Node.js in the Digital Media Universe

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João Varajão - Software Development in Disruptive Times
In this project, the challenge was to "deploy software faster than the coronavirus spread." In a project with such peculiar characteristics, several factors can influence success, but some clearly stand out: top management support, agility, understanding and commitment of the project team, and the technology used. Conventional development approaches and technologies would simply not be able to meet the requirements promptly.

Nicole Forsgren, Margaret-Anne Storey, Chandra Maddila, Thomas Zimmermann, Brian Houck, Jenna Butler - The SPACE of Developer Productivity
Developer productivity is about more than an individual's activity levels or the efficiency of the engineering systems relied on to ship software, and it cannot be measured by a single metric or dimension. The SPACE framework captures different dimensions of productivity, and here we demonstrate how this framework can be used to understand productivity in practice and why using it will help teams better understand developer productivity and create better measures to inform their work and teams.

Chris Nokleberg, Brad Hawkes - Best Practice: Application Frameworks
While frameworks can be a powerful tool, they have some disadvantages and may not make sense for all organizations. Framework maintainers need to provide standardization and well-defined behavior while not being overly prescriptive. When frameworks strike the right balance, however, they can offer large developer productivity gains. The consistency provided by widespread use of frameworks is a boon for other teams such as SRE and security that have a vested interest in the quality of applications. Additionally, the structure of frameworks provides a foundation for building higher-level abstractions such as microservices platforms, which unlock new opportunities for system architecture and automation.

J. Paul Reed - Beyond the Fix-it Treadmill
Given that humanity’s study of the sociological factors in safety is almost a century old, the technology industry’s post-incident analysis practices and how we create and use the artifacts those practices produce are all still in their infancy. So don’t be surprised that many of these practices are so similar, that the cognitive and social models used to parse apart and understand incidents and outages are few and cemented in the operational ethos, and that the byproducts sought from post-incident analyses are far-and-away focused on remediation items and prevention.