知鸦日报2022-10-19

越来越多设计师使用3D软件辅助海报制作，通过3D软件可以帮助我们快速构建画面的基础光影、结构、透视等关系，提高设计效率，快速出图。

在良好画面结构层次的基础上进行后期处理，丰富细节突出作品质感，海报设计就事半功倍！本文通过具体案例讲解3D辅助海报制作的全流程，主要包括以下4个步骤，希望能为你带来一些启发~

At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations. A large number of batch workflows run daily to serve various business needs. These include ETL pipelines, ML model training workflows, batch jobs, etc. As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company.

In this blog post, we introduce and share learnings on Maestro, a workflow orchestrator that can schedule and manage workflows at a massive scale.

Feature flags are everywhere in modern software development: They’re a great tool for running A/B experiments, slowly rolling out changes to users, and even turning off problematic codepaths during incidents. When an engineer implements a new feature, it’s practically second-nature to gate it behind a feature flag.

While this practice is largely beneficial for the most part, incidents are occasionally caused when a feature flag enables a buggy codepath and causes a crash or an otherwise degraded user experience. A feature flag that causes a crash immediately upon app launch is particularly painful because even if the feature flag is disabled remotely after an engineer identifies the issue, once an app has the bad configuration it will continue to crash before it’s able to successfully fetch the corrected configuration.

We’ve experienced this issue a few times at Lyft over the years. When a crash on launch was introduced by turning on a feature flag or changing other remote configurations, we usually had to ship a hotfix to get users out of infinite crash loops since we had no way of pushing configuration updates to the app when it was crashing so early in its lifecycle. This inevitably resulted in disappointed users, fewer rides, and lost revenue.

To help mitigate these crash loops, we created Safe Mode.

At Uber, over 120,000 production workflows are orchestrated, scheduled, and executed every day. These workflows are owned by over 3,000 users across many teams within Uber, powering critical ETL jobs, business metrics, dashboards, machine learning models, or critical regulatory reports. Internally, the Data Workflow Platform (DWP) team makes this happen by developing Uber’s centralized workflow management system with high infrastructure reliability and minimum scheduling latency. The workflow management system also comes with a user-friendly application that allows users to create, author, and manage both streaming and batch workflows in a self-serve way.

Unorganized pull requests are the bane of large codebases. Follow Yash's tips to prevent your PRs from getting roasted in the group chat.

Data is a vital component in how we improve Figma day by day. Learn more about what kind of data we use and how we use it.

薯条广告业务作为小红书商业化中重要的一部分，可以让内容创作者和企业商家轻松使用手机 app 进行笔记推广和广告投放。虽然从用户视角来看，薯条广告的投放方式简单便捷，但从平台视角来看却面临着很多挑战。如何在薯条广告和其他广告的竞争过程中，合理分配小红书的商业化流量，最大化广告主和平台的整体收益，需要深入地思考和探索。对此，小红书商业技术团队从策略视角出发，对薯条竞价广告的投放进行建模，推导出理论最优解法，再结合业务实践，思考出简单有效的调控方案，让薯条广告业务快速达到了预期水准，推动了商业化平台的发展。

本文将带你一步一步了解virtual机制，为你揭开virtual的神秘面纱。

我们平时会写各种各样或简单或复杂的sql语句，提交后就会得到我们想要的结果集。比如sql语句，”select * from t_user where user_id > 10;”，意在从表t_user中筛选出user_id大于10的所有记录。你有没有想过从一条sql到一个结果集，这中间经历了多少坎坷呢？

一个快速而又稳定的接口，对于客户端的用户体验来说是大有裨益的。