知鸦日报2022-02-03

TOB内容如何避免自嗨？

To leverage the scale and agility of the public cloud, we have built a new generation of infrastructure platform for Salesforce.

A framework prescribing the minimal set of indicators that every service needs to discern its health and performance.

This is the fourth and final post in the series on how we scaled our development practices at Lyft in the face of an ever-increasing number of developers and services.

Despite the explosive growth of the internet over the past couple of decades, much of the digitized knowledge has been curated for human understanding and has stayed unfriendly for machine comprehension. Even promising efforts towards creating semantic web like the Resource Description Framework in Attributes (RDFA), Ontology Web Language (OWL), JSON-LD, and Open Graph Protocol are in infancy and fall short for commercial applications due to data sparsity and high variance in data quality across websites. Hence Web Information Extraction (WIE), colloquially known as scraping, is the dominant knowledge acquisition strategy for several organizations in advertising, commerce, search engines, travel, etc. For our purposes, Pinterest uses this approach to bring high-level information (like price and product description) from saved websites to the Pin-level, to help provide Pinners with more information, along with a link back to the original website for more details, and to ultimately take action.

Uber is a worldwide marketplace of services, processing thousands of monetary transactions every second. As a marketplace, Uber takes on all of the risks associated with payment processing. Uber partners who use the marketplace to provide services are paid for their work even if Uber was unable to collect the payment. Fraud response is thus a very important operational component of Uber’s global marketplace.

Industry-wide, payment fraud losses are measured in terms of the fraction of gross amounts processed. Though only a small fraction of gross bookings, these losses impact profits significantly. Furthermore, if a fraudulent activity is not discovered and mitigated immediately, it could soon be further exploited, resulting in serious losses for the company. These dynamics make early fraud detection vital to the company’s financial health.

Modern fraud detection systems are a combination of classic 1980s AI (also known as an “expert system”) and modern machine learning. We would like to share the journey on how we build the best-in-class automatic fraud detection system and process, leveraging both machine algorithms and human knowledge.

在写本文时，本人成功替某一个知名在线答题平台的后台优化一个Swift代码模块的运行效率，并使之上线后运行效率提升了数十倍。经过这次经历，本人决定将优化的经验进行一一分享，优化包括了字符串解析、数组操作、指针操作等。

构建属于你自己的认知体系。