知鸦日报2023-02-14

表单是一个包含了若干表单控件、组件的合集，其中包含的所有表单都具有一定的联系和共性。所以当我们展开表单内容的设计时，就要先构建整体的框架，确定表单间联系和共性，再进行细节的设计。

AI绘画爆发一个很重要的原因就是 Stable Diffusion 的开源，这也来不开这几年 Diffusion Model 扩散模型在这几年里的迅猛发展。

本文从命令行迭代的 4 个阶段出发，重点介绍了 Ruby 脚本的命令行化以及 CLI 的 GUI 化。

随着闲鱼前端架构的不断演进，一些关键技术设施需要结合业务特征逐步自建，技术方案也要拥抱社区来提升可扩展性。一方面， 闲鱼跨端开发框架kun 让前端开发者使用JS/CSS/HTML即可交付终端页面，同时兼顾了动态性和高性能，另一方面，前端UI框架也正从集团 rax 逐步转向社区 React 方案。在这个大背景下，围绕 kun 和 web 两个容器的跨端组件建设也势在必行，因此 Fish UI 应运而生，它将全面拥抱 react 生态，并借助 kun 容器的能力，为闲鱼终端开发者提供一套高易用性和稳定性的跨端（kun & web）UI组件库。

FishUI 的最大的技术创新点在于跨端，它通过跨端组件的形式对齐 kun 和 web 两个容器的体验标准，让使用者更加专注于业务逻辑而非容器差异，从而实现为业务开发提效的目标。围绕着该创新和目标，一些可预知的技术难题也随之出现，比如组件库的工程结构如何组织、分端构建如何做、开发规范是什么样等等，这些问题将在下文中一一得到解答。

Ant Design V5 已经发布也有一段时间了，作为 V5 设计研发小组成员，在这几个月中我们也第一时间升级了手上的业务应用到 antd v5，并针对实际的业务场景研究了 antd 动态主题的实践用法。截至目前近 3 个月的时间，我们总共完成了 6 个应用/组件/站点的 antd v5 升级改造，且均已支持亮暗色主题切换。

B站基于k8s构建的私有云平台已达万级节点规模，托管了公司大部分的在线业务以及机器学习、大数据、转码等离线业务。在推进降本增效的过程中，我们发现业务申请的容器资源规格存在不合理的现象，通常设置的资源配额会比实际负载偏大很多。这就造成节点的cpu分配率很高，已经无法调度新容器，但是节点的实际cpu峰值利用率却很低。

腾讯文档智能表格的界面是用 Canvas 进行绘制的，今天来讲讲 feature 层的设计。

本文将为大家分享我们在与商品的演变成长性和商品的购买体验相关的三个环节中探索实践的经验。

At Dropbox, we store data about how people use our products and services in a Hadoop-based data lake. Various teams rely on the information in this data lake for all kinds of business purposes—for example, analytics, billing, and developing new features—and our job is to make sure that only good quality data reaches the lake.

Our data lake is over 55 petabytes in size, and quality is always a big concern when working with data at this scale. The features we build, the decisions we make, and the financial results we report all hinge on our data being accurate and correct. But with so much data to sift through, quality problems can be incredibly hard to find—if we even know they exist in the first place. It's the data engineering equivalent of looking for a black cat in a dark room.

Like many companies, Dropbox runs experiments that compare two product versions—A and B—against each other to understand what works best for our users. When a company generates revenue from selling advertisements, analyzing these A/B experiments can be done promptly; did a user click on an ad or not? However, at Dropbox we sell subscriptions, which makes analysis more complex. What is the best way to analyze A/B experiments when a user’s experience over several months can affect their decision to subscribe?

For example, let’s say we wanted to measure the effect of a change in how we onboard a new trial user on the first day of their trial. We could pick some metric that is available immediately—such as the number of files uploaded—but this might not be well correlated with user satisfaction. We could wait 90 days to see if the user converts and continues on a paid subscription, but that takes a long time. Is there a metric that is both available immediately and highly correlated with user satisfaction?

We found that, yes, there is a better metric: eXpected Revenue (XR). Using machine learning, we can make a prediction about the probable value of a trial user over a two-year period, measured as XR. This prediction is made a few days after the start of a trial, and it is highly correlated with user satisfaction. With machine learning we can now draw accurate conclusions from A/B experiments in a matter of days instead of months—meaning we can run more experiments every year, giving us more opportunities to make the Dropbox experience even better for our users.

We tackle some of the unique challenges of scaling multimodal machine learning models that operate on media assets (video, audio, and text).

随着“元宇宙”概念的兴起，各类“元宇宙”App也是层出不穷，而在“元宇宙”的概念中，最吸引人的莫过于“捏脸”了，本篇文章就将介绍我们是如何基于Spine这个框架来在移动端探索和实现一个“捏脸”功能的。

哈啰出海项目目前是基于Google地图提供的服务进行地图相关能力的场景应用。基于出海现有的需求，我们采用Native地图+H5页面的设计原则。

ESLint 是前端工程化领域中必须的，我们几乎每天都会用到它。本文会从如何使用开始，到如何编写一个 ESLint 插件，以及插件的运行机制，最后到 ESLint 的部分核心源码，逐步来解析 ESLint 的工作机制。

在云音乐全面转跨端的时代，H5 / RN 缓存模块是非常重要的组成部分，目前云音乐使用的缓存库已经“历史悠久”，没法在现有的基础上支撑日益庞大的跨端需求，因此我们基于缓存库的可扩展架构，从问题出发，重新设计了一套新的跨端缓存库。