[OC] Hockeystick project growth (0 PyPI downloads for 12 months, then 120k in 3 months) Visualization

What This Visualization Shows

This data visualization displays "[OC] Hockeystick project growth (0 PyPI downloads for 12 months, then 120k in 3 months)" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on Data sources: GitHub and PyPI Data associated with [this project](https://github.com/UKGovernmentBEIS/inspect_evals). Tools used: GitHub CLI, PyPI API, Python, Gource.

[The full visualisation here.](https://sdsimmons.com/experiments/inspect-evals/)

I work on an open source project that hosts a large number of AI evaluations ("evals"). As of today, there are > 120 evals in this project, which use a framework that is written by some of the same people behind [Rmarkdown](https://rmarkdown.rstudio.com/index.html).

An eval is just a way to characterise an AI's capabilities / behaviour in some dimension, letting us assign numbers so that we can rank and compare different AI models, as well as to help us quantify "how fast" new models are improving in certain dimensions.

Examples of characteristics that evals can help us quantify:

1. [How honest is the model?](https://ukgovernmentbeis.github.io/inspect_evals/evals/safeguards/mask/) 2. [How good at math competition questions?](https://ukgovernmentbeis.github.io/inspect_evals/evals/mathematics/aime2026/) 3. [How is their chemistry knowledge?](https://ukgovernmentbeis.github.io/inspect_evals/evals/knowledge/chembench/) 4. [What about their medical knowledge?](https://ukgovernmentbeis.github.io/inspect_evals/evals/knowledge/healthbench/)

I made a dashboard that shows how the number of evals in the project has increased over time (as well as various other metrics, using data from github and PyPI, such as the one you see in the image).

**Spoiler:** Regarding the image in the post, downloads were flat for 12 months until we started releasing to PyPI with a more regular release cadence! If you look at the github stars chart you can see linear growth, so the PyPI download explosion was effectively just pent up demand.

I also used [Gource](https://github.com/acaudwell/gource). I previously saw a cool video using Gource on the Linux kernel and thought it was a great way to show the collaboration that happens in open source projects.

(if you read this far, [here is the link](https://sdsimmons.com/experiments/inspect-evals/) again to the actual visualisation! I hope some people find it cool), which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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