Author here, happy to answer any questions! Accompanying description for the video:
Which repositories show the fastest growth? Are there any notable patterns worth highlighting? Let’s find out! The analysis considers 158 public GitHub code repositories at OpenAI, Anthropic and Cohere, created since August 2014, with an aggregate of 12k commits.
The S-curve in project management is a graphical representation that illustrates the cumulative progress of a project over time. It is called an "S-curve" because its characteristic shape resembles the letter "S": It starts slowly, accelerates, and then levels off.
The cumulative number of code commits over time can be used as raw data to model development progress “cost” with S-curves. Similar results can be obtained with the count of distinct authors (harder to control) and the count of modified files.
The animation illustrates the progression of commits over time, with normalisation applied to both axes. Each frame captures a snapshot of the repositories at a specific moment in time. A combination of the fastest and slowest repositories is highlighted with colors and labels. Quiet projects cluster in the top-left corner, and accelerating projects are found in the bottom-right area.
Over time, patterns tend to stabilise. Projects with synchronised acceleration can be attributed to coordinated commits from private repositories. The Python APIs for OpenAI, Anthropic, and Cohere stand out as some of the most active repositories, with OpenAI taking a prominent role in the Node.js / Typescript API development. 5 out of 8 of the most active repositories belong to OpenAI.
S-curves in software development are well-equipped to run simulations, comparative performance analyses, identify project delays and anomalies, and optimise resources in large teams with multiple projects.
