5 Repos
Tools for creating, saving, and reusing components and logic flows across multiple projects.
Distinguishing note: Focuses on reusable building blocks rather than generic development environments.
Explore 5 awesome GitHub repositories matching development tools & productivity · Modular Development Tools. Refine with filters or upvote what's useful.
ToolJet is a low-code development platform designed for building and deploying internal business applications. It provides a visual interface where users can drag and drop components to design layouts, connect to various data sources, and execute custom logic. The platform is built on a containerized architecture, ensuring that applications remain portable and consistent across different cloud and server environments. The platform distinguishes itself through integrated artificial intelligence capabilities that assist in the generation of user interfaces, database schemas, and data queries fr
Enables the creation of modular components and logic flows that can be saved and shared across multiple projects.
Node-RED is a visual, low-code automation framework designed for building event-driven data processing workflows. It provides a browser-based programming environment where users connect hardware devices, APIs, and online services by wiring together functional nodes in a directed graph. This visual approach allows for the creation of complex logic paths without the need for traditional source code. The platform is distinguished by its pluggable node architecture and portable flow serialization. Logic is represented as JSON-based data structures, enabling flows to be easily versioned, shared, a
Supports modular development through reusable subflows and custom nodes that simplify the maintenance of complex automation logic.
Theia is a modular framework designed for building professional-grade development environments that function as both local desktop applications and remote browser-based services. It provides a comprehensive toolkit for constructing specialized coding tools, allowing developers to assemble custom interfaces and backend logic through a flexible, contribution-based architecture. The platform distinguishes itself through a highly extensible workbench that supports the integration of existing third-party editor plugins and standard language servers. By utilizing a dependency injection container an
Supplies a collection of reusable components and services for assembling professional-grade development tools with custom UI and backend logic.
Tuist is a Swift manifest–based project manager for Xcode projects. It replaces manual Xcode project file editing with a declarative Swift DSL where build targets, settings, and dependencies are defined in code. The tool then generates Xcode workspaces and projects from that manifest, and can bootstrap new projects with a given platform target and initial structure. Beyond generation and scaffolding, Tuist provides a command-line build capability that compiles Apple platform projects without launching Xcode. It resolves the dependency graph between targets, detects cycles, and determines buil
Organizes Xcode projects into smaller, reusable modules for better maintainability and compile times.
Dieses Projekt ist eine umfassende Lehrressource und ein Kurs zum Aufbau neuronaler Netze mit PyTorch. Es deckt die grundlegenden Bausteine des Deep Learning ab, einschließlich Tensor-Manipulation, automatischer Differenzierung und der Konstruktion modularer Komponenten für neuronale Netze. Das Repository dient als technischer Leitfaden für verschiedene spezialisierte Bereiche. Es bietet Implementierungsdetails für Computer-Vision-Aufgaben wie Bildklassifizierung, Objekterkennung und semantische Segmentierung sowie Workflows für die Verarbeitung natürlicher Sprache (NLP) mit Transformern, rekurrenten Netzen und generativen Modellen. Zudem enthält es eine Referenz für generative KI, mit Fokus auf die Synthese von Bildern mittels Diffusionsmodellen und adversarialen Netzwerken. Das Material erstreckt sich auf Modelloptimierung und Deployment-Pipelines. Es behandelt Techniken zur Reduzierung der Modellgröße und zur Erhöhung der Inferenzgeschwindigkeit durch Quantisierung und den Export von Modellen in Formate wie ONNX und TensorRT. Weitere Kompetenzbereiche umfassen Data Engineering für paralleles Laden, Modellevaluierung mittels benutzerdefinierter Metriken und das Deployment von Open-Source Large Language Models. Das Projekt wird primär als eine Reihe von Jupyter Notebooks bereitgestellt.
Constructs neural network architectures using modular containers and hooks to manage parameters and data flow.