知鸦日报2022-10-20

2015年，Notion的联合创始人Ivan Zhao（赵伊）和Simon Last的生活精简到只剩“写代码”以及“吃饭”两件事。

这是他们创业的第三年，因为前期产品定位错误，所以不得不从头开始写代码进行开发。这个时候，整个团队只剩他们两个人，为了节省开支，他们从旧金山搬到了日本京都，居住在一间小到只用一个屏风来划分工作区和“卧室”的房间里。他们每天有18个小时坐在电脑前工作，不停地写代码、写代码、出门吃饭、写代码。

两个20出头的年轻人，对于生活条件的艰苦并不在意，他们只想着如何让这款名为Notion的软件尽快上线。在他们的构想中，这款软件能够彻底改变人与各类APP、电脑软件的关系，它能够让每一个人在不懂代码的前提下都能够随意开发自己想要的APP和软件工具，从而释放出更多的生产力与创造力。

如今，Notion已经有超2000万的用户，估值达到了数十亿美元，成为了世界范围内正在流行的生产力软件。不过，Simon Last、Ivan Zhao以及后期加入的第三位联合创始人Akshay Kothari还有着更为远大的目标——让每一个人只用Notion一个APP就能够达成高效的生活与工作。

通过对麦序列表性能优化，最终减少一半以上机器。

在我们之前分享的《Dutter | 钉钉 Flutter 跨四端方案设计与技术实践》《Dutter | 前车之鉴：聊聊钉钉 Flutter 落地桌面端踩过的“坑”》文章中，有为大家简单介绍过钉钉 Flutter 桌面端应用的一些情况。在文章中我们有提到，因为需要支持多窗口、窗口内嵌等场景，在桌面端我们无法使用 FlutterBoost 一类的中间件来共享 FlutterEngine，只能采用多引擎方案来驱动多画布同时渲染。

此方案虽然能满足现阶段钉钉业务使用，但未来随着业务盖度、复杂度的提升，方案的弊端也愈加明显：引擎启动偶现卡顿、首帧耗时略长、内存占用高等。尤其是钉钉 Windows 端因为32位兼容问题，目前仍以 JIT 模式在运行 Flutter 页面，情况相比 AOT 模式更加差一些。以钉钉 Windows 目前线上业务为例，若不做任何优化，启动首帧展示耗时大概在 1000ms~2600ms 之间，每个引擎内存占用大概在 70MB 左右。

我们选择基于 Flutter 来构建钉钉跨4+端研发框架（Dutter） 的主要初衷即看中其在性能和体验上具备可媲美 Native 运行的能力，在完成前期基础设施搭建和核心链路验证之后，多引擎下的性能问题已成为阻碍我们达成既定目标的主要障碍，需要攻坚解决掉。

本文主要为大家介绍一下我们在此问题上探索方向以及最终方案，并以实际应用收益来为做一个直观展示，希望能为关注此领域的同学和团队提供一定参考。

闲鱼号在闲鱼业务中一直承担着非常重要的角色，它既是卖家组织商品的货架，又是达人自我表达的载体，既是大 V 私域运营的阵地，又是小铺开店经营的门面。它是闲鱼各产品线的交汇点，号店浑然一体，一定要类比的话，它更像是抖音/小红书个人主页 + 淘宝店的综合体。

观察者模式（Observer Pattern）是一种发布/订阅模式，定义了对象间的一种一对多的依赖关系，一个主题对象（Subject）持有若干个依赖其状态的观察者对象（Observer），而观察者模式则允许多个观察者对象自动接收主题对象的状态变更事件，以各自处置。

转转是一家主营二手商品交易的电商平台。根据交易主体的不同，可以形成C2C、C2B、B2C等交易关系。比如个人用户在转转app的自由市场发布商品进行售卖属于C2C模式、转转公司提供手机和其他电子产品的邮寄与上门回收C2B服务，转转app还提供官方验和质保与售后的二手B2C商品。本文将重点介绍转转 B2C 卖场商品上架审核过程中图像算法的应用。

由于二手商品的非标品性质，即便同一sku下的不同库存商品间也存在着成色差异。平台为了提升用户体验，增加商品信息的透明度，在展示二手商品时全部采用实拍商品图，避免使用渲染的标品图片。这就涉及到对每个上架商品的相关展示图片进行信息准确性、图片质量等各方面的审核问题。

随着 5G、AI 技术发展，视频行业迎来一个高速的增长期，视频在网络带宽的占 2019 年为 43%，预计到 2025 年占比将高达 76%，仍然保持着高速的增长，其中有大部分是超高清的视频，据某权威机构预测，2022 年超高清的视频规模有望达到 4 万亿人民币，相比于 2019 年的 1.2 万亿人民币翻了 3 倍之多，可见超高清市场的规模有很大的空间。

我们目前处于逼近真实，基本达到真实的阶段。从分辨率来讲我们从标清、高清慢慢往超高清的方向发展，从动态范围、色域和视角范围来看，从标准动态范围到高动态范围，还有窄色域、窄视角到宽色域、宽视角的方向发展，未来肯定会朝着超越真实的视觉通信互动媒体方向发展，其中会涌现更多的视频技术，包括更高的分辨率，还有多视角、多自由度，还有超低时延、实时互动、实时渲染、数字孪生等技术。

At Duolingo, we believe it’s important to use data to drive our decisions. This includes decisions about how best to teach languages. You probably aren’t thinking about it while you’re doing your daily Spanish lessons, but in the background, we have dedicated teams of volunteers and staff working on improving the courses we teach you and developing new versions of the courses, which we call “trees”. Once these trees are done, we release them as A/B experiments to a portion of learners and track the impact on engagement with the app. This lets us ensure that the changes we made would actually be beneficial to the learning experience on Duolingo.

