OpenAI's Pando Git backend cuts push latency nearly 5x and serves almost a petabyte a day

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controls robots in simulation and on hardware

OpenAI's Pando Git backend cuts push latency nearly 5x and serves almost a petabyte a day

Pando, a new Git backend OpenAI built, now powers all development in its monorepo: it cut median push latency by nearly 5x and delivers nearly a petabyte of Git data per day.
Pando coordinates updates to each branch independently so pushes to unrelated branches run in parallel, and it reuses packfiles across fetches instead of generating one per client, letting the server send the same bytes to many clients with little computation; the tradeoff is that a reader looking at several branches can see them at different points in time.
Commits landed on master grew +1,378% in Q3 2026 relative to Q1 2024, with a Q4 2026 estimate of +2,442%; OpenAI attributes the pace to Codex letting engineers run far more work in parallel.
Reliability of the monorepo systems fell below 90% around the end of 2025, with slow fetches and pushes and failed merges, which pushed the rebuild; Pando kept Git protocol and client compatibility so engineers and agents changed nothing in their workflow.
OpenAI maintains its own copy of Git and plans to contribute client improvements upstream, and says it will keep investing in the Git ecosystem for agent-scale work.

Hacker News 의견들

Technical details aside, this volume is ridiculous. How is exponential commit growth an asset and not a liability?

This is obviously just a response to Anthropic's redesigning source control post. I doubt anyone in the chain writing this even thought about that question.

That volume is a liability only for human engineers, with limited memory, short attention spans and quirky coding preferences. LLMs don't have any of those problems.

Not really, this is fairly mild for OpenAI's size. For my own work I've stopped counting commits and started looking at disk size changes.

I have no particular interest in the topic but this is a great blog post. No AI in every second line, no needless hype, just engineers talking in their own voice with numbers and charts.

Naive to think you can tell whether a human or their unreleased model wrote this.

What is this massive volume of code actually doing? They have model architectures, training, serving, and app source. Why would any of it grow exponentially while the products barely seem to change?

It's AI slop. Coding agents write much more voluminous code, more tests, and many more commits from fixing and fixing over and over.

RL environments. RL environments as far as the eye can see.

Engineers run multiple sessions now and the LLM does most of the commits and pushes. That's becoming the norm at big tech.

Supply was always the limiting factor, not demand. When an engineer shipped 100 lines of production code a week you only did the critical things, and cheaper code makes the marginal projects worth doing.