作为智能客服产品全剖析（中篇），本文将继续介绍智能客服产品的用户细分。我们将分别从「积极型探索者」和「消极型探索者」的需求视角分析用户痛点和用户旅程地图，从而探索个性化的智能产品策略。

As Airbnb has grown to a company with over 1,200 developers, the number of platforms and channels for pushing changes to our product — and the number of daily changes we push into production — has also grown tremendously. In the face of this growth, we constantly need to scale our ability to detect errors before they reach production. However, errors inevitably slip past pre-production validation, so we also invest heavily in mechanisms to detect errors quickly when they do make it to production. In this blog post we will cover the motivations and foundations for a system for safeguarding changes in production, which we call Safe Deploys. Two following posts will cover the technical architecture in detail for how we applied this to traditional A/B tests, and code deploys respectively.

How we applied qualitative learning, human labeling and machine learning to iteratively develop Airbnb’s Community Support Taxonomy.

Learn how Linaria, Airbnb’s newest choice for web styling, improved both developer experience and web performance.

How Airbnb is leveraging graph neural networks to up-level our machine learning.

How we redesigned payments data read flow to optimize client integrations, while achieving up to 150x performance gains.

"Empower families and groups of friends who travel together in a collaborative way to document, organise, and share their travel experiences and stays with the larger Airbnb community"

The above brief was given during the Airbnb + Adobe Creative Jam held during April 2020 for US, UK and Canadian students. Unfortunately the competition was not open to Australian students, meaning I could not participate. However, I thought it would work as a good practice brief that I could do in my own time as a way to really delve into the UX design process through research, ideation and testing.

An important part of running Airbnb’s infrastructure is ensuring our cloud spending automatically scales with demand, both up and down. Our traffic fluctuates heavily every day, and our cloud footprint should scale dynamically to support this.

To support this scaling, Airbnb utilizes Kubernetes, an open source container orchestration system. We also utilize OneTouch, a service configuration interface built on top of Kubernetes, and is described in more detail in a previous post.

In this post, we’ll talk about how we dynamically size our clusters using the Kubernetes Cluster Autoscaler, and highlight functionality we’ve contributed to the sig-autoscaling community. These improvements add customizability and flexibility to meet Airbnb’s unique business requirements.

How Airbnb migrated from Webpack to Metro and made the development feedback loop nearly instantaneous, the largest production build 50% faster, with marginal end-user runtime improvements.

随着有关数据泄露的新闻报道日益增多，加上国际监管和安全要求的出台，数据监管与保护已成为备受关注且亟待解决的重要议题。

我们的房东和房客社区相信：爱彼迎会保障用户数据安全，同时尊重用户的隐私权利。

在爱彼迎，数据的收集、存储和传输会通过不同的数据存储和基础设施来完成。工程师很难通过手动跟踪来了解用户及敏感数据在技术环境中的流转过程。其实，这反而会让我们的数据保护难上加难。虽然我们现在有从不同维度保障数据安全的供应商，但我们更希望设计一套理想的数据安全工具，既能支持我们生态系统中的数据存储器，又能满足我们在数据开发和自动化数据保护方面的所有需求。

在《爱彼迎数据隐私与安全工程》系列分享中，我们会和各位展开聊聊：如何通过创建和维护数据安全平台来化解上述挑战。

第一篇文章，我们会快速回顾构建数据保护平台（Data Protection Platform, 以下简称 DPP）的背景和技术架构，并深入解读我们的数据清单组件——Madoka。

Viaduct, a GraphQL-based data-oriented service mesh, is Airbnb’s paved road solution for fetching internal data and serving public-facing API requests. As a unified data access layer, the Viaduct framework handles high throughput and is capable of dynamically routing to hundreds of downstream destinations when executing arbitrary GraphQL queries.

在之前的文章中，我们已经研究了 Airbnb 的产品架构、为它构建的 mock 系统以及这个测试生态系统对产品功能运行自动化测试的具体方法。但一个值得注意的缺失部分是我们没有讨论如何以及何时运行这些测试。

我们现在来详细拆解一下把所有这些测试基础设施联系在一起的工具，看看这个工具是怎么为我们的工程师带来愉快的开发体验。

How we maintained reliable money movement while migrating Airbnb’s payment orchestration system from the legacy monolithic application to a service-oriented architecture.

在 Airbnb 自动化测试框架系列文章的第六篇，我们将讲述造成测试结果不一致的常见因素，以及我们如何解决这些问题。

本文将介绍爱彼迎的智能自动化平台是如何通过支持对话式 AI 和客服自动化，来提升爱彼迎的用户体验的。

在该系列前面的文章里，我们已经介绍了我们的状态模拟系统以及如何基于该系统做交互测试。在这篇文章里，我们将深入介绍整个自动化测试系统的架构，包括如何检测应用的空闲状态，以及如何优雅地处理错误等。

为了让读者们可以基于所讲述的内容重新搭建一个类似的系统，并且避免我们遇到的大多数问题，这篇文章将会涉及较多具体的实现细节。