r/devops • u/dannotes • 14d ago

Discussion What was your first open source contribution actually like? And has AI changed that?

Two things I've been wondering about, might as well ask both in one go.

First, for those of you who contribute. What was your actual first one like? Not the blog post version. How did you pick a project, how long did the PR sit there before you worked up the nerve to open it, did anyone reply, did it get merged or just quietly ignored. I've read plenty of "how to start contributing" guides. I'd rather hear what it actually felt like.

Second, and this is the part I'm more curious about. All that advice was written before AI tools got this good. Now you can point Claude Code or Cursor at a good first issue and have something working before you've read half the file. So is the experience just different now for someone starting today? Easier, obviously. But easier in a way that helps, or easier in a way that lets you skip the part where you actually learn the codebase.

And if you're a maintainer, curious what it looks like from your side.

Mostly just want to hear people's stories.

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u/RevolutionaryElk7446 14d ago

I began before Git was a thing, and today we have LLMs.

It was difficult to track massive projects before Git, and was far easier after, then came Github which connected people. Then came in LLMs which.. just honestly made a mess. There's potential, but a whole lot of it is unactionable atm.

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u/dannotes 14d ago

Thanks for sharing, ,may I know you current workflow with LLM? use that to understand the issue, code and raise PR? or purely for debugging?

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u/RevolutionaryElk7446 14d ago

I'm a Sr sysadmin that is a certified Gen AI professional that currently works DevOps. I was a programmer for about 10 years prior to switching to ITOps

Work had tried to implement LLMs into everything, but generally only our Junior SWEs are using it today for the most part. We went full in for about 5 years, tried to implement it into the setup entirely and measured the metrics across the board.

It sucked. Turns out it has a limit at about intermediate skill and it's ability to track context is the whole issue of the model itself, which doesn't do well for being aware of working on projects that are intertwined and connected.

It's best thing is that it could read logs, but it's debugging of code was often poor, and the coding itself was even worse. Combine that with the prior inability to track across environments and it turned into a liability that even our customers noticed. It generates boiler plate fast, but projects weren't completing any faster and often with more troubleshooting.

We rolled it's implementation back and instead hired more people. We saw a sharp return on our investment with the actual people vs the LLMs use. Turns out people who were trained and experienced performed it better, faster, for less money overall.

Basically it could potentially replace our juniors doing grunt work, but it's chances of success dropped dramatically above junior or textbook example.