨大模型提升思维能力的路径。

本文为《事件CPU开销压降》揭榜报告，旨在解决风控系统间信息传递时事件体持续膨胀导致的序列化/反序列化CPU消耗过高的问题。

At Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members. To achieve this, we are committed to building robust systems that deliver comprehensive observability, enabling us to take full accountability for every title on our service.

At Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engineers and analysts collaborate to provide actionable insights to the engineering organization in a continuous feedback loop that ultimately enhances the business.

One crucial way in which we do this is through the democratization of highly curated data sources that sunshine usage and cost patterns across Netflix’s services and teams. The Data & Insights organization partners closely with our engineering teams to share key efficiency metrics, empowering internal stakeholders to make informed business decisions.

How did the Threads iOS team maintain the app’s performance during its incredible growth? Here’s how Meta’s Threads team thinks about performance, including the key metrics we mon…

How Airbnb is adapting ranking for our map interface.

Delivering personalized recommendations is key to engaging Zalando’s customers, but traditional models can miss the complexity of user-content interactions. By integrating graph neural networks...

本文将以一个经典的 RAG（检索增强生成）知识问答系统为例，详细介绍从智能体设计到最终应用部署的全流程。

近两年，检索增强生成（RAG，Retrieval-Augmented Generation）技术正在成为提升大模型性能的关键工具。RAG技术通过引入外部知识，结合检索与生成的双重能力，为大模型在复杂场景中的应用提供了更多可能性。无论是文档解析的质量、上下文信息的精确性，还是针对任务的合理规划，RAG的每一步都在为模型能力的上限奠定基础。

查询改写让分析更简单、更高效！

数据一致性校验如何在小红书实践并落地。

本文深入探讨了如何在多品牌、多终端的环境中，建立一个支持多个平台和品牌的企业级设计系统。该系统不仅提供高效、可靠、统一的设计管理方案，还实现了设计的复用和资源共享，大幅提升设计效率和质量，同时降低设计成本。