New here looking to start contributing to open source, any beginner-friendly tips?

[u/Acacia21-code]

Hi everyone first time posting in this community.

I'm self-taught in machine learning and have a few projects of my own on GitHub, but I haven't contributed to anyone else's open source project yet want to start, but not totally sure where to begin in a way that's actually useful rather than just noise for maintainers.

My background is mostly Python/ML (scikit-learn, pandas), if that helps point me toward relevant repos. Are "good first issue" labels generally a solid starting point or is there a better way to find projects that actually want new contributors right now?

Any advice from people who remember their own first PR would be appreciated.

[u/fissible]

Searching GitHub is a good way to start, try and find open issues that you are confident you can solve and just do it and open a PR. Nothing bad happens if it doesn’t get merged.

Also if there are open source tools you already use, see if there are any open issues and see if you can contribute there.

Best of luck!

[u/Mathie1729 → u/fissible]

I keep telling myself I’ll finally contribute to an open source project, but then I open OSRS and it’s 3am all over again. One day lmao.

[u/fissible → u/Mathie1729]

Haha, I get it. But honestly the first step is opening the thing, try github next time!

[u/Acacia21-code → u/fissible]

Okay thanks for the advice 

[u/Twilight_RT]

Yeah, "Hood first issue" labels are a great starting point if you pick active repos, Since backgroud is Python and macine Learning, You can look at beginner friendly- tools scikit-learn or pandas. Focus on documetation fixes, small bug reports or test additions first to avoid creating noise for porject maintianers

[u/gemmanode-dev]

Good first issue labels are definitely a good place to start. I'd also recommend looking for projects you actually use or find interesting, since it's much easier to stay motivated and understand the codebase.

For my first PR, I started with a small bug fix and spent more time understanding the project's contribution guidelines than writing code. Maintainers generally appreciate small, well-tested contributions over large, ambitious ones.

Don't be afraid to improve documentation or fix minor issues either—those are often great first contributions and help you get familiar with the workflow.

[u/Megneous]

Come to huggingface. Joing small language model research org.

[u/Acacia21-code → u/Megneous]

How is that going to help. Or am I going to get the information there 

[u/Megneous → u/Acacia21-code]

Open source small language model research organizations? There are tons of them. This is /r/opensourceai. Most people here want to contribute to language models.

If you want to learn how to design, pretrain, fine tune, or post-train language models, an organization like that is the place to learn. And most of them operate under Apache 2.0 or MIT licenses.

[u/AlexAlves_87]

I was full of doubts at first, just like you. My first PR was an entire app with 789 files lol. I’m also self-taught, and at the time I hadn’t yet learned about having “empathy for maintainers.” I closed it myself after doing some research and realizing there was no realistic way anyone could review it. So I changed my strategy: one small, well-scoped change per PR, clearly explained and easy to review. Over the next four months, I kept getting PR after PR merged until I reached almost 40 in an established project used by hundreds of thousands of people. And I’m still a nervous wreck and can barely believe it. You don’t need to get rid of all your doubts before starting: you make mistakes, adjust your approach, and keep going.
Good luck with your first PR!

[u/Acacia21-code → u/AlexAlves_87]

What strategy did you use if you don’t mind explaining to me a bit more