A curated list of boilerplates and templates for mobile and web apps across various programming languages and frameworks.
Awesome Projects Boilerplates is a curated GitHub repository listing boilerplates and templates for mobile and web applications. It serves as a directory to help developers quickly find starter code for various technologies, frameworks, and platforms, reducing initial setup time. The collection spans Android, iOS, JavaScript frameworks, Python, microservices, and more.
Developers and teams starting new projects who want to avoid repetitive configuration and leverage community-vetted starter templates. It's especially useful for full-stack developers, mobile app creators, and those exploring new frameworks.
It centralizes hundreds of boilerplates across the tech stack in one place, saving hours of research. The list is community-maintained, ensuring it includes up-to-date, production-ready starters following best practices.
Boilerplates for mobile and web apps
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Includes boilerplates for Android, iOS, web, microservices, and multiple languages like JavaScript and Python, ensuring developers can find starters for nearly any stack without scattered searches.
Actively accepts contributions via a clear contributing guide, helping keep the list refreshed with modern tools and best practices over time.
Aggregates hundreds of production-ready starters in one place, from HTML5 Boilerplate to React and Flask templates, eliminating hours of configuration research.
Features sections for IDE templates like Emacs Yasnippet and Vim, plus GitHub guidelines, which are often overlooked in similar directories.
As a community list, entries vary in quality and maintenance; some linked projects may be outdated or abandoned, with no vetting process mentioned in the README.
The directory only points to external boilerplates, so users must rely on those projects for updates and bug fixes, adding overhead if issues arise.
With multiple options per category (e.g., several React starters), the README offers no comparisons or recommendations, making it hard to pick the best fit.