A curated list of awesome curated lists covering computer management, programming languages, sciences, and more.
Awesome Awesome is a meta-directory of 'awesome' lists, which are themselves curated collections of the best resources, tools, libraries, and software for specific topics. It solves the problem of discovering high-quality, community-vetted lists across diverse fields like programming, science, security, and web development by providing a centralized index.
Developers, researchers, sysadmins, and tech enthusiasts looking for a starting point to find the best tools and resources in a specific niche without searching through scattered repositories.
It saves significant time and effort by aggregating hundreds of specialized curated lists into one place, ensuring users can quickly find authoritative, community-maintained resources rather than relying on generic search results.
A curated list of awesome curated lists of many topics.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The README spans from Computer Management to Sciences, linking to hundreds of specialized lists like awesome-python and awesome-react, ensuring wide resource discovery across diverse fields.
Each linked list is itself a curated collection, as per the project philosophy, meaning resources are pre-screened by domain experts for reliability.
It provides a single entry point to navigate multiple awesome lists, saving time compared to scattered searches on GitHub or other platforms.
The README encourages community input via issues and pull requests, allowing the directory to grow and adapt to missing topics.
Sections like awesome-biology or awesome-math have 'Not yet! Do it yourself!' notes, indicating significant gaps where users must create lists themselves.
As a GitHub README, it lacks automated updates or link validation, so broken links or outdated lists are common risks without active monitoring.
The meta-list doesn't assess the curation standards of linked lists, so some may be poorly maintained, biased, or lack recent updates, relying solely on community vetting.
Users must manually browse categories, which is inefficient for specific queries compared to search-enabled directories or platforms with filtering options.