[devhouse]

The really interesting part is what this system runs on.

Spotify built Fleet Management back in 2022 to apply changes across thousands of repos. Half their PRs were already flowing through it before any AI was involved, but only for mechanical stuff like dep bumps and config updates. Claude Code is what let it understand what the code means, not just its structure.

The thing nobody's talking about is that none of this AI automation works without Backstage doing the boring work of tracking who owns what, how things build, and what depends on what.

I went through the Anthropic case study, Spotify's engineering blog series, and the full earnings call transcript here https://www.everydev.ai/p/blog-spotify-built-an-ai-coding-ag...

[joshribakoff]

> a framework for applying code changes across hundreds or thousands of repositories at once

Statements like this raise fair questions. Is there code duplication across 1,000s of repos, and why respond by increasing surface area further with bespoke tooling?

[narnarpapadaddy]

Imagine you initialized 10,000 NPM repos identically simultaneously. Then had 100 different teams each take of 100 those repos for 10 different projects, and let each repo run for 1,000 commits. How distinct would each of those repos be? How might have they evolved independently? What types of interesting patterns might be adopted to improve development experience, or detect bugs by each team? What packages at what version might be most popular?

Now imagine you had the tools to do a diff across all those repos simultaneously, and classify, group, and review those patterns. What could you learn NPM teams and practices?

Now imagine you could pick best of breed, and propagate those back to all the other projects automatically to improve their productivity, security, etc. How fast would your productivity improve and your engineering culture change if everyone could automatically learn the best of what everyone else had to offer?

Companies like Spotify have sophisticated tooling for detecting repo changes and enforcing policy like that, and they run that experiment 1,000 times a day. Small evolutions in what was an identical build script, like a version bump, are detected, and if it passes a threshold it can be rolled out everywhere else immediately.

Having all the copies that you can sync up centrally periodically puts natural selection to work on internal best practices.

Basically, things work differently at scale. When the number developers you employ approaches a meaningful percentage of the total number of developers globally, your internal diversity starts to mirror the global diversity. So you have to manage that diversity. If you freeze policy entirely, you fall behind the global average. If you let things run wild, your company fractures technologically.

So, make a 1,000 copies, see what pops up, adopt and enforce things that look good, then do it again. Evolve to the next best place you can be from where you are.

[Taikonerd]

> The thing nobody's talking about is that none of this AI automation works without Backstage doing the boring work of tracking who owns what, how things build, and what depends on what.

Do you mean that Backstage has the metadata like what services call which other services, and AI needs that to make changes safely? Sounds helpful to both AI and human developers ;-)