However, one downside to this approach is that it is a very retrospective measure: it gives us information on how a tree performed after we have finished making changes. But what if we wanted to see how we’re doing on improving a course while we’re still working on it?

Have you ever wondered how Duolingo figures out whether your answer counts as right or wrong? Let’s say you’re learning Portuguese and get “Eu tenho gatos demais” (literally I have cats too many) so you type “I have too many dogs.” You'll get a red ribbon and a sad Duo. How does Duolingo know? Although this example is pretty simple, deciding which translations should be accepted turns out to be a complex problem!

In this post, we’ll talk about why translation grading is so hard, and we'll report on a recent research project we organized to develop sophisticated artificial intelligence solutions to this problem.

Daily practice is essential for language learning, so Duolingo helps learners stay on track by sending daily practice reminders. In fact, Duo's persistence is so well known that it's even become a popular internet meme. And let's be honest, most of us have probably swiped away one of these notifications...and probably felt a bit guilty in the process.

But, have you ever wondered how Duo decides what message to send? Well, last year Duolingo’s Machine Learning Engineers built a really neat AI system to find the perfect reminder to send each learner each day! We recently published this novel algorithm in a paper and short presentation at the Knowledge Discovery and Data Mining (KDD) Conference 2020. In this post we’ll take a peek at the AI behind these notorious notifications.

Duolingo is the world’s most popular language learning app with more than 150 million users (at the time of this writing). At the core of the Duolingo experience, users learn in bite-sized lessons which consists of several interactive exercises (internally, we call them “challenges”).

So, for any given lesson, which exercises should a user see and in what order? This is the responsibility of Session Generator, our backend module which gets data from one of our 88 language courses (and counting!) in the Duolingo Incubator, sprinkles some machine learning magic, and proceeds to serve a sequence of exercises tailored to the needs of each of our millions of users.

Duolingo works to improve the performance of our apps for all of our learners. As Duolingo has grown over time with new courses, features, and millions of additional users, several teams are working behind the scenes to keep things running smoothly. This post will focus on a backend feature called Request Tracing that has led to significant improvements in performance.

Duolingo is steadily moving from a monolithic architecture (a few large, tightly-coupled services) to a microservice architecture (many small, loosely-coupled services). This has its advantages, but also has tradeoffs, including operational complexity and observability.

At Duolingo, striving for excellence is one of our core operating principles. We strive for excellence in our mobile app by devoting ourselves to a high-quality experience for every learner. As part of our commitment to building for a globally diverse audience, we've dedicated our efforts across the plethora of languages, operating systems, and mobile devices that our learners use. We run hundreds of A/B tests simultaneously, and staying on top of all possible versions of the app a learner might see depending on their user interface (UI) language, course, and device can become overwhelming. Until last year, there was no easy way to visually compare the same learner experience across multiple languages and devices to ensure a smooth, enjoyable experience for everyone.

At Duolingo, our goal is to make language learning fun and effective. We think the best education should be full of play, so we're constantly developing new features that make learning new things — and practicing old things — feel like a game! At the same time, we're serious about taking a scientific, data-driven approach to all of our products, and about sharing what we learn with the world. In this post, we'll take a look at the science behind the Duolingo skill strength meter, which we published in an Association of Computational Linguistics article earlier this year....

本篇文章主要介绍如何实现一个SQL解析器来应用的业务当中，同时结合具体的案例来介绍SQL解析器的实践过程。

本文从内容审核概览入手，向读者呈现了vivo在内容安全建设上的努力与尝试，如：治理手段的探索、系统及功能设计、降本增效的尝试等，并分享建设过程中遇到的挑战及解决方案。

在前后端分离和服务化的环境下，通常需要一个API网关统一对接C端、B端和运营等前端项目。这个API网关的主要作用有两个：

一是为所有前端项目提供统一的入口，然后按照规则转发到后端服务上，之所以多一层转发，是因为服务化后的项目与前端是多对多的关系，一个前端项目可能会访问多个后端的项目，一个后端项目也可能对多个前端项目提供服务。

二是对所有的前端项目进行统一鉴权，鉴权后将用户ID写入请求头并转发给后端项目，所有的后端项目都是无会话状态的，只需要根据ID对外提供接口。

Spring Cloud Gateway 和 Netflix Zuul 都是这样一个API网关项目，能够按照业务规则实现请求的转发功能，也可以将统一鉴权很好的集成进去。最开始使用的 Zuul，和其他业务项目一样基于 Web Servlet 技术栈。随着 Spring 不断的升级，后来的项目也就从 Zuul 改成了 Spring Cloud Gateway。由于 Spring Cloud Gateway 构建在 Web Reactive技术栈之上，因此相关的业务就开始使用 WebFlux、WebClient 和 Reactor。

本次跟大家分享的是智能创意在哈啰的应用实践，包括以下几个部分：什么是创意、如何进行创意生成与创意优选、哈啰智能创意系统展示。