Three new Campus Experts are joining the fall 2022 batch of the MLH Fellowship to work with open source maintainers and get real-world experience.
Always wanted to get involved in an open source project but don’t know where to begin? Looking to build up your reputation but need to start with something simple? GitHub now helps you find good first issues to start contributing to open source.
There are several ways to find more information and get started with contributing to open source:
- If there’s a particular topic that interests you, visit
github.com/topics/<topic>. For example, machine learning enthusiasts can visit github.com/topics/machine-learning to find relevant projects and starter issues. You can also browse popular topics by visiting github.com/topics.
- If you already know which project you want to work on, find beginner-friendly issues for that project by visiting
github.com/<owner>/<repository>/contribute. For example, you can find ways to make your first contribution to
- If you’ve been active on GitHub, your past contributions, stars, and other activities are used to offer personalized recommendations for projects you might like. Visit github.com/explore to see your curated list.
Using any of these options, you can see a list of projects and a few beginner-friendly issues we recommend reviewing to help you get started. You can also click More good first issues or visit
github.com/<owner>/<repo>/contribute for the project you’re interested in to see a complete list.
We’re glad you asked. Learn about the machine learning algorithms that made this feature a reality in our engineering post. We cover everything from how we detect positive training samples to the deep learning algorithms used and the infrastructure needed to keep the results fresh and relevant.
Our algorithm is constantly evolving—be sure to come back to see what new recommendations we have for you.
Do you have an idea about how to improve the good first issues feature? Contact us and let us know. Happy open-sourcing!