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HelloGitHub is a curated list of interesting and entry-level open source projects suitable for newcomers to contribute to. Discover projects across various programming languages and domains.
HelloGitHub is a popular GitHub repository that serves as a curated collection of open source projects intended to help developers, especially beginners, discover and engage with the open source community.
Finding suitable open source projects to contribute to as a beginner can be overwhelming. HelloGitHub simplifies this by hand-picking accessible projects.
Weekly selection of high-quality, beginner-friendly open source projects.
Provides brief, insightful introductions for each listed project.
Organized by issue number for easy navigation and tracking.
The HelloGitHub repository is used in various scenarios by its target audience:
Individuals new to open source can browse the list to find projects marked as beginner-friendly with simple issues.
Helps beginners overcome the initial hurdle of finding a suitable project to contribute to, building confidence and experience.
Developers looking for inspiration or specific examples of how certain technologies are used in practice can explore the listed projects.
Facilitates discovery of new tools, libraries, or project ideas across different domains and tech stacks.
You might be interested in these projects
Trino is a distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. It allows organizations to analyze data where it lives without migrating it.
Logstash is a powerful, open-source data processing pipeline that can ingest data from a multitude of sources simultaneously, transform it, and then send it to your favorite "stash", like Elasticsearch.
Efficient implementations of state-of-the-art linear attention models in Torch and Triton. This project provides high-performance, memory-efficient alternatives to traditional quadratic attention mechanisms, specifically optimized for long sequences and large-scale deep learning models.