10 Insights from Adopting TypeScript at Scale
tl;dr: Adopting Typescript at scale was a net positive for the Bloomberg team. Principles core to the project were (1) scalability, (2) ecosystem coherence, so packages work together, (3) standards alignment, sticking to standards like ECMAScript. This article outlines some of the "surprising corners" turned.
featured in #217
How LinkedIn Handles Merging Code In High-velocity Repositories
tl;dr: This post focuses on how the CI system works with repositories of different sizes, "specifically ones with a high velocity of commits being merged into master, to ensure timeliness and code correctness."
featured in #182
When Scaling Your Workload Is A Matter Of Saving Lives
tl;dr: Werner received a call to scale the data model that governors were using to plan their response to COVID-19. He talks through how he did so with the Amazon team.
featured in #180
Building The DataDog Platform For Processing Timeseries Data At Massive Scale
tl;dr: Podcast interview with Vadim Semenov discussing "the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale."
featured in #167
Speed At Scale - What's New In Web Performance?
tl;dr: (1) Lighthouse supports Performance Budgeting (2) Native image and iframe lazy-loading comes to the web (3) Google Fonts now supports font-display as a query parameter.
featured in #143
How Is Software Developed At Amazon?
tl;dr: Fireside chat with Ken Exner, GM at AWS Dev Tools. The article summarizes it more extensively than I will. Key ideas are:
Create more autonomy and higher velocity by moving away from monolithic architecture towards microservices and two pizza teams 🍕🍕
Culture of ownership and accountability. Two pizza teams own everything relevant to their product. They acts like startups, managers oversee startups
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featured in #131