we try to understand the large scale um utility of of the of these patterns.

Scaling a single VCS to hundreds of developers, hundreds of millions lines of code, and a rapid rate of submissions is a monumental task. Twitter’s monorepo roll-out about 5 years ago (based on git) was one of the biggest software engineering boondoggles I have ever witnessed in my career. Running simple commands such as git status would take minutes. If an individual clone got too far behind, it took hours to catch up (for a time there was even a practice of shipping hard drives to remote employees with a recent clone to start out with). I bring this up not specifically to make fun of Twitter engineering, but to illustrate how hard this problem is. I’m told that 5 years later, the performance of Twitter’s monorepo is still not what the developer tooling team there would like, and not for lack of trying.

In very large code bases, it is likely impossible to make a change to a fundamental API and get it code reviewed by every affected team before merge conflicts force the process to start over again.

Such scaled-up communication and collaboration processes would also require meta-design principles to collaboratively construct the required design rationale, media and environments [23].

Etzioni astutely observed that all communities have a serious defect: they exclude. To prevent communities from over-excluding, they should be able to maintain some limitations on membership, yet at the same time greatly restrict the criteria that communities may use to enforce such exclusivity. He therefore proposed the idea of “megalogues”: society-wide dialogues that link many community dialogues into one, often nation-wide conversation [7].

For large-scale software systems, Van Roy believes we need to embrace a self-sufficient style of system design in which systems become self-configuring, healing, adapting, etc.. The system has components as first class entities (specified by closures), that can be manipulated through higher-order programming. Components communicate through message-passing. Named state and transactions support system configuration and maintenance. On top of this, the system itself should be designed as a set of interlocking feedback loops.

The creation and exploitation of large-scale quantitative atlases will lead to a more precise understanding of development.

Just as comprehensive datasets of genomic sequence have revolutionalized biological discovery, large-scale quantitative measurements of gene expression and morphology will certainly be of great assistance in enabling computational embryology in the future. Such datasets will form the essential basis for systems level, computational models of molecular pathways and how gene expression concentrations and interactions alter to drive changes in cell shape, movement, connection, and differentiation. In this review, we discuss the strategies and methods used to generate such datasets.