知鸦日报2022-06-03

AARRR 模型是一套适用于移动 App 的分析框架，又称海盗指标，是“增长黑客”中驱动用户增长的核心模型。

提出 PLG（Product-Led-Growth）这个概念的 OpenView Partners 最近分享了一个新的观点，认为 PLG 进入了一个新的时代：The Age of Connected Work（互联工作时代）。在这个分享里，OV 谈到了目前正在进行的 3 个趋势，以及新时代 PLG 应遵循的 11 条原则。

Uber has adopted Golang (Go for short) as a primary programming language for developing microservices. Our Go monorepo consists of about 50 million lines of code (and growing) and contains approximately 2,100 unique Go services (and growing).

Go makes concurrency a first-class citizen; prefixing function calls with the go keyword runs the call asynchronously. These asynchronous function calls in Go are called goroutines. Developers hide latency (e.g., IO or RPC calls to other services) by creating goroutines. Two or more goroutines can communicate data either via message passing (channels) or shared memory. Shared memory happens to be the most commonly used means of data communication in Go.

In an introductory article, we talked about the importance of Graph Networks in fraud detection. In this article, we will be adding some further context on graphs, graph technology and some common use cases.

Connectivity is the most prominent feature of today’s networks and systems. From molecular interactions, social networks and communication systems to power grids, shopping experiences or even supply chains, networks relating to real-world systems are not random. This means that these connections are not static and can be displayed differently at different times. Simple statistical analysis is insufficient to effectively characterise, let alone forecast, networked system behaviour.

As the world becomes more interconnected and systems become more complex, it is more important to employ technologies that are built to take advantage of relationships and their dynamic properties. There is no doubt that graphs have sparked a lot of attention because they are seen as a means to get insights from related data. Graph theory-based approaches show the concepts underlying the behaviour of massively complex systems and networks.

At Salesforce, Trust is our number-one value, and it has its own special meaning to each part of the company. In our Technology, Marketing, & Products (TMP) organization, a big part of Trust is providing highly reliable Salesforce experiences to our customers, which can be challenging because of the scale of the Salesforce infrastructure, its range of tech stacks, and the many products that those tech stacks support. Because of that challenge — and because TMP must gauge reliability at both that high level (across products) and from a zoomed-in view (for individual services supporting those products) — agreeing on what “highly reliable” means and how to measure it is absolutely critical. So just as Salesforce employees refer to standardized branding guidelines to speak the same product language, we also need standardized service ownership guidelines to ensure that we’re speaking the same reliability language. This blog post is about the Salesforce journey to framing reliability in terms of service-level indicators (SLIs) and objectives (SLOs), which are often used in the enterprise software business to represent the true customer experience in a clear, quantitative, and actionable way.

减小应用安装包的体积，对提升用户体验和下载转化率都大有益处。本文将结合美团平台的实践经验，分享一下 so 体积优化的思路、收益。

本文介绍了GDI和DXGI这两种截图方法，同时在Qt中实现了这几种截图方法，并进行了性能和效果上的比较。

本人是做教师端开发的，不论是最常用的三分屏教师端还是半身直播教师端，都离不开截图、截屏功能。三分屏中，截窗口、截桌面区域是上课是老师的一种教学工具，可以给学生们展示更加丰富的课程内容和一些课外补充。半身直播中（附图为半身直播间的3D图），老师在播放课件中的动画片时，可以直接通过截取课件截图，来给学生更加沉浸的动画体验。

功耗优化是应用体验优化的一个重要课题，高功耗会引发用户的电量焦虑，也会导致糟糕的发热体验，从而降低了用户的使用意愿。而功耗又是涉及整机的长时间多场景的综合性复杂指标，影响因素很多。不论是功耗的量化拆解，还是异常问题的监控，以及主动的功耗优化对于开发人员来说都是很有挑战性的。

本文结合抖音的功耗优化实践中产出了一些实验结论，优化思路，从功耗的基础知识，功耗组成，功耗分析，功耗优化等几个方面，对 Android 应用的功耗优化做一个总结沉淀。

本文从性能优化的价值、性能指标的定义域衡量以及如何优化性能这几部分内容来帮助大家快速对小程序性能优化相关的方法有一定的理解。

本文介绍 Shopee Off-Platform Ads 团队将 GPU 技术应用到图片渲染场景的设计思路与实践经验。

店铺浏览闭环之后，因各端数据与渲染割裂导致代码无法复用，从而引起研发成本增加；同时多年以来技术栈未升级且不统一导致严重落后于竞品店铺，对于ISV开放化的支持程度也停滞不前。针对这一系列的问题，店铺研发团队发起了多端融合降本提效项目，下定决心将多端渲染全部升级重写，从底层能力上解决了以上所有问题，使店铺浏览从原有的重复开发1.0时代，步入了全新的一套代码多端复用2.0新时代。

以一种新的思路实现了响应式。

数据量化的意识，用户视角出发优化和解决问题。

随着越来越多的业务在深入使用flutter技术且迭代频繁，flutter在线上的实际表现受到了广泛的关注。在此背景下，一套完善的高可用监控体系变得尤为重要，它不仅要能准确地定位线上的性能和稳定性问题，还要能提供一些辅助信息帮助开发同学去定位问题，从而提升用户使用感受。这篇文章将介绍hummer在基于集团原有的flutter高可用方案的基础上在流畅度相关问题的探索和优化。

系统思考将重新定义创新和学习。长远看将重构互联网基本形式。