Finding an Entry Point Into Open Source

Getting involved in open source often feels like the classic chicken-and-egg problem: you need experience to contribute, but you need contributions to build experience. GitHub's "good first issues" feature is designed to break that loop by directing newcomers to maintainers who are actively looking for beginner-friendly work.

Several entry points exist depending on how you prefer to search:

  • Browse by topic. If you have a specific interest, visit github.com/topics/<topic>—for example, github.com/topics/machine-learning—to see relevant projects and their recommended starter issues. A full list of popular topics is available at github.com/topics.
  • Target a known repository. When you already have a project in mind, github.com/<owner>/<repository>/contribute shows the beginner-friendly issues for that repo. For instance, github.com/nodejs/node/contribute surfaces opportunities for first-time contributors to Node.js.
  • Get personalized recommendations. GitHub tracks your activity—stars, prior contributions, and more—to build a tailored set of suggested projects. Your curated list appears at github.com/explore.

From any of these views, you can review a handful of handpicked starter issues. Links labeled "More good first issues" or direct navigation to a repository's /contribute page will expand that list to show everything available.

Behind the Recommendations

These suggestions don't come from a simple label filter. The engineering team built a machine learning pipeline to identify which issues are genuinely suitable for newcomers—from detecting positive training samples through to the deep learning models that score issues—plus the infrastructure required to keep recommendations current as projects evolve. The full technical walkthrough is available in the engineering post.

The algorithm itself is under continuous development, so recommendations will improve over time. Feature feedback can be sent through GitHub support.