Scaling Git at OpenAI

Can anyone explain to me what this massive volume of code could actually be doing? They obviously have variations of model architectures, training infrastructure, serving infrastructure, and the source for their apps.

Why would the volume that any of this be increasing exponentially? And what are all the engineers doing when (besides the model quality) their products don't seem to be changing very much?

Bare in mind that OpenAI|Anthropic (kill me, I can't recall which one), made a massive improvement on their app by... gziping the served content!

I don't see benchmarks anywhere in here. For example, how many pushes per second does this handle? And how many concurrent pushers? etc..

Disclaimer: I am building a code forge at https://juju.bi

While I don't have any particular interest in the topic this is a great blog post. No AI in every second line, no needless marketing or hype. Just a bunch of engineers talking in their own voice about a topic in great detail with numbers, charts, visualizations.

> We built Pando with Codex, moving from kick-off to production, in just 10 weeks. The core of Pando was designed by a single engineer, implemented by teams of agents, and deployed by a handful of engineers. Pando began serving production traffic in April of this year, and is one part of a larger system of source control management tools used at OpenAI.

Impressive!?

Another way to avoid having to maximize download speed is to use a global git cache per machine in a similar way that pnpm optimizes installing packages:

https://git-dedup.ben3d.ca

Technical details aside, this volume is ridiculous. How on earth do they see this kind of "growth" as an asset and not a liability?

lol nice try openai but its shallow and too late

Impressive. We're definitely seeing LLMs help create real novel software