Explica en lenguaje sencillo qué estás construyendo y obtén los mejores repositorios de GitHub que coincidan.
awesome-repositories.com te ayuda a encontrar el proyecto open-source adecuado en GitHub. Dile a nuestra búsqueda por IA qué quieres construir, en lenguaje sencillo, y clasificará miles de repositorios curados según su relevancia. Cada proyecto es monitoreado continuamente, organizados en categorías claras y votados por otros desarrolladores. Explora las selecciones a continuación o realiza una búsqueda para comenzar.
Lo más destacado del directorio: una selección nueva cada pocas horas.
Búsquedas comunes, organizadas en un árbol navegable.
Es un directorio curado de repositorios open-source de GitHub que puedes buscar en lenguaje natural. Describe lo que estás construyendo y una IA clasificará miles de proyectos verificados según su relevancia, incluyendo una breve nota sobre por qué encajan.
La búsqueda de GitHub coincide con las palabras clave que escribes. Aquí describes el problema en lenguaje natural y la IA interpreta tu intención, por lo que aparecerá un proyecto que resuelva tu necesidad incluso si no utiliza tus palabras exactas.
Escribe lo que quieres construir, por ejemplo: una página de estado autohospedada que haga ping a mis servicios y me avise por Discord. Obtendrás repositorios coincidentes clasificados por relevancia en lugar de por número de estrellas.
Están curados, no es una extracción bruta. Una IA analiza cada proyecto, lo clasifica en una categoría y lo ordena por relevancia bajo reglas que ajustamos y revisamos, eliminando entradas duplicadas o de baja calidad.
Sí. Buscar y navegar por el directorio es gratuito.
LLMs, agents, and the tools to build with them.
Notes, tasks, docs, and knowledge bases.
Chat, calls, photos, music, and personal files.
Databases, pipelines, and analytics.
Containers, deployment, monitoring, and automation.
Passwords, secrets, and offensive security.
Languages, CLIs, frameworks, and version control.
Courses, books, interviews, and CS foundations.
OpenSpec is a specification-driven development tool designed to maintain living documentation directly within a software repository. It functions as an automated documentation engine that tracks technical requirements alongside source code, ensuring that system specifications evolve in tandem with the implementation to prevent documentation drift. The framework distinguishes itself through differential change tracking, which compares current code states against stored specifications to identify deviations between intended requirements and actual implementation. By integrating specification st
This project is a community-maintained, open-source knowledge base that serves as a structured index for cybersecurity resources. It provides a centralized directory of tools, frameworks, and documentation designed to assist security researchers, penetration testers, and developers in hardening digital infrastructure and navigating the security tooling ecosystem. The repository distinguishes itself through a collaborative curation model that relies on distributed user contributions to maintain an accurate and up-to-date registry of technical assets. By organizing information into structured m
V is a statically typed, compiled programming language designed for high-performance systems development. It prioritizes memory safety and execution speed by enforcing strict type checking and immutable defaults, while generating native machine code for multiple hardware architectures. The language is built around an integrated toolchain that includes a compiler, package manager, formatter, and testing utilities within a single executable, facilitating rapid development cycles. What distinguishes V is its focus on developer productivity and interoperability. It provides a direct interface for
TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